{"meta":{"query_hash":"823cf2c5d6fa","filters":{"topic":"Medical Image Segmentation Techniques"},"cohort_total":1412,"direct_labels_cover":3,"predictions_cover":1412,"exported":1412,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/823cf2c5d6fa","api":"https://metacan.xera.ac/api/v1/cohort?topic=Medical+Image+Segmentation+Techniques"},"results":[{"id":"W104645259","doi":"10.1007/978-3-642-23094-3_11","title":"Interactive Segmentation with Super-Labels","year":2011,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Institute of Biomedical Imaging and Bioengineering","keywords":"Computer science; Segmentation; Artificial intelligence; Pattern recognition (psychology); Pixel; Object (grammar); Histogram; Image segmentation; Coherence (philosophical gambling strategy); Computer vision; Image (mathematics); Mathematics","score_opus":0.020407307880424108,"score_gpt":0.2787925118847826,"score_spread":0.2583852040043585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W104645259","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013313989,0.000015433909,0.9855129,0.0002277597,0.00032669143,0.00026130758,4.2085347e-7,0.00023277878,0.00010868872],"genre_scores_gemma":[0.46020532,0.0000018962965,0.538729,0.0010222901,0.000021889618,0.000014591445,5.129274e-7,0.0000038320345,6.4750344e-7],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980854,0.0000738466,0.00022984316,0.0006604941,0.00058138394,0.00036905147],"domain_scores_gemma":[0.9989104,0.0001654498,0.000092838825,0.0005365082,0.00016588665,0.00012886393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005052438,0.00016534339,0.00014697533,0.00035280213,0.00011815915,0.00021432624,0.0014471627,0.00004295259,0.00004178042],"category_scores_gemma":[0.00007823219,0.00012613427,0.000021910128,0.0016099793,0.00040744361,0.0019191125,0.00037159093,0.00022788504,0.000020671516],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010663926,0.000108428925,0.002297237,0.0000074477493,0.0000047386457,0.000049998693,0.008725988,0.00024235353,0.010913542,0.00026009587,0.000010172162,0.9773693],"study_design_scores_gemma":[0.0003785558,0.00042084962,0.004316232,0.00007345561,0.0000024431104,0.0000587795,0.0000052829746,0.118487604,0.86842126,0.007572984,0.0000033230765,0.00025922287],"about_ca_topic_score_codex":0.00008117316,"about_ca_topic_score_gemma":0.000030982166,"teacher_disagreement_score":0.9771101,"about_ca_system_score_codex":0.00013470887,"about_ca_system_score_gemma":0.00016405362,"threshold_uncertainty_score":0.5143606},"labels":[],"label_agreement":null},{"id":"W105164381","doi":"10.1016/b978-012077790-7/50015-1","title":"Fully Automated Hybrid Segmentation of the Brain","year":2000,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Segmentation; Artificial intelligence; Computer science; Pattern recognition (psychology); Computer vision","score_opus":0.011436887969475985,"score_gpt":0.2566730134144004,"score_spread":0.24523612544492443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W105164381","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000014351389,0.00013608322,0.015913084,0.0004031515,0.000299796,0.00082545565,0.00002358909,0.00079020794,0.98159426],"genre_scores_gemma":[0.00024069296,0.000038258564,0.03252974,0.00279747,0.00007693655,0.000043520406,0.00002026382,0.00004425803,0.96420884],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979325,0.00009045969,0.00061321555,0.00042662545,0.00073315366,0.00020406631],"domain_scores_gemma":[0.9982454,0.00010033222,0.0004692158,0.000988278,0.000105304,0.00009147302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032497945,0.00028963853,0.00033806651,0.0001408656,0.00008775175,0.00007289254,0.0013120959,0.00013995475,0.00037504983],"category_scores_gemma":[0.000029145058,0.00022038301,0.00020730207,0.000033895758,0.00021851808,0.00012751119,0.00028249025,0.00031828092,0.00009604033],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021101494,0.0000064181536,4.8601163e-7,0.00003536761,0.000029946867,0.000009746491,0.00015926485,6.4882386e-7,0.00084376853,0.00260778,0.003781142,0.9925233],"study_design_scores_gemma":[0.000834112,0.00022758574,0.000059517697,0.0013921699,0.00011369952,0.00011293311,0.0000067371952,0.002278701,0.13835806,0.07452302,0.7810595,0.0010339691],"about_ca_topic_score_codex":0.000001000203,"about_ca_topic_score_gemma":0.000002317684,"teacher_disagreement_score":0.99148935,"about_ca_system_score_codex":0.00008689937,"about_ca_system_score_gemma":0.0001923396,"threshold_uncertainty_score":0.8986958},"labels":[],"label_agreement":null},{"id":"W117083209","doi":"","title":"Shape Model and Threshold Extraction via Shape Gradients","year":2001,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial intelligence; Active shape model; Computer vision; Computer science; Computation; Computer graphics; Grayscale; Shape analysis (program analysis); Metric (unit); A priori and a posteriori; Representation (politics); Noise (video); Visualization; Object (grammar); Pattern recognition (psychology); Image (mathematics); Algorithm; Segmentation","score_opus":0.03308320384702466,"score_gpt":0.30660132249658745,"score_spread":0.27351811864956277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W117083209","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039833248,0.00002995347,0.9530153,0.0004884096,0.00006692013,0.000116929805,2.0602046e-7,0.00039073743,0.006058258],"genre_scores_gemma":[0.67359173,0.00010861304,0.32339707,0.0019201125,0.000023919685,0.000016805068,0.0000017168569,0.00000612804,0.0009338775],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991426,0.000013304678,0.00015302062,0.0002752385,0.00025095165,0.00016490572],"domain_scores_gemma":[0.99954355,0.000022322527,0.00004029436,0.00022692914,0.000038955015,0.0001279378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015901029,0.00008461434,0.000075894895,0.000076278855,0.00006831275,0.00009842444,0.0002769466,0.000044500026,0.00024320996],"category_scores_gemma":[0.0000137894685,0.000072939365,0.0000202739,0.00016185392,0.000038658694,0.0008895619,0.00011888322,0.00009019208,0.00003621263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004927124,0.000110163324,0.0014177816,0.000008838482,0.000007526379,0.000025971267,0.00021263833,0.00004213158,0.022771895,0.006794595,0.003915976,0.9646876],"study_design_scores_gemma":[0.00013642521,0.000032284228,0.0016583306,0.0000054075927,0.0000020939926,0.000038939772,0.000005539835,0.9839073,0.0058441684,0.008181914,0.000094724885,0.000092885966],"about_ca_topic_score_codex":0.000016380724,"about_ca_topic_score_gemma":0.0000043145283,"teacher_disagreement_score":0.98386514,"about_ca_system_score_codex":0.000021522701,"about_ca_system_score_gemma":0.000011898678,"threshold_uncertainty_score":0.2974381},"labels":[],"label_agreement":null},{"id":"W1171165778","doi":"10.71781/10211","title":"TONGA : un algorithme de gradient naturel pour les problèmes de grande taille","year":2007,"lang":"fr","type":"dissertation","venue":"Open MIND","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Geography; Humanities; Forestry; Art","score_opus":0.04013708482387094,"score_gpt":0.3693566295650704,"score_spread":0.32921954474119947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1171165778","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027404275,0.001336766,0.9517418,0.0015348162,0.0007927589,0.001712995,0.00003628553,0.0000324726,0.015407796],"genre_scores_gemma":[0.0078047453,0.00023176068,0.9486455,0.00046095825,0.00022954422,0.00015411415,0.00034211634,0.000049620736,0.04208165],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9963919,0.00035132223,0.00072750734,0.0009507487,0.0007168871,0.0008616517],"domain_scores_gemma":[0.9978059,0.00026151742,0.0004951375,0.00057752855,0.00037338145,0.00048651674],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0019923486,0.0004896296,0.0004998186,0.00026981565,0.00044671132,0.0009419212,0.0025824523,0.00068808533,0.0026968264],"category_scores_gemma":[0.00036011258,0.00049165974,0.00017874547,0.00054779975,0.00017375682,0.00070174,0.00039293143,0.0009727429,0.0002679324],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003449961,0.00033950742,0.00031620948,0.00007967409,0.00007777732,0.00022690979,0.0126144355,0.000011506554,0.0054432936,0.0006330634,0.0031294215,0.9770937],"study_design_scores_gemma":[0.0016772436,0.0004077862,0.005810365,0.0013691236,0.0001974701,0.00024553918,0.004473739,0.018036004,0.9356757,0.004892033,0.025894184,0.0013208608],"about_ca_topic_score_codex":0.0014768068,"about_ca_topic_score_gemma":0.00076546584,"teacher_disagreement_score":0.97577286,"about_ca_system_score_codex":0.00040055782,"about_ca_system_score_gemma":0.0011332666,"threshold_uncertainty_score":0.99975353},"labels":[],"label_agreement":null},{"id":"W1178809019","doi":"10.71781/10904","title":"Simulation des fonctions de texture bidirectionnelles","year":2014,"lang":"fr","type":"dissertation","venue":"Library and Archives Canada (Government of Canada)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Texture (cosmology); Mathematics; Computer science; Artificial intelligence","score_opus":0.006135584614080993,"score_gpt":0.19137598946518358,"score_spread":0.1852404048511026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1178809019","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014271738,0.0009179082,0.8749167,0.0024080302,0.0011138454,0.00041558038,0.00010682279,0.00007876852,0.1057706],"genre_scores_gemma":[0.91107213,0.0004130947,0.05117493,0.0015937135,0.0001870509,0.000029321327,0.00006557407,0.000037748316,0.035426445],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968654,0.00022080618,0.00047681516,0.00041108634,0.0016553667,0.0003705176],"domain_scores_gemma":[0.9980191,0.00092074863,0.0003751914,0.00027324082,0.0000035952721,0.00040810223],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000028183376,0.00030328945,0.0003170763,0.000056979952,0.0005049621,0.00010132753,0.00040284856,0.000102699465,0.00011503382],"category_scores_gemma":[0.000043582233,0.00032313998,0.0000545146,0.0002019307,0.00016853746,0.0008119192,0.000106845866,0.0002898486,9.544199e-9],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037885,0.00018600261,0.016856167,0.00242336,0.00032601628,0.00011430095,0.003146833,0.01759372,0.0638846,0.19050483,0.003350871,0.70123446],"study_design_scores_gemma":[0.0004596476,0.00019783706,0.061607216,0.0010496798,0.00010319256,0.0000141795435,0.0046359403,0.52438796,0.38192725,0.010233693,0.014642807,0.00074058346],"about_ca_topic_score_codex":0.009880187,"about_ca_topic_score_gemma":0.08145293,"teacher_disagreement_score":0.8968004,"about_ca_system_score_codex":0.000028642015,"about_ca_system_score_gemma":0.0029198297,"threshold_uncertainty_score":0.99992204},"labels":[],"label_agreement":null},{"id":"W118910867","doi":"10.1007/978-3-642-40811-3_58","title":"Segmentation of Cells with Partial Occlusion and Part Configuration Constraint Using Evolutionary Computation","year":2013,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Constraint (computer-aided design); Computation; Evolutionary computation; Segmentation; Artificial intelligence; Algorithm; Geometry; Mathematics","score_opus":0.014253024818479565,"score_gpt":0.2681570906929637,"score_spread":0.2539040658744841,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W118910867","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10354763,0.000022541479,0.8955161,0.00021937594,0.00019777888,0.0004247525,9.0036605e-7,0.000064398184,0.000006500453],"genre_scores_gemma":[0.5091296,0.000002706451,0.49064106,0.00019616204,0.000020589072,0.000005486042,0.0000020180216,0.0000022873758,1.1470334e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998346,0.00009487823,0.00033810697,0.00044866142,0.0005455706,0.00022680944],"domain_scores_gemma":[0.99904794,0.00019251839,0.00019907254,0.00020675198,0.00025845206,0.00009529948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039420062,0.00013143674,0.00015300122,0.00024066636,0.00014436195,0.00017648838,0.0003186956,0.0000498984,0.000017120317],"category_scores_gemma":[0.000038594615,0.0001091876,0.00001556846,0.00077326375,0.00063636183,0.0012614093,0.00017764646,0.00011227991,0.0000026536013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008192586,0.00007532715,0.0015498995,0.000035402474,0.0000046573346,0.000008001043,0.0014268875,0.062994294,0.30316243,0.00024158493,0.00001388408,0.63047945],"study_design_scores_gemma":[0.000215275,0.00015199592,0.0013393002,0.000060922615,0.000002033855,0.000026562626,0.0000037845539,0.67868596,0.3177431,0.0016748789,5.421571e-7,0.00009563764],"about_ca_topic_score_codex":0.000108337474,"about_ca_topic_score_gemma":0.0000043659725,"teacher_disagreement_score":0.6303838,"about_ca_system_score_codex":0.000092721224,"about_ca_system_score_gemma":0.00019071517,"threshold_uncertainty_score":0.4452541},"labels":[],"label_agreement":null},{"id":"W118997998","doi":"10.1007/978-3-642-23629-7_68","title":"Random Walks for Deformable Image Registration","year":2011,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Image registration; Random walk; Computer vision; Artificial intelligence; Computer graphics (images); Image (mathematics); Mathematics; Statistics","score_opus":0.02408593641414553,"score_gpt":0.2815436391959181,"score_spread":0.2574577027817726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W118997998","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007952081,0.000024269935,0.9974281,0.00035860264,0.00050719164,0.00046585,7.7990614e-7,0.00023833399,0.0001816339],"genre_scores_gemma":[0.2544903,0.000003566405,0.74438053,0.0010205584,0.0000559598,0.000041234252,0.0000012125865,0.000004505832,0.00000216031],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822485,0.000043333912,0.00031861613,0.000566334,0.00042548528,0.00042136933],"domain_scores_gemma":[0.99875706,0.0002161786,0.00012220994,0.00059939804,0.00018875403,0.00011639687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013299529,0.00014197417,0.00016242583,0.0002489577,0.00017881884,0.00026930883,0.0015683306,0.00005979738,0.00001443994],"category_scores_gemma":[0.00031827475,0.00011871636,0.000051192706,0.0009493372,0.0003409342,0.0016344233,0.000257306,0.0001449407,0.000009808031],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022365113,0.00007544745,0.00012641297,0.000025795818,0.0000027596752,0.000012657415,0.0024733746,0.00035247338,0.015239423,0.0011216259,0.00012103059,0.9804266],"study_design_scores_gemma":[0.00060579705,0.00014792876,0.000349376,0.00002674701,0.0000016148322,0.000018958257,6.131232e-7,0.54278255,0.41919017,0.03669217,0.000024478888,0.00015963665],"about_ca_topic_score_codex":0.000052970907,"about_ca_topic_score_gemma":0.000020464708,"teacher_disagreement_score":0.980267,"about_ca_system_score_codex":0.00008524891,"about_ca_system_score_gemma":0.00018098029,"threshold_uncertainty_score":0.48411128},"labels":[],"label_agreement":null},{"id":"W119341417","doi":"10.1007/978-3-642-40811-3_90","title":"Higher-Order CRF Tumor Segmentation with Discriminant Manifold Potentials","year":2013,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Pattern recognition (psychology); Segmentation; Artificial intelligence; Conditional random field; Computer science; Context (archaeology); Scale-space segmentation; Image segmentation; Subspace topology; Pairwise comparison; Manifold (fluid mechanics); Discriminant; Mathematics","score_opus":0.011988847784916474,"score_gpt":0.2617474468103238,"score_spread":0.24975859902540734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W119341417","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009879282,0.00003589973,0.98620915,0.0024180568,0.0005552945,0.0005814853,5.752095e-7,0.00027775072,0.000042497963],"genre_scores_gemma":[0.4230388,0.0000025342288,0.5740452,0.0027854,0.000060904964,0.000052797433,0.0000013581315,0.000007373965,0.000005612436],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9971544,0.000091707225,0.00036215948,0.0008639879,0.000957462,0.00057026464],"domain_scores_gemma":[0.9984881,0.00016460041,0.00016003111,0.000720298,0.00027394274,0.00019301762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039543898,0.00024271094,0.00022061674,0.00034074986,0.00020319558,0.0008176315,0.0016882082,0.0000475855,0.00012019939],"category_scores_gemma":[0.000064932276,0.00017364959,0.000032393647,0.00185418,0.00035784443,0.0020817085,0.00039064995,0.00022159242,0.000070471084],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004617721,0.00015320165,0.0012951917,0.000034996236,0.0000073023575,0.000083806044,0.0013090757,0.0022583923,0.06434618,0.0009778027,0.00016927224,0.92936015],"study_design_scores_gemma":[0.0007873488,0.0005175573,0.02386635,0.00017941449,0.000008818872,0.00012031136,0.000007648071,0.34249339,0.6133807,0.017915469,0.0000234606,0.0006995656],"about_ca_topic_score_codex":0.00029881092,"about_ca_topic_score_gemma":0.000029181787,"teacher_disagreement_score":0.9286606,"about_ca_system_score_codex":0.00013323915,"about_ca_system_score_gemma":0.00017630168,"threshold_uncertainty_score":0.7884439},"labels":[],"label_agreement":null},{"id":"W1219611103","doi":"10.1007/s11548-015-1199-9","title":"Registration of 3D shapes under anisotropic scaling","year":2015,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Iterative closest point; Image registration; Translation (biology); Computer science; Rotation (mathematics); Rigid transformation; Artificial intelligence; Scaling; Algorithm; Computer vision; Context (archaeology); Orientation (vector space); Extrapolation; Mathematics; Point cloud; Geometry; Image (mathematics)","score_opus":0.04539397022582947,"score_gpt":0.3064155319050907,"score_spread":0.2610215616792612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1219611103","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05950408,0.00028941274,0.93732387,0.0016158234,0.0011357059,0.00002444848,6.59454e-7,0.000021880216,0.0000840939],"genre_scores_gemma":[0.75904316,0.00010862388,0.23952766,0.0009592853,0.00033921158,8.243075e-7,0.0000031338693,0.0000037125374,0.000014401221],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9985848,0.00019845,0.00062830327,0.00011885482,0.0003798349,0.00008974328],"domain_scores_gemma":[0.9981626,0.0004996302,0.00056254386,0.00008299925,0.00059115485,0.000101015714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091507053,0.00007814046,0.00025215093,0.0002878908,0.000020359903,0.00005452945,0.00039720107,0.00007172369,0.000007648062],"category_scores_gemma":[0.00014113936,0.00006521297,0.000075096046,0.00009397209,0.0001193748,0.00044413566,0.00007342209,0.00013206319,8.8252904e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021914592,0.00036837047,0.023419835,0.00003438515,0.00080554193,0.0006368206,0.0011795766,0.0012647944,0.0046844864,0.01493801,0.038086127,0.9143629],"study_design_scores_gemma":[0.0074774264,0.0020214806,0.60218155,0.0014857751,0.00017954636,0.035546318,0.00045361492,0.2559916,0.026078608,0.05958807,0.0074835573,0.0015124247],"about_ca_topic_score_codex":0.0000054795632,"about_ca_topic_score_gemma":5.7879515e-7,"teacher_disagreement_score":0.9128505,"about_ca_system_score_codex":0.000042422726,"about_ca_system_score_gemma":0.0001764721,"threshold_uncertainty_score":0.26593077},"labels":[],"label_agreement":null},{"id":"W135546504","doi":"10.1007/978-3-642-33454-2_47","title":"Efficient Global Optimization Based 3D Carotid AB-LIB MRI Segmentation by Simultaneously Evolving Coupled Surfaces","year":2012,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Segmentation; Computer science; Magnetic resonance imaging; Consistency (knowledge bases); Relaxation (psychology); Image segmentation; Artificial intelligence; Lumen (anatomy); Repeatability; Carotid arteries; Computer vision; Radiology; Mathematics; Medicine","score_opus":0.00796063068306078,"score_gpt":0.2665269464248773,"score_spread":0.2585663157418165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W135546504","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073991823,0.00020177546,0.99001056,0.0004320612,0.00095170963,0.000559537,0.0000051072193,0.0004201679,0.000019927966],"genre_scores_gemma":[0.48084533,0.0000033338047,0.5181256,0.00095015694,0.00004855037,0.000013022211,0.0000074591458,0.0000061401743,4.091988e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99653554,0.00017596874,0.0004583205,0.00078383763,0.0012610127,0.00078529655],"domain_scores_gemma":[0.9980686,0.00053026446,0.00021768153,0.0006062269,0.00027929948,0.0002979648],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013612523,0.00028683667,0.00024200967,0.00021832658,0.00029727665,0.0005343021,0.0014291949,0.00010744429,0.000043505865],"category_scores_gemma":[0.00037934506,0.00026435102,0.000048309656,0.0024145711,0.00035075907,0.00095613353,0.00037209492,0.00021165337,0.000014268002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027896967,0.00012650507,0.0029721307,0.000014090631,0.0000025503314,0.0000036913154,0.00050082325,0.9584964,0.0057924706,0.000009520189,0.000052337757,0.032026708],"study_design_scores_gemma":[0.00035738212,0.00008268527,0.0004422424,0.000048685524,0.0000041147177,0.000013251737,0.0000018880505,0.953161,0.04556286,0.000045455454,0.0000028370525,0.00027757828],"about_ca_topic_score_codex":0.00011473065,"about_ca_topic_score_gemma":0.000008924583,"teacher_disagreement_score":0.47344616,"about_ca_system_score_codex":0.0005831063,"about_ca_system_score_gemma":0.00024344002,"threshold_uncertainty_score":0.99998087},"labels":[],"label_agreement":null},{"id":"W13685733","doi":"10.1007/978-3-642-38868-2_26","title":"Efficient 3D Multi-region Prostate MRI Segmentation Using Dual Optimization","year":2013,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Consistency (knowledge bases); Segmentation; Relaxation (psychology); Image segmentation; Artificial intelligence; Prostate; Algorithm; Pattern recognition (psychology); Mathematical optimization; Mathematics","score_opus":0.019938405559570486,"score_gpt":0.2860397709251951,"score_spread":0.2661013653656246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W13685733","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014284082,0.00002853008,0.9834678,0.0005242366,0.00059399876,0.0008179791,4.002259e-7,0.0002796295,0.0000032988726],"genre_scores_gemma":[0.27736455,0.0000037499838,0.7216769,0.0008667752,0.000047577534,0.000029956787,0.0000020078564,0.000007630561,8.7606554e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974646,0.00011971842,0.00039899038,0.0007923862,0.0007325345,0.00049178785],"domain_scores_gemma":[0.9987142,0.00013346388,0.00018300026,0.0005199621,0.00029010553,0.00015923889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056081975,0.00020849307,0.00016863026,0.00046378627,0.0002506427,0.000649455,0.0008582577,0.00007227008,0.000018621526],"category_scores_gemma":[0.00012595158,0.00018377339,0.00003405855,0.0019162053,0.00033977535,0.0010688277,0.00047241765,0.0002010084,0.000018983648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.988491e-7,0.000058967053,0.00027832726,0.000007987379,0.0000012360467,0.000008467468,0.0010372984,0.77196795,0.008234228,0.000008435262,0.0000055511578,0.21839069],"study_design_scores_gemma":[0.00033047132,0.00006437828,0.0006220899,0.000053622764,0.0000018274972,0.000037670667,0.000001954472,0.94750464,0.050930455,0.00024821973,4.9752555e-7,0.00020417242],"about_ca_topic_score_codex":0.00012061869,"about_ca_topic_score_gemma":0.0000043391433,"teacher_disagreement_score":0.26308048,"about_ca_system_score_codex":0.00027534424,"about_ca_system_score_gemma":0.00018230958,"threshold_uncertainty_score":0.74940616},"labels":[],"label_agreement":null},{"id":"W1434226134","doi":"10.1007/978-3-642-38085-3_13","title":"Generation of Synthetic 4D Cardiac CT Images by Deformation from Cardiac Ultrasound","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Deformation (meteorology); Cardiac Ultrasound; Computer vision; Artificial intelligence; Ultrasound; Synthetic data; Cardiac imaging; Radiology; Medicine; Geology","score_opus":0.015456944228606813,"score_gpt":0.24726208220634832,"score_spread":0.23180513797774152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1434226134","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00053173286,0.00086823385,0.9952216,0.00020560768,0.0015539081,0.00059260253,0.000071213384,0.00017332668,0.0007817488],"genre_scores_gemma":[0.09935334,0.00035631025,0.89864844,0.00069695694,0.00047887495,0.00004644112,0.0001609445,0.000039829993,0.00021887144],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99642175,0.00010868779,0.00072529446,0.0010371435,0.0013061981,0.00040089272],"domain_scores_gemma":[0.99713844,0.0006540604,0.00049013284,0.0012219138,0.00033031078,0.0001651377],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008180184,0.00042724647,0.00061683153,0.000434914,0.0001728352,0.00063492486,0.0018827296,0.00018784808,0.000087702196],"category_scores_gemma":[0.00018818342,0.00038475054,0.00017415365,0.00034691027,0.0008126799,0.0013110613,0.00047705136,0.0004658327,0.000051640498],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011838378,0.000025941405,0.000056071203,0.000039270977,0.00003026041,0.000005271557,0.00060051575,0.0007702216,0.059971355,0.0013201728,0.0018471339,0.9353326],"study_design_scores_gemma":[0.00016368022,0.00013964775,0.00011567415,0.00033737853,0.000032520427,0.00001059498,7.4486036e-7,0.109025456,0.8534547,0.0353195,0.00058990536,0.00081018003],"about_ca_topic_score_codex":0.00022825293,"about_ca_topic_score_gemma":0.00000466114,"teacher_disagreement_score":0.93452245,"about_ca_system_score_codex":0.00027146513,"about_ca_system_score_gemma":0.00029953592,"threshold_uncertainty_score":0.99986047},"labels":[],"label_agreement":null},{"id":"W1480048967","doi":"10.1007/978-3-540-28626-4_40","title":"3D Automatic Fiducial Marker Localization Approach for Frameless Stereotactic Neuro-surgery Navigation","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials","funders":"","keywords":"Fiducial marker; Artificial intelligence; Computer science; Medicine","score_opus":0.02076482293416335,"score_gpt":0.2696439825261536,"score_spread":0.24887915959199028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1480048967","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000025031166,0.00007472148,0.99625224,0.00024727665,0.0013578966,0.0012063477,0.0000063084703,0.00047286015,0.00035734402],"genre_scores_gemma":[0.025705082,0.000015901245,0.97069687,0.0029030323,0.00040596983,0.00009305273,0.00007263712,0.00005387009,0.000053573218],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995628,0.0000799953,0.00085705804,0.001528209,0.0013101127,0.0005966211],"domain_scores_gemma":[0.99660414,0.0011698423,0.000574172,0.0010880767,0.00036999193,0.00019378772],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013489282,0.00051787065,0.0006152677,0.0008108502,0.0002855925,0.0007345782,0.0018820238,0.0003993415,0.00003345347],"category_scores_gemma":[0.00045725613,0.0004985503,0.00017178139,0.00071972015,0.0005639508,0.00090096245,0.00051586627,0.0006121137,0.000011597693],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006324959,0.000057327226,0.00003425014,0.00038436736,0.000012516691,0.000030070045,0.0005076428,0.012694485,0.000073366355,0.003286603,0.00008657829,0.9828265],"study_design_scores_gemma":[0.00023514417,0.00009691082,0.000056155244,0.0006122665,0.000015246552,0.00004707475,1.8536063e-7,0.93675387,0.0022736744,0.05928277,0.00007513383,0.0005515975],"about_ca_topic_score_codex":0.000014525465,"about_ca_topic_score_gemma":0.00000262628,"teacher_disagreement_score":0.9822749,"about_ca_system_score_codex":0.00051869726,"about_ca_system_score_gemma":0.00088628294,"threshold_uncertainty_score":0.9997466},"labels":[],"label_agreement":null},{"id":"W1480957316","doi":"10.1109/iembs.1991.683841","title":"Multispectral Tissue Characterization In Magnetic Resonance Imaging Using Bayesian Estimation And Markov Random Fields","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Western Hospital","funders":"","keywords":"Markov random field; A priori and a posteriori; Random field; Computer science; Stack (abstract data type); Multispectral image; Bayesian probability; Parametric statistics; Artificial intelligence; Maximum a posteriori estimation; Markov process; Stochastic process; Pattern recognition (psychology); Algorithm; Mathematics; Image segmentation; Image (mathematics); Statistics; Maximum likelihood","score_opus":0.007222918737328938,"score_gpt":0.27188273664515816,"score_spread":0.2646598179078292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1480957316","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015049781,0.00016593393,0.9830167,0.0011579614,0.000042459207,0.00022135078,6.8756964e-7,0.0001342585,0.00021085703],"genre_scores_gemma":[0.36960286,0.0000236074,0.62965107,0.0005618366,0.000024624836,0.0000088492025,0.000004326339,0.0000040583545,0.00011878787],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999191,0.000058617497,0.00022796114,0.00022577874,0.00014536605,0.00015127493],"domain_scores_gemma":[0.99968785,0.00004422641,0.000044990764,0.00014952463,0.000021078924,0.00005230997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021521888,0.00008096174,0.00009622767,0.00011709559,0.000042711774,0.00012122992,0.00014805706,0.00003293181,0.00009193859],"category_scores_gemma":[0.00005118049,0.000080272024,0.000009183098,0.00018430909,0.00003091075,0.0009258506,0.000057534264,0.00007742274,0.000003420214],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004137149,0.000014029723,0.00081926235,0.000005514314,2.176442e-7,0.0000058346445,0.00027300516,0.000012069451,0.025907872,0.00021476073,0.00002498045,0.9727183],"study_design_scores_gemma":[0.00063303375,0.0000148386725,0.012591216,0.00003386033,0.0000013061014,0.000017284085,0.0000052218747,0.9357159,0.050596736,0.00021197749,0.00008418666,0.00009444108],"about_ca_topic_score_codex":0.000054117205,"about_ca_topic_score_gemma":0.00002449649,"teacher_disagreement_score":0.9726239,"about_ca_system_score_codex":0.00004173409,"about_ca_system_score_gemma":0.000016935113,"threshold_uncertainty_score":0.3273398},"labels":[],"label_agreement":null},{"id":"W1481719636","doi":"10.1007/11566465_83","title":"Toward Automatic Computer Aided Dental X-ray Analysis Using Level Set Method","year":2005,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Support vector machine; Segmentation; Artificial intelligence; Pattern recognition (psychology); Scheme (mathematics); Classifier (UML); Computer vision; Image segmentation; Mathematics","score_opus":0.05790426927556042,"score_gpt":0.36032894302337387,"score_spread":0.30242467374781346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1481719636","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015175699,0.000041051324,0.98294824,0.00067770615,0.0004793769,0.00028745053,0.0000032310224,0.0003823327,0.0000048907386],"genre_scores_gemma":[0.3788784,0.0000014780608,0.61825943,0.002672871,0.000170371,0.000006471105,0.000002525448,0.00000771302,7.4136534e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99588287,0.00029436784,0.00064533757,0.0012118045,0.0012487208,0.0007169009],"domain_scores_gemma":[0.9978154,0.0004877618,0.00022439717,0.0010211052,0.00017644823,0.00027490192],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020113676,0.00032777805,0.00048112823,0.001311711,0.00024051797,0.0007221388,0.0028799952,0.00011621133,0.000045514553],"category_scores_gemma":[0.0001446064,0.00029332415,0.00017375141,0.0058337734,0.00036497923,0.0012743975,0.0012052853,0.00035438905,0.000023060249],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011781002,0.00006165098,0.0008650221,0.000012757633,0.00003501562,0.000025645277,0.0019611425,0.21646239,0.0035466333,0.000048363556,0.000022503848,0.7769577],"study_design_scores_gemma":[0.00026454538,0.00006776666,0.0033691425,0.000040425934,0.000030205172,0.000058885926,0.000001542761,0.9501197,0.04492529,0.00078430347,0.000008245859,0.00032998182],"about_ca_topic_score_codex":0.00009026754,"about_ca_topic_score_gemma":0.000050793235,"teacher_disagreement_score":0.7766277,"about_ca_system_score_codex":0.00037986247,"about_ca_system_score_gemma":0.00028033566,"threshold_uncertainty_score":0.9999519},"labels":[],"label_agreement":null},{"id":"W1482074272","doi":"10.1007/11558484_59","title":"A Bayesian Approach for Weighting Boundary and Region Information for Segmentation","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Boundary (topology); Segmentation; Weighting; Computer science; Image segmentation; Artificial intelligence; Scale-space segmentation; Bayesian probability; Pattern recognition (psychology); Decision boundary; Pixel; Computer vision; Mathematics","score_opus":0.016789385779584484,"score_gpt":0.2643120375307236,"score_spread":0.24752265175113908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1482074272","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000028296852,0.00013232157,0.9964961,0.00056287,0.00032023442,0.0016474308,0.0000066695065,0.00019159714,0.000639943],"genre_scores_gemma":[0.0016575145,0.000034542867,0.99542797,0.0022844658,0.0002997984,0.00013722376,0.000057934376,0.000016520888,0.00008400822],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978125,0.000018119355,0.00053537596,0.0007258022,0.00053102704,0.00037718067],"domain_scores_gemma":[0.99841243,0.0003624953,0.0003831018,0.0004591243,0.00025722015,0.00012562405],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00085859466,0.0003057197,0.00029614367,0.00063342275,0.0003257205,0.0007849486,0.00094200025,0.00022149147,0.0000024510339],"category_scores_gemma":[0.00012931046,0.00028857082,0.00007608081,0.00022507743,0.000362017,0.0021798015,0.00031634152,0.00025869245,0.0000011771822],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006907915,0.000009194378,0.0000046307077,0.00012552031,0.000004452684,8.217161e-7,0.0008156522,0.0005815874,0.00007102795,0.0036408019,0.00009899809,0.9946404],"study_design_scores_gemma":[0.00050711184,0.00020901063,0.000009136776,0.00016277729,0.000009514784,0.000041806834,9.0598144e-7,0.9364775,0.0044208555,0.056274503,0.001494166,0.00039273346],"about_ca_topic_score_codex":0.0000040890773,"about_ca_topic_score_gemma":0.000003869479,"teacher_disagreement_score":0.9942477,"about_ca_system_score_codex":0.00025083649,"about_ca_system_score_gemma":0.00027300225,"threshold_uncertainty_score":0.99995667},"labels":[],"label_agreement":null},{"id":"W1482635442","doi":"10.1007/978-3-540-74260-9_83","title":"Fuzzy C-Means Clustering for Segmenting Carotid Artery Ultrasound Images","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Artificial intelligence; Carotid arteries; Ultrasound; Fuzzy logic; Cluster analysis; Market segmentation; Computer science; Radiology; Medicine; Internal medicine; Business","score_opus":0.025033052005786573,"score_gpt":0.2881967337312117,"score_spread":0.2631636817254251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1482635442","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000007705521,0.00015210334,0.99170095,0.00024640502,0.0017265987,0.0009886269,0.000012552198,0.00047598832,0.004689065],"genre_scores_gemma":[0.003751022,0.00003868828,0.99230856,0.0026129913,0.0006741241,0.000036172438,0.0000147535375,0.00005355079,0.00051016704],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9951896,0.00003515971,0.000835063,0.0017074702,0.0012303671,0.0010023256],"domain_scores_gemma":[0.99630123,0.0013948103,0.00042135446,0.0012652256,0.00034636603,0.00027103862],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0021312449,0.0006072334,0.000578253,0.0010296978,0.0003631152,0.00090450107,0.0031247954,0.0003322508,0.000021884178],"category_scores_gemma":[0.00030326826,0.00059286144,0.00020346299,0.00049685535,0.0007477846,0.0009834191,0.0010538483,0.00076217495,0.000021896565],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000070613655,0.00004181032,0.000070704795,0.00019955987,0.000027481927,0.00008663679,0.001124747,0.0032723846,0.011084692,0.0019294751,0.00024090789,0.9819145],"study_design_scores_gemma":[0.0023512677,0.0012054618,0.00037965714,0.0037628715,0.00008765363,0.0010586772,0.000005055341,0.29484305,0.39208522,0.2970867,0.0018236784,0.005310738],"about_ca_topic_score_codex":0.00001584514,"about_ca_topic_score_gemma":0.00005385043,"teacher_disagreement_score":0.9766038,"about_ca_system_score_codex":0.00041207875,"about_ca_system_score_gemma":0.00033121876,"threshold_uncertainty_score":0.99965227},"labels":[],"label_agreement":null},{"id":"W1483719133","doi":"","title":"A Triple-diagonal Gradient-based Edge Detection.","year":2003,"lang":"en","type":"article","venue":"UTS ePRESS (University of Technology Sydney)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of California, Irvine; National Taiwan University; Shanghai Jiao Tong University; Zhejiang University; Technische Universität Berlin; Universität Wien; Hongik University; University of Bristol; Universidad EAFIT; University of Massachusetts Dartmouth; Chinese Academy of Sciences; University of Regina; Universidad de Málaga; City University of Hong Kong; Universidad Pública de Navarra; Nanyang Technological University; University of Missouri; Universidad de Navarra; University of Toronto; University of Bedfordshire; Dartmouth College; University of Miami","keywords":"Diagonal; Edge detection; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Mathematics; Image gradient; Morphological gradient; Computer vision; Computer science; Geometry; Algorithm; Pattern recognition (psychology); Image processing; Image (mathematics)","score_opus":0.010421698609543208,"score_gpt":0.21430488358839736,"score_spread":0.20388318497885416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1483719133","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013144583,0.00009371684,0.9826545,0.001129403,0.00020194381,0.00020401608,0.00000447225,0.0008284461,0.0017388929],"genre_scores_gemma":[0.6478254,0.00001939286,0.35144353,0.00017713552,0.0000059062627,0.000003020977,0.0000024339145,0.000008342172,0.00051485153],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998864,0.00008167085,0.00014284569,0.00040539936,0.00024337282,0.0002627172],"domain_scores_gemma":[0.9988909,0.0000591674,0.0001545572,0.00065351167,0.00013352743,0.000108375716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020502268,0.00013182708,0.00021782516,0.00076750375,0.00018275664,0.00001491523,0.0011789424,0.00023402757,0.00016015556],"category_scores_gemma":[0.00020527658,0.00015555252,0.00009701,0.0009832057,0.00047470655,0.00033638158,0.00019626063,0.00025118593,0.000051498126],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000827164,0.0012624506,0.0086139105,0.00017693796,0.00026972697,0.00052098307,0.0011470093,0.00004504551,0.123585075,0.20815744,0.016006649,0.64013207],"study_design_scores_gemma":[0.0023722928,0.00037048446,0.0012860345,0.00006577524,0.00004631729,0.000057503807,0.0005128264,0.007159731,0.95153195,0.013324715,0.022796046,0.00047630657],"about_ca_topic_score_codex":0.00005037999,"about_ca_topic_score_gemma":0.000025805737,"teacher_disagreement_score":0.8279469,"about_ca_system_score_codex":0.00006761557,"about_ca_system_score_gemma":0.00008974707,"threshold_uncertainty_score":0.6343248},"labels":[],"label_agreement":null},{"id":"W1484405646","doi":"10.15837/ijccc.2007.1.2333","title":"Deformable Atlases for the Segmentation of Internal Brain Nuclei in Magnetic Resonance Imaging","year":2007,"lang":"en","type":"article","venue":"International Journal of Computers Communications & Control","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Harvard University; University of Oxford; NIH Clinical Center; National Institutes of Health; Institut national de recherche en informatique et en automatique (INRIA); University of Bern","keywords":"Artificial intelligence; Segmentation; Computer science; Magnetic resonance imaging; Computer vision; Atlas (anatomy); Grey matter; Affine transformation; Image segmentation; Brain atlas; Image registration; Pattern recognition (psychology); Image (mathematics); Anatomy; Radiology; Medicine; Mathematics","score_opus":0.013561967480862629,"score_gpt":0.3177810355113612,"score_spread":0.3042190680304986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1484405646","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017637495,0.0024613778,0.98487955,0.009893668,0.0005336215,0.00031560197,0.0000069849348,0.000022448063,0.00012298403],"genre_scores_gemma":[0.68628836,0.00013747215,0.31109732,0.002361359,0.00007485793,0.000014803373,0.0000027408305,0.0000073466636,0.000015777065],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979777,0.00014650807,0.0010505943,0.00011674771,0.0005327862,0.00017569936],"domain_scores_gemma":[0.99471813,0.0030691356,0.0007383514,0.0005531565,0.0008605874,0.00006066421],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021369846,0.00010959889,0.0001991155,0.00041546594,0.0000774662,0.00013046649,0.003969236,0.000027916227,0.000008691036],"category_scores_gemma":[0.0003379818,0.000088230794,0.00014113414,0.00023755615,0.00018283795,0.0007925329,0.0003045449,0.0002348512,0.000001375899],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016690555,0.00021358127,0.004589673,0.000006316316,0.00005955513,0.000013113428,0.0010432277,0.0004351369,0.006057133,0.009883418,0.0023758141,0.9751561],"study_design_scores_gemma":[0.009860607,0.0005061645,0.047608428,0.0006986587,0.000051867017,0.0004012263,0.0006700132,0.89131373,0.019768776,0.008048285,0.020723715,0.00034853557],"about_ca_topic_score_codex":0.00006555601,"about_ca_topic_score_gemma":0.000039774633,"teacher_disagreement_score":0.97480756,"about_ca_system_score_codex":0.0001713987,"about_ca_system_score_gemma":0.00010571295,"threshold_uncertainty_score":0.7375894},"labels":[],"label_agreement":null},{"id":"W1488988499","doi":"10.1109/i2mtc.2015.7151236","title":"Stochastic color image segmentation using spatial constraints","year":2015,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Canada Research Chairs","keywords":"Adjacency list; Artificial intelligence; Segmentation; Computer science; Image segmentation; Scale-space segmentation; Pattern recognition (psychology); Histogram; Computer vision; Graph; Scale (ratio); Minimum spanning tree-based segmentation; Segmentation-based object categorization; Scale space; Image (mathematics); Image processing; Algorithm; Geography; Theoretical computer science; Cartography","score_opus":0.05242080753303619,"score_gpt":0.3330149764157243,"score_spread":0.2805941688826881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1488988499","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038929286,0.0000042457523,0.99340045,0.00018052338,0.00024599893,0.00024880833,0.000001524732,0.00035150757,0.0016740137],"genre_scores_gemma":[0.21322277,2.4407203e-7,0.78601336,0.0006178377,0.00003913979,0.000011145337,0.000004042242,0.0000049612127,0.00008650591],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989681,0.00006315837,0.00020187293,0.00021784885,0.00038204895,0.00016697464],"domain_scores_gemma":[0.9993277,0.0000484459,0.00007559184,0.00019883583,0.00015147423,0.00019796011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029814232,0.000087351335,0.00009451483,0.00007737659,0.00004401118,0.00013835433,0.0003044519,0.000034581008,0.00019189135],"category_scores_gemma":[0.0001515934,0.000079237594,0.000021634001,0.00016121364,0.00014793806,0.0006456022,0.00012487521,0.000064317086,0.000101061705],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028102053,0.0002787476,0.00013933075,0.000027771672,0.000043213364,0.00010126487,0.0031886268,0.00041204685,0.40463355,0.009651153,0.014572341,0.56692386],"study_design_scores_gemma":[0.0014712967,0.00021533815,0.0000919526,0.000025287281,0.000013532971,0.00007923103,0.00050039025,0.67668426,0.31716457,0.0034051843,0.000021761829,0.00032720988],"about_ca_topic_score_codex":0.00011417013,"about_ca_topic_score_gemma":0.000005321753,"teacher_disagreement_score":0.6762722,"about_ca_system_score_codex":0.000103786435,"about_ca_system_score_gemma":0.00016616151,"threshold_uncertainty_score":0.32312152},"labels":[],"label_agreement":null},{"id":"W1490487555","doi":"10.1007/978-3-642-10331-5_30","title":"Automated Segmentation of Brain Tumors in MRI Using Force Data Clustering Algorithm","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Cluster analysis; Segmentation; Artificial intelligence; Boundary (topology); Pattern recognition (psychology); Image segmentation; Cluster (spacecraft); Magnetic resonance imaging; Algorithm; Computer vision; Mathematics","score_opus":0.033398451891719534,"score_gpt":0.3209498056747834,"score_spread":0.28755135378306385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1490487555","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000036367113,0.00011928179,0.99799204,0.00032110146,0.00039137545,0.00056419015,0.000013418928,0.00037519363,0.00018703254],"genre_scores_gemma":[0.002700515,0.000019078874,0.99578476,0.0012823455,0.000094740026,0.0000034717289,0.000038321457,0.000022459066,0.00005431715],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99594796,0.00007859162,0.0009265416,0.0013934743,0.0011512373,0.00050222734],"domain_scores_gemma":[0.99715173,0.00036834393,0.00053145207,0.0016637342,0.00015517318,0.00012956004],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016264381,0.0003995074,0.00054517895,0.0012036313,0.00010151915,0.00026376054,0.0041908533,0.00022195412,0.000012704177],"category_scores_gemma":[0.00014800433,0.0004035658,0.0000535293,0.0009376637,0.00048466877,0.0014652247,0.0020686851,0.00053029385,0.000003274412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021116264,0.000024944764,0.000010769238,0.000045758108,0.0000042608203,0.000078184836,0.00052946067,0.020543672,0.002971464,0.00009728599,0.000029690229,0.9756624],"study_design_scores_gemma":[0.00027736212,0.00010423885,0.00006135364,0.00081763027,0.000004112115,0.000056380883,5.4397265e-7,0.974689,0.014831219,0.008783326,0.000012746737,0.00036209985],"about_ca_topic_score_codex":0.00009314397,"about_ca_topic_score_gemma":0.00006594251,"teacher_disagreement_score":0.9753003,"about_ca_system_score_codex":0.000398571,"about_ca_system_score_gemma":0.0005020222,"threshold_uncertainty_score":0.99984163},"labels":[],"label_agreement":null},{"id":"W1492385901","doi":"10.1007/11566465_42","title":"Automatic Segmentation of the Left Ventricle in 3D SPECT Data by Registration with a Dynamic Anatomic Model","year":2005,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Segmentation; Artificial intelligence; Computer science; Computer vision; Matching (statistics); Ventricle; 3d model; Pattern recognition (psychology); Medicine; Cardiology","score_opus":0.012288245488034711,"score_gpt":0.2856267100590427,"score_spread":0.273338464571008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1492385901","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10301396,0.000042208456,0.8953576,0.0011433912,0.00006229634,0.00030755906,0.0000032537273,0.00006412942,0.000005596578],"genre_scores_gemma":[0.53405285,0.0000034901798,0.4655664,0.00036187583,0.000006188264,0.000002959151,0.0000029608082,0.000002694576,6.2072354e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809694,0.000080614125,0.00034416173,0.0005479939,0.000684061,0.000246226],"domain_scores_gemma":[0.99853104,0.000120426594,0.00019471066,0.0010592921,0.000048321755,0.00004618231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008161318,0.00012086496,0.00013849536,0.00020639854,0.000073559226,0.00013806204,0.0024173749,0.000032704585,0.0000054072007],"category_scores_gemma":[0.000098562174,0.000086151646,0.000014522214,0.0014125799,0.00029104744,0.0013442874,0.00048391562,0.00017816029,0.0000014613823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030517197,0.00011198104,0.0014536848,0.000021016187,0.0000022650077,0.0000037921477,0.001325693,0.11196408,0.0209254,0.000041084575,0.000050314815,0.86409765],"study_design_scores_gemma":[0.00025020874,0.00003940808,0.0014970831,0.00006709803,0.0000017603405,0.00001619577,0.0000010976323,0.9125298,0.08449883,0.001005932,8.231777e-7,0.000091718284],"about_ca_topic_score_codex":0.000051168823,"about_ca_topic_score_gemma":0.0003459804,"teacher_disagreement_score":0.8640059,"about_ca_system_score_codex":0.00024516214,"about_ca_system_score_gemma":0.00029431834,"threshold_uncertainty_score":0.44921243},"labels":[],"label_agreement":null},{"id":"W1492466763","doi":"10.1007/978-3-642-41181-6_20","title":"Evaluation of Interactive Segmentation Algorithms Using Densely Sampled Correct Interactions","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Segmentation; Artificial intelligence; Computer science; Scale-space segmentation; Observer (physics); Segmentation-based object categorization; Image segmentation; Computer vision; Range (aeronautics); Set (abstract data type); Pattern recognition (psychology); Engineering","score_opus":0.06434496983224125,"score_gpt":0.3576914320468806,"score_spread":0.29334646221463934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1492466763","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00037760334,0.00009525842,0.9952705,0.000086744476,0.0020450712,0.0010248161,0.0000062309205,0.000128293,0.00096551585],"genre_scores_gemma":[0.068477064,0.00001457673,0.9307645,0.0003998304,0.00017691254,0.000039931994,0.00001522991,0.000025984207,0.00008594679],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99523103,0.00020599877,0.00078392675,0.0010310418,0.0023897532,0.0003582322],"domain_scores_gemma":[0.99556327,0.00074375764,0.0008034444,0.00082285755,0.0019302884,0.00013637064],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0022860235,0.00038955463,0.0004546583,0.0011368714,0.00015000302,0.00033404172,0.0014763162,0.00017046169,0.00032758765],"category_scores_gemma":[0.00049014477,0.00037404682,0.00011911158,0.0006009306,0.0004808111,0.00166186,0.00061311485,0.0006118676,0.000032762637],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002918811,0.00003714458,0.000009466941,0.000016571503,0.000025877147,0.000004378983,0.0011994948,0.006894736,0.009310305,0.00024018735,0.000030911542,0.982228],"study_design_scores_gemma":[0.00030174607,0.00011621616,0.000052607495,0.0004408907,0.000051170984,0.00005033729,0.0000019738416,0.88947636,0.06450217,0.044656757,0.000013694912,0.0003360654],"about_ca_topic_score_codex":0.00014272124,"about_ca_topic_score_gemma":0.000046619647,"teacher_disagreement_score":0.98189193,"about_ca_system_score_codex":0.0011943008,"about_ca_system_score_gemma":0.0008436023,"threshold_uncertainty_score":0.99987113},"labels":[],"label_agreement":null},{"id":"W1493246429","doi":"10.1007/978-3-540-30074-8_9","title":"Neuroanatomy Registration: An Algebraic-Topology Based Approach","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; Bishop's University","funders":"","keywords":"Computer science; ENCODE; Topology (electrical circuits); Algebraic number; Decomposition; Image (mathematics); Algebraic topology; Physical law; Differential (mechanical device); Scheme (mathematics); Deformation (meteorology); Applied mathematics; Theoretical computer science; Algebra over a field; Artificial intelligence; Mathematics; Mathematical analysis; Pure mathematics","score_opus":0.02057634803163264,"score_gpt":0.2769336381050219,"score_spread":0.25635729007338925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1493246429","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000064356727,0.00009872233,0.9902397,0.0013623952,0.00079403823,0.0005205446,0.0000026083396,0.0005361216,0.0064394637],"genre_scores_gemma":[0.027964668,0.000012244025,0.963536,0.007935995,0.00032053093,0.000023781085,0.0000246213,0.000034528563,0.00014760923],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99536586,0.00008995622,0.0006413895,0.001961262,0.0013241831,0.0006173247],"domain_scores_gemma":[0.99680287,0.0002452095,0.00037058783,0.0019801552,0.0002694708,0.00033171222],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009912248,0.00052655325,0.0004904674,0.00087608595,0.00023850526,0.0005892867,0.0042718807,0.000411209,0.0000877991],"category_scores_gemma":[0.00013441285,0.00049782934,0.0001121657,0.0006784652,0.001477117,0.0010777406,0.00060914806,0.00096510316,0.000023741915],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006531955,0.00014160616,0.0000162163,0.00007884138,0.000008459021,0.00028434006,0.00048645746,0.012634685,0.0002845664,0.18770567,0.00009643112,0.7982562],"study_design_scores_gemma":[0.00059984403,0.0005594382,0.000055700606,0.0001932509,0.000010526314,0.00017419853,2.5706635e-7,0.5582787,0.011034205,0.4275638,0.00051541807,0.0010146645],"about_ca_topic_score_codex":0.000039234536,"about_ca_topic_score_gemma":0.00002407961,"teacher_disagreement_score":0.7972415,"about_ca_system_score_codex":0.00035307018,"about_ca_system_score_gemma":0.0014391068,"threshold_uncertainty_score":0.99974734},"labels":[],"label_agreement":null},{"id":"W1493313087","doi":"10.1007/978-3-642-15711-0_70","title":"Probabilistic Multi-Shape Representation Using an Isometric Log-Ratio Mapping","year":2010,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Simplex; Probabilistic logic; Computer science; Euclidean space; Shape analysis (program analysis); Principal component analysis; Statistical model; Algorithm; Artificial intelligence; Mathematics; Pattern recognition (psychology); Combinatorics","score_opus":0.06032258157147319,"score_gpt":0.3429956427986851,"score_spread":0.2826730612272119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1493313087","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08722935,0.000015612355,0.9108491,0.00020005685,0.00094934733,0.00046361174,4.913706e-7,0.00028812382,0.0000042844645],"genre_scores_gemma":[0.4564702,9.808955e-7,0.54293257,0.0004906119,0.000088449306,0.0000104079445,0.0000011978838,0.000005247552,3.138943e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997167,0.00012598211,0.00043568332,0.0010251391,0.00076602824,0.00048017118],"domain_scores_gemma":[0.9979611,0.00038532476,0.00017771205,0.0009945066,0.00027113777,0.00021019498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013320143,0.00019430013,0.00020660408,0.0010159819,0.00027514706,0.00071981875,0.0019890333,0.00010447329,0.000026110993],"category_scores_gemma":[0.0013378252,0.00017916012,0.000038807317,0.0051702564,0.00048171362,0.002236585,0.0005429503,0.00046447874,0.0000092950695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011731647,0.00017070312,0.0023252668,0.0000245262,0.0000016902733,0.000020439442,0.0015588063,0.013683505,0.16559114,0.00016801864,0.0000016657334,0.8164531],"study_design_scores_gemma":[0.0001891855,0.00005975594,0.004170049,0.00004957352,0.000001358582,0.00003825752,0.0000012080413,0.9060115,0.08584989,0.0034291956,8.102466e-7,0.00019918742],"about_ca_topic_score_codex":0.00008550189,"about_ca_topic_score_gemma":0.000076275566,"teacher_disagreement_score":0.892328,"about_ca_system_score_codex":0.000112413996,"about_ca_system_score_gemma":0.00032626188,"threshold_uncertainty_score":0.7305938},"labels":[],"label_agreement":null},{"id":"W1494093226","doi":"10.1007/978-3-642-17688-3_5","title":"Neural Image Thresholding Using SIFT: A Comparative Study","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Thresholding; Artificial intelligence; Computer science; Pattern recognition (psychology); Scale-invariant feature transform; Artificial neural network; Image segmentation; Computer vision; Segmentation; Image processing; Image (mathematics)","score_opus":0.05166302053514781,"score_gpt":0.3427319140114214,"score_spread":0.29106889347627357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1494093226","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017624457,0.00008231148,0.9940176,0.00019079783,0.0014745006,0.0010536301,0.0000031963386,0.000371155,0.0010443492],"genre_scores_gemma":[0.17599653,0.000003683422,0.82255936,0.0010173556,0.00031652485,0.00001352602,0.0000022144648,0.000029705641,0.000061122555],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99516773,0.000081913524,0.00071607786,0.0017668598,0.0015679983,0.0006994353],"domain_scores_gemma":[0.99691874,0.0004657086,0.00042515012,0.0015699771,0.00034875819,0.00027164974],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0012249246,0.00062480726,0.0007243577,0.00096417964,0.000416276,0.0010824748,0.0040972177,0.00027769132,0.00006779938],"category_scores_gemma":[0.00010647874,0.00056122005,0.00012687514,0.00072603615,0.0012578096,0.0013735094,0.0021802217,0.0018061382,0.000028071003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002465702,0.00057632173,0.00039591122,0.000100235295,0.00007634186,0.0014163284,0.021669727,0.0071249236,0.06272199,0.0042203506,0.00007034171,0.90160286],"study_design_scores_gemma":[0.00058312353,0.0005083859,0.00015993473,0.00028204182,0.000022723993,0.00014448797,0.0000034980776,0.9031676,0.050710585,0.043179072,0.000069962516,0.0011685482],"about_ca_topic_score_codex":0.00005206454,"about_ca_topic_score_gemma":0.00007777804,"teacher_disagreement_score":0.9004343,"about_ca_system_score_codex":0.00026920214,"about_ca_system_score_gemma":0.00042292243,"threshold_uncertainty_score":0.9999545},"labels":[],"label_agreement":null},{"id":"W1495728280","doi":"10.1007/11867586_32","title":"Posterior Sampling of Scientific Images","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Sample (material); Image resolution; Resolution (logic); Sampling (signal processing); Artificial intelligence; Superresolution; Computer vision; Image (mathematics); Image processing; Porous medium; Pattern recognition (psychology); Geology; Porosity; Physics","score_opus":0.02292459763073616,"score_gpt":0.2858964746626657,"score_spread":0.26297187703192954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1495728280","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011655455,0.00028729753,0.99540305,0.00025646164,0.0013745502,0.00036812178,0.00001140008,0.00022573624,0.0019568412],"genre_scores_gemma":[0.029709155,0.000009445416,0.9687628,0.0005019385,0.00017636533,0.00000707818,0.000009550683,0.000027069334,0.00079660764],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9957393,0.000033569206,0.00077006436,0.0014309607,0.0014873297,0.00053877186],"domain_scores_gemma":[0.9970363,0.00038161717,0.00047378254,0.0015078125,0.00045124805,0.00014925592],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001297311,0.0004065956,0.0005217447,0.0012896616,0.00021483576,0.00078291853,0.0036289785,0.00022446633,0.00004273707],"category_scores_gemma":[0.00013915767,0.00037637822,0.00013740273,0.00080602313,0.0019872217,0.0007819565,0.0014918208,0.000526473,0.000023718663],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021300618,0.000031866417,0.000033177766,0.000074830015,0.0000051150378,0.000037154477,0.00021482936,0.0012460414,0.013439443,0.0011678014,0.00018629628,0.98356134],"study_design_scores_gemma":[0.000701109,0.0005058407,0.0006700844,0.0022777908,0.000027857752,0.00019392573,3.4467124e-7,0.12902817,0.6509866,0.21205282,0.0015234998,0.00203192],"about_ca_topic_score_codex":0.000031400574,"about_ca_topic_score_gemma":0.000013447528,"teacher_disagreement_score":0.9815294,"about_ca_system_score_codex":0.00019355651,"about_ca_system_score_gemma":0.0005641623,"threshold_uncertainty_score":0.9998688},"labels":[],"label_agreement":null},{"id":"W1499400216","doi":"10.1007/978-3-540-74260-9_17","title":"Automatic Closed Edge Detection Using Level Lines Selection","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Enhanced Data Rates for GSM Evolution; Pattern recognition (psychology); Edge detection; Probabilistic logic; Selection (genetic algorithm); Line (geometry); Set (abstract data type); Reduction (mathematics); Curvature; Flexibility (engineering); Algorithm; Image (mathematics); Image processing; Mathematics; Statistics","score_opus":0.06129480763329023,"score_gpt":0.32126037504094146,"score_spread":0.2599655674076512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1499400216","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003979423,0.00009827218,0.9959763,0.000085492014,0.0018195397,0.00050923316,0.0000017936296,0.0006741569,0.00043726215],"genre_scores_gemma":[0.041933686,0.000013782266,0.95609385,0.0011408352,0.0006236295,0.0000074729696,0.0000028899783,0.00003666183,0.00014719294],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99604815,0.000054347238,0.00072707806,0.0012659562,0.0012880679,0.0006164135],"domain_scores_gemma":[0.99781656,0.00037102203,0.00043189104,0.0007789431,0.00040079132,0.00020078346],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014396264,0.00048441594,0.0004481008,0.0015381706,0.00032323066,0.0004962416,0.0018213851,0.0004342934,0.000047719626],"category_scores_gemma":[0.00021525176,0.00046659308,0.000120224206,0.0011508832,0.0005191759,0.0009341503,0.00061829196,0.00086412334,0.000030942454],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001869004,0.000018728153,0.0000094968555,0.000041565287,0.000006465344,0.000024414245,0.00023448006,0.0012181284,0.0060051247,0.00033719125,0.0000054822317,0.9920971],"study_design_scores_gemma":[0.00017444445,0.00013173526,0.00011107933,0.00031465045,0.00000977767,0.00013147006,1.268209e-7,0.8594838,0.11809223,0.020986639,0.000086537635,0.000477513],"about_ca_topic_score_codex":0.000056827583,"about_ca_topic_score_gemma":0.00015404313,"teacher_disagreement_score":0.9916195,"about_ca_system_score_codex":0.0007403545,"about_ca_system_score_gemma":0.0005117287,"threshold_uncertainty_score":0.99977857},"labels":[],"label_agreement":null},{"id":"W1500434646","doi":"10.1109/icip.2004.1421731","title":"Estimation of mixtures of probabilistic pca with stochastic em for the 3d biplanar reconstruction of scoliotic rib cage","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université de Montréal","funders":"","keywords":"Probabilistic logic; Principal component analysis; Expectation–maximization algorithm; Minification; Maximization; Computer science; Mathematics; Artificial intelligence; Energy minimization; Iterative reconstruction; Pattern recognition (psychology); Mathematical optimization; Algorithm; Statistics; Maximum likelihood","score_opus":0.0130000198637166,"score_gpt":0.2642931776818289,"score_spread":0.25129315781811234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1500434646","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012834537,0.000037436334,0.9861405,0.00017220638,0.000040861905,0.00066767685,0.0000046380587,0.0000471894,0.00005495298],"genre_scores_gemma":[0.55239964,0.0000012967441,0.4475155,0.000027026399,0.000008013318,0.000024018467,0.000001267414,0.0000026761643,0.000020569041],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991573,0.000032750017,0.00033662046,0.00013861863,0.00024531808,0.00008937252],"domain_scores_gemma":[0.9989252,0.00033082103,0.0002728581,0.00027110035,0.00017137526,0.000028645522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000292204,0.00007172482,0.00014914082,0.00009594632,0.000031152555,0.000013912015,0.00027988554,0.00003127263,0.000031745025],"category_scores_gemma":[0.00023247546,0.000043568423,0.000029620916,0.0002342391,0.00017184483,0.00023127091,0.000027521002,0.000043635817,7.2704887e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006065343,0.00015919998,0.000034705423,0.0004580933,0.00005518301,2.857761e-7,0.0014589836,0.045166302,0.008534811,0.011480665,0.0004883575,0.93210274],"study_design_scores_gemma":[0.0003258463,0.00032788803,0.0002977442,0.00014554693,0.00003383443,0.000015843434,0.00007153042,0.80929726,0.18757525,0.0018427907,0.0000015767464,0.0000648756],"about_ca_topic_score_codex":0.00003817501,"about_ca_topic_score_gemma":0.000038072394,"teacher_disagreement_score":0.9320379,"about_ca_system_score_codex":0.000022065187,"about_ca_system_score_gemma":0.00007559612,"threshold_uncertainty_score":0.17766687},"labels":[],"label_agreement":null},{"id":"W1500681480","doi":"10.1007/978-3-642-15711-0_2","title":"Fast Random Walker with Priors Using Precomputation for Interactive Medical Image Segmentation","year":2010,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Precomputation; Computer science; Random walker algorithm; Segmentation; Speedup; Artificial intelligence; Image segmentation; Prior probability; Session (web analytics); Image (mathematics); Computer vision; Algorithm; Bayesian probability; Parallel computing","score_opus":0.009279635495244364,"score_gpt":0.31166758753036866,"score_spread":0.3023879520351243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1500681480","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05212377,0.0000049885707,0.94537896,0.00063243235,0.00088885415,0.0007819118,0.0000012564052,0.00018126662,0.0000065449312],"genre_scores_gemma":[0.33083615,9.322443e-7,0.66827506,0.0007308961,0.000111081245,0.00003361953,0.0000032895862,0.0000085549,4.2564463e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99735814,0.0000866487,0.00037384377,0.00078005565,0.000980572,0.00042072704],"domain_scores_gemma":[0.9980365,0.00080096116,0.00019933694,0.00041140645,0.0003461896,0.0002056288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001329545,0.00020966459,0.00022547452,0.00038805912,0.00021524861,0.0004852036,0.0012416807,0.00010224916,0.000028204402],"category_scores_gemma":[0.00053059845,0.00016395215,0.000046962832,0.0011332672,0.00052301044,0.0018618028,0.0003001929,0.00044136113,0.000004008243],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037760896,0.00008212625,0.00028383918,0.000020729622,0.0000063293187,0.000014049557,0.0023143548,0.0042492053,0.060917187,0.000060500486,0.0000071524787,0.9320068],"study_design_scores_gemma":[0.0010176284,0.00012840363,0.00028260838,0.00006192166,0.0000034285633,0.00006177961,0.0000027237943,0.74750257,0.2489193,0.0018495116,0.0000026904074,0.00016743843],"about_ca_topic_score_codex":0.00003362686,"about_ca_topic_score_gemma":0.000067996196,"teacher_disagreement_score":0.93183935,"about_ca_system_score_codex":0.00012562265,"about_ca_system_score_gemma":0.00047610575,"threshold_uncertainty_score":0.6685775},"labels":[],"label_agreement":null},{"id":"W1501798870","doi":"10.1109/iembs.2006.260668","title":"3D Prostate Boundary Segmentation From Ultrasound Images Using 2D Active Shape Models","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Segmentation; Boundary (topology); Artificial intelligence; Slicing; Computer science; Gold standard (test); Rotation (mathematics); 3D ultrasound; Computer vision; Image segmentation; Active shape model; Volume (thermodynamics); Ultrasound; Pattern recognition (psychology); Mathematics; Medicine; Radiology; Statistics; Mathematical analysis; Physics","score_opus":0.01945674121403477,"score_gpt":0.28082329416531093,"score_spread":0.26136655295127614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1501798870","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038546216,0.00006104728,0.9568233,0.00012278136,0.00012078146,0.00036667677,0.000023113342,0.0005576026,0.003378496],"genre_scores_gemma":[0.11186084,0.000018786248,0.88682705,0.00071217946,0.00007740992,0.000029729012,0.00008134643,0.000015204578,0.00037746274],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99835235,0.00009273339,0.00032570492,0.0004677126,0.00049107306,0.00027041137],"domain_scores_gemma":[0.99916565,0.00015759327,0.00014678977,0.0003263059,0.000118941876,0.000084692074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016055763,0.00017368693,0.00015251139,0.00010670777,0.00017770621,0.0004879701,0.0004134884,0.000057734662,0.00029710837],"category_scores_gemma":[0.000021086787,0.00015961906,0.000045997524,0.00026902702,0.00011904661,0.0028167807,0.00012902587,0.00012749126,0.000030459756],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001850541,0.0001762044,0.00031705588,0.000012261828,0.000033337812,0.000029125096,0.0012216441,0.00051875715,0.8361604,0.0013365071,0.0048015113,0.15537468],"study_design_scores_gemma":[0.00037971893,0.000038847633,0.00091458956,0.00002310337,0.00001301555,0.0000098997,0.00012554134,0.16380845,0.79038966,0.044003382,0.000032782205,0.00026097897],"about_ca_topic_score_codex":0.0016207603,"about_ca_topic_score_gemma":0.00002402367,"teacher_disagreement_score":0.1632897,"about_ca_system_score_codex":0.00015141252,"about_ca_system_score_gemma":0.00011229074,"threshold_uncertainty_score":0.6509076},"labels":[],"label_agreement":null},{"id":"W1501826617","doi":"10.1109/pacrim.1991.160808","title":"Inhomogeneity test for unsupervised texture segmentation","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Texture (cosmology); Artificial intelligence; Image texture; Segmentation; Pattern recognition (psychology); Computer science; A priori and a posteriori; Uncorrelated; Image segmentation; Multivariate statistics; Computer vision; Mathematics; Image (mathematics); Machine learning; Statistics","score_opus":0.03190129939014009,"score_gpt":0.27737835616441775,"score_spread":0.24547705677427767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1501826617","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030588798,0.00004138542,0.9941384,0.0015264194,0.000082911894,0.00040476047,0.0000040792665,0.000480483,0.0030156607],"genre_scores_gemma":[0.067673504,0.000026831205,0.92507046,0.004266918,0.00006305303,0.00014024867,0.000011016919,0.000008399154,0.0027395359],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99919116,0.000022347062,0.0001740626,0.00024373388,0.00020905645,0.0001596513],"domain_scores_gemma":[0.99934506,0.00016228299,0.00004246856,0.00027814077,0.00008183208,0.00009023102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014693054,0.00008568824,0.00008084256,0.00005799555,0.00008118127,0.00010114586,0.00040204753,0.000047065816,0.0005916384],"category_scores_gemma":[0.00014476097,0.00007242289,0.000042143358,0.00021543843,0.000026727485,0.00046368598,0.000062313295,0.000050397615,0.00008332284],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011360681,0.00020567406,0.0006130928,0.000028233448,0.000008572205,0.00000314528,0.0005010978,0.0000016704813,0.068477824,0.0029435996,0.10712958,0.82008636],"study_design_scores_gemma":[0.0009864271,0.00026520222,0.000704915,0.000012992654,0.000008038977,0.000010577881,0.000057585297,0.15634678,0.8350209,0.002535133,0.0037570423,0.00029441566],"about_ca_topic_score_codex":0.000005566206,"about_ca_topic_score_gemma":0.0000030537524,"teacher_disagreement_score":0.819792,"about_ca_system_score_codex":0.000030941523,"about_ca_system_score_gemma":0.000008975157,"threshold_uncertainty_score":0.64780253},"labels":[],"label_agreement":null},{"id":"W1502687757","doi":"10.1109/iembs.2006.259219","title":"A Non-Rigid Image Registration Technique for 3D Ultrasound Carotid Images using a \"Twisting and Bending\" Model","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Image registration; Computer vision; Artificial intelligence; Mutual information; Similarity (geometry); Computer science; Metric (unit); Medicine; Image (mathematics); Engineering","score_opus":0.02144911016104652,"score_gpt":0.30346399043591715,"score_spread":0.28201488027487065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1502687757","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012188186,0.000010636961,0.99408454,0.0001093115,0.000029715804,0.0008821287,0.000010169595,0.00038164682,0.003273031],"genre_scores_gemma":[0.07870013,0.000004319774,0.92051184,0.00015304246,0.00005746808,0.00014976427,0.00001483096,0.000015029603,0.00039354365],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99866605,0.000022837792,0.00035459606,0.00043966994,0.0002432118,0.0002736453],"domain_scores_gemma":[0.9991625,0.00014291411,0.00016598644,0.0003067126,0.00014715985,0.00007471903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052633375,0.00016666623,0.00016480596,0.00013381564,0.00019458275,0.00036526745,0.00029229134,0.00007577325,0.0000058611313],"category_scores_gemma":[0.0001005046,0.00015879425,0.000045152276,0.00019403276,0.000117980366,0.0010019668,0.00006682335,0.00010143919,0.0000010373302],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018537315,0.000029492601,0.00016032215,0.00005424789,0.0000032212893,0.0000025711504,0.00007560047,0.000049524453,0.9930702,0.0032750592,0.0022903685,0.0009875408],"study_design_scores_gemma":[0.00018495155,0.00004133227,0.00008454239,0.000040899547,0.000008367161,0.000055093195,0.000013772709,0.30086526,0.69101125,0.007496464,0.000013207016,0.00018488428],"about_ca_topic_score_codex":0.00034982114,"about_ca_topic_score_gemma":0.000014155076,"teacher_disagreement_score":0.30205896,"about_ca_system_score_codex":0.00007616458,"about_ca_system_score_gemma":0.00008883378,"threshold_uncertainty_score":0.64754415},"labels":[],"label_agreement":null},{"id":"W1505077723","doi":"10.1007/11559573_106","title":"Carotid Artery Ultrasound Image Segmentation Using Fuzzy Region Growing","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Image segmentation; Segmentation; Ultrasound; Carotid arteries; Region growing; Radiology; Medicine; Scale-space segmentation; Internal medicine","score_opus":0.0229647488472599,"score_gpt":0.2781774407225861,"score_spread":0.2552126918753262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1505077723","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001600108,0.00013801051,0.9956094,0.000408375,0.001146984,0.00060373533,0.0000026414214,0.00040173985,0.0015290867],"genre_scores_gemma":[0.012686129,0.00006847473,0.98313797,0.003121853,0.0007301471,0.000012778276,0.00001215501,0.000045903224,0.00018456498],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99549466,0.00007874982,0.0007508084,0.0015783539,0.0014066674,0.0006907471],"domain_scores_gemma":[0.99733937,0.00046995177,0.00045143254,0.0012250803,0.00027240388,0.00024176612],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008936559,0.0005715595,0.00049482525,0.0010092751,0.00031940461,0.0008937475,0.0023750525,0.00030969293,0.00003443333],"category_scores_gemma":[0.00013573903,0.0005686531,0.00015219956,0.00063897733,0.0007933081,0.0029790855,0.0007139922,0.00081644615,0.00003742061],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000050608514,0.00005249666,0.00009402543,0.00008569083,0.000022953,0.00025650224,0.0016096953,0.005210753,0.060867015,0.0027482368,0.00013302368,0.92891455],"study_design_scores_gemma":[0.0015484809,0.00056180806,0.00029766664,0.0028347105,0.00008463612,0.002673317,0.0000048587035,0.3831615,0.42066237,0.18365441,0.00043852802,0.0040777246],"about_ca_topic_score_codex":0.00002898274,"about_ca_topic_score_gemma":0.000015468308,"teacher_disagreement_score":0.9248368,"about_ca_system_score_codex":0.00082716136,"about_ca_system_score_gemma":0.00044803388,"threshold_uncertainty_score":0.99967647},"labels":[],"label_agreement":null},{"id":"W1506020096","doi":"10.1007/978-3-642-15819-3_72","title":"A Neural Approach to Image Thresholding","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Thresholding; Computer science; Artificial intelligence; Pattern recognition (psychology); Image segmentation; Artificial neural network; Segmentation; Image (mathematics); Set (abstract data type); Computer vision; Balanced histogram thresholding; Otsu's method; Sample (material); Image processing","score_opus":0.021830107660583433,"score_gpt":0.27921527672487584,"score_spread":0.25738516906429243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1506020096","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000022101842,0.00005836197,0.9864353,0.0008734979,0.0012422274,0.0006432373,0.000002835804,0.0004646751,0.010257764],"genre_scores_gemma":[0.006901784,0.000006430092,0.9862889,0.00607654,0.0004006628,0.000027384678,0.0000040477,0.000035776473,0.00025848675],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955476,0.000030173007,0.00053085963,0.0017783287,0.001385129,0.00072791777],"domain_scores_gemma":[0.99717337,0.0002538836,0.00020699002,0.001703477,0.0002472112,0.00041504932],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010713926,0.00051289675,0.00048221598,0.0010273666,0.00024163187,0.000985816,0.00511038,0.00034199256,0.0000378409],"category_scores_gemma":[0.00020656617,0.00045953784,0.00012166323,0.00074776984,0.0007813062,0.0009150058,0.0023022187,0.0015031598,0.00007005022],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031174666,0.00004550864,0.0000075125677,0.000040326948,0.000005421069,0.00007815589,0.0010235577,0.00087604544,0.011685352,0.007888348,0.00013934438,0.9782073],"study_design_scores_gemma":[0.0004570313,0.0003434212,0.000089494635,0.0004054406,0.000012736468,0.00029170708,4.0047402e-7,0.71752334,0.12201933,0.15519272,0.001708324,0.0019560507],"about_ca_topic_score_codex":0.000016927654,"about_ca_topic_score_gemma":0.000009863265,"teacher_disagreement_score":0.97625124,"about_ca_system_score_codex":0.00019000014,"about_ca_system_score_gemma":0.0003046275,"threshold_uncertainty_score":0.99978566},"labels":[],"label_agreement":null},{"id":"W1510433709","doi":"10.1007/978-3-540-85988-8_92","title":"Nonrigid Registration of Dynamic Renal MR Images Using a Saliency Based MRF Model","year":2008,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"York University","keywords":"Artificial intelligence; Computer science; Computer vision; Markov random field; Contrast (vision); Image registration; Pattern recognition (psychology); Histogram; Pixel; Orientation (vector space); Mutual information; Similarity (geometry); Invariant (physics); Image (mathematics); Mathematics; Image segmentation","score_opus":0.02464400333445046,"score_gpt":0.30114708970803994,"score_spread":0.2765030863735895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1510433709","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02851068,0.00003603856,0.97056186,0.00035784813,0.00016498475,0.00020124468,0.0000018490751,0.00014447693,0.000021033462],"genre_scores_gemma":[0.48227438,0.0000037373638,0.51727843,0.00042041938,0.000014150571,0.0000033118286,9.609154e-7,0.0000037890084,8.00467e-7],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99742657,0.00008093176,0.00047129724,0.00069077726,0.0009542625,0.0003761762],"domain_scores_gemma":[0.99846464,0.00018852588,0.00024673034,0.0007548273,0.00023114329,0.00011411734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081383163,0.0001798899,0.00022154802,0.00044840638,0.00019040669,0.000109460234,0.0015609483,0.00007300788,0.0000049685987],"category_scores_gemma":[0.00023392604,0.00016376832,0.000057490968,0.0017524061,0.0008177773,0.0010592239,0.0002769568,0.00021475027,0.0000016157996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009399028,0.00019023594,0.00038665358,0.00004100651,0.0000032950616,0.000071277274,0.0013478971,0.32687905,0.4249331,0.00021855814,0.00003601942,0.24588352],"study_design_scores_gemma":[0.00015649512,0.00006823785,0.00031225683,0.00005356901,0.0000013321294,0.000036770125,1.625837e-7,0.7289967,0.26678494,0.0034625304,3.874094e-7,0.00012664884],"about_ca_topic_score_codex":0.00007011767,"about_ca_topic_score_gemma":0.000014356303,"teacher_disagreement_score":0.4537637,"about_ca_system_score_codex":0.00016351018,"about_ca_system_score_gemma":0.00077201356,"threshold_uncertainty_score":0.66782784},"labels":[],"label_agreement":null},{"id":"W1511224494","doi":"10.1109/iembs.2006.260000","title":"Segmentation of Prostate from 3-D Ultrasound Volumes Using Shape and Intensity Priors in Level Set Framework","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Prior probability; Artificial intelligence; Segmentation; Computer science; Image segmentation; Ground truth; Computer vision; Level set (data structures); Intensity (physics); Pattern recognition (psychology); Bayesian probability; Physics","score_opus":0.03346061836036333,"score_gpt":0.2979099303786115,"score_spread":0.2644493120182482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1511224494","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48528576,0.00001976397,0.51443076,0.000049618993,0.000030445934,0.000117859265,0.0000053149656,0.000039476527,0.000020988582],"genre_scores_gemma":[0.48169056,0.000007763913,0.51806474,0.00018468301,0.000010479069,0.0000027845363,0.000009223626,0.0000031414618,0.000026667034],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990607,0.0000490752,0.00029078842,0.0002493572,0.0002246687,0.00012542428],"domain_scores_gemma":[0.99946016,0.00014470264,0.00011001378,0.0001753504,0.000071785464,0.000037966376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002066866,0.00008709683,0.00013918302,0.00009379107,0.00003015359,0.00007288565,0.00016913678,0.000056426226,0.000037618214],"category_scores_gemma":[0.00010103206,0.00008178212,0.000016302633,0.00023771072,0.00009066294,0.00037296247,0.00010187195,0.000096926815,0.0000014774198],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043239004,0.00020128852,0.42762208,0.00006465216,0.000024730776,0.000019783605,0.0067970855,0.000084010055,0.46579573,0.0015364011,0.0017083535,0.09610264],"study_design_scores_gemma":[0.0004275637,0.00006530658,0.3254316,0.00013890128,0.000007684551,0.000008950166,0.0005361187,0.061674576,0.5889375,0.02253606,0.0000073427145,0.0002283968],"about_ca_topic_score_codex":0.0022895087,"about_ca_topic_score_gemma":0.000105982486,"teacher_disagreement_score":0.1231418,"about_ca_system_score_codex":0.00003901259,"about_ca_system_score_gemma":0.000032640073,"threshold_uncertainty_score":0.34610677},"labels":[],"label_agreement":null},{"id":"W1512293023","doi":"10.1007/11919476_26","title":"Shape Tracking and Registration for 4D Visualization of MRI and Structure","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Visualization; Fluoroscopy; Matching (statistics); Real-time MRI; Chin; Face (sociological concept); Pattern recognition (psychology); Magnetic resonance imaging; Radiology; Medicine; Anatomy","score_opus":0.017864903433592167,"score_gpt":0.29184546350112733,"score_spread":0.2739805600675352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1512293023","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016075863,0.0002652588,0.99849945,0.00020957922,0.00020252955,0.00046607762,0.000008218711,0.00007504284,0.00011305754],"genre_scores_gemma":[0.09956406,0.00004060379,0.8997322,0.00044368277,0.00013518467,0.000005698485,0.000018419027,0.00001581507,0.00004438709],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812776,0.000018617302,0.00041898497,0.00073851953,0.0004937729,0.0002023616],"domain_scores_gemma":[0.9987265,0.00027632705,0.00034732025,0.0003514426,0.00023090401,0.00006751808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044733752,0.00022884036,0.00028327017,0.00037789228,0.00013224373,0.00036523913,0.0005725637,0.00020368787,0.000004972015],"category_scores_gemma":[0.00010013129,0.00021260917,0.00003127277,0.00021251726,0.0006404895,0.00056126283,0.00023254624,0.00017598369,9.042211e-8],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000040437426,0.000010003345,0.00007359713,0.00017214811,0.000004515678,0.000004226583,0.00043836684,0.00085189193,0.0042120153,0.027614536,0.0000565419,0.9665581],"study_design_scores_gemma":[0.0002902929,0.00022567256,0.00032778786,0.00036135266,0.000010377288,0.00003421083,2.066355e-7,0.77042943,0.050110996,0.17774351,0.00012136925,0.00034481267],"about_ca_topic_score_codex":0.000012791522,"about_ca_topic_score_gemma":0.000025537884,"teacher_disagreement_score":0.9662133,"about_ca_system_score_codex":0.00005524415,"about_ca_system_score_gemma":0.00015321882,"threshold_uncertainty_score":0.86699504},"labels":[],"label_agreement":null},{"id":"W1514214094","doi":"10.1007/978-3-642-17274-8_34","title":"Automatic Liver Segmentation from CT Scans Using Multi-layer Segmentation and Principal Component Analysis","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Segmentation; Principal component analysis; Artificial intelligence; Pattern recognition (psychology); Component (thermodynamics); Layer (electronics); Image segmentation; Scale-space segmentation; Computer vision","score_opus":0.03484164948683444,"score_gpt":0.30208166818681076,"score_spread":0.2672400186999763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1514214094","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012708685,0.00017130487,0.98537374,0.0000942514,0.0006266465,0.00070961984,0.000016918548,0.0002648667,0.000033996057],"genre_scores_gemma":[0.08775624,0.00004351361,0.9111548,0.000798649,0.00012108042,0.000016491014,0.0000520811,0.0000256132,0.000031539097],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99584913,0.00009877446,0.00076041283,0.0015132949,0.0013114095,0.000466973],"domain_scores_gemma":[0.99757814,0.00041616271,0.0005649729,0.00095601,0.0002154813,0.0002692229],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007969741,0.0005258929,0.00061776926,0.0013413501,0.00032290586,0.00075208326,0.0014350798,0.00021480405,0.00014632096],"category_scores_gemma":[0.00006637911,0.00049906824,0.00013108029,0.00091624545,0.0007195718,0.0010501771,0.00094367843,0.0007439726,0.000013880855],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034581542,0.0000847526,0.00095882674,0.000053696123,0.0001549108,0.00013342453,0.002362181,0.005374897,0.04439284,0.00019011971,0.0000026366308,0.9462883],"study_design_scores_gemma":[0.00037817718,0.000051637646,0.0021157581,0.00014972428,0.00014722602,0.000028469743,0.0000010596935,0.9404683,0.053823914,0.0023167683,0.000008521614,0.00051041483],"about_ca_topic_score_codex":0.0004744539,"about_ca_topic_score_gemma":0.0004475383,"teacher_disagreement_score":0.94577783,"about_ca_system_score_codex":0.0004350515,"about_ca_system_score_gemma":0.0002956957,"threshold_uncertainty_score":0.9997461},"labels":[],"label_agreement":null},{"id":"W1519712624","doi":"10.1007/978-3-540-89639-5_26","title":"A Continuous Labeling for Multiphase Graph Cut Image Partitioning","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Electric (Canada); Institut National de la Recherche Scientifique","funders":"","keywords":"Cut; Graph partition; Computer science; Iterated function; Piecewise; Graph; Maximum cut; Partition (number theory); Minimum cut; Segmentation; Image (mathematics); Image segmentation; Artificial intelligence; Algorithm; Mathematics; Theoretical computer science; Combinatorics","score_opus":0.02123429327167649,"score_gpt":0.2865642261312774,"score_spread":0.26532993285960094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1519712624","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000016515633,0.0004014556,0.9963391,0.00043246776,0.0008839746,0.0008526297,0.000012556443,0.00048016055,0.0005811377],"genre_scores_gemma":[0.0021568073,0.00014673069,0.99461544,0.0024278169,0.00030615,0.00006751599,0.000015480979,0.000036621448,0.0002274132],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996292,0.000034563855,0.00066564087,0.0014059125,0.00091015664,0.00069175893],"domain_scores_gemma":[0.9972086,0.0007196767,0.00036509312,0.0009828716,0.00048225006,0.0002415189],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00084863976,0.0004619195,0.0005312534,0.0007746241,0.0004140893,0.0005262327,0.002270298,0.00025336872,0.000023583456],"category_scores_gemma":[0.00038289666,0.00044679004,0.0001837718,0.00047603872,0.0009682419,0.00088525505,0.00063744234,0.00059823506,0.00002312918],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000872688,0.000059995098,0.000012904312,0.00006798313,0.000015539068,0.00019555523,0.0010542131,0.0016313965,0.002955066,0.0019519107,0.00063819275,0.9914085],"study_design_scores_gemma":[0.0018177254,0.00064801786,0.000014214072,0.0013253557,0.000026766944,0.0003003797,6.826024e-7,0.73626506,0.12956505,0.124605894,0.0035869435,0.0018439265],"about_ca_topic_score_codex":0.000019218602,"about_ca_topic_score_gemma":0.000021590193,"teacher_disagreement_score":0.9895646,"about_ca_system_score_codex":0.00018643298,"about_ca_system_score_gemma":0.0003848347,"threshold_uncertainty_score":0.99979836},"labels":[],"label_agreement":null},{"id":"W1520226455","doi":"","title":"Probabilistic deformable models for weld defact contour estimation in radiography","year":2006,"lang":"en","type":"article","venue":"International Conference on Computer Vision and Graphics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Robustness (evolution); Artificial intelligence; Parametric statistics; Active contour model; Computer science; Computer vision; Probabilistic logic; Segmentation; Image segmentation; Parametric model; Maximum likelihood; Estimation theory; Statistical model; Pattern recognition (psychology); Mathematics; Algorithm; Statistics","score_opus":0.03033442797742568,"score_gpt":0.31212574118432834,"score_spread":0.28179131320690265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1520226455","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004561068,0.000019580897,0.9921966,0.0014336865,0.00028194967,0.00038986508,0.000009503021,0.00013071501,0.0009770103],"genre_scores_gemma":[0.77471006,0.000048760034,0.22426228,0.0007992036,0.000038927235,0.00006152953,0.0000411154,0.0000063409893,0.0000317625],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986612,0.00004581934,0.0003738114,0.0003731474,0.00036896043,0.00017705168],"domain_scores_gemma":[0.9992205,0.00016334822,0.00012288315,0.00018960639,0.00023526521,0.00006843466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034616506,0.00015460233,0.0001647827,0.0005082884,0.00007276483,0.00033439777,0.0004671471,0.00007580433,0.000009532651],"category_scores_gemma":[0.000020527623,0.0001346199,0.00007116095,0.00024063313,0.000074093965,0.0007531306,0.00009256295,0.00013789683,0.0000023192954],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003974554,0.0001579837,0.00013707658,0.000022558539,0.0000107291,0.00000583221,0.00008721696,0.0034881437,0.00009111202,0.9147798,0.001621768,0.07955802],"study_design_scores_gemma":[0.00050228206,0.00017851792,0.0014315324,0.00008154682,0.0000018302886,0.0000061331602,0.0000028153838,0.75310135,0.0002340674,0.24422131,0.00012421791,0.000114373885],"about_ca_topic_score_codex":0.000091119044,"about_ca_topic_score_gemma":0.000040679246,"teacher_disagreement_score":0.770149,"about_ca_system_score_codex":0.000032826447,"about_ca_system_score_gemma":0.00003888237,"threshold_uncertainty_score":0.548964},"labels":[],"label_agreement":null},{"id":"W1521924723","doi":"10.1007/3-540-45468-3_157","title":"Analysis of 3D Deformation Fields for Appearance-Based Segmentation","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Computer science; Segmentation; A priori and a posteriori; Novelty; Artificial intelligence; Atlas (anatomy); Pattern recognition (psychology); Computer vision","score_opus":0.01888360649441305,"score_gpt":0.28403211938718936,"score_spread":0.2651485128927763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1521924723","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000040013307,0.00008989232,0.9980063,0.00025697018,0.00034366944,0.00057519454,0.000008650352,0.00011601821,0.00056331634],"genre_scores_gemma":[0.044584446,0.000026171809,0.95357186,0.0015850697,0.00008003974,0.000035794048,0.000045211367,0.0000123576765,0.00005905819],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975121,0.000027601829,0.000630962,0.0006858341,0.0008440678,0.00029943444],"domain_scores_gemma":[0.99802643,0.00032487462,0.00048368308,0.0007340022,0.0003458213,0.00008520191],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008452744,0.0002582375,0.00046363164,0.0016139665,0.0001036529,0.00016042717,0.0013755664,0.0002172714,0.000032128708],"category_scores_gemma":[0.0000921086,0.00024043328,0.00017548367,0.0013020599,0.00029991765,0.0005623056,0.00018262184,0.00023815501,0.0000032622165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006502179,0.000022007252,0.000062092375,0.00006590125,0.00003964364,0.0000029616565,0.0003345159,0.06176788,0.00023366706,0.0009011561,0.000012748085,0.9365509],"study_design_scores_gemma":[0.00026240156,0.00015964133,0.00008454789,0.0001733972,0.000071674185,0.0000019321828,1.7925522e-7,0.96826816,0.019487344,0.01117987,0.00005352918,0.00025735653],"about_ca_topic_score_codex":0.000019462814,"about_ca_topic_score_gemma":0.00004578244,"teacher_disagreement_score":0.93629354,"about_ca_system_score_codex":0.0001985288,"about_ca_system_score_gemma":0.0002652593,"threshold_uncertainty_score":0.98045844},"labels":[],"label_agreement":null},{"id":"W1524663122","doi":"10.1007/978-3-642-02611-9_3","title":"Hierarchical Sampling with Constraints","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Sampling (signal processing); Simulated annealing; Convergence (economics); Task (project management); Hierarchical database model; Algorithm; Scale (ratio); Artificial intelligence; Data mining; Computer vision","score_opus":0.023570253470611018,"score_gpt":0.28485284954127515,"score_spread":0.2612825960706641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1524663122","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000007894642,0.00008784243,0.9917358,0.0009460603,0.00033245923,0.0003787056,0.0000024064773,0.00038693976,0.0061218645],"genre_scores_gemma":[0.008486177,0.000022501952,0.9867368,0.0043070214,0.0002166339,0.0000063635866,0.0000044584554,0.00002098865,0.00019910572],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99610394,0.0000343231,0.000474022,0.001406096,0.0013628047,0.00061882474],"domain_scores_gemma":[0.99771297,0.00045437049,0.00022447744,0.0011101994,0.00020348681,0.00029450408],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000763967,0.00045446958,0.00046408898,0.0007009412,0.00018363456,0.00053115183,0.0028942872,0.00025790994,0.00006738331],"category_scores_gemma":[0.00010547157,0.00036688722,0.00007640833,0.0005027635,0.0020120116,0.0005391704,0.00061391946,0.0010981862,0.000029994302],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003090079,0.000016994387,0.000007693727,0.000011580919,0.0000046418736,0.00012551577,0.00020006327,0.00055537024,0.00015974026,0.009923233,0.000013593602,0.9889785],"study_design_scores_gemma":[0.0012830783,0.001645874,0.00039044456,0.0026716965,0.000025841146,0.0010454775,3.8015398e-7,0.18312152,0.0239919,0.7810162,0.002007242,0.0028003913],"about_ca_topic_score_codex":0.0000045236816,"about_ca_topic_score_gemma":0.000010332429,"teacher_disagreement_score":0.9861781,"about_ca_system_score_codex":0.00022355875,"about_ca_system_score_gemma":0.0007058642,"threshold_uncertainty_score":0.9998783},"labels":[],"label_agreement":null},{"id":"W1530802675","doi":"10.1109/icip.2004.1418813","title":"Approximation of images by basis functions for multiple region segmentation with level sets","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Basis (linear algebra); Artificial intelligence; Image segmentation; Segmentation; Computer science; Basis function; Pattern recognition (psychology); Computer vision; Scale-space segmentation; Mathematics; Algorithm; Geometry; Mathematical analysis","score_opus":0.036575047272807415,"score_gpt":0.2846522756258054,"score_spread":0.248077228352998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1530802675","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011462566,0.000014262748,0.99615186,0.0014463167,0.000029510638,0.0005471917,0.000019067447,0.00021980201,0.00042570662],"genre_scores_gemma":[0.12099455,0.0000071202157,0.8771617,0.0003788386,0.000016534876,0.00017517574,0.000071739014,0.0000073392785,0.0011870156],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991385,0.000032242162,0.00023541629,0.00022798058,0.00024357412,0.00012232011],"domain_scores_gemma":[0.9992756,0.00012332693,0.00014619317,0.0002288735,0.00017223973,0.00005376525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016692806,0.000088637105,0.00009916224,0.000091162685,0.00007214191,0.00004573035,0.00019373267,0.00003587596,0.000026140873],"category_scores_gemma":[0.000055814948,0.000071357776,0.00003227099,0.0002060529,0.000046689376,0.0009719879,0.00002967645,0.00003861933,0.000006101939],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026858004,0.00021959182,0.00052398804,0.00005814075,0.000024381801,2.9059677e-7,0.00045233822,0.000064064014,0.09276744,0.00060265535,0.12804407,0.7772162],"study_design_scores_gemma":[0.00078167627,0.00016850796,0.00039046138,0.000017097056,0.000010360009,0.000005110402,0.0001398521,0.04964675,0.94808215,0.00015347384,0.00049018976,0.00011439012],"about_ca_topic_score_codex":0.000031162246,"about_ca_topic_score_gemma":0.000014239878,"teacher_disagreement_score":0.8553147,"about_ca_system_score_codex":0.000048185524,"about_ca_system_score_gemma":0.000028665097,"threshold_uncertainty_score":0.29098856},"labels":[],"label_agreement":null},{"id":"W1532068777","doi":"10.1007/bfb0028121","title":"Minimum loss of information and image segmentation","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Thresholding; Histogram; Image segmentation; Artificial intelligence; Balanced histogram thresholding; Segmentation; Information loss; Computer science; Pattern recognition (psychology); Scale-space segmentation; Image (mathematics); Region growing; Computer vision; Segmentation-based object categorization; Mathematics; Histogram matching","score_opus":0.009378960805021187,"score_gpt":0.2609454593000441,"score_spread":0.2515664984950229,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1532068777","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008149403,0.00011942443,0.99726146,0.0004689752,0.00029511584,0.00035417982,0.000005311051,0.00010728661,0.0013067267],"genre_scores_gemma":[0.00827766,0.0001215804,0.9901864,0.0012252915,0.0000968183,0.000007158753,0.000010315346,0.000008993759,0.00006581338],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99778056,0.000022945113,0.0006092023,0.00048718593,0.00083991466,0.00026017884],"domain_scores_gemma":[0.99844915,0.00020902477,0.00042301448,0.0005534991,0.00025000755,0.000115324285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066240143,0.00025703377,0.00029582536,0.0007316235,0.00007668738,0.00028686156,0.0011622938,0.00016642016,0.00003139074],"category_scores_gemma":[0.00009206719,0.00024094187,0.000046603163,0.00031951163,0.0007639113,0.0023016923,0.0006367701,0.00030941362,0.000016843633],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023602267,0.000009430815,0.000018027271,0.000054555367,0.00000345406,0.000006023796,0.00082442444,0.00014714894,0.0011320302,0.0022443389,0.000032162337,0.995526],"study_design_scores_gemma":[0.0014334837,0.0006717797,0.0005437744,0.0011098147,0.000027706657,0.00019033793,0.0000023278512,0.5697198,0.2715218,0.15215704,0.0010816674,0.0015404937],"about_ca_topic_score_codex":0.000011561817,"about_ca_topic_score_gemma":0.0000068386853,"teacher_disagreement_score":0.99398553,"about_ca_system_score_codex":0.00013958446,"about_ca_system_score_gemma":0.0002348487,"threshold_uncertainty_score":0.98253244},"labels":[],"label_agreement":null},{"id":"W1532695445","doi":"","title":"Barycentric Label Space","year":2009,"lang":"en","type":"article","venue":"Medical Image Computing and Computer-Assisted Intervention","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Barycentric coordinate system; Smoothing; Artificial intelligence; Context (archaeology); Mathematics; Pattern recognition (psychology); Prior probability; Space (punctuation); Segmentation; Computer science; Algorithm; Computer vision; Bayesian probability; Geometry","score_opus":0.0147270269734733,"score_gpt":0.31334122412510296,"score_spread":0.2986141971516297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1532695445","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011536001,0.00038366189,0.98214275,0.0034912922,0.0006240421,0.00020394474,7.927684e-7,0.0010536445,0.0005639005],"genre_scores_gemma":[0.43211892,0.00006085775,0.5641623,0.0031473832,0.00033617453,0.000004897546,0.000020037103,0.0000146095235,0.00013484359],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99684024,0.00036344596,0.0007210222,0.00070183893,0.00090023817,0.00047319828],"domain_scores_gemma":[0.9984005,0.00019604777,0.0002520212,0.00046637226,0.00017602644,0.0005090498],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001160084,0.00027716675,0.00035682067,0.00026169294,0.00022544246,0.000492133,0.0009748692,0.00016919457,0.00009492865],"category_scores_gemma":[0.00026502737,0.00025704756,0.00015421682,0.0006049586,0.00014511723,0.0005609402,0.0005387107,0.00048502837,0.000046675108],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000052321834,0.0004917058,0.0000540802,0.00004229892,0.000018618342,0.00012846375,0.00013244309,6.2592875e-7,0.00042232906,0.0024392456,0.006434149,0.9898308],"study_design_scores_gemma":[0.0028681902,0.0013770964,0.02001429,0.0011355109,0.000027533584,0.0005712651,0.000033678243,0.96562415,0.0045760227,0.0023515995,0.000826015,0.0005946413],"about_ca_topic_score_codex":0.000021987364,"about_ca_topic_score_gemma":0.0000012042566,"teacher_disagreement_score":0.9892362,"about_ca_system_score_codex":0.00007209282,"about_ca_system_score_gemma":0.000049881706,"threshold_uncertainty_score":0.9999882},"labels":[],"label_agreement":null},{"id":"W1535647113","doi":"10.1007/978-3-540-74260-9_20","title":"Enhancing Contour Primitives by Pairwise Grouping and Relaxation","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Silhouette; Computer science; Artificial intelligence; Pairwise comparison; Computer vision; Matching (statistics); Curvature; Pattern recognition (psychology); Binary number; Object (grammar); Mathematics; Geometry","score_opus":0.017765657929045983,"score_gpt":0.2737278084279845,"score_spread":0.2559621504989385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1535647113","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006550339,0.00074571226,0.9960409,0.00035684597,0.0004887888,0.0003980206,0.0000020958776,0.00029391857,0.0016082379],"genre_scores_gemma":[0.024456795,0.00016417436,0.97153014,0.0032649674,0.00022156657,0.000009532605,0.000007606684,0.000027875689,0.00031732488],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9967779,0.000043685646,0.0005497381,0.0012051282,0.000937651,0.00048591945],"domain_scores_gemma":[0.9979524,0.00066177093,0.00036797023,0.00061598053,0.0001901343,0.0002117215],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016054511,0.0003728892,0.00037161718,0.00060279167,0.0002098141,0.00045981724,0.0012543593,0.00027990085,0.000016567914],"category_scores_gemma":[0.0002777971,0.0003554093,0.00005528774,0.00034392063,0.0007044981,0.0009797633,0.00075544167,0.0006558646,0.000009160872],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024325375,0.000013581874,0.000024018034,0.000040256822,0.00000534281,0.00003795174,0.0009624598,0.0000307964,0.005848594,0.0035548282,0.00004212811,0.9894376],"study_design_scores_gemma":[0.0015160199,0.0009684067,0.00089674495,0.004034318,0.000036281308,0.00027733194,0.00000415224,0.23415695,0.5361209,0.21642745,0.0021995956,0.003361842],"about_ca_topic_score_codex":0.000025247315,"about_ca_topic_score_gemma":0.00003827473,"teacher_disagreement_score":0.98607576,"about_ca_system_score_codex":0.00026616323,"about_ca_system_score_gemma":0.00019824159,"threshold_uncertainty_score":0.9998898},"labels":[],"label_agreement":null},{"id":"W1536437679","doi":"","title":"Experimental Analysis of the MRF Algorithm for Segmentation of Noisy Medical Images","year":2011,"lang":"en","type":"article","venue":"Algorithmic operations research","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Fraser Health; Simon Fraser University","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Image segmentation; Computer vision; Toolbox; Market segmentation; Image (mathematics); Noise reduction; Medical imaging; Pattern recognition (psychology); Scale-space segmentation; Markov random field","score_opus":0.08648639396389575,"score_gpt":0.4188762014867848,"score_spread":0.33238980752288905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1536437679","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046961126,0.00009271469,0.9936492,0.00028357116,0.00011108913,0.00077098765,0.00004673571,0.000037059835,0.00031254935],"genre_scores_gemma":[0.15152197,0.000031830863,0.8475551,0.00009481857,0.000038477458,0.00040631983,0.000030134284,0.000010085644,0.00031127082],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99702156,0.00039801636,0.0005351696,0.00032618415,0.00146031,0.00025875831],"domain_scores_gemma":[0.99809575,0.00026854296,0.00007031898,0.00063891686,0.0008070464,0.000119434146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019293625,0.000100327,0.00023555069,0.00056664913,0.00023458194,0.000051124374,0.0013544754,0.00008380123,0.00045728002],"category_scores_gemma":[0.00035744064,0.00007388579,0.00016166968,0.0021858474,0.00047741926,0.0004418821,0.0003990894,0.00019564926,0.000004859213],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014144312,0.0016331315,0.00035836015,0.000046666653,0.0009921109,0.000007082701,0.011796302,0.00008892261,0.24241522,0.009330782,0.003132333,0.730185],"study_design_scores_gemma":[0.0002507042,0.00013898504,0.0006776279,0.0000127838985,0.00003282465,0.0000018487748,0.0004843513,0.3013429,0.69689,0.00009948737,0.000010134323,0.000058322545],"about_ca_topic_score_codex":0.00064838777,"about_ca_topic_score_gemma":0.00003079629,"teacher_disagreement_score":0.7301266,"about_ca_system_score_codex":0.00007341481,"about_ca_system_score_gemma":0.00033876498,"threshold_uncertainty_score":0.50068957},"labels":[],"label_agreement":null},{"id":"W1537247569","doi":"10.1007/978-3-642-04667-4_22","title":"Automatic Classification of Image Registration Problems","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Image registration; Computer science; Artificial intelligence; Variation (astronomy); Image (mathematics); Flexibility (engineering); Pattern recognition (psychology); Computer vision; Algorithm; Mathematics; Statistics","score_opus":0.024529212598800892,"score_gpt":0.282875417088396,"score_spread":0.2583462044895951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1537247569","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000022251474,0.00009501662,0.9930021,0.00083712616,0.00029143857,0.0005536031,0.0000017679249,0.00029118927,0.0049055195],"genre_scores_gemma":[0.023705663,0.000036857702,0.97529787,0.0005565997,0.000103331535,0.000012619848,0.00001009221,0.000015571479,0.00026138447],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99671245,0.000043843178,0.0007989093,0.0009288328,0.0011974421,0.00031852315],"domain_scores_gemma":[0.9973795,0.00022051368,0.0007083107,0.0012296803,0.00034679598,0.00011521],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010922622,0.00031191504,0.0003934488,0.00068097445,0.000091671754,0.00028789113,0.0022318077,0.00023460868,0.000035681333],"category_scores_gemma":[0.00017854769,0.00028906256,0.00008550409,0.00055147865,0.000709427,0.0008954241,0.00028312978,0.00044515586,0.000018955257],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.453344e-7,0.000027821208,0.0000034142597,0.00007059167,0.0000028001416,0.000008583243,0.00029579087,0.00018251812,0.009007356,0.0075901533,0.000055659482,0.98275465],"study_design_scores_gemma":[0.00019553331,0.00028979595,0.0004319463,0.0007806484,0.0000084595495,0.00003395903,1.6133785e-7,0.7115872,0.041598726,0.24448557,0.000137666,0.00045035788],"about_ca_topic_score_codex":0.000010501602,"about_ca_topic_score_gemma":0.000013709038,"teacher_disagreement_score":0.98230433,"about_ca_system_score_codex":0.00023026708,"about_ca_system_score_gemma":0.00045802048,"threshold_uncertainty_score":0.99995613},"labels":[],"label_agreement":null},{"id":"W1537403352","doi":"10.1002/9780470973134.ch4","title":"Evolutionary Deformable Models for Medical Image Segmentation: A Genetic Algorithm Approach to Optimizing Learned, Intuitive, and Localized Medial‐Based Shape Deformation","year":2010,"lang":"en","type":"other","venue":"Genetic and evolutionary computation","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Artificial intelligence; Segmentation; Deformation (meteorology); Computer science; Image (mathematics); Computer vision; Genetic algorithm; Image segmentation; Algorithm; Pattern recognition (psychology); Machine learning; Geography","score_opus":0.015601758397120318,"score_gpt":0.2669453030105406,"score_spread":0.2513435446134203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1537403352","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000047421756,0.0014003126,0.9923682,0.0004245382,0.00033567694,0.0019777992,0.00007529925,0.0005040888,0.0028666412],"genre_scores_gemma":[0.00027981185,0.00042899573,0.9955016,0.00086440414,0.00029472582,0.00067189656,0.0007687243,0.00013185132,0.00105803],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967569,0.0001828838,0.00069953175,0.00089440274,0.0010099829,0.00045630307],"domain_scores_gemma":[0.99840575,0.00017410608,0.0003522585,0.000304305,0.00026812922,0.0004954443],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004177314,0.00044199804,0.0004391138,0.0006454061,0.00030783747,0.00018215271,0.00045024903,0.00051415496,0.0001243338],"category_scores_gemma":[0.00008695093,0.00045046033,0.00008558454,0.00039203887,0.00028877764,0.00053556665,0.00030752056,0.00033274604,0.000017361695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050520608,0.00022745828,0.0000099819545,0.0005004814,0.00009247186,0.000011551762,0.0006625169,0.025348304,0.00011329595,0.0005210622,0.07880748,0.8936549],"study_design_scores_gemma":[0.0014675815,0.00019747675,0.00016936951,0.00015139098,0.000051237974,0.00014054106,0.00007322038,0.9907428,0.000057554502,0.004883762,0.0015814224,0.00048364257],"about_ca_topic_score_codex":0.0000801878,"about_ca_topic_score_gemma":0.00000532656,"teacher_disagreement_score":0.9653945,"about_ca_system_score_codex":0.0001519763,"about_ca_system_score_gemma":0.00044162487,"threshold_uncertainty_score":0.9997947},"labels":[],"label_agreement":null},{"id":"W1538973951","doi":"10.1109/icassp.1988.196750","title":"Shape matching using curvature processes","year":2003,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Curvature; Matching (statistics); Metric (unit); Sign (mathematics); Mathematics; Constant (computer programming); Interpretation (philosophy); Constant curvature; Artificial intelligence; Computer science; Geometry; Computer vision; Algorithm; Mathematical analysis; Engineering; Statistics","score_opus":0.030662512541463006,"score_gpt":0.3108651053741619,"score_spread":0.2802025928326989,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1538973951","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022543904,0.000080101316,0.9892083,0.00013453484,0.0000771568,0.00007476204,1.2690151e-7,0.00039047477,0.0077801775],"genre_scores_gemma":[0.057878677,0.000006900827,0.9399711,0.0018121011,0.000014229175,0.000004042584,3.5533142e-7,0.000004652764,0.00030793983],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993309,0.000035279536,0.00011281247,0.0001803691,0.00020462867,0.00013603803],"domain_scores_gemma":[0.9996038,0.000043472708,0.00003716537,0.00018250062,0.000070315546,0.00006273177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001736212,0.00006541392,0.00006278097,0.00004381951,0.00006395261,0.00013029735,0.00031465688,0.000032948003,0.00024553822],"category_scores_gemma":[0.00015237882,0.00005255694,0.000014032607,0.00035082098,0.00001836832,0.0006138648,0.00005214822,0.00008324304,0.000019512918],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000036841316,0.00041148474,0.0017368547,0.0006053103,0.00006250065,0.00013211164,0.006250166,0.00009230808,0.16106243,0.40206963,0.019628817,0.4079447],"study_design_scores_gemma":[0.00027892753,0.00004594986,0.000087908884,0.00009967535,0.000007677137,0.00010514613,0.00016370627,0.027080746,0.92050546,0.047468103,0.0037065083,0.00045019374],"about_ca_topic_score_codex":0.0000081486105,"about_ca_topic_score_gemma":0.0000012455943,"teacher_disagreement_score":0.75944304,"about_ca_system_score_codex":0.000016586047,"about_ca_system_score_gemma":0.000092001435,"threshold_uncertainty_score":0.26884714},"labels":[],"label_agreement":null},{"id":"W15394667","doi":"10.1007/978-3-642-11840-1_12","title":"SRAD, Optical Flow and Primitive Prior Based Active Contours for Echocardiography","year":2010,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Vector flow; Active contour model; Speckle pattern; Artificial intelligence; Computer vision; Computer science; Optical flow; Speckle noise; Sensitivity (control systems); Flow (mathematics); Pattern recognition (psychology); Image (mathematics); Image segmentation; Mathematics; Geometry; Engineering","score_opus":0.025682665331086905,"score_gpt":0.30500427117869383,"score_spread":0.2793216058476069,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W15394667","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000139586,0.00007353944,0.9710181,0.0010443772,0.00016855991,0.00091292185,0.000033635075,0.00012199854,0.026612941],"genre_scores_gemma":[0.0039958637,0.00065047806,0.9932641,0.00179215,0.000026544694,0.00012280245,0.000056691773,0.000007789385,0.000083603794],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841374,0.000029856117,0.0005389251,0.00034375628,0.000443884,0.00022983235],"domain_scores_gemma":[0.9967469,0.0008132946,0.00028415295,0.001388478,0.0005816736,0.00018552945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001084362,0.000221818,0.000284496,0.0009827497,0.00047449156,0.00060720503,0.0020584567,0.00019115431,0.0000044709386],"category_scores_gemma":[0.0001826837,0.00021709657,0.00007022638,0.00029544637,0.0018718077,0.0047325287,0.0011542558,0.0005325339,0.000004857408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000055500655,0.000015816706,0.000009785961,0.000031826276,0.000007920707,2.4083332e-7,0.0008623107,0.000008079386,0.000036177462,0.13995679,0.000114404276,0.8589511],"study_design_scores_gemma":[0.0021084768,0.00042272595,0.003907718,0.00063291076,0.000042008163,0.00003179225,0.00007058215,0.90149355,0.0048082103,0.025205033,0.060151886,0.0011251265],"about_ca_topic_score_codex":0.0000033795282,"about_ca_topic_score_gemma":0.0000047468784,"teacher_disagreement_score":0.90148544,"about_ca_system_score_codex":0.000078452795,"about_ca_system_score_gemma":0.00039497725,"threshold_uncertainty_score":0.88529414},"labels":[],"label_agreement":null},{"id":"W1539882801","doi":"10.1109/ijcnn.1992.227305","title":"Multiresolution edge detection","year":2003,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Enhanced Data Rates for GSM Evolution; Edge detection; Computer science; Artificial intelligence; Representation (politics); Scale (ratio); Position (finance); Range (aeronautics); Artificial neural network; Computer vision; Image (mathematics); Detector; Pattern recognition (psychology); Image processing; Physics","score_opus":0.01598769001768295,"score_gpt":0.2684181272422167,"score_spread":0.25243043722453373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1539882801","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00036832676,0.000012950994,0.98313236,0.00006398276,0.00016463127,0.00007213972,3.735376e-8,0.0004712111,0.015714334],"genre_scores_gemma":[0.45281425,0.0000041929648,0.5458803,0.0004789414,0.000010852702,0.000013395957,2.2144287e-7,0.0000022220274,0.0007956481],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995069,0.000053700907,0.00008647798,0.00013148726,0.00012771427,0.00009375501],"domain_scores_gemma":[0.99969727,0.000022215596,0.000021041855,0.00017809644,0.000031281746,0.000050075952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018414474,0.00003864016,0.000033438406,0.000045866665,0.00004741839,0.000039368264,0.00014018641,0.00002552368,0.00009642471],"category_scores_gemma":[0.00011059962,0.000033716024,0.00001679837,0.00016633958,0.000016027,0.00030886946,0.000021846048,0.000043394586,0.00011455481],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.9908086e-7,0.00004628234,0.000062163796,0.0000039593083,0.0000029404043,0.0000029211958,0.00011490678,0.0000015543089,0.10335585,0.04290059,0.002810681,0.8506976],"study_design_scores_gemma":[0.00010675462,0.000027439373,0.0002869257,0.0000018415035,6.490998e-7,0.000007763241,0.000008180686,0.009333361,0.984886,0.0020044271,0.0032777428,0.000058941012],"about_ca_topic_score_codex":0.0000125435445,"about_ca_topic_score_gemma":0.0000041991334,"teacher_disagreement_score":0.8815301,"about_ca_system_score_codex":0.000028079114,"about_ca_system_score_gemma":0.000013057342,"threshold_uncertainty_score":0.14724085},"labels":[],"label_agreement":null},{"id":"W154056955","doi":"10.1007/978-3-642-23626-6_59","title":"Automatic View Planning for Cardiac MRI Acquisition","year":2011,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Siemens (Canada)","funders":"","keywords":"Computer science; Computer vision; Workflow; Segmentation; Ventricle; Artificial intelligence; Process (computing); Medicine; Cardiology","score_opus":0.029079452560573385,"score_gpt":0.30446686640723286,"score_spread":0.2753874138466595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W154056955","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027313065,0.00014838985,0.9951688,0.00025384812,0.00084218924,0.00044820216,0.0000010239272,0.0003747471,0.000031484837],"genre_scores_gemma":[0.2947737,0.0000038832527,0.7037296,0.0013695466,0.000070199516,0.000046484765,0.0000011156781,0.000005134929,3.1423272e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815595,0.00006934835,0.0002959796,0.00060500996,0.00044858886,0.00042513624],"domain_scores_gemma":[0.9987838,0.00029348148,0.00010479143,0.00057177804,0.00012246081,0.00012367926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011147172,0.00015440412,0.00020963205,0.0002931183,0.00016785078,0.00020668376,0.0014968327,0.0000615629,0.000019980242],"category_scores_gemma":[0.000099404184,0.0001350005,0.000060814782,0.0011066018,0.00023449624,0.0009248711,0.00034707584,0.00013716237,0.00001134312],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014691078,0.000033458113,0.0005673934,0.000035560555,0.0000031477728,0.000008256991,0.0029625208,0.00027849208,0.0019313322,0.00040445194,0.000060090857,0.99371386],"study_design_scores_gemma":[0.00018742736,0.00018654074,0.004042401,0.00016552728,0.0000037474215,0.000011309638,0.0000011852285,0.77417994,0.19250794,0.028445339,0.000022621529,0.00024602265],"about_ca_topic_score_codex":0.000011943354,"about_ca_topic_score_gemma":8.0179956e-7,"teacher_disagreement_score":0.9934678,"about_ca_system_score_codex":0.00009522679,"about_ca_system_score_gemma":0.00013358773,"threshold_uncertainty_score":0.55051607},"labels":[],"label_agreement":null},{"id":"W1540704818","doi":"10.1007/3-540-45053-x_33","title":"Multimodal Elastic Matching of Brain Images","year":2000,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Computer science; Image registration; Artificial intelligence; Matching (statistics); Transformation (genetics); Iterated function; Computer vision; Image (mathematics); Pattern recognition (psychology); Mathematics","score_opus":0.011044005041093256,"score_gpt":0.2686283494709561,"score_spread":0.25758434442986283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1540704818","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004112327,0.00016712352,0.99489635,0.00060654344,0.00052537717,0.00032682918,0.0000070011615,0.00024090095,0.003188731],"genre_scores_gemma":[0.033955567,0.000036100733,0.96363384,0.0018476452,0.00017706769,0.0000076161646,0.0000044459252,0.00002937045,0.00030833503],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99642676,0.000051441217,0.00067754585,0.0011372698,0.0012117766,0.00049519964],"domain_scores_gemma":[0.9973017,0.0009311307,0.00033059678,0.0010982762,0.00016188395,0.00017641483],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00083358155,0.00041826602,0.0005272285,0.00083975185,0.000119289434,0.00026192656,0.0032162997,0.00023938663,0.00013580205],"category_scores_gemma":[0.00016073146,0.0003836045,0.0001260117,0.0004606599,0.0009733181,0.00068854605,0.0008834015,0.00071074255,0.000044268305],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035002627,0.00002758448,0.000005524226,0.000046422167,0.0000068436757,0.000085483174,0.0006131323,0.004155665,0.0021131341,0.002573147,0.00006441263,0.9903051],"study_design_scores_gemma":[0.00067507994,0.00044240564,0.00022747001,0.0016442828,0.000016067177,0.00019196456,3.8951924e-7,0.27438948,0.10809278,0.6125994,0.00035137686,0.0013693299],"about_ca_topic_score_codex":0.000047177782,"about_ca_topic_score_gemma":0.000010962668,"teacher_disagreement_score":0.9889358,"about_ca_system_score_codex":0.00015200608,"about_ca_system_score_gemma":0.0003870087,"threshold_uncertainty_score":0.9998616},"labels":[],"label_agreement":null},{"id":"W1540871397","doi":"10.1007/978-3-540-75759-7_61","title":"Is a Single Energy Functional Sufficient? Adaptive Energy Functionals and Automatic Initialization","year":2007,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Canadian Institutes of Health Research; University of British Columbia","keywords":"Initialization; Artificial intelligence; Segmentation; Image segmentation; Energy functional; Image (mathematics); Pattern recognition (psychology); Minification; Energy (signal processing); Computer science; Ideal (ethics); Context (archaeology); Set (abstract data type); Scale-space segmentation; Manifold (fluid mechanics); Mathematics; Computer vision; Mathematical optimization; Statistics; Engineering","score_opus":0.025156991252737577,"score_gpt":0.26768392699837257,"score_spread":0.242526935745635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1540871397","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020411257,0.0001167374,0.99628586,0.00047395175,0.0006800412,0.000077832934,0.0000010222626,0.00023977611,0.00008363202],"genre_scores_gemma":[0.60627705,0.000003824915,0.387798,0.0057944697,0.00010820485,0.000007006683,0.000002677652,0.000005350318,0.000003373378],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99750197,0.000070822665,0.00037905472,0.00072703644,0.00089942536,0.00042168173],"domain_scores_gemma":[0.99855363,0.0005213439,0.00013953223,0.00036096844,0.0002507784,0.00017372439],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092879834,0.0001840279,0.00016622701,0.00063224835,0.000227523,0.0002993043,0.0005929873,0.00008715772,0.000043875018],"category_scores_gemma":[0.00014390302,0.0001699579,0.000034939058,0.0021139584,0.00046325036,0.0009042834,0.0003935318,0.000119152995,0.0000033360125],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000066693387,0.00013375214,0.00022461722,0.0000068214235,0.0000052992136,0.000024121598,0.0008203974,0.0016146385,0.0050307233,0.0074205003,0.00012210383,0.98459035],"study_design_scores_gemma":[0.0002550555,0.00023167806,0.0028998216,0.000050968305,0.0000026808514,0.00008294399,0.0000024898075,0.81916004,0.15759572,0.01940595,0.00008273734,0.00022991185],"about_ca_topic_score_codex":0.00006813795,"about_ca_topic_score_gemma":0.000058391055,"teacher_disagreement_score":0.98436046,"about_ca_system_score_codex":0.0001739854,"about_ca_system_score_gemma":0.00018145436,"threshold_uncertainty_score":0.6930682},"labels":[],"label_agreement":null},{"id":"W1542499753","doi":"10.1109/mwscas.2003.1562307","title":"Region of Interest Identification in Prostate TRUS mages Based on Gabor Filter","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; University of Waterloo","funders":"","keywords":"Grey level; Region of interest; Artificial intelligence; Gabor filter; Computer vision; Contrast (vision); Computer science; Pattern recognition (psychology); Feature (linguistics); Texture (cosmology); Filter (signal processing); Image texture; Feature extraction; Image (mathematics); Image segmentation","score_opus":0.03449454355969277,"score_gpt":0.2861676181601013,"score_spread":0.2516730746004085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1542499753","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019438455,0.0000043172845,0.977097,0.0011777241,0.000055926514,0.00021729979,9.87066e-7,0.00012509413,0.0018832282],"genre_scores_gemma":[0.9310912,0.0000023292678,0.06736789,0.00041718796,0.000009622902,0.00002943578,0.000008342903,0.0000040556824,0.0010699716],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991914,0.00006490189,0.00029503656,0.00020274831,0.00015055142,0.000095370444],"domain_scores_gemma":[0.9994414,0.000059047088,0.00010328439,0.00032118897,0.000052319087,0.000022764276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021644252,0.00006186838,0.00007748316,0.00019006681,0.000011698397,0.000049322407,0.00028577007,0.000022988614,0.000039034443],"category_scores_gemma":[0.000044547392,0.000052090352,0.00002187328,0.00026286868,0.00004105234,0.0002659345,0.000033342607,0.000056770274,0.00001567417],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012386919,0.0018400102,0.01361999,0.00029882052,0.000008536946,0.00022419059,0.0007498362,0.0003017863,0.26424608,0.067807004,0.10349135,0.54728854],"study_design_scores_gemma":[0.00036935226,0.00009702214,0.03011935,0.000073970354,0.0000011389592,0.0000020730686,0.0000123640275,0.037624694,0.927922,0.003585923,0.00009763691,0.00009446943],"about_ca_topic_score_codex":0.000115093215,"about_ca_topic_score_gemma":0.000038876104,"teacher_disagreement_score":0.9116527,"about_ca_system_score_codex":0.000029005927,"about_ca_system_score_gemma":0.000018794515,"threshold_uncertainty_score":0.2124183},"labels":[],"label_agreement":null},{"id":"W1542739256","doi":"10.1007/978-3-540-89639-5_5","title":"Enhancing Boundary Primitives Using a Multiscale Quadtree Segmentation","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Boundary (topology); Quadtree; Computer science; Segmentation; Superposition principle; Artificial intelligence; Computer vision; Curvature; Image segmentation; Object (grammar); Binary number; Binary image; Algorithm; Pattern recognition (psychology); Image (mathematics); Geometry; Mathematics; Image processing; Mathematical analysis; Arithmetic","score_opus":0.025115508303298673,"score_gpt":0.2917443832834117,"score_spread":0.266628874980113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1542739256","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014535975,0.0005672591,0.99610424,0.00015055222,0.00095049065,0.0006024885,0.0000041514186,0.00035802575,0.0011174614],"genre_scores_gemma":[0.005163068,0.00011972537,0.9926606,0.0013896354,0.00026853848,0.000013814621,0.0000085019865,0.00003670893,0.00033940485],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99570924,0.00007020882,0.0007100184,0.0014742363,0.001419474,0.00061684824],"domain_scores_gemma":[0.9977081,0.00041959403,0.00042685232,0.0009744561,0.0002498191,0.00022120897],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00070052355,0.0005094143,0.0004940628,0.00089780305,0.0004432317,0.0005167032,0.002089856,0.00027990073,0.00007150474],"category_scores_gemma":[0.00013339816,0.00050061557,0.00012876495,0.0005493956,0.0011761459,0.0013798294,0.0010578467,0.000730131,0.00004792824],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000038953804,0.000047181376,0.00004107445,0.00006200752,0.000014111194,0.00020920845,0.003319644,0.00090592384,0.020499954,0.00017265663,0.000057744644,0.9746666],"study_design_scores_gemma":[0.00085904106,0.00039073374,0.00027475102,0.0017425953,0.000024398769,0.000518918,0.000002638686,0.42755058,0.5441497,0.022133172,0.0004110769,0.0019423879],"about_ca_topic_score_codex":0.00004475635,"about_ca_topic_score_gemma":0.00005412392,"teacher_disagreement_score":0.9727242,"about_ca_system_score_codex":0.00065103394,"about_ca_system_score_gemma":0.00086707016,"threshold_uncertainty_score":0.99974453},"labels":[],"label_agreement":null},{"id":"W1543055822","doi":"10.1109/aiccsa.2005.1387120","title":"Watershed segmentation for carotid artery ultrasound images","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Segmentation; Artificial intelligence; Computer vision; Watershed; Preprocessor; Computer science; Image segmentation; Scheme (mathematics); Ultrasound; Carotid arteries; Pattern recognition (psychology); Radiology; Medicine; Mathematics; Surgery","score_opus":0.015408319033306465,"score_gpt":0.2836176369585118,"score_spread":0.2682093179252053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1543055822","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00069211586,0.000009146451,0.9950981,0.0017627816,0.000095934825,0.00042748897,0.0000030382275,0.00049151666,0.0014198916],"genre_scores_gemma":[0.04731902,0.000010207902,0.94753087,0.002672063,0.00011068336,0.00013738492,0.0000251009,0.000007647834,0.0021870518],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990949,0.000031674983,0.00021153971,0.00025525817,0.00020647109,0.00020015183],"domain_scores_gemma":[0.9993993,0.00013681922,0.000049590733,0.00025730542,0.00007276938,0.00008422248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024277605,0.0000945104,0.00009007414,0.00007211734,0.00007051813,0.00016601308,0.00035368078,0.000032364194,0.00017997235],"category_scores_gemma":[0.000049783048,0.000078100034,0.000048320908,0.00009901172,0.000035968893,0.00092086574,0.00004114125,0.00004390753,0.00008003671],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000028848303,0.000060276605,0.00019807905,0.000015484067,0.000013498607,8.8700455e-7,0.0006406865,0.000007811713,0.87001896,0.0010955825,0.06725792,0.060687963],"study_design_scores_gemma":[0.0003245707,0.000055163993,0.000303959,0.0000038855746,0.000003724723,0.000011027473,0.00005314391,0.00047881564,0.99753606,0.00069953385,0.00041766022,0.000112435664],"about_ca_topic_score_codex":0.000007795031,"about_ca_topic_score_gemma":0.0000037200016,"teacher_disagreement_score":0.12751715,"about_ca_system_score_codex":0.00005187645,"about_ca_system_score_gemma":0.000020428339,"threshold_uncertainty_score":0.3184827},"labels":[],"label_agreement":null},{"id":"W1544898917","doi":"10.1007/978-3-642-04271-3_116","title":"3D Prostate Segmentation in Ultrasound Images Based on Tapered and Deformed Ellipsoids","year":2009,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Cancer Agency; University of British Columbia","funders":"National Cancer Institute; National Institutes of Health","keywords":"Segmentation; Computer science; Repeatability; Ellipsoid; 3D ultrasound; Computer vision; Artificial intelligence; Prostate cancer; Brachytherapy; Prostate brachytherapy; Image segmentation; Prostate; Ultrasound; Medicine; Radiation therapy; Radiology; Cancer; Mathematics; Physics","score_opus":0.00804747775476821,"score_gpt":0.2685411104758444,"score_spread":0.2604936327210762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1544898917","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028052408,0.0000333428,0.9698226,0.0013297994,0.00016122615,0.00041353292,0.00000103866,0.00015318785,0.000032906348],"genre_scores_gemma":[0.51584953,0.000009672899,0.48076543,0.0033413966,0.00001859223,0.000010013969,0.0000018036399,0.000002924751,5.9920416e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977712,0.00010059975,0.00032115143,0.00073420955,0.00063642464,0.0004364339],"domain_scores_gemma":[0.99883753,0.00043447834,0.00009183499,0.00043822604,0.000070714676,0.00012720766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088313397,0.00019722334,0.00018174095,0.000553061,0.00012102434,0.00041537732,0.0007966812,0.00006085586,0.0000061301166],"category_scores_gemma":[0.00026015405,0.0001665941,0.000022066206,0.0016672903,0.00029818976,0.00096351455,0.00009743491,0.00025524056,0.0000043034056],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010097522,0.00010136341,0.002000497,0.000010845761,6.065056e-7,0.00002603805,0.0009909265,0.0123452265,0.04927606,0.000035078123,0.000012652435,0.9351906],"study_design_scores_gemma":[0.0006963221,0.00046854076,0.026848266,0.00010720775,0.000001120817,0.000018400333,0.0000016207218,0.5870155,0.3784527,0.0061316853,0.0000025470515,0.0002560864],"about_ca_topic_score_codex":0.0000238626,"about_ca_topic_score_gemma":0.00001625945,"teacher_disagreement_score":0.9349345,"about_ca_system_score_codex":0.00017460306,"about_ca_system_score_gemma":0.00015710447,"threshold_uncertainty_score":0.67935103},"labels":[],"label_agreement":null},{"id":"W1545235289","doi":"10.1007/978-3-642-04271-3_31","title":"High-Quality Model Generation for Finite Element Simulation of Tissue Deformation","year":2009,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"","keywords":"Finite element method; Mesh generation; Computer science; Interpolation (computer graphics); Segmentation; Deformation (meteorology); Feature (linguistics); Algorithm; Artificial intelligence; Computer vision; Image (mathematics); Structural engineering; Materials science","score_opus":0.040745924981870636,"score_gpt":0.3538679032349211,"score_spread":0.3131219782530505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1545235289","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0074009285,0.000011142495,0.9911735,0.00065239356,0.00019552236,0.00047020047,0.0000023874945,0.000091424154,0.0000024705841],"genre_scores_gemma":[0.5048021,0.0000011103064,0.49437317,0.00076843955,0.00004027466,0.0000074509417,0.0000058307537,0.0000013583171,2.4630168e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983028,0.000052939355,0.00048565812,0.00038792263,0.0005431003,0.00022754146],"domain_scores_gemma":[0.9987912,0.0002529547,0.00021218625,0.00041005848,0.00027518498,0.000058381247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012637118,0.00011145437,0.00015767307,0.00024965417,0.000106955355,0.00012577568,0.0006670929,0.000054422013,0.000002208283],"category_scores_gemma":[0.00030990006,0.00010052255,0.000028959665,0.0007404601,0.00008149571,0.0010942994,0.0000917684,0.00007811348,0.0000010946443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015812644,0.000022510274,0.0000057280836,0.0000057451625,3.755031e-7,1.0014559e-7,0.00030429143,0.56039214,0.028324403,0.000723037,0.0000029536232,0.4102171],"study_design_scores_gemma":[0.00015065489,0.00011674218,0.00013498607,0.000009751266,8.2157715e-7,2.8825576e-7,1.1447746e-7,0.62434167,0.3524756,0.022699002,9.867703e-7,0.00006939212],"about_ca_topic_score_codex":0.000010475725,"about_ca_topic_score_gemma":0.000006268539,"teacher_disagreement_score":0.49740118,"about_ca_system_score_codex":0.000111640984,"about_ca_system_score_gemma":0.00011241961,"threshold_uncertainty_score":0.40991905},"labels":[],"label_agreement":null},{"id":"W1546217776","doi":"10.1007/11566489_28","title":"2D/3D Deformable Registration Using a Hybrid Atlas","year":2005,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Artificial intelligence; Point distribution model; Atlas (anatomy); Computer vision; Range (aeronautics); Surface reconstruction; Point (geometry); Object (grammar); Image registration; Surface (topology); Computer graphics (images); Image (mathematics); Geometry; Anatomy; Mathematics","score_opus":0.01984461026091034,"score_gpt":0.29143815596994077,"score_spread":0.27159354570903044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1546217776","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014575658,0.000052116357,0.9835487,0.00093429524,0.00035736134,0.0002103335,5.5245897e-7,0.00025842374,0.00006257967],"genre_scores_gemma":[0.42226896,0.0000031717,0.5761437,0.0014496803,0.00012430502,0.000004214941,7.7063436e-7,0.0000037357133,0.0000014911741],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99770194,0.000058241327,0.00036088933,0.0006468044,0.0007414266,0.0004906955],"domain_scores_gemma":[0.9987807,0.00012539426,0.00014299963,0.0006740818,0.00013249373,0.00014430788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000969941,0.00016766918,0.00015918238,0.00033018313,0.00022577879,0.00050386775,0.0015613243,0.00004814057,0.00001526846],"category_scores_gemma":[0.00016709622,0.00014964536,0.00003711369,0.0013999185,0.00031572473,0.0022774695,0.00043260012,0.00024087109,0.000023372044],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017702783,0.00004238379,0.00016502495,0.0000082452625,0.0000013943148,0.000018004304,0.0004780587,0.0423566,0.014560328,0.0001612024,0.0000524649,0.9421545],"study_design_scores_gemma":[0.0001261265,0.000043672648,0.00010054957,0.000030891548,0.0000010573215,0.00008334797,1.9939272e-7,0.7485667,0.2485082,0.0023166158,0.000082306404,0.00014032105],"about_ca_topic_score_codex":0.00006938487,"about_ca_topic_score_gemma":0.00003157619,"teacher_disagreement_score":0.9420142,"about_ca_system_score_codex":0.00028475208,"about_ca_system_score_gemma":0.0003067263,"threshold_uncertainty_score":0.61023605},"labels":[],"label_agreement":null},{"id":"W1547801334","doi":"10.1007/11559573_9","title":"Mutual Information-Based Methods to Improve Local Region-of-Interest Image Registration","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Image registration; Computer science; Mutual information; Artificial intelligence; Region of interest; Computer vision; Similarity (geometry); Similarity measure; Image (mathematics); Pattern recognition (psychology)","score_opus":0.030484618543668962,"score_gpt":0.3253488648489821,"score_spread":0.29486424630531316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1547801334","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000004131563,0.000027071792,0.99484646,0.0016530508,0.0008105405,0.0006426352,0.0000060171164,0.00023017691,0.0017799072],"genre_scores_gemma":[0.0073648547,0.0000054300303,0.98798966,0.0042537465,0.00019667743,0.000022724544,0.000012070834,0.000016947382,0.0001378868],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967058,0.00008335285,0.0010175994,0.00088089734,0.00086509343,0.00044726252],"domain_scores_gemma":[0.99645394,0.00066888274,0.00061468483,0.0013933822,0.0005942649,0.0002748492],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014845531,0.00042395198,0.00047601934,0.0011308715,0.00011311807,0.00045019086,0.002830244,0.0002957264,0.000033624336],"category_scores_gemma":[0.0005110797,0.00039738763,0.00013176289,0.0006623694,0.00095503655,0.0017042716,0.0006881691,0.0006259471,0.000050545812],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008076049,0.000017573822,0.000001160741,0.000045964884,0.0000041134253,0.000016049344,0.00039260703,0.001534484,0.0009625354,0.006943982,0.00020555724,0.9898679],"study_design_scores_gemma":[0.00041654703,0.00057078974,0.000018655777,0.00056065,0.000010625748,0.000047308116,8.796802e-7,0.74159926,0.22341971,0.030023225,0.0025597499,0.0007726143],"about_ca_topic_score_codex":0.00003318145,"about_ca_topic_score_gemma":0.000040576328,"teacher_disagreement_score":0.9890953,"about_ca_system_score_codex":0.00040412397,"about_ca_system_score_gemma":0.00087244995,"threshold_uncertainty_score":0.9998478},"labels":[],"label_agreement":null},{"id":"W1548953334","doi":"10.1007/978-3-642-15555-0_16","title":"Superpixels and Supervoxels in an Energy Optimization Framework","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":441,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision","score_opus":0.01308034700212096,"score_gpt":0.2652442031675602,"score_spread":0.25216385616543924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1548953334","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000092980474,0.00018664787,0.9977176,0.00049351086,0.00072021404,0.00023743573,0.0000022750926,0.00020489597,0.00034442992],"genre_scores_gemma":[0.017268848,0.00013659275,0.97935486,0.0029122834,0.00022305915,0.000015076924,0.000008720991,0.000028358883,0.000052198146],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966908,0.00006224747,0.0005190614,0.0014113358,0.0008247111,0.0004918247],"domain_scores_gemma":[0.99782133,0.000440465,0.00014730834,0.0011443975,0.0001810845,0.0002654159],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008088847,0.00040894738,0.00042734272,0.0009710492,0.00014455047,0.00067593734,0.0022661076,0.00061444275,0.00007415045],"category_scores_gemma":[0.00018476017,0.0003901528,0.000045263994,0.0005724954,0.0008481796,0.0014183795,0.0009142905,0.0010920558,0.0000036171205],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033930692,0.00004514043,0.00014075346,0.000022463544,0.0000029068137,0.000078833975,0.001199478,0.010754817,0.0011648448,0.02253675,0.0000039133456,0.9640467],"study_design_scores_gemma":[0.00021727075,0.00021620312,0.00009730757,0.00039734558,0.0000036851086,0.000051902123,2.983754e-7,0.72732836,0.015092534,0.255758,0.00014587684,0.00069122144],"about_ca_topic_score_codex":0.000051002437,"about_ca_topic_score_gemma":0.00015629738,"teacher_disagreement_score":0.9633555,"about_ca_system_score_codex":0.0001312585,"about_ca_system_score_gemma":0.00029655546,"threshold_uncertainty_score":0.99985504},"labels":[],"label_agreement":null},{"id":"W1549854013","doi":"10.1007/978-3-540-30135-6_20","title":"Image Segmentation Adapted for Clinical Settings by Combining Pattern Classification and Level Sets","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Classifier (UML); Segmentation; Image segmentation; Computer science","score_opus":0.06571400330680793,"score_gpt":0.35235838206945164,"score_spread":0.2866443787626437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1549854013","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006781952,0.00014708501,0.9956322,0.0020940884,0.00063582323,0.000978417,0.000038030947,0.0002513576,0.00015520545],"genre_scores_gemma":[0.018165955,0.00012302694,0.97680974,0.0045341477,0.00013274522,0.00004953423,0.0000915328,0.000036477915,0.000056846184],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99616474,0.00006639694,0.0009097166,0.001533921,0.00086617214,0.00045906167],"domain_scores_gemma":[0.99724543,0.00089661527,0.00059869856,0.0007112372,0.00032254623,0.00022549373],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018372185,0.00041964362,0.0004797207,0.0003861284,0.0002475836,0.0005827748,0.0014365014,0.00034580458,0.000015902528],"category_scores_gemma":[0.00026368815,0.0004125089,0.00009848985,0.00026066488,0.0008136996,0.00089697546,0.00056507194,0.0006307633,0.000009406623],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004301324,0.00003533073,0.00006786341,0.00006454978,0.000009341831,0.0000079957845,0.00042180353,0.000018583185,0.0017764934,0.0006350861,0.00048737676,0.9964713],"study_design_scores_gemma":[0.0027907633,0.0009813067,0.0013504513,0.0013176466,0.00004336436,0.00006455245,0.0000034106583,0.84868145,0.019991878,0.122591525,0.00054965843,0.0016339734],"about_ca_topic_score_codex":0.000015745907,"about_ca_topic_score_gemma":0.00000812912,"teacher_disagreement_score":0.9948373,"about_ca_system_score_codex":0.00028598245,"about_ca_system_score_gemma":0.00036820528,"threshold_uncertainty_score":0.9998327},"labels":[],"label_agreement":null},{"id":"W1550192491","doi":"10.1023/a:1008163913937","title":"Color Image Segmentation for Multimedia Applications","year":2000,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Pixel; Image segmentation; Range segmentation; Segmentation; Segmentation-based object categorization; Scale-space segmentation; Minimum spanning tree-based segmentation; Image texture","score_opus":0.021553808500069157,"score_gpt":0.31071570521017905,"score_spread":0.2891618967101099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1550192491","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002586959,0.0003948729,0.9970458,0.00032519226,0.0005790278,0.0010424304,0.0000025003706,0.00006974262,0.00028172781],"genre_scores_gemma":[0.017209876,0.00026417593,0.97991854,0.0003092585,0.00052868767,0.00027749717,0.00000789758,0.000020069494,0.001464024],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99806523,0.0000987149,0.00096520194,0.00018390114,0.00048334117,0.00020361217],"domain_scores_gemma":[0.99819785,0.00033555814,0.0005220811,0.000273306,0.0004839559,0.00018723294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078254583,0.00013157378,0.00028557022,0.0001748917,0.00008433168,0.0002092529,0.0007101877,0.00006137827,0.00013222209],"category_scores_gemma":[0.000088324974,0.00010799091,0.00014525485,0.00025768843,0.000052464406,0.0005709958,0.000025148622,0.00012195756,0.00011144923],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074788375,0.0006782739,0.00008724177,0.00033575107,0.00023362545,0.00003489754,0.0021677443,0.013204923,0.044441868,0.0026436148,0.034788884,0.9013084],"study_design_scores_gemma":[0.0022830865,0.0016746168,0.00015574666,0.00065628084,0.00018766346,0.0009288163,0.0016340658,0.6300588,0.3336726,0.0018314577,0.026137697,0.0007791396],"about_ca_topic_score_codex":0.0000121385265,"about_ca_topic_score_gemma":6.1891467e-7,"teacher_disagreement_score":0.90052927,"about_ca_system_score_codex":0.00015938637,"about_ca_system_score_gemma":0.00009678484,"threshold_uncertainty_score":0.44037417},"labels":[],"label_agreement":null},{"id":"W1550835464","doi":"10.1007/978-3-642-36961-2_10","title":"Hierarchical Conditional Random Fields for Myocardium Infarction Detection","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Conditional random field; Computer science; Segmentation; Artificial intelligence; Infarction; Probabilistic logic; Pattern recognition (psychology); Classifier (UML); Myocardial infarction; Magnetic resonance imaging; Radiology; Cardiology; Medicine","score_opus":0.013787823703578976,"score_gpt":0.2635684421208188,"score_spread":0.24978061841723984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1550835464","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000104045275,0.00006228597,0.99488026,0.0011699441,0.0016165054,0.0009805835,0.0000073196934,0.00028053072,0.0009921597],"genre_scores_gemma":[0.057090495,0.000035029767,0.9360958,0.0049879905,0.0009771385,0.00020764119,0.00003722953,0.00003136351,0.00053735916],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970195,0.000046375284,0.0005141024,0.001052577,0.00092616613,0.00044123977],"domain_scores_gemma":[0.99768424,0.0008683657,0.00023044476,0.0006756543,0.00035616168,0.00018511977],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00080954,0.00034578206,0.00039618553,0.0006604888,0.0002593406,0.0004454979,0.0014174593,0.00040058498,0.00009976021],"category_scores_gemma":[0.00025179604,0.00031791764,0.00017386478,0.00028612162,0.00060721545,0.00076494983,0.00044249496,0.000735318,0.000047432495],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013489415,0.000017297947,0.0000021219566,0.000031683,0.000013266721,0.0000059237673,0.00014719817,0.0014115857,0.0008445683,0.006036964,0.00029596238,0.99117994],"study_design_scores_gemma":[0.0009382009,0.0003441102,0.00007376087,0.00013380812,0.000010973962,0.00006686928,9.638598e-8,0.44207573,0.02650388,0.52722543,0.0021006798,0.00052645657],"about_ca_topic_score_codex":0.00001614504,"about_ca_topic_score_gemma":0.0000140554685,"teacher_disagreement_score":0.99065346,"about_ca_system_score_codex":0.00024511965,"about_ca_system_score_gemma":0.00029602266,"threshold_uncertainty_score":0.9999273},"labels":[],"label_agreement":null},{"id":"W1550913451","doi":"10.1007/978-3-540-30125-7_11","title":"A Multistage Image Segmentation and Denoising Method – Based on the Mumford and Shah Variational Approach","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Smoothing; Piecewise; Segmentation; Computer science; Image segmentation; Scale-space segmentation; Noise reduction; Artificial intelligence; Segmentation-based object categorization; Noise (video); Computer vision; Active contour model; Level set (data structures); Pattern recognition (psychology); Image denoising; Algorithm; Image (mathematics); Mathematics; Mathematical analysis","score_opus":0.01961389575014118,"score_gpt":0.28636367099322524,"score_spread":0.2667497752430841,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1550913451","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000016803653,0.0000572545,0.99702626,0.0011874295,0.00015973223,0.0006326228,0.0000063415923,0.000120585166,0.0007929773],"genre_scores_gemma":[0.0065267864,0.000011888003,0.989166,0.0041141086,0.00008945276,0.000025112939,0.000009660041,0.000018362563,0.000038585356],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99713916,0.0001020981,0.00036158768,0.0011062085,0.0009792533,0.00031167248],"domain_scores_gemma":[0.9977801,0.0011210657,0.00024085124,0.00060155906,0.0001261048,0.00013034618],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001659766,0.00034709458,0.00027372493,0.00045877107,0.00032949122,0.0007675135,0.0010199441,0.00016241129,0.000020979605],"category_scores_gemma":[0.0001869679,0.00025883238,0.000045313176,0.000322169,0.00079042796,0.0004527911,0.00051685044,0.0005469382,0.0000023875532],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008849511,0.000051666368,0.000016611823,0.000086754604,0.000010846063,0.000035259975,0.001741187,0.010130374,0.0023746034,0.048477996,0.000012871786,0.93705297],"study_design_scores_gemma":[0.00035905428,0.00010910722,0.00014088259,0.00019548673,0.000007853322,0.00003174435,8.8541805e-7,0.9100186,0.007306259,0.081499696,0.00001682283,0.000313579],"about_ca_topic_score_codex":0.000028061135,"about_ca_topic_score_gemma":0.0000058304963,"teacher_disagreement_score":0.9367394,"about_ca_system_score_codex":0.00023791619,"about_ca_system_score_gemma":0.00030442647,"threshold_uncertainty_score":0.9999864},"labels":[],"label_agreement":null},{"id":"W1551244780","doi":"10.1007/978-3-540-30125-7_2","title":"Hierarchical Regions for Image Segmentation","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Segmentation; Image segmentation; Artificial intelligence; Straddle; Block (permutation group theory); Image (mathematics); Computer vision; Market segmentation; Iterated function; Scale-space segmentation; Pattern recognition (psychology); Mathematics","score_opus":0.02111746710785406,"score_gpt":0.2967925874741635,"score_spread":0.2756751203663094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1551244780","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000022057934,0.0000801748,0.99401814,0.0023789222,0.00083592784,0.001001418,0.000009464395,0.00036845187,0.001305297],"genre_scores_gemma":[0.0015127133,0.000037661717,0.9948029,0.0029245587,0.00031702974,0.00006629384,0.000022482945,0.00003188522,0.00028447702],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9964877,0.000030371402,0.00056121004,0.0013552621,0.0009990078,0.0005664788],"domain_scores_gemma":[0.99763715,0.00053529517,0.00027019458,0.0010355982,0.00028401212,0.00023775386],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00078066933,0.00041174694,0.00039655573,0.0007555014,0.00026168273,0.00052180944,0.0025216222,0.0002700619,0.00004272067],"category_scores_gemma":[0.00020053073,0.0003867643,0.0001558799,0.0004344785,0.0009780333,0.0008557857,0.00069260277,0.00062768126,0.000029782066],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000055886153,0.000040123123,0.0000014110514,0.00006615505,0.000008795738,0.000058905705,0.00061258447,0.0007190134,0.0020744517,0.12743779,0.0001919265,0.8687833],"study_design_scores_gemma":[0.0005005406,0.00026325436,0.0000095696,0.0003388561,0.000008903977,0.000052064363,1.6969166e-7,0.071960114,0.03446113,0.89160717,0.00026627118,0.00053197925],"about_ca_topic_score_codex":0.000011274873,"about_ca_topic_score_gemma":0.000013182369,"teacher_disagreement_score":0.86825126,"about_ca_system_score_codex":0.00054081634,"about_ca_system_score_gemma":0.0008052796,"threshold_uncertainty_score":0.99985844},"labels":[],"label_agreement":null},{"id":"W1551287518","doi":"10.1007/978-3-540-85988-8_35","title":"Bone Segmentation and Fracture Detection in Ultrasound Using 3D Local Phase Features","year":2008,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; 3D ultrasound; Artificial intelligence; Segmentation; Speckle pattern; Imaging phantom; Computer vision; Phase congruency; Ridge; Filter (signal processing); Pattern recognition (psychology); Ultrasound; Feature extraction; Geology; Radiology; Acoustics; Physics; Medicine","score_opus":0.01581582623786595,"score_gpt":0.3019868058840909,"score_spread":0.28617097964622495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1551287518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15330476,0.000160158,0.8457695,0.00015784567,0.00023043927,0.00022770892,4.6622216e-7,0.00014740495,0.0000017004605],"genre_scores_gemma":[0.5600624,0.000016997645,0.43885383,0.0010147628,0.000041162893,0.0000050749627,7.4745566e-7,0.0000048544507,1.7063567e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979952,0.00010015816,0.0002962525,0.00067188893,0.0005719882,0.00036451058],"domain_scores_gemma":[0.99907583,0.00028900645,0.00010646276,0.00033186362,0.00007573782,0.000121083525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005577973,0.00018199313,0.00018864872,0.0005191918,0.00023651315,0.0001969765,0.0005123815,0.00010335555,0.0000040516816],"category_scores_gemma":[0.00016221394,0.00016510714,0.000022795968,0.0016591828,0.0005923983,0.001257055,0.00018949893,0.0003670102,0.0000015157209],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000887326,0.00009256339,0.0007231218,0.000011420439,0.0000015162942,0.00008964918,0.0025426438,0.014013729,0.099679425,0.000005424341,0.000005688708,0.882826],"study_design_scores_gemma":[0.0006539432,0.00012948811,0.0043614036,0.000041514293,0.0000015240856,0.00052930746,0.0000029601952,0.50896347,0.48395807,0.0011663182,0.000003736503,0.0001882427],"about_ca_topic_score_codex":0.00019211702,"about_ca_topic_score_gemma":0.000094604184,"teacher_disagreement_score":0.8826377,"about_ca_system_score_codex":0.00022648255,"about_ca_system_score_gemma":0.00013415443,"threshold_uncertainty_score":0.67328733},"labels":[],"label_agreement":null},{"id":"W1551472421","doi":"10.1109/isbi.2015.7164079","title":"Fast and efficient image registration based on gradient orientations of minimal uncertainty","year":2015,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Image registration; Computer science; Voxel; Artificial intelligence; Matching (statistics); Context (archaeology); Sampling (signal processing); Computer vision; Image (mathematics); Pattern recognition (psychology); Mathematics; Statistics","score_opus":0.02468355415368978,"score_gpt":0.2951020276709205,"score_spread":0.2704184735172307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1551472421","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013542221,0.000003997778,0.97916144,0.00080864795,0.00007993824,0.00018488769,0.0000032876233,0.00011376467,0.006101834],"genre_scores_gemma":[0.56866693,7.947779e-7,0.43077177,0.00039238986,0.000010459392,0.000017462631,0.000008894962,0.0000031300262,0.00012818542],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989744,0.000060975028,0.00022564677,0.00022385543,0.00040709914,0.000108060485],"domain_scores_gemma":[0.9992244,0.00008411015,0.000098312594,0.00027080704,0.00017269592,0.00014966517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036589598,0.00007531237,0.00008801161,0.00010297387,0.0000356008,0.000054133205,0.00018864193,0.000024433868,0.0000185118],"category_scores_gemma":[0.00017588798,0.000061931314,0.000021666103,0.00023526068,0.00011950396,0.00013844822,0.000044545028,0.000048706886,0.0000064328424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003257954,0.0040397984,0.0023546377,0.00027415864,0.000059888,0.00009104349,0.017190207,0.028430799,0.10256207,0.40227252,0.099156976,0.3432421],"study_design_scores_gemma":[0.0008678905,0.0006545433,0.0011999948,0.000028963004,0.00000621359,0.000004082641,0.00042925292,0.8848595,0.11099122,0.00071602094,0.0000997235,0.00014260436],"about_ca_topic_score_codex":0.00006143135,"about_ca_topic_score_gemma":0.0000067053998,"teacher_disagreement_score":0.8564287,"about_ca_system_score_codex":0.000045977973,"about_ca_system_score_gemma":0.000093801755,"threshold_uncertainty_score":0.25254858},"labels":[],"label_agreement":null},{"id":"W1552642215","doi":"","title":"Extraction of blurred contours and noise","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec; Queen's University","funders":"","keywords":"Extraction (chemistry); Computer science; Noise (video); Computer vision; Artificial intelligence; Image (mathematics); Chromatography","score_opus":0.011565185414374839,"score_gpt":0.30009828866065835,"score_spread":0.2885331032462835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1552642215","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076127164,0.000033686432,0.98671234,0.00093898014,0.000027790767,0.000053158772,1.5355553e-7,0.000106244646,0.004514962],"genre_scores_gemma":[0.46194762,0.000019488509,0.53712666,0.00039331088,0.00001686298,0.0000028449915,2.5551276e-7,0.0000011775021,0.0004917708],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996525,0.000013751276,0.00009743865,0.00008357335,0.000104394116,0.000048351205],"domain_scores_gemma":[0.9997512,0.000035664914,0.00003616521,0.000106923435,0.000029210849,0.000040825562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010667234,0.000027932827,0.000044791224,0.00003352007,0.000011158715,0.00001782693,0.0001016167,0.000016115418,0.00008042387],"category_scores_gemma":[0.000025994636,0.000023546441,0.00000899528,0.000053861077,0.000028420538,0.00038048343,0.000030631134,0.000026758471,0.0000070981946],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001353266,0.00003618711,0.0001276069,0.000004935758,0.0000025090076,8.4771267e-7,0.00018671427,8.482797e-7,0.09912532,0.005184898,0.0049560163,0.89037275],"study_design_scores_gemma":[0.0002656216,0.00005037036,0.004106622,0.000009533207,0.0000022690433,0.000010994843,0.000032251508,0.016597146,0.9763994,0.0006733204,0.0017839625,0.000068483656],"about_ca_topic_score_codex":0.000016165748,"about_ca_topic_score_gemma":0.000003232998,"teacher_disagreement_score":0.89030427,"about_ca_system_score_codex":0.000007132123,"about_ca_system_score_gemma":0.000008845807,"threshold_uncertainty_score":0.0960196},"labels":[],"label_agreement":null},{"id":"W1552686093","doi":"10.1023/a:1011273631202","title":"Complexity, Confusion, and Perceptual Grouping. Part I: The Curve-like Representation","year":2001,"lang":"en","type":"article","venue":"Journal of Mathematical Imaging and Vision","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Espace pour la vie","funders":"","keywords":"Measure (data warehouse); Curse of dimensionality; Element (criminal law); Context (archaeology); Tangent; Enhanced Data Rates for GSM Evolution; Mathematics; Artificial intelligence; Orientation (vector space); Texture (cosmology); Computer science; Edge detection; Segmentation; Basis (linear algebra); Image (mathematics); Geometry; Image processing; Geography; Data mining","score_opus":0.04834786120524981,"score_gpt":0.36360035579256744,"score_spread":0.31525249458731763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1552686093","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046013433,0.00025428436,0.94519013,0.007881871,0.00011318541,0.00009369889,2.4910145e-7,0.000037922673,0.00041521096],"genre_scores_gemma":[0.7151077,0.0010604889,0.28036973,0.0029872493,0.00024081605,0.0000039972824,0.0000014076384,0.00001532177,0.00021333038],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986941,0.00016027075,0.00044011237,0.00014256749,0.0004351686,0.00012773326],"domain_scores_gemma":[0.99893963,0.00038201403,0.00022517925,0.00018579575,0.000136157,0.00013120525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011628532,0.00008972532,0.0001815419,0.00007566555,0.00015983504,0.00028537473,0.00026463746,0.000023411361,0.00006931466],"category_scores_gemma":[0.0002967408,0.00005285825,0.000043329776,0.00012725731,0.00027891522,0.0006260889,0.00020730248,0.00019554072,0.0000055166856],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005323678,0.00045355887,0.004587316,0.00013792852,0.000044001026,0.00018478246,0.009894129,0.0000024151468,0.0098377485,0.03474265,0.046101127,0.89396113],"study_design_scores_gemma":[0.0037373505,0.0010073902,0.038217016,0.002041457,0.00014405168,0.012526122,0.006036461,0.39057046,0.0041132756,0.5321017,0.008721318,0.00078337995],"about_ca_topic_score_codex":0.000004338741,"about_ca_topic_score_gemma":3.0038836e-7,"teacher_disagreement_score":0.89317775,"about_ca_system_score_codex":0.000012128442,"about_ca_system_score_gemma":0.000014304582,"threshold_uncertainty_score":0.27518752},"labels":[],"label_agreement":null},{"id":"W1553404127","doi":"10.1007/11559573_15","title":"Efficient Global Weighted Least-Squares Translation Registration in the Frequency Domain","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Frequency domain; Invariant (physics); Image registration; Algorithm; Fractal; Translation (biology); Function (biology); Image translation; Image (mathematics); Coding (social sciences); Artificial intelligence; Computer vision; Mathematics; Statistics","score_opus":0.017862459543023963,"score_gpt":0.2755722656234865,"score_spread":0.25770980608046257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1553404127","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018207407,0.0004547683,0.98784244,0.003468783,0.0004798074,0.0007803055,0.0000063330885,0.00018646866,0.006599001],"genre_scores_gemma":[0.091536365,0.000025194773,0.90577316,0.0022685616,0.00031617374,0.000027926222,0.00001365779,0.000013925058,0.000025022853],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99568844,0.00014965687,0.00074549776,0.001169389,0.0017310001,0.000515996],"domain_scores_gemma":[0.9979577,0.00036239903,0.00031788842,0.001105419,0.00014784081,0.00010872037],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018134962,0.0004173931,0.00033482118,0.0004676266,0.00019167103,0.0005287413,0.002811958,0.00029458303,0.00003225102],"category_scores_gemma":[0.000066348126,0.00031980584,0.00010765613,0.0010543953,0.00067574106,0.0004228428,0.0001983849,0.0006961608,0.000024026549],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000049342707,0.000066058376,0.000045320412,0.000023504794,0.000003730238,0.00008107451,0.0019468358,0.0043418068,0.00020712131,0.048936438,0.00003281956,0.94431037],"study_design_scores_gemma":[0.000611189,0.0002616209,0.0007949109,0.00049631775,0.000009253748,0.00013035261,0.0000020079522,0.47073886,0.0010031846,0.5247861,0.00036680727,0.0007993583],"about_ca_topic_score_codex":0.00007178347,"about_ca_topic_score_gemma":0.0004293585,"teacher_disagreement_score":0.943511,"about_ca_system_score_codex":0.00053605903,"about_ca_system_score_gemma":0.000461847,"threshold_uncertainty_score":0.9999254},"labels":[],"label_agreement":null},{"id":"W1553738992","doi":"10.1007/978-3-642-19315-6_30","title":"Compressed Sensing for Robust Texture Classification","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Texture (cosmology); Artificial intelligence; Computer science; Pattern recognition (psychology); Compressed sensing; Computer vision; Image (mathematics)","score_opus":0.05823035430574065,"score_gpt":0.28460980108116657,"score_spread":0.22637944677542593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1553738992","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[8.6216625e-7,0.00014449088,0.99407345,0.00060667173,0.0011199387,0.00085583585,0.000006485152,0.00040991136,0.0027823646],"genre_scores_gemma":[0.005114235,0.000027636503,0.9919425,0.0021088077,0.00034036406,0.000014998433,0.000017222248,0.000037249712,0.0003969795],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965756,0.000038348226,0.00057471206,0.001457901,0.0008134947,0.00053995725],"domain_scores_gemma":[0.99705577,0.00051902427,0.00041322154,0.0013598845,0.00045665057,0.00019542182],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00086890673,0.00044848962,0.00045612105,0.00066136144,0.00025144685,0.00044673786,0.002611291,0.0003919678,0.000030817795],"category_scores_gemma":[0.00016717143,0.00041403697,0.00013110264,0.00038631825,0.00070376316,0.0006377082,0.0006471207,0.0006355393,0.000023631243],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000044141743,0.000017456854,0.0000026045127,0.000045640885,0.0000065911327,0.000013838975,0.00040354402,0.0006935483,0.0009748405,0.00839881,0.000242636,0.98919606],"study_design_scores_gemma":[0.00026373626,0.00013353174,0.00003998067,0.0003547758,0.000010516274,0.000034708326,1.8037574e-7,0.8469773,0.012038994,0.13848367,0.001093617,0.0005689824],"about_ca_topic_score_codex":0.000013754825,"about_ca_topic_score_gemma":0.000018603692,"teacher_disagreement_score":0.9886271,"about_ca_system_score_codex":0.00024586686,"about_ca_system_score_gemma":0.00038321663,"threshold_uncertainty_score":0.99983114},"labels":[],"label_agreement":null},{"id":"W1554247483","doi":"10.1007/978-3-540-77046-6_22","title":"Multiscale Boundary Identification for Medical Images","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Identification (biology); Boundary (topology); Computer science; Artificial intelligence; Segmentation; Task (project management); Computer vision; Object (grammar); Operator (biology); Image (mathematics); Image segmentation; Pattern recognition (psychology); Mathematics; Engineering","score_opus":0.022179599693414615,"score_gpt":0.3218266724970275,"score_spread":0.2996470728036129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1554247483","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000003655377,0.00036662156,0.994286,0.0012458115,0.001771261,0.0007711062,0.00001000111,0.0003776915,0.0011678495],"genre_scores_gemma":[0.0022811084,0.0000726187,0.99257565,0.003485691,0.00057689135,0.000042670727,0.00002373243,0.000036177793,0.0009054464],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99493104,0.000032609863,0.0008592688,0.0014711451,0.0020823348,0.0006236016],"domain_scores_gemma":[0.99670535,0.0009817302,0.00035883574,0.0011940558,0.00041629412,0.00034371373],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0031337377,0.00040210746,0.00042439235,0.00087141857,0.00028650297,0.0006854228,0.003819046,0.00046599656,0.000084959676],"category_scores_gemma":[0.0007158783,0.0003762661,0.00014643346,0.00044560243,0.0013744972,0.0008101193,0.0009285363,0.0007235252,0.000047820817],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034376144,0.000028116938,0.0000067492797,0.000047128386,0.0000054943575,0.00004633636,0.00019099396,0.00005317754,0.0005144407,0.0028419537,0.00035714358,0.99590504],"study_design_scores_gemma":[0.0011068844,0.00038026218,0.00029586363,0.0010459239,0.000022622777,0.00020859095,4.2979704e-7,0.5104857,0.14756408,0.32881042,0.0084014535,0.0016777971],"about_ca_topic_score_codex":0.000013066244,"about_ca_topic_score_gemma":0.00003795816,"teacher_disagreement_score":0.99422723,"about_ca_system_score_codex":0.0003047645,"about_ca_system_score_gemma":0.00078105787,"threshold_uncertainty_score":0.9998689},"labels":[],"label_agreement":null},{"id":"W1556718077","doi":"10.1007/11566465_11","title":"Elastic Registration of 3D Ultrasound Images","year":2005,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Voxel; Artificial intelligence; Computer vision; Image registration; 3D ultrasound; Segmentation; Speckle pattern; Ultrasound; Process (computing); Image (mathematics); Acoustics","score_opus":0.01120584617764754,"score_gpt":0.2798254599276722,"score_spread":0.2686196137500247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1556718077","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035498133,0.000068536276,0.9949695,0.0008014526,0.00024339261,0.00014196114,7.100734e-7,0.00013922225,0.00008543821],"genre_scores_gemma":[0.47896212,0.0000049899813,0.52048135,0.00048305292,0.00006118928,0.0000032615048,4.6824007e-7,0.0000022420493,0.0000013260722],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99810505,0.000056531775,0.00036772896,0.00049601006,0.0006805348,0.00029412046],"domain_scores_gemma":[0.99850625,0.0005187643,0.00015706183,0.00058003893,0.0001505593,0.00008732637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008523852,0.00012639603,0.00015328889,0.00028665754,0.00008226322,0.00018579644,0.0014096271,0.000050037394,0.000018201088],"category_scores_gemma":[0.0005590376,0.00011126665,0.00002828638,0.0013378876,0.0005117028,0.0011180994,0.00021298885,0.00017095481,0.0000106126245],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016701705,0.00006632173,0.0004216886,0.000013910111,0.0000016009329,0.000005155971,0.0006358384,0.009949193,0.0894489,0.00032641485,0.000060580685,0.8990687],"study_design_scores_gemma":[0.00017350538,0.00010931752,0.0027094602,0.000054825086,0.0000016583512,0.000038134745,3.9496246e-7,0.2298557,0.7618867,0.004994296,0.000017506169,0.0001584842],"about_ca_topic_score_codex":0.000024423569,"about_ca_topic_score_gemma":0.000018993846,"teacher_disagreement_score":0.8989102,"about_ca_system_score_codex":0.00009103652,"about_ca_system_score_gemma":0.00017695481,"threshold_uncertainty_score":0.45373222},"labels":[],"label_agreement":null},{"id":"W1557471985","doi":"","title":"Multiple Sclerosis Lesions Segmentation using Spectral Gradient and Graph Cuts","year":2008,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"","keywords":"Segmentation; Artificial intelligence; Graph; Computer science; Pattern recognition (psychology); Image segmentation; Market segmentation; Theoretical computer science","score_opus":0.040967320393712495,"score_gpt":0.245849297138442,"score_spread":0.20488197674472952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1557471985","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26999363,0.00018435852,0.7268315,0.0013426576,0.00006452463,0.00022515218,0.0000049379896,0.00027986345,0.0010733899],"genre_scores_gemma":[0.50360256,0.0004193962,0.49536476,0.00015325143,0.000007285863,0.000020180649,0.000022078553,0.000012265482,0.00039821037],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9968555,0.0015236738,0.00036381593,0.0005254267,0.00042948124,0.0003021104],"domain_scores_gemma":[0.99753636,0.0006137859,0.0002103446,0.00084853766,0.0005460669,0.0002448919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001549366,0.00018199546,0.00017910881,0.00021663739,0.0006428559,0.00020226502,0.000674183,0.00007333668,0.000037650123],"category_scores_gemma":[0.0005106865,0.00018911966,0.000080993115,0.000608643,0.0003104782,0.00063154846,0.000372728,0.00018202403,0.000011956501],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015818045,0.001454046,0.03592187,0.00009373738,0.0000964643,0.00004772356,0.04639536,0.0000550009,0.5310807,0.063294955,0.0035368572,0.31800744],"study_design_scores_gemma":[0.0011206578,0.0000020384455,0.03517065,0.00045658753,0.000021223264,0.00011758288,0.00015505003,0.072338685,0.8865426,0.0032557896,0.00033967124,0.00047942487],"about_ca_topic_score_codex":0.0005902763,"about_ca_topic_score_gemma":0.0001861632,"teacher_disagreement_score":0.35546193,"about_ca_system_score_codex":0.000083184226,"about_ca_system_score_gemma":0.0000916968,"threshold_uncertainty_score":0.77120763},"labels":[],"label_agreement":null},{"id":"W1558244650","doi":"10.1007/978-3-642-04271-3_65","title":"Tumor Invasion Margin on the Riemannian Space of Brain Fibers","year":2009,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Geodesic; Margin (machine learning); Glioma; Euclidean distance; Computer science; Euclidean space; Fiber tract; Lesion; Brain tumor; Pathology; Artificial intelligence; Mathematics; Medicine; Radiology; Pure mathematics; Geometry; Magnetic resonance imaging; Diffusion MRI; Cancer research; Machine learning","score_opus":0.014122522865845803,"score_gpt":0.27062858985566063,"score_spread":0.2565060669898148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1558244650","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014863459,0.000020717976,0.95627004,0.028159164,0.00022772611,0.00026500132,4.3886007e-7,0.000112332804,0.00008110372],"genre_scores_gemma":[0.57714486,0.0000016391691,0.41019827,0.012611717,0.000035388686,0.0000033998913,2.3267982e-7,0.0000026489138,0.0000018272013],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978656,0.0001354415,0.0002758396,0.00055557204,0.0008126591,0.00035488754],"domain_scores_gemma":[0.99796426,0.0008303278,0.000139756,0.00087239034,0.000089652196,0.000103640356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014134829,0.0001574979,0.00017223897,0.00028922883,0.00014310729,0.00017920017,0.0023112013,0.00004097966,0.0000150781325],"category_scores_gemma":[0.0006333395,0.000104606006,0.000047302357,0.0019307988,0.00043672742,0.00043276954,0.00029901805,0.00027940847,0.000010649282],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007189059,0.00010793017,0.00014290081,0.000010247179,0.0000018260444,0.000036034646,0.0020505742,0.0032071618,0.030747104,0.0054417686,0.0008525281,0.9573947],"study_design_scores_gemma":[0.00021627315,0.0004885367,0.004499123,0.0001767681,0.000001067392,0.000021305634,0.000001842696,0.23756753,0.7182536,0.03850276,0.00006282249,0.00020836983],"about_ca_topic_score_codex":0.00001977384,"about_ca_topic_score_gemma":0.000006735506,"teacher_disagreement_score":0.95718634,"about_ca_system_score_codex":0.00008009105,"about_ca_system_score_gemma":0.00015101396,"threshold_uncertainty_score":0.42948258},"labels":[],"label_agreement":null},{"id":"W1558923211","doi":"10.1007/978-3-642-04268-3_100","title":"A General PDE-Framework for Registration of Contrast Enhanced Images","year":2009,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Terry Fox Foundation","keywords":"Regularization (linguistics); Computer science; Contrast (vision); Parametric statistics; Image registration; Term (time); Artificial intelligence; Exploit; Computer vision; Algorithm; Image (mathematics); Mathematics","score_opus":0.013338396922364762,"score_gpt":0.3103721865101055,"score_spread":0.29703378958774074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1558923211","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030537115,0.00004749347,0.9941226,0.0019209821,0.0003229975,0.00037637315,0.0000015393074,0.0001302341,0.000024043968],"genre_scores_gemma":[0.46153563,0.000003799883,0.53709006,0.0012857905,0.00007220201,0.000008847912,7.9238936e-7,0.0000019641095,8.974347e-7],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981739,0.000043884596,0.00038034806,0.00054543786,0.0005135652,0.00034289056],"domain_scores_gemma":[0.99852306,0.00042236253,0.0001944329,0.0005426479,0.00022811459,0.00008936022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080552045,0.00013854261,0.00020736667,0.0002166047,0.00009245098,0.0001921167,0.0013063906,0.00007538244,0.0000045928778],"category_scores_gemma":[0.0006839282,0.00012286029,0.000052733954,0.0010606691,0.00030493079,0.000667256,0.00009859701,0.00015404174,0.0000010273438],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000061278283,0.000056607485,0.000018846273,0.000009717794,0.0000012838066,0.0000021498477,0.0004301302,0.0013297545,0.16236341,0.005708553,0.00003438989,0.830039],"study_design_scores_gemma":[0.00018316407,0.00025207349,0.0007564147,0.00005688651,0.000001178587,0.000004388304,2.7296164e-7,0.14693461,0.71155477,0.1401444,0.00000290886,0.00010891923],"about_ca_topic_score_codex":0.000010529533,"about_ca_topic_score_gemma":0.000003585674,"teacher_disagreement_score":0.8299301,"about_ca_system_score_codex":0.00006221157,"about_ca_system_score_gemma":0.00016932692,"threshold_uncertainty_score":0.5010097},"labels":[],"label_agreement":null},{"id":"W1570328450","doi":"10.1007/11505730_52","title":"Unified Statistical Approach to Cortical Thickness Analysis","year":2005,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Smoothing; Geodesic; Computer science; Surface (topology); Statistical inference; Euclidean distance; Kernel (algebra); Population; Artificial intelligence; Euclidean geometry; Computer vision; Algorithm; Pattern recognition (psychology); Mathematics; Geometry; Statistics; Discrete mathematics","score_opus":0.018312749511144318,"score_gpt":0.3035165372907087,"score_spread":0.2852037877795644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1570328450","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014564767,0.000012675982,0.9961275,0.0016072548,0.00017282122,0.00024345265,0.0000017677829,0.00026107943,0.000116994466],"genre_scores_gemma":[0.45132664,7.3754654e-7,0.5453212,0.0032716498,0.00006138714,0.000012198646,0.0000018790407,0.0000032676462,9.892711e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967658,0.00016287527,0.00040757714,0.0010214124,0.0010529577,0.00058936834],"domain_scores_gemma":[0.99792445,0.00057502603,0.000060927443,0.0008820263,0.00016460096,0.000392964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014363505,0.00019198176,0.00030447028,0.0008006869,0.00017841486,0.00046419963,0.0023013675,0.00008026877,0.000025883804],"category_scores_gemma":[0.0005363135,0.0001625281,0.000056607434,0.0063363803,0.000440671,0.0006200069,0.0007355412,0.0003879591,0.000041218722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004939252,0.00021747436,0.0007684247,0.000008237199,0.000017712011,0.000015545364,0.0015928671,0.07129427,0.0018916061,0.0071186693,0.000063240244,0.917007],"study_design_scores_gemma":[0.00013694668,0.00006687232,0.0071402127,0.000008206997,0.000013313087,0.000015465366,8.135088e-7,0.97433937,0.015288399,0.0027326052,0.00002898172,0.00022881247],"about_ca_topic_score_codex":0.000033433083,"about_ca_topic_score_gemma":0.000025803662,"teacher_disagreement_score":0.9167782,"about_ca_system_score_codex":0.00017025118,"about_ca_system_score_gemma":0.00019989243,"threshold_uncertainty_score":0.66277033},"labels":[],"label_agreement":null},{"id":"W1573704087","doi":"10.1007/11866763_94","title":"Fast and Robust Clinical Triple-Region Image Segmentation Using One Level Set Function","year":2006,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; CARE Canada; General Electric (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Image segmentation; Level set (data structures); Level set method; Scale-space segmentation; Pattern recognition (psychology); Computer vision; Image processing; Image (mathematics)","score_opus":0.08333183951679882,"score_gpt":0.3384066075782218,"score_spread":0.25507476806142293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1573704087","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025040137,0.00004959494,0.97326654,0.00044901206,0.000703667,0.00031427105,0.0000013462538,0.00016615939,0.00000925471],"genre_scores_gemma":[0.3185434,0.000006758973,0.68044245,0.0008235417,0.00016615109,0.000005635268,0.00000414444,0.000006207981,0.0000017157796],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99746674,0.00017356807,0.00052122696,0.0008447951,0.00062252954,0.0003711502],"domain_scores_gemma":[0.99886495,0.00022774115,0.00019565514,0.0004485598,0.00016228722,0.00010082289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012653635,0.00018345033,0.00021487121,0.00035302708,0.00023138634,0.0005075946,0.00068192236,0.000100516234,0.000004521502],"category_scores_gemma":[0.00014029685,0.0001749849,0.000043542223,0.0013150667,0.00057399616,0.0012613784,0.0004193755,0.0002663213,0.0000038979406],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010749344,0.00010739738,0.004387849,0.000017169055,0.0000034748646,0.000016081336,0.00022289851,0.009745435,0.020561825,0.0001267786,0.00006590411,0.96473444],"study_design_scores_gemma":[0.0006654407,0.0001997832,0.0254966,0.0000655912,0.000007061487,0.000055862252,0.0000017809299,0.9272655,0.04027175,0.005711447,0.000004351415,0.00025481614],"about_ca_topic_score_codex":0.00013387036,"about_ca_topic_score_gemma":0.0000355935,"teacher_disagreement_score":0.9644796,"about_ca_system_score_codex":0.00012934855,"about_ca_system_score_gemma":0.00015399409,"threshold_uncertainty_score":0.71356773},"labels":[],"label_agreement":null},{"id":"W1573811715","doi":"","title":"Shape identification via metrics constructed from the oriented distance function","year":2005,"lang":"en","type":"article","venue":"Control and Cybernetics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Identification (biology); Function (biology); Computer science; Mathematics","score_opus":0.006666476019390006,"score_gpt":0.22264424961775392,"score_spread":0.21597777359836393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1573811715","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007539575,0.0010605678,0.9889151,0.0018391211,0.00017402181,0.00018124051,0.000009467179,0.00014629534,0.00013464087],"genre_scores_gemma":[0.9759159,0.00006765784,0.022089291,0.0016414701,0.000108144355,0.000019581606,0.000012454926,0.000004769797,0.00014074118],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990934,0.00007104251,0.0002399957,0.00022669228,0.0002517515,0.00011712819],"domain_scores_gemma":[0.9991333,0.0002517363,0.000121420526,0.00029928275,0.00012551068,0.00006874728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018190275,0.00008494827,0.00009425766,0.000031486237,0.00010851846,0.00013694112,0.000283322,0.00004513897,0.00005396112],"category_scores_gemma":[0.00014565689,0.000061868224,0.000023408484,0.00026766732,0.00012094562,0.00022447921,0.000049372386,0.00010146829,0.000024994115],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011728784,0.00002946293,0.004150696,0.0000020366874,0.000023107134,8.7396137e-7,0.0002260534,0.0000028226218,0.0069979536,0.0053154635,0.0027529667,0.9804868],"study_design_scores_gemma":[0.0024952858,0.00011479557,0.19044237,0.00002538049,0.000120369885,0.000009294711,0.00013723495,0.7538615,0.011015021,0.006819815,0.034591984,0.000366984],"about_ca_topic_score_codex":0.00011747049,"about_ca_topic_score_gemma":0.000063226245,"teacher_disagreement_score":0.9801198,"about_ca_system_score_codex":0.000029006085,"about_ca_system_score_gemma":0.000026543976,"threshold_uncertainty_score":0.2522913},"labels":[],"label_agreement":null},{"id":"W1574826636","doi":"10.1007/978-3-642-15745-5_10","title":"Automatic Prostate Segmentation Using Fused Ultrasound B-Mode and Elastography Images","year":2010,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Cancer Agency; University of British Columbia","funders":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Elastography; Segmentation; Artificial intelligence; Computer vision; Ultrasound; Computer science; Ultrasound elastography; 3D ultrasound; Image segmentation; Pattern recognition (psychology); Radiology; Medicine","score_opus":0.008557780598168781,"score_gpt":0.28763591601341926,"score_spread":0.27907813541525045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1574826636","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33675534,0.00001814794,0.66222906,0.00017434145,0.00037965327,0.00024248869,0.000001046763,0.00019705632,0.000002879204],"genre_scores_gemma":[0.474248,0.0000039647766,0.5251358,0.0005675216,0.000031042604,0.0000081256685,7.739979e-7,0.000004632731,1.5035724e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979453,0.00007081693,0.0003056387,0.00068262307,0.0005862401,0.0004093489],"domain_scores_gemma":[0.99866533,0.00038026285,0.0001344818,0.0005099094,0.00014913444,0.00016087267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007695829,0.00019111462,0.00017149175,0.000511049,0.00024350976,0.00070340314,0.00092432956,0.000065882596,0.000012049106],"category_scores_gemma":[0.0002642089,0.00016800522,0.00003156751,0.0016416059,0.000728011,0.0014870929,0.00031164385,0.00034446004,0.0000027675023],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.969565e-7,0.000025988804,0.004149947,0.000017052458,0.0000020849811,0.0000067615465,0.0015116815,0.00047134922,0.48125675,0.000034763496,0.000004553162,0.51251817],"study_design_scores_gemma":[0.00024607743,0.00006140803,0.0060348166,0.000037020265,0.0000028665647,0.00008113608,0.0000021330216,0.58997196,0.3967559,0.0066077663,0.0000013879646,0.00019750913],"about_ca_topic_score_codex":0.00006008948,"about_ca_topic_score_gemma":0.000029985962,"teacher_disagreement_score":0.5895006,"about_ca_system_score_codex":0.000045596717,"about_ca_system_score_gemma":0.00014618687,"threshold_uncertainty_score":0.68510544},"labels":[],"label_agreement":null},{"id":"W1576481657","doi":"10.1007/978-3-540-74260-9_21","title":"Image Segmentation Using Level Set and Local Linear Approximations","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Segmentation; Image segmentation; Image (mathematics); Scale-space segmentation; Artificial intelligence; Set (abstract data type); Segmentation-based object categorization; Linear approximation; Computer vision; Scope (computer science); Algorithm; Pattern recognition (psychology); Nonlinear system","score_opus":0.06645478620908808,"score_gpt":0.3354902658013269,"score_spread":0.26903547959223884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1576481657","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004155122,0.00010818141,0.99807173,0.00018665941,0.0004097959,0.00047039113,0.000010285911,0.0001801383,0.0005212767],"genre_scores_gemma":[0.0023537062,0.00002629247,0.9959064,0.0014017079,0.00018548245,0.0000058909927,0.000015739568,0.00002418181,0.00008056518],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99693906,0.000036967846,0.0005305182,0.0010569507,0.0009953739,0.0004411388],"domain_scores_gemma":[0.998316,0.00026307057,0.00026563415,0.00069249095,0.0002566348,0.00020616432],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011205671,0.0003687855,0.00031982074,0.0008478717,0.00024524497,0.00038976214,0.0012088655,0.00024524043,0.000021801821],"category_scores_gemma":[0.00008943685,0.0003591075,0.00005771297,0.00050591305,0.001176476,0.0008257008,0.00088266504,0.0005535573,0.00001375255],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027620308,0.000018850254,0.00001407444,0.00005739727,0.000007427698,0.000049159753,0.00083340105,0.0017504226,0.0026311085,0.0018844648,0.000024710771,0.9927262],"study_design_scores_gemma":[0.00024186517,0.00009197337,0.0000406615,0.00023054665,0.00000912739,0.00011585835,0.0000011438926,0.954839,0.019762687,0.024139468,0.000063752195,0.0004639386],"about_ca_topic_score_codex":0.000032042455,"about_ca_topic_score_gemma":0.000026147001,"teacher_disagreement_score":0.9922623,"about_ca_system_score_codex":0.00030506324,"about_ca_system_score_gemma":0.00035044938,"threshold_uncertainty_score":0.9998861},"labels":[],"label_agreement":null},{"id":"W1579330142","doi":"","title":"Semi-Automatic Snake-Based Segmentation of Carotid Artery Ultrasound Images","year":2010,"lang":"en","type":"article","venue":"Communications of The Arab Computer Society","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Contouring; Active contour model; Artificial intelligence; Computer vision; Segmentation; Robustness (evolution); Computer science; Ultrasound; Sensitivity (control systems); Image segmentation; Pattern recognition (psychology); Radiology; Medicine; Engineering","score_opus":0.01612002685894837,"score_gpt":0.2788243948723146,"score_spread":0.2627043680133662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1579330142","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017868344,0.000054046126,0.9785409,0.00242812,0.0002806472,0.00043768302,0.000012627205,0.0001931038,0.00018452342],"genre_scores_gemma":[0.3833081,0.000026211274,0.61604595,0.00053858594,0.000016427042,0.00003223996,0.000011397729,0.0000070778824,0.000013996523],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99842143,0.00026927193,0.0005426125,0.00021382615,0.0003922342,0.00016059834],"domain_scores_gemma":[0.9933122,0.0008386037,0.00048823416,0.00501854,0.0002816038,0.000060818315],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0006572852,0.00014478872,0.00022199261,0.000052933792,0.00023576357,0.00007431027,0.0059104264,0.00008084812,0.000015492697],"category_scores_gemma":[0.000114996474,0.000117066345,0.00028309267,0.00049717847,0.00067376025,0.00037824904,0.0013228871,0.0003809595,0.000004640548],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018794453,0.0008936483,0.007980641,0.0002502263,0.00018887933,2.2355873e-7,0.0059695146,0.00026603034,0.9049758,0.0044411244,0.033871524,0.041160524],"study_design_scores_gemma":[0.0006644039,0.00007314976,0.020357281,0.00015066522,0.000045331228,0.00001534211,0.00012698809,0.14352718,0.8321691,0.0024674782,0.00014134782,0.00026169108],"about_ca_topic_score_codex":0.00002634893,"about_ca_topic_score_gemma":0.000006432835,"teacher_disagreement_score":0.36543977,"about_ca_system_score_codex":0.000034907804,"about_ca_system_score_gemma":0.00013814506,"threshold_uncertainty_score":0.9994681},"labels":[],"label_agreement":null},{"id":"W1581645353","doi":"10.1007/978-3-642-10331-5_100","title":"Optimal Weights for Convex Functionals in Medical Image Segmentation","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Initialization; Energy functional; Computer science; Segmentation; Image segmentation; Regular polygon; Minification; Energy (signal processing); Image (mathematics); Artificial intelligence; Set (abstract data type); Energy minimization; Convex optimization; Mathematical optimization; Algorithm; Pattern recognition (psychology); Computer vision; Mathematics","score_opus":0.016648513566191462,"score_gpt":0.2960550388763512,"score_spread":0.27940652531015975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1581645353","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000029305611,0.00016939596,0.9943325,0.0022785857,0.0009293229,0.0009177243,0.000006364582,0.00021891479,0.0011178842],"genre_scores_gemma":[0.001165665,0.00006546003,0.99221367,0.005667735,0.0004016864,0.00005950136,0.000031470725,0.000023796487,0.0003710153],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99519217,0.000055341883,0.0008149957,0.0014038107,0.0019343557,0.0005993194],"domain_scores_gemma":[0.9975682,0.0008212944,0.0003006868,0.00071761635,0.00030667504,0.00028552304],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017503103,0.00044224237,0.0005324815,0.00104934,0.00014837962,0.00038406078,0.0023800551,0.00042201477,0.00019907182],"category_scores_gemma":[0.000313176,0.00040669699,0.00013149215,0.00052420323,0.0006726486,0.0009881235,0.00048571316,0.00071873056,0.00003273687],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001057675,0.000053667496,0.000009422502,0.000035462715,0.000005965594,0.00009211465,0.00029260956,0.0005764511,0.0004361912,0.0053852,0.00022013401,0.9928822],"study_design_scores_gemma":[0.0014593609,0.00059836166,0.00019388353,0.0008450859,0.000011093417,0.00012332338,5.832665e-7,0.7531037,0.033824295,0.20763879,0.0011572717,0.0010442936],"about_ca_topic_score_codex":0.000011344117,"about_ca_topic_score_gemma":0.000025775147,"teacher_disagreement_score":0.9918379,"about_ca_system_score_codex":0.00042103726,"about_ca_system_score_gemma":0.000854958,"threshold_uncertainty_score":0.9998385},"labels":[],"label_agreement":null},{"id":"W1582875974","doi":"10.1007/978-3-642-21073-0_39","title":"An Entropy-Based Technique for Nonrigid Medical Image Alignment","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision","score_opus":0.017956009144947563,"score_gpt":0.29223853256918547,"score_spread":0.2742825234242379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1582875974","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000013738278,0.00006156339,0.99523914,0.0008216157,0.00080315256,0.0016132253,0.000012859305,0.0004924795,0.0009545803],"genre_scores_gemma":[0.0031482624,0.0000220917,0.99043834,0.0056369277,0.00035040578,0.00024334174,0.000023865174,0.00005153174,0.000085250686],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9943331,0.00008089489,0.0007993323,0.0018822639,0.0020909053,0.00081352284],"domain_scores_gemma":[0.99616367,0.0005125404,0.00037880422,0.0019401837,0.00038560104,0.0006192072],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0024052085,0.00061340217,0.0006043037,0.0008448545,0.00021501374,0.00042658846,0.0058411174,0.0005818939,0.00028650204],"category_scores_gemma":[0.00025658167,0.00054367806,0.00019088211,0.00037111779,0.0012848745,0.0007931841,0.00075065554,0.00077803107,0.00003130927],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022483227,0.00025133337,0.000010084716,0.00014448585,0.000017130613,0.00026178022,0.00043700688,0.00016114535,0.014816701,0.028352695,0.00037905292,0.9551461],"study_design_scores_gemma":[0.000678685,0.0010918191,0.0000089958185,0.0006955841,0.000015735865,0.00007588111,1.2159191e-7,0.2303171,0.5237466,0.24061114,0.0016320557,0.0011262947],"about_ca_topic_score_codex":0.000024815772,"about_ca_topic_score_gemma":0.000018068282,"teacher_disagreement_score":0.9540198,"about_ca_system_score_codex":0.00038778537,"about_ca_system_score_gemma":0.0014210709,"threshold_uncertainty_score":0.9997015},"labels":[],"label_agreement":null},{"id":"W1583737440","doi":"10.1007/11866763_64","title":"4D Shape Registration for Dynamic Electrophysiological Cardiac Mapping","year":2006,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Iterative closest point; Computer science; Rotation (mathematics); Translation (biology); Artificial intelligence; Computer vision; Point (geometry); Algorithm; Range (aeronautics); Mathematics; Point cloud","score_opus":0.01240216626173031,"score_gpt":0.27333396378410113,"score_spread":0.26093179752237083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1583737440","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015795052,0.00007427516,0.98216987,0.0008615147,0.0004059821,0.00040370354,0.0000012174995,0.00027523137,0.000013159384],"genre_scores_gemma":[0.46062443,0.0000029379503,0.5387217,0.0005177622,0.00009513769,0.000029514276,0.0000043454556,0.0000033392278,8.4067557e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99791497,0.00006534911,0.00032019883,0.00075787766,0.00045813233,0.00048348785],"domain_scores_gemma":[0.99887687,0.0003253111,0.000116267765,0.00047679624,0.00013653694,0.00006819576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007181799,0.0001621942,0.00020001577,0.00023406696,0.00024394986,0.00043078838,0.0012511854,0.00008133423,0.0000038103658],"category_scores_gemma":[0.00016597149,0.0001391156,0.00007146225,0.0012762753,0.00036754092,0.00058913906,0.00022330049,0.00018380118,0.0000033919803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000039448732,0.00005200276,0.00013792128,0.000015857158,0.00000193451,0.000006274977,0.00010364205,0.0040913275,0.18955788,0.0021143795,0.00006341993,0.8038514],"study_design_scores_gemma":[0.00012785531,0.00017404779,0.0037661684,0.000021905871,0.0000010898727,0.0000054937973,2.3551245e-7,0.87070745,0.07462337,0.05036437,0.000030265774,0.00017773797],"about_ca_topic_score_codex":0.00003282059,"about_ca_topic_score_gemma":0.0000094917,"teacher_disagreement_score":0.86661613,"about_ca_system_score_codex":0.00017324465,"about_ca_system_score_gemma":0.00018313581,"threshold_uncertainty_score":0.5672969},"labels":[],"label_agreement":null},{"id":"W1585060786","doi":"10.1007/11559573_58","title":"Object Shape Extraction Based on the Piecewise Linear Skeletal Representation","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Piecewise linear function; Computer science; Segmentation; Piecewise; Artificial intelligence; Pixel; Vertex (graph theory); Computer vision; Image segmentation; Planar; Pattern recognition (psychology); Algorithm; Mathematics; Geometry; Computer graphics (images); Theoretical computer science; Graph","score_opus":0.026489472866013927,"score_gpt":0.3070585636167025,"score_spread":0.28056909075068853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1585060786","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00002554606,0.00008262482,0.9911876,0.0033822544,0.00089510926,0.00064567453,0.0000030025697,0.00034851232,0.0034297258],"genre_scores_gemma":[0.04691496,0.00006424879,0.93930006,0.011957676,0.0011004728,0.0000517306,0.000016987491,0.000047560392,0.0005462928],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957682,0.00011798051,0.0005553463,0.0013576032,0.0017335877,0.00046726762],"domain_scores_gemma":[0.99632734,0.0013398654,0.00037989492,0.0015631393,0.00023650464,0.00015325072],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013904142,0.0004393381,0.00033027053,0.00069469545,0.0002979244,0.00048481068,0.002592986,0.00026469585,0.0002587414],"category_scores_gemma":[0.00039508697,0.0003269029,0.0001564147,0.00069424836,0.0006450174,0.00076100416,0.0004430843,0.001076982,0.00012108264],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000054708767,0.000042121686,0.000007300848,0.000010950861,0.000004549595,0.000039669067,0.00017458481,0.011416525,0.0005489003,0.0022519836,0.00015895418,0.985339],"study_design_scores_gemma":[0.00019072724,0.00019094645,0.00011059381,0.00022247319,0.0000066627354,0.000023952658,1.9389368e-7,0.964193,0.019836845,0.014062275,0.0007735703,0.00038874795],"about_ca_topic_score_codex":0.000015450683,"about_ca_topic_score_gemma":0.00001628864,"teacher_disagreement_score":0.98495024,"about_ca_system_score_codex":0.00034941355,"about_ca_system_score_gemma":0.00042268497,"threshold_uncertainty_score":0.9999183},"labels":[],"label_agreement":null},{"id":"W1585210950","doi":"10.1007/11919476_37","title":"An Improved Representation of Junctions Through Asymmetric Tensor Diffusion","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Robustness (evolution); Asymmetry; Representation (politics); Diffusion MRI; Structure tensor; Tensor (intrinsic definition); Diffusion; Artificial intelligence; Convolutional neural network; Algorithm; Pattern recognition (psychology); Image (mathematics); Mathematics; Physics; Geometry","score_opus":0.019795122062193558,"score_gpt":0.2940108031782403,"score_spread":0.2742156811160467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1585210950","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000051527277,0.000108438515,0.99471706,0.0002457866,0.0010710601,0.00053144875,0.0000061086284,0.00028407818,0.0029844968],"genre_scores_gemma":[0.033563886,0.000049733306,0.96477973,0.0008217035,0.00026713,0.000017238517,0.000026706984,0.000028532304,0.00044533683],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99629176,0.00006615862,0.0007567545,0.0013212732,0.0011604945,0.0004035577],"domain_scores_gemma":[0.9969387,0.00035569374,0.0005439681,0.0015796507,0.00045253013,0.00012946353],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005315934,0.0003715245,0.00048823387,0.0011354838,0.00018314632,0.00026787253,0.0022940713,0.00028904533,0.000026020241],"category_scores_gemma":[0.00019462986,0.00033258935,0.00012987482,0.0016375723,0.00070586347,0.0012949124,0.0006059508,0.00052943605,0.000009487323],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000051611764,0.00012563466,0.00010079244,0.000037580543,0.0000074963295,0.000019682899,0.0003907543,0.0020310762,0.008689507,0.0026853469,0.00021684123,0.9856901],"study_design_scores_gemma":[0.0006088914,0.0007408868,0.0011077272,0.00030916836,0.000024980869,0.00004747247,7.5266985e-7,0.7389268,0.12552388,0.1315391,0.0003211898,0.0008491355],"about_ca_topic_score_codex":0.00025826358,"about_ca_topic_score_gemma":0.000036314505,"teacher_disagreement_score":0.984841,"about_ca_system_score_codex":0.0002152825,"about_ca_system_score_gemma":0.00028444832,"threshold_uncertainty_score":0.9999126},"labels":[],"label_agreement":null},{"id":"W1586482675","doi":"10.1109/cic.1996.542560","title":"3D motion/structure estimation using temporal and stereoscopic point matching in biplane cineangiography","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Biplane; Computer vision; Artificial intelligence; Stereoscopy; Computer science; Structure from motion; Matching (statistics); Point (geometry); Motion estimation; Calibration; Motion (physics); Point set registration; Cineangiography; Mathematics; Geometry","score_opus":0.01943338377511131,"score_gpt":0.26234931352109847,"score_spread":0.24291592974598716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1586482675","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.128348,0.00004630668,0.8708908,0.00024876144,0.000054028427,0.00012611422,9.269399e-7,0.00015303204,0.0001320261],"genre_scores_gemma":[0.44637275,0.0000038676294,0.5533473,0.00024675508,0.000007997112,0.0000012870859,0.0000026095317,0.0000027803255,0.000014673578],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924314,0.00004527495,0.00020398754,0.00020860966,0.00017093401,0.00012803957],"domain_scores_gemma":[0.9996688,0.000026752363,0.000058572827,0.0001699854,0.000017002169,0.00005890266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011040654,0.00008756102,0.0000989863,0.00029522646,0.000044913886,0.0001183822,0.00015194884,0.000041918218,0.00016696546],"category_scores_gemma":[0.000013506149,0.00007610143,0.000016691221,0.00037318858,0.000029465733,0.0006816415,0.00007217077,0.00008753488,0.0000030027902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000044307985,0.0001717021,0.048683904,0.00021707293,0.000032280408,0.00012130712,0.0063030366,0.00089437404,0.024987204,0.0033939413,0.0015293194,0.9136614],"study_design_scores_gemma":[0.00040798495,0.000043684267,0.007219834,0.00007761856,0.000004218937,0.00006276962,0.00005176124,0.9786129,0.007369365,0.00596708,0.000007200902,0.00017559066],"about_ca_topic_score_codex":0.00018231965,"about_ca_topic_score_gemma":0.000020632478,"teacher_disagreement_score":0.97771853,"about_ca_system_score_codex":0.000020941205,"about_ca_system_score_gemma":0.00000400582,"threshold_uncertainty_score":0.31033263},"labels":[],"label_agreement":null},{"id":"W1587643749","doi":"10.1007/978-3-642-04271-3_67","title":"Robust Atlas-Based Brain Segmentation Using Multi-structure Confidence-Weighted Registration","year":2009,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Segmentation; Atlas (anatomy); Computer science; Artificial intelligence; Image registration; Pattern recognition (psychology); Image segmentation; Computer vision; Image (mathematics); Medicine","score_opus":0.038143309534800676,"score_gpt":0.3132261708070502,"score_spread":0.2750828612722495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1587643749","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016150609,0.000035802823,0.9799622,0.0026697086,0.00043301043,0.0004372657,0.0000018767895,0.0003052215,0.0000042507804],"genre_scores_gemma":[0.4297486,9.53835e-7,0.5661032,0.0040726596,0.000061165236,0.0000028228344,0.0000056809536,0.0000041882618,7.0261717e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99696934,0.00015906326,0.00048145128,0.0009450714,0.00094934035,0.00049575546],"domain_scores_gemma":[0.9983356,0.00027238496,0.00025920215,0.0007148105,0.0002454047,0.00017261574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007910613,0.00026235334,0.00022640913,0.00049980095,0.00027944928,0.00064297987,0.0015105512,0.00013150448,0.000019279358],"category_scores_gemma":[0.00029141761,0.00023849447,0.000050932165,0.0023274603,0.00035946962,0.0015037678,0.0001246663,0.000330513,0.000003715467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008287059,0.000104404186,0.0005803522,0.000016427537,0.0000026717526,0.000044631255,0.00090902305,0.09687538,0.32418808,0.0003484088,0.000041615705,0.5768807],"study_design_scores_gemma":[0.00035461536,0.00011137882,0.0013561276,0.000059391452,0.0000018522419,0.00002166733,7.1111316e-7,0.6764158,0.317166,0.0043178857,0.000002136752,0.00019242379],"about_ca_topic_score_codex":0.00006330191,"about_ca_topic_score_gemma":0.000049217346,"teacher_disagreement_score":0.57954043,"about_ca_system_score_codex":0.0003015396,"about_ca_system_score_gemma":0.00044084364,"threshold_uncertainty_score":0.9725522},"labels":[],"label_agreement":null},{"id":"W1587803272","doi":"10.1007/978-3-540-85990-1_123","title":"Deformable Ultrasound Registration without Reconstruction","year":2008,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Modality (human–computer interaction); Artificial intelligence; Data set; Computer vision; Imaging phantom; Similarity (geometry); Volume (thermodynamics); Set (abstract data type); Task (project management); Adaptability; Pattern recognition (psychology); Image (mathematics); Radiology; Medicine","score_opus":0.0171317479272489,"score_gpt":0.2671213998095198,"score_spread":0.24998965188227093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1587803272","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0348583,0.000026991169,0.9635704,0.00031793313,0.00064305577,0.00017908901,2.735799e-7,0.00028497668,0.00011893423],"genre_scores_gemma":[0.48539332,0.000014465664,0.5139868,0.00053542893,0.000058152826,0.0000062879412,6.511596e-7,0.0000026448809,0.0000022110776],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807763,0.000057574813,0.00031316682,0.0005757824,0.00061615097,0.000359694],"domain_scores_gemma":[0.998863,0.00016253778,0.00013083451,0.000581878,0.0001435856,0.00011816461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007072974,0.00013943904,0.00014460385,0.0002940892,0.00031513043,0.00021818496,0.0010872711,0.00006546249,0.000011867112],"category_scores_gemma":[0.00028021587,0.00012320082,0.0000315272,0.0015161359,0.0006232846,0.001988928,0.00015987015,0.00022805629,0.000016850605],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033896322,0.00004264702,0.009405238,0.0000091504235,0.000002114161,0.000025413621,0.0012575681,0.004100302,0.019779218,0.00025879082,0.000048780843,0.9650674],"study_design_scores_gemma":[0.0003124192,0.00013505945,0.006589066,0.000056461813,0.0000013499582,0.0016192462,9.844363e-7,0.5731285,0.40421265,0.013630107,0.000022323833,0.00029183415],"about_ca_topic_score_codex":0.000062030915,"about_ca_topic_score_gemma":0.000024596424,"teacher_disagreement_score":0.96477556,"about_ca_system_score_codex":0.0001584679,"about_ca_system_score_gemma":0.0002864302,"threshold_uncertainty_score":0.5023984},"labels":[],"label_agreement":null},{"id":"W1588596779","doi":"10.1007/3-540-44745-8_42","title":"3D Flux Maximizing Flows","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Vector flow; Computer science; Segmentation; Balanced flow; Active contour model; Flow (mathematics); Image segmentation; Level set (data structures); Algorithm; Contrast (vision); Vector field; Energy functional; Set (abstract data type); Energy (signal processing); Artificial intelligence; Geometry; Mathematics; Mathematical analysis","score_opus":0.02112393225142152,"score_gpt":0.2695281358672332,"score_spread":0.24840420361581167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1588596779","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000005541364,0.0002834925,0.9774287,0.000639838,0.0015033756,0.00041402824,0.0000022168372,0.0005751397,0.019147698],"genre_scores_gemma":[0.0015107685,0.000079349426,0.9933835,0.0031379287,0.00047618494,0.000015038263,0.0000075890152,0.000038699483,0.0013509213],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99533266,0.00004445973,0.0006554594,0.0016631659,0.0015382495,0.00076603366],"domain_scores_gemma":[0.99711865,0.00037816254,0.00029054892,0.0016724179,0.00024710433,0.00029308713],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010614967,0.0005489224,0.0005360412,0.0009878454,0.00024004882,0.0006993394,0.0044224565,0.00035472063,0.0002715806],"category_scores_gemma":[0.0001642912,0.0005107684,0.00013236904,0.00077097234,0.0006244434,0.00092992745,0.0017159254,0.0010033781,0.00018744875],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014961873,0.000017063909,0.0000053992544,0.000020577349,0.0000054744896,0.0003226798,0.0002708345,0.001250089,0.00039561323,0.0026490728,0.00015408176,0.9949076],"study_design_scores_gemma":[0.0004932078,0.0002804029,0.000027360235,0.00088825304,0.000014390333,0.000602898,2.0430504e-7,0.7617274,0.0181729,0.20721596,0.009001208,0.0015757971],"about_ca_topic_score_codex":0.000020137506,"about_ca_topic_score_gemma":0.000022250339,"teacher_disagreement_score":0.99333185,"about_ca_system_score_codex":0.00038350903,"about_ca_system_score_gemma":0.00048222416,"threshold_uncertainty_score":0.9997344},"labels":[],"label_agreement":null},{"id":"W1588716475","doi":"10.1109/icip.2003.1246714","title":"A probabilistic framework for image segmentation","year":2004,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Image segmentation; Markov random field; Probabilistic logic; Artificial intelligence; Markov chain; Random field; Computer science; Scale-space segmentation; Segmentation-based object categorization; Markov process; Segmentation; Pattern recognition (psychology); Grayscale; Markov model; Mathematics; Image (mathematics); Machine learning; Statistics","score_opus":0.020369731535270715,"score_gpt":0.32607854746431253,"score_spread":0.3057088159290418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1588716475","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022978129,0.0000063234656,0.99601054,0.001873971,0.00010768158,0.0005877369,0.0000011376795,0.00052246347,0.00066035846],"genre_scores_gemma":[0.00764496,0.0000023866298,0.9902715,0.0017148086,0.000034433346,0.00022293604,0.000004335662,0.0000056540584,0.00009895103],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992717,0.000014176152,0.00016054978,0.00022375848,0.00018112341,0.00014871682],"domain_scores_gemma":[0.9994355,0.000114983275,0.00004726367,0.00025106748,0.000079297286,0.000071928785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017250098,0.00006956734,0.00006954578,0.00004593036,0.00005737066,0.00012242385,0.00033432798,0.000038364968,0.000055326716],"category_scores_gemma":[0.00030120404,0.000059457372,0.000035781624,0.00017367303,0.000040377337,0.0004650221,0.0000549505,0.0000552945,0.00004720678],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032520409,0.00010371941,0.0000041306635,0.00004362322,0.0000066270136,0.0000042731535,0.00056292384,0.000017989087,0.012985283,0.9027742,0.0014450423,0.082048915],"study_design_scores_gemma":[0.00032530032,0.00010877256,0.000036489786,0.000022317981,0.0000030406584,0.000004665775,0.00003217287,0.0013588198,0.2760674,0.72188336,0.000058865444,0.000098819626],"about_ca_topic_score_codex":0.000009042493,"about_ca_topic_score_gemma":0.0000014200212,"teacher_disagreement_score":0.26308212,"about_ca_system_score_codex":0.000070961774,"about_ca_system_score_gemma":0.00005565029,"threshold_uncertainty_score":0.24246012},"labels":[],"label_agreement":null},{"id":"W1589180502","doi":"10.1007/11559573_113","title":"Automated Snake Initialization for the Segmentation of the Prostate in Ultrasound Images","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Initialization; Computer science; Segmentation; Artificial intelligence; Image segmentation; Computer vision; Automation; Active contour model; Pattern recognition (psychology)","score_opus":0.015494784296847513,"score_gpt":0.2889345879727446,"score_spread":0.2734398036758971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1589180502","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00003745714,0.00015745481,0.99604577,0.0011979744,0.0006344153,0.0014802516,0.000013771599,0.00018103932,0.00025185334],"genre_scores_gemma":[0.081502944,0.00016825265,0.91414917,0.0034750905,0.00024091169,0.00013459689,0.00002253304,0.00003807505,0.00026841593],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99757004,0.00006725697,0.0006086586,0.00062801805,0.0008127062,0.0003132962],"domain_scores_gemma":[0.99731976,0.0011456446,0.00045198758,0.0007708892,0.00026709668,0.00004463348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001139802,0.0002625168,0.00025927037,0.00035247632,0.0001774931,0.00027145536,0.0022082976,0.00014185508,0.000015012463],"category_scores_gemma":[0.00031729657,0.00016590559,0.000082717044,0.00069537445,0.0007755814,0.00056716945,0.00040804554,0.0003274872,0.0000021054059],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010423535,0.00005328123,0.0001865483,0.00010054847,0.000013568933,0.0000047309572,0.0029960275,0.027312178,0.008544029,0.003489403,0.0002980595,0.9569912],"study_design_scores_gemma":[0.000790529,0.00018336157,0.0018875104,0.0007081684,0.000020565827,0.00003495096,0.0000022712372,0.67515165,0.26511958,0.055284455,0.00027797496,0.0005389533],"about_ca_topic_score_codex":0.000026158079,"about_ca_topic_score_gemma":0.000099032244,"teacher_disagreement_score":0.95645225,"about_ca_system_score_codex":0.00022277846,"about_ca_system_score_gemma":0.00036787466,"threshold_uncertainty_score":0.67654335},"labels":[],"label_agreement":null},{"id":"W1592201811","doi":"10.1007/978-3-642-10331-5_88","title":"Automatic Data-Driven Parameterization for Phase-Based Bone Localization in US Using Log-Gabor Filters","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Phase congruency; Hessian matrix; Artificial intelligence; Feature extraction; Pattern recognition (psychology); Filter (signal processing); Gabor filter; Curvature; Computer vision; Invariant (physics); Algorithm; Mathematics","score_opus":0.05062558824357933,"score_gpt":0.333652538710691,"score_spread":0.2830269504671117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1592201811","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001762851,0.00006153555,0.9973125,0.00033621932,0.00052857446,0.0012345967,0.000031855685,0.000290802,0.000027631073],"genre_scores_gemma":[0.021505719,0.000011681349,0.97414225,0.003923,0.00012440588,0.000021414686,0.0002307366,0.000030850715,0.0000099344725],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961024,0.00007860013,0.0008630965,0.0015080586,0.00091472006,0.00053314766],"domain_scores_gemma":[0.9970536,0.0004847273,0.0005106787,0.0015836089,0.00022006108,0.00014732446],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010329527,0.00043622486,0.000552834,0.001199713,0.00015748931,0.0005264586,0.0028189267,0.00028687762,0.00002050604],"category_scores_gemma":[0.00034873336,0.00043526056,0.00006970272,0.00090349396,0.00043238807,0.0011930807,0.00052444683,0.00035504476,0.0000038464786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007056166,0.00008167568,0.00001679112,0.00009323365,0.000004173713,0.000035822995,0.00015656548,0.12918478,0.00096981606,0.00023093857,0.000033409804,0.86918575],"study_design_scores_gemma":[0.0008125811,0.00026924335,0.000018652521,0.0006828694,0.00001226597,0.000012919491,8.973128e-8,0.98139995,0.005768916,0.010467287,0.000115341594,0.00043987736],"about_ca_topic_score_codex":0.000027157326,"about_ca_topic_score_gemma":0.00003279301,"teacher_disagreement_score":0.86874586,"about_ca_system_score_codex":0.00046175127,"about_ca_system_score_gemma":0.0006895353,"threshold_uncertainty_score":0.9998099},"labels":[],"label_agreement":null},{"id":"W1593431850","doi":"10.1023/a:1011141618114","title":"Complexity, Confusion, and Perceptual Grouping. Part II: Mapping Complexity","year":2001,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Espace pour la vie","funders":"","keywords":"Element (criminal law); Texture (cosmology); Tangent; Context (archaeology); Mathematics; Computation; Artificial intelligence; Orientation (vector space); Enhanced Data Rates for GSM Evolution; Curse of dimensionality; Computer science; Image (mathematics); Pattern recognition (psychology); Geometry; Algorithm; Geography","score_opus":0.061013875360453995,"score_gpt":0.33651376704504443,"score_spread":0.27549989168459044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1593431850","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08666331,0.00007680448,0.9058467,0.005371623,0.0015645599,0.00008760002,0.000002148451,0.00008071054,0.00030651112],"genre_scores_gemma":[0.4655234,0.00025081678,0.52984786,0.00334816,0.00094295904,0.0000013602889,0.000008450989,0.00001033039,0.000066705055],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99758023,0.00014223551,0.00074779056,0.0002596071,0.0010791606,0.00019096707],"domain_scores_gemma":[0.998251,0.00013230773,0.00047176497,0.0002054746,0.0007320816,0.00020733601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070912874,0.00016344154,0.00026534995,0.00036026846,0.00016374163,0.00035161583,0.0013245584,0.000060246613,0.0002098448],"category_scores_gemma":[0.000050823728,0.00014219398,0.00010725394,0.00017094308,0.00025248696,0.0011242538,0.0010667294,0.00028043007,0.00001272292],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010255319,0.00057376374,0.001847069,0.000017276465,0.00016803219,0.0007101179,0.0038691247,0.00005459935,0.00427614,0.030666582,0.044329837,0.9133849],"study_design_scores_gemma":[0.008944414,0.0045358213,0.14939632,0.0024213362,0.000046347537,0.021974206,0.0004687532,0.53498036,0.005047318,0.11447962,0.15605916,0.0016463759],"about_ca_topic_score_codex":0.000017323131,"about_ca_topic_score_gemma":0.0000027286087,"teacher_disagreement_score":0.9117385,"about_ca_system_score_codex":0.0000853431,"about_ca_system_score_gemma":0.000053061838,"threshold_uncertainty_score":0.5798502},"labels":[],"label_agreement":null},{"id":"W1594874717","doi":"10.1007/11559573_17","title":"Edge Detection Models","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Piecewise; Constant (computer programming); Laplace operator; Linear approximation; Contrast (vision); Piecewise linear function; Computer science; Approximation algorithm; Image (mathematics); Edge detection; Algorithm; Segmentation; Image segmentation; Enhanced Data Rates for GSM Evolution; Applied mathematics; Artificial intelligence; Mathematics; Image processing; Mathematical analysis; Nonlinear system; Physics","score_opus":0.02259402804802146,"score_gpt":0.2675673996897302,"score_spread":0.24497337164170874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1594874717","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000043636596,0.00027009923,0.9899971,0.0004398357,0.0011164634,0.00037920615,0.0000017443095,0.0005218058,0.0072694197],"genre_scores_gemma":[0.02452088,0.00009020941,0.9707778,0.0030521695,0.00065897766,0.000018804954,0.0000028611428,0.00003413882,0.00084412406],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963282,0.000033586617,0.0005244369,0.0013649174,0.0012060166,0.00054287823],"domain_scores_gemma":[0.99779654,0.00023364293,0.00025143864,0.0012658988,0.00023063697,0.00022186768],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008125152,0.00042890754,0.0003843251,0.00089173525,0.00019045903,0.00047169445,0.0029828716,0.000333962,0.000060672093],"category_scores_gemma":[0.00006869031,0.000404817,0.00011303313,0.000526124,0.00058272877,0.0013977536,0.0010524368,0.0008310631,0.000080891616],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012952929,0.000013503925,5.3282196e-7,0.000011606023,0.0000033190843,0.000024029821,0.00027366896,0.005591059,0.000509412,0.004142831,0.000029385206,0.9893994],"study_design_scores_gemma":[0.00015262808,0.00011069806,0.000005786543,0.00016807645,0.0000044478647,0.000057315367,5.7090777e-8,0.7340591,0.038439795,0.2257778,0.0007294601,0.0004948382],"about_ca_topic_score_codex":0.000015024306,"about_ca_topic_score_gemma":0.000038724316,"teacher_disagreement_score":0.98890454,"about_ca_system_score_codex":0.00045045296,"about_ca_system_score_gemma":0.00034778452,"threshold_uncertainty_score":0.9998404},"labels":[],"label_agreement":null},{"id":"W1595337542","doi":"10.1109/isbi.2015.7164001","title":"Random walker image registration with inverse consistency","year":2015,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Inverse; Consistency (knowledge bases); Computer science; Property (philosophy); Image registration; Extension (predicate logic); Algorithm; Graph; Image (mathematics); Inverse problem; Set (abstract data type); Artificial intelligence; Mathematics; Theoretical computer science","score_opus":0.029240098519613365,"score_gpt":0.2764946640013875,"score_spread":0.24725456548177413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1595337542","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003986101,0.0000068497507,0.94676214,0.001381773,0.000056812794,0.00016443476,2.1032449e-7,0.00037277426,0.05085641],"genre_scores_gemma":[0.018003419,0.0000043647856,0.9765146,0.0018352574,0.000023304132,0.000019090387,0.0000035549185,0.000004502444,0.0035919193],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99915105,0.000057998473,0.0001536504,0.00019217645,0.00033437117,0.00011073321],"domain_scores_gemma":[0.9992015,0.00003732385,0.00006226812,0.00033489117,0.00020202389,0.0001619882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036543913,0.0000707174,0.00008697337,0.00004455008,0.000030400199,0.000117099335,0.00026104526,0.000026224709,0.00006596258],"category_scores_gemma":[0.00014049276,0.000048979222,0.00001716924,0.00017241396,0.00010822931,0.000747927,0.00005000833,0.00005909743,0.00011340941],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017139946,0.0002909346,0.0007562077,0.000043427975,0.00005073645,0.00034561462,0.002696663,0.000009948702,0.019326352,0.08627763,0.8089932,0.08103788],"study_design_scores_gemma":[0.021808278,0.0017577229,0.0006208127,0.00013418098,0.000057028436,0.00043859574,0.001125711,0.09163871,0.8269938,0.042123593,0.011796117,0.0015054264],"about_ca_topic_score_codex":0.00004718346,"about_ca_topic_score_gemma":0.000020796007,"teacher_disagreement_score":0.8076675,"about_ca_system_score_codex":0.000029769732,"about_ca_system_score_gemma":0.0001606968,"threshold_uncertainty_score":0.19973147},"labels":[],"label_agreement":null},{"id":"W1596933240","doi":"10.1109/iembs.2003.1279808","title":"Fast reconstruction of volumetric models of anatomical structures","year":2004,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Delaunay triangulation; Computer vision; Artificial intelligence; Computer science; Triangulation; 3D reconstruction; Segmentation; Surface reconstruction; Smoothing; Process (computing); Constrained Delaunay triangulation; Visual hull; Iterative reconstruction; Tetrahedron; Image segmentation; Marching cubes; Algorithm; Mathematics; Surface (topology); Visualization; Geometry","score_opus":0.017048867146080848,"score_gpt":0.2666759077269421,"score_spread":0.24962704058086127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1596933240","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04616505,0.000022000702,0.9521488,0.0000594961,0.00007697823,0.00007369445,0.0000011373174,0.000085505395,0.0013673248],"genre_scores_gemma":[0.5141466,0.0000067660026,0.4857918,0.00003488165,0.000004639754,0.0000010803891,3.7257135e-7,0.0000014644016,0.000012396147],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992342,0.000020460371,0.00027181153,0.00014395735,0.00024824866,0.00008134838],"domain_scores_gemma":[0.99948406,0.000024875535,0.00011242268,0.00022614346,0.00010298264,0.00004949454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010674917,0.00005288448,0.00012396488,0.000228367,0.000012665337,0.0000112797225,0.00033184802,0.000044108176,0.00005612607],"category_scores_gemma":[0.000045307308,0.00004459328,0.000039072635,0.0005218703,0.00010174104,0.00040277632,0.00007282414,0.000056641442,0.0000013651934],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003930859,0.000054901117,0.00020256727,0.000029964007,0.000014393231,0.0000012432338,0.00023554577,0.0007936886,0.02665942,0.1984449,0.0001809454,0.7733785],"study_design_scores_gemma":[0.00027103786,0.00006610888,0.00061956205,0.00001325864,0.0000027361973,0.00001646983,0.000035234472,0.025060844,0.8125839,0.16126949,0.0000011671016,0.00006014695],"about_ca_topic_score_codex":0.000112606715,"about_ca_topic_score_gemma":0.0000024536455,"teacher_disagreement_score":0.7859245,"about_ca_system_score_codex":0.000027925393,"about_ca_system_score_gemma":0.0000561705,"threshold_uncertainty_score":0.18184611},"labels":[],"label_agreement":null},{"id":"W1597605992","doi":"10.1007/978-3-642-15745-5_19","title":"A Generative Model for Brain Tumor Segmentation in Multi-Modal Images","year":2010,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":201,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Institute of Biomedical Imaging and Bioengineering; National Center for Research Resources; National Institute of Neurological Disorders and Stroke; Institut national de recherche en informatique et en automatique (INRIA); National Institutes of Health; National Science Foundation","keywords":"Computer science; Segmentation; Artificial intelligence; Atlas (anatomy); Prior probability; Probabilistic logic; Generative model; Image segmentation; Pattern recognition (psychology); Modal; Generative grammar; Bayesian probability; Medicine","score_opus":0.023990697327000043,"score_gpt":0.32836743723209866,"score_spread":0.3043767399050986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1597605992","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016753836,0.000011733183,0.9797128,0.0022378133,0.00047145074,0.0006624773,0.00000404814,0.00014326815,0.0000025852532],"genre_scores_gemma":[0.3546342,6.950943e-7,0.64212507,0.0030970115,0.000045645545,0.00008713551,0.0000020023037,0.0000059144486,0.0000023556881],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99784607,0.0000650049,0.00035103477,0.00081230566,0.0004594048,0.0004662119],"domain_scores_gemma":[0.998696,0.00042978735,0.000112050206,0.00047836493,0.00016003895,0.00012374556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012882205,0.00019043517,0.00019169723,0.00046128285,0.00014227176,0.0003561308,0.0014219916,0.00006626062,0.0000044914755],"category_scores_gemma":[0.00056095765,0.00017139936,0.0000430545,0.0011288753,0.00036568876,0.0012278475,0.0003522631,0.00036157237,0.0000031592324],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000058714486,0.00013803446,0.0006533467,0.000015788759,0.0000016357974,0.000014280192,0.003888782,0.045923837,0.49483722,0.00039394078,0.000052724532,0.45407453],"study_design_scores_gemma":[0.00040529127,0.000045020355,0.0005411484,0.000013034967,4.9671337e-7,0.0000076080482,8.576712e-7,0.65644693,0.33521205,0.007193278,9.124544e-7,0.0001333881],"about_ca_topic_score_codex":0.000044685064,"about_ca_topic_score_gemma":0.0003846549,"teacher_disagreement_score":0.6105231,"about_ca_system_score_codex":0.00011026845,"about_ca_system_score_gemma":0.00031561533,"threshold_uncertainty_score":0.6989463},"labels":[],"label_agreement":null},{"id":"W1599213198","doi":"10.1007/978-3-540-27816-0_15","title":"A Multi-scale Geometric Flow for Segmenting Vasculature in MRI","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Computer science; Scale (ratio); Market segmentation; Artificial intelligence; Computer vision; Flow (mathematics); Segmentation; Cartography; Geometry; Mathematics; Geography","score_opus":0.018881183917839064,"score_gpt":0.27646564371463733,"score_spread":0.2575844597967983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1599213198","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000011338661,0.00070239574,0.9961078,0.00045602475,0.0010326933,0.0011928641,0.000008123714,0.00027992448,0.00020879369],"genre_scores_gemma":[0.002231877,0.00007177524,0.99550956,0.0016735038,0.00023001194,0.0000596756,0.0000127925505,0.00003866819,0.00017211941],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953742,0.00003529188,0.0007432279,0.0018084593,0.0011818684,0.00085697894],"domain_scores_gemma":[0.9975042,0.0004984854,0.00031504547,0.0011812575,0.00028322494,0.00021778831],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015747119,0.00054140046,0.00062519324,0.0023064022,0.0002048727,0.0005622314,0.0031051335,0.00046837988,0.000026413403],"category_scores_gemma":[0.00028049282,0.00051054434,0.00021468918,0.0018265886,0.0005101022,0.0008494424,0.0011129677,0.0009857913,0.000016297128],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004463488,0.00009421271,0.00007751723,0.00015126618,0.0000122219735,0.000076053904,0.0010907452,0.02572437,0.0007731863,0.0007449368,0.000044109118,0.9712069],"study_design_scores_gemma":[0.0012742248,0.00017722059,0.00029695677,0.00097225147,0.000010601577,0.000035821064,3.7593793e-7,0.9336233,0.009674428,0.052846834,0.0002125958,0.0008753713],"about_ca_topic_score_codex":0.000031545533,"about_ca_topic_score_gemma":0.00009967843,"teacher_disagreement_score":0.97033155,"about_ca_system_score_codex":0.0007180467,"about_ca_system_score_gemma":0.0005896629,"threshold_uncertainty_score":0.99973464},"labels":[],"label_agreement":null},{"id":"W1600408421","doi":"10.1109/iscas.1998.698801","title":"Genetic algorithms for active contour optimization","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Genetic algorithm; Algorithm; Artificial intelligence; Mathematical optimization; Machine learning; Mathematics","score_opus":0.03566001155821601,"score_gpt":0.2924873265654066,"score_spread":0.2568273150071906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1600408421","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000012188456,0.000021844022,0.9956409,0.0006043862,0.00008487459,0.00028106914,0.000001172373,0.00028764023,0.003065948],"genre_scores_gemma":[0.002309799,0.000022542068,0.99486125,0.0011279742,0.000041585336,0.000072525305,0.0000014125453,0.000004348339,0.0015585602],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945265,0.0000181546,0.0001128146,0.00017146845,0.0001315471,0.00011336681],"domain_scores_gemma":[0.99959105,0.000059998598,0.000038413833,0.00016736246,0.00008270351,0.000060493676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000053427313,0.000053275347,0.000059750877,0.000041562897,0.000043152453,0.00006033957,0.00026479267,0.000028857577,0.00048332836],"category_scores_gemma":[0.000052877826,0.000046627196,0.000024916933,0.00010664914,0.000018929504,0.00029137888,0.000038526305,0.000027570964,0.00002379947],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.64945e-7,0.00005039109,0.000005185243,0.0000047117564,0.000007740954,0.0000020295959,0.00027517442,0.00049888296,0.0004372494,0.0013061874,0.021741625,0.97566986],"study_design_scores_gemma":[0.00023823162,0.0000651722,0.000041438834,0.0000026146663,0.0000020069187,0.000002808755,0.000012580359,0.96686095,0.031800743,0.00048404213,0.0004191429,0.000070286835],"about_ca_topic_score_codex":0.000005999438,"about_ca_topic_score_gemma":5.234843e-7,"teacher_disagreement_score":0.9755996,"about_ca_system_score_codex":0.000022096649,"about_ca_system_score_gemma":0.0000062943336,"threshold_uncertainty_score":0.5292107},"labels":[],"label_agreement":null},{"id":"W1603012830","doi":"10.1109/ccv.1988.590048","title":"Singularities Of Principal Direction Fields From 3-D Images","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Gravitational singularity; Principal (computer security); Computer science; Artificial intelligence; Computer vision; Mathematics; Mathematical analysis","score_opus":0.012697085064188644,"score_gpt":0.2731175322098845,"score_spread":0.26042044714569584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1603012830","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050616516,0.000052262774,0.9829922,0.00086216733,0.000082505474,0.000047774134,0.0000011035239,0.00026407227,0.0106362915],"genre_scores_gemma":[0.33511963,0.00001458662,0.66306925,0.0005096908,0.000058856444,0.000004253312,0.0000015607018,0.0000020171733,0.0012201703],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994397,0.000026094569,0.00015388522,0.00012653417,0.00017846566,0.00007528003],"domain_scores_gemma":[0.9996139,0.000061970095,0.000042996973,0.00020213709,0.00004466799,0.000034345063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009927526,0.000046247613,0.00007254525,0.000043119588,0.000023032191,0.000038157115,0.00024358327,0.000035095887,0.00029042855],"category_scores_gemma":[0.00004909086,0.000040323575,0.000026485402,0.00007814104,0.000039567156,0.00038875424,0.00008703007,0.00005286707,0.000012829732],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030203712,0.00011437801,0.0006196664,0.000011766779,0.000016367554,0.000003223237,0.0009940254,0.000011632455,0.06464142,0.0070508975,0.013052127,0.9134815],"study_design_scores_gemma":[0.00008569484,0.000026243351,0.0019910121,0.000009937254,0.0000018808704,0.0000010421624,0.000020216024,0.0025500807,0.99253726,0.0017928387,0.0009270786,0.00005672812],"about_ca_topic_score_codex":0.00022635385,"about_ca_topic_score_gemma":0.000022225477,"teacher_disagreement_score":0.92789584,"about_ca_system_score_codex":0.000014460452,"about_ca_system_score_gemma":0.000015853315,"threshold_uncertainty_score":0.3179989},"labels":[],"label_agreement":null},{"id":"W1603749598","doi":"10.1109/icip.2003.1246686","title":"Image registration using collinear virtual circles","year":2004,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer vision; Translation (biology); Hausdorff distance; Image registration; Artificial intelligence; Rotation (mathematics); Similarity (geometry); Computer science; Transformation (genetics); Rigid transformation; Geometric transformation; Line (geometry); Boundary (topology); Set (abstract data type); Image (mathematics); Line segment; Similarity measure; Mathematics; Geometry; Mathematical analysis","score_opus":0.026053900840637,"score_gpt":0.30569841692457067,"score_spread":0.27964451608393365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1603749598","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055383267,0.000005261964,0.99122614,0.0006387662,0.00007484428,0.00010516546,4.6781716e-7,0.00040274087,0.0020082805],"genre_scores_gemma":[0.075316176,0.0000042868414,0.9237156,0.00076855515,0.00004014994,0.0000036711424,0.0000018617691,0.000004054607,0.00014566252],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99924713,0.000022146793,0.00017010536,0.00018807506,0.00025562776,0.00011688809],"domain_scores_gemma":[0.9995432,0.000019215215,0.00005597288,0.00024811766,0.000064322274,0.00006915939],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017885848,0.000060110826,0.000062637504,0.0000524641,0.00006728439,0.00015322604,0.00030277818,0.00003222775,0.000049090613],"category_scores_gemma":[0.00006517631,0.000057031637,0.000023839726,0.0002087486,0.00006105696,0.0007417437,0.00006309791,0.00005655427,0.00004901942],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003654294,0.00015206957,0.000024564233,0.000013519249,0.000010679867,0.000066791756,0.00059834943,0.00018595222,0.8078467,0.11699269,0.0026406385,0.07146439],"study_design_scores_gemma":[0.0004937282,0.00012828203,0.00016653395,0.000025412171,0.0000035853661,0.000036531008,0.000082192186,0.046019446,0.94258845,0.010116335,0.00014380648,0.00019568103],"about_ca_topic_score_codex":0.00009273099,"about_ca_topic_score_gemma":0.000005944086,"teacher_disagreement_score":0.13474177,"about_ca_system_score_codex":0.00006542519,"about_ca_system_score_gemma":0.00009967762,"threshold_uncertainty_score":0.23256826},"labels":[],"label_agreement":null},{"id":"W1606542664","doi":"","title":"Non-linear registration of histological images for 3-D middle-ear modelling","year":2007,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Image warping; Artificial intelligence; Scanner; Computer vision; Pixel; Computer science; Similarity (geometry); Block (permutation group theory); Image registration; Transformation (genetics); Pattern recognition (psychology); Mathematics; Image (mathematics); Biology","score_opus":0.0548498535552958,"score_gpt":0.2877726159772841,"score_spread":0.23292276242198828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1606542664","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08397821,0.00007095271,0.90462404,0.000081564496,0.00032041554,0.0007632863,0.000072862735,0.0004736998,0.009614934],"genre_scores_gemma":[0.51559746,0.000023469293,0.48357654,0.0003066467,0.00002498217,0.00003419599,0.000016532325,0.000021890275,0.00039829552],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99701667,0.00009632816,0.00095136435,0.0007067395,0.00070079986,0.0005280978],"domain_scores_gemma":[0.99765784,0.00043583778,0.00048265056,0.0006991589,0.00045465963,0.00026982828],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0026087381,0.00027572838,0.00037857954,0.00023577109,0.0003881464,0.00004751232,0.0010027604,0.00025425814,0.00002728036],"category_scores_gemma":[0.0006436961,0.00026906742,0.00020001551,0.000454312,0.00016492153,0.0011348095,0.00020171139,0.00040953793,0.000025680652],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017642083,0.0005590585,0.000040086823,0.00031485024,0.000073794465,0.00008584827,0.000025609335,0.0006588294,0.40173954,0.26478463,0.00009670538,0.33144462],"study_design_scores_gemma":[0.00073549594,0.00034733483,0.00007648801,0.0000976442,0.000024975783,0.000025583411,0.000029357032,0.019296085,0.94423604,0.031064996,0.0036794324,0.0003865755],"about_ca_topic_score_codex":0.00007318611,"about_ca_topic_score_gemma":0.0000109721195,"teacher_disagreement_score":0.5424965,"about_ca_system_score_codex":0.00023842865,"about_ca_system_score_gemma":0.00003374405,"threshold_uncertainty_score":0.99997616},"labels":[],"label_agreement":null},{"id":"W1606932326","doi":"10.1007/11539506_55","title":"Fuzzy Sets Theory Based Region Merging for Robust Image Segmentation","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Weighting; Computer science; Artificial intelligence; Pattern recognition (psychology); Similarity (geometry); Image segmentation; Fuzzy set; Fuzzy logic; Image (mathematics); Segmentation; Data mining","score_opus":0.024032472384366885,"score_gpt":0.28435333846001914,"score_spread":0.26032086607565225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1606932326","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000038541,0.00014998646,0.9942855,0.0013796738,0.00084064866,0.0010404006,0.000006356768,0.00044279106,0.001850753],"genre_scores_gemma":[0.001304389,0.000027496937,0.9930578,0.0047287256,0.00035522348,0.00006473473,0.000027206775,0.000044531564,0.00038987905],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99611807,0.00008676111,0.00063192094,0.0014907167,0.0010498503,0.000622665],"domain_scores_gemma":[0.9968877,0.0009912434,0.0004449865,0.001122298,0.00035022697,0.0002035664],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016565165,0.00050781184,0.00043857106,0.00092106365,0.00030046844,0.0005783509,0.0024318038,0.00028008543,0.000033283955],"category_scores_gemma":[0.00022257274,0.00048044987,0.00016831521,0.0004638707,0.0006667622,0.0011847595,0.0005029523,0.0005357247,0.000020271515],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010457359,0.000025838423,0.0000027842877,0.0000603963,0.0000069892762,0.000033143726,0.00033042673,0.006579543,0.0009855062,0.008041386,0.00019754839,0.98372597],"study_design_scores_gemma":[0.0006876989,0.0002273311,0.000013728127,0.0005480409,0.000019869447,0.000042257765,5.956404e-7,0.7492743,0.04851317,0.19948752,0.0003485438,0.0008369454],"about_ca_topic_score_codex":0.000005237284,"about_ca_topic_score_gemma":0.000011659965,"teacher_disagreement_score":0.98288906,"about_ca_system_score_codex":0.00049667084,"about_ca_system_score_gemma":0.0004897315,"threshold_uncertainty_score":0.99976474},"labels":[],"label_agreement":null},{"id":"W1607711335","doi":"10.1007/11866763_84","title":"Piecewise-Quadrilateral Registration by Optical Flow – Applications in Contrast-Enhanced MR Imaging of the Breast","year":2006,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto","funders":"Canadian Institutes of Health Research; Terry Fox Foundation; Canadian Breast Cancer Research Alliance","keywords":"Computer science; Optical flow; Quadrilateral; Contrast (vision); Interpolation (computer graphics); Computer vision; Algorithm; Piecewise; Image registration; Artificial intelligence; Consistency (knowledge bases); Grid; Image (mathematics); Mathematics; Geometry; Mathematical analysis","score_opus":0.004690790960680132,"score_gpt":0.24639316324479146,"score_spread":0.24170237228411134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1607711335","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007548176,0.000050731036,0.9899422,0.0017482355,0.00016833072,0.0003942658,0.0000034978702,0.00008156878,0.00006300834],"genre_scores_gemma":[0.6610194,0.0000012597579,0.3385609,0.00034981134,0.000037178615,0.00002446545,0.0000025850907,0.0000033032134,0.0000011281722],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980323,0.000069126894,0.00043566982,0.0005396061,0.000582203,0.00034110938],"domain_scores_gemma":[0.9988526,0.00021719195,0.0001481216,0.0005999436,0.00012333071,0.000058828653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056122127,0.00014426139,0.0001685164,0.00016571736,0.00011255674,0.00020311831,0.0015235262,0.0000484386,0.0000041218473],"category_scores_gemma":[0.00006141563,0.00010871261,0.00004097123,0.0016728443,0.0006017546,0.0006396254,0.00027006905,0.00022618793,0.0000014802092],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048589627,0.0002509879,0.010588837,0.000026937987,0.0000017765734,0.0000044071026,0.0005560814,0.01807541,0.2328414,0.001469096,0.000127774,0.73605245],"study_design_scores_gemma":[0.00023500303,0.000016189784,0.0129975,0.00005090759,0.0000012785633,0.000020313435,7.264494e-7,0.5169461,0.4602367,0.009359862,0.0000060093735,0.00012946701],"about_ca_topic_score_codex":0.00013673394,"about_ca_topic_score_gemma":0.000076432145,"teacher_disagreement_score":0.735923,"about_ca_system_score_codex":0.00011882924,"about_ca_system_score_gemma":0.00014706558,"threshold_uncertainty_score":0.44331715},"labels":[],"label_agreement":null},{"id":"W1608409036","doi":"10.1109/pria.2015.7161626","title":"Assessment of trabecular bone structure using fuzzy distance transform based on Min-Max operations","year":2015,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Trabecular bone; Weighting; Pixel; Fuzzy logic; Measure (data warehouse); Computer science; Image processing; Artificial intelligence; Volume (thermodynamics); Pattern recognition (psychology); Mathematics; Computer vision; Biomedical engineering; Algorithm; Image (mathematics); Data mining; Osteoporosis; Acoustics; Engineering; Physics","score_opus":0.029160048169225555,"score_gpt":0.332859107834199,"score_spread":0.3036990596649734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1608409036","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044657257,0.0000149823845,0.99031746,0.0006178803,0.0000843318,0.00025816043,0.000009845626,0.00010838082,0.0041232174],"genre_scores_gemma":[0.4987278,6.160501e-7,0.5007505,0.00045378951,0.00000819272,0.000005161195,0.00000611026,0.0000036403646,0.000044183962],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987415,0.000059153415,0.0002731222,0.00022830778,0.00056096044,0.00013694234],"domain_scores_gemma":[0.99927336,0.000030197507,0.00004428573,0.00037539034,0.00013559815,0.00014118376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024187675,0.00010480056,0.00015388103,0.00008448078,0.000051632527,0.00006748246,0.00032507902,0.000045959805,0.000069001166],"category_scores_gemma":[0.000031466236,0.00008424313,0.00004339082,0.0002716779,0.00005617323,0.0003480854,0.000020839138,0.00009835238,9.2527335e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036957634,0.0013952848,0.0009767306,0.00024896875,0.000086467975,0.00011531435,0.0028347012,0.07005409,0.4951001,0.26093435,0.0051099546,0.16310705],"study_design_scores_gemma":[0.000534926,0.00016804023,0.00025169714,0.0000385791,0.0000080936525,0.0000031605855,0.000051283998,0.74602956,0.25123447,0.0014259729,0.000114888026,0.00013935269],"about_ca_topic_score_codex":0.000045754314,"about_ca_topic_score_gemma":0.000038329672,"teacher_disagreement_score":0.67597544,"about_ca_system_score_codex":0.000102949954,"about_ca_system_score_gemma":0.00025682955,"threshold_uncertainty_score":0.34353352},"labels":[],"label_agreement":null},{"id":"W160920134","doi":"10.1007/978-3-642-22092-0_15","title":"A Convex Max-Flow Segmentation of LV Using Subject-Specific Distributions on Cardiac MRI","year":2011,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St Joseph's Health Care; CARE Canada; Robarts Clinical Trials; Western University","funders":"","keywords":"Bhattacharyya distance; Mathematics; Regular polygon; Mathematical optimization; Segmentation; Convex optimization; Algorithm; Computer science; Artificial intelligence; Geometry","score_opus":0.03459903390378626,"score_gpt":0.2852454452730724,"score_spread":0.2506464113692861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W160920134","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018048665,0.00005760877,0.9804871,0.000110064364,0.0008054614,0.0003240241,0.000008851637,0.000116985495,0.00004125905],"genre_scores_gemma":[0.43544036,0.000009040263,0.5643345,0.00017046374,0.00003186085,0.0000056978793,0.000003865446,0.000004005932,2.0356329e-7],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977145,0.00013269424,0.0003967958,0.0006566647,0.00070672255,0.0003925912],"domain_scores_gemma":[0.9985609,0.00022563407,0.00017655491,0.00071170885,0.00019435704,0.00013086322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007950651,0.00018372397,0.00025045493,0.00038445395,0.00015950583,0.00012731811,0.0012489858,0.00007222362,0.00004362386],"category_scores_gemma":[0.00008435192,0.0001668147,0.00007245373,0.0019297132,0.0005658393,0.0006917919,0.00033499787,0.00022030108,0.000008877559],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012626685,0.00025095916,0.0030796505,0.000028541925,0.000011447058,0.000024985286,0.004728143,0.0037391623,0.071456954,0.0017668244,0.000072905576,0.9148278],"study_design_scores_gemma":[0.00018607876,0.00017096628,0.0034626366,0.000071744544,0.000003135316,0.000006655038,0.0000028712948,0.264781,0.7264803,0.004638573,0.000008340282,0.00018771752],"about_ca_topic_score_codex":0.00005277484,"about_ca_topic_score_gemma":0.000003652474,"teacher_disagreement_score":0.91464007,"about_ca_system_score_codex":0.00020399726,"about_ca_system_score_gemma":0.00020113177,"threshold_uncertainty_score":0.6802506},"labels":[],"label_agreement":null},{"id":"W1615425248","doi":"10.1109/acssc.1990.523432","title":"SMESH: An IO-Computation Balanced Architecture for Parallel Implementations of Convolution and Morph","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Convolution (computer science); Parallel computing; Computation; Architecture; Implementation; Computer architecture; Parallel architecture; Computational science; Algorithm; Programming language; Artificial intelligence; Artificial neural network","score_opus":0.021874570659864083,"score_gpt":0.3350081083341034,"score_spread":0.3131335376742393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1615425248","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007023294,0.000016680338,0.99027246,0.0019115015,0.000029797444,0.00043544514,0.000008743249,0.00016161428,0.00014043726],"genre_scores_gemma":[0.2709479,0.000006257447,0.7282611,0.0006300292,0.000025551264,0.000049451333,0.00003533195,0.0000035532994,0.000040836356],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992187,0.000040464354,0.00024812325,0.00020959335,0.00015498252,0.00012814204],"domain_scores_gemma":[0.9994934,0.00009015947,0.000099407574,0.00014470008,0.00009643933,0.000075866956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019394363,0.000074592164,0.000101218706,0.000087974535,0.00006346523,0.000039826107,0.00016978454,0.000030322852,0.000030673244],"category_scores_gemma":[0.00002752495,0.000067171284,0.00002497699,0.00011508849,0.00005043162,0.00044459794,0.000040701845,0.000040548326,0.0000014381262],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018664354,0.00013415152,0.00045687528,0.000049565704,0.00002169549,3.9327628e-7,0.0018618351,0.0010947727,0.037817247,0.040328424,0.005985083,0.91223127],"study_design_scores_gemma":[0.0032313182,0.0007909549,0.012785523,0.000030257892,0.000026084927,0.000020738502,0.0003553144,0.7664411,0.16206224,0.05259535,0.0012425259,0.00041858543],"about_ca_topic_score_codex":0.00002834721,"about_ca_topic_score_gemma":0.000037277303,"teacher_disagreement_score":0.9118127,"about_ca_system_score_codex":0.00002123436,"about_ca_system_score_gemma":0.000031024665,"threshold_uncertainty_score":0.27391654},"labels":[],"label_agreement":null},{"id":"W1626816967","doi":"10.1007/s11548-015-1216-z","title":"Estimation of intraoperative brain shift by combination of stereovision and doppler ultrasound: phantom and animal model study","year":2015,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Computer vision; Point cloud; Imaging phantom; Displacement (psychology); Image registration; Artificial intelligence; Finite element method; Doppler effect; Medicine; Physics; Radiology; Image (mathematics)","score_opus":0.02500294314783334,"score_gpt":0.3110096688904543,"score_spread":0.2860067257426209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1626816967","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48013884,0.00012603324,0.51898247,0.0005549078,0.00012967142,0.000054416392,0.0000030366787,0.000006427844,0.0000042037786],"genre_scores_gemma":[0.95206445,0.00005474464,0.047605462,0.00022993234,0.000029963881,0.0000011916292,0.000007634061,0.0000035824114,0.000003060824],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984382,0.00032575903,0.0006400696,0.00015763172,0.00036079518,0.00007758121],"domain_scores_gemma":[0.99787617,0.0010244511,0.0005240336,0.00007626784,0.000401246,0.00009784541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012862511,0.00010250987,0.00033844885,0.0003035352,0.000025899502,0.00005690299,0.00021264455,0.00006446152,0.000002023873],"category_scores_gemma":[0.00020587041,0.00008474863,0.000035120207,0.000082467835,0.00017637882,0.0006534806,0.00009534095,0.00012772845,7.676112e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012861013,0.0027037193,0.1186771,0.00009939531,0.0013777708,0.00017921645,0.025332712,0.0014368698,0.025263168,0.0056267623,0.029671155,0.78834605],"study_design_scores_gemma":[0.0048084636,0.002782807,0.16313802,0.00027113603,0.00006211238,0.0024756447,0.0004036348,0.8048764,0.014157519,0.00664634,0.000023119985,0.00035479723],"about_ca_topic_score_codex":0.0000088745755,"about_ca_topic_score_gemma":7.752739e-7,"teacher_disagreement_score":0.80343956,"about_ca_system_score_codex":0.000026391464,"about_ca_system_score_gemma":0.000085722146,"threshold_uncertainty_score":0.34559488},"labels":[],"label_agreement":null},{"id":"W1628667175","doi":"10.1007/978-3-540-85990-1_99","title":"Fast Musculoskeletal Registration Based on Shape Matching","year":2008,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Université de Genève; National Research Foundation","keywords":"Computer science; Matching (statistics); Context (archaeology); Image registration; Simple (philosophy); Stiffness; Algorithm; Artificial intelligence; Computer vision; Image (mathematics); Mathematics; Structural engineering","score_opus":0.016002577884301733,"score_gpt":0.28390124731188976,"score_spread":0.267898669427588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1628667175","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018961238,0.000008870707,0.97865236,0.0013517519,0.000409319,0.00019256285,4.3733704e-7,0.00031676024,0.00010671449],"genre_scores_gemma":[0.5143147,0.0000015223079,0.48347852,0.0021297194,0.00006265347,0.000007115084,0.0000010624348,0.000003855393,8.40061e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974865,0.000088859306,0.00029086365,0.00074483687,0.0009974422,0.0003915065],"domain_scores_gemma":[0.99863803,0.00030209747,0.00010932473,0.0007146843,0.00009710601,0.000138741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007233085,0.00017843768,0.00015412996,0.00038593682,0.00029595045,0.00021839786,0.001551915,0.000065184686,0.000020245327],"category_scores_gemma":[0.00019406927,0.00015630467,0.000054921024,0.0015219185,0.00042681987,0.0008517812,0.00022478368,0.00030426652,0.000023257291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003485695,0.00009533806,0.0004191531,0.000013098359,0.0000010067115,0.000089133704,0.0008539981,0.03761257,0.0055176304,0.00029821185,0.000037503847,0.9550589],"study_design_scores_gemma":[0.00020855015,0.00018986662,0.0033965963,0.000052387386,6.5132167e-7,0.000036244102,3.4273796e-7,0.9520255,0.040881436,0.0030089042,0.000007062085,0.00019250884],"about_ca_topic_score_codex":0.000030491026,"about_ca_topic_score_gemma":0.000008499901,"teacher_disagreement_score":0.95486635,"about_ca_system_score_codex":0.00013212905,"about_ca_system_score_gemma":0.00026541096,"threshold_uncertainty_score":0.637392},"labels":[],"label_agreement":null},{"id":"W1629391210","doi":"10.1142/9789812796752_0007","title":"WAVELET ANALYSIS OF SATELLITE IMAGES IN OCEAN APPLICATIONS","year":2003,"lang":"en","type":"book-chapter","venue":"WORLD SCIENTIFIC eBooks","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Space Agency; Goddard Space Flight Center; Jet Propulsion Laboratory; National Aeronautics and Space Administration","keywords":"Satellite; Wavelet; Remote sensing; Geology; Computer science; Artificial intelligence; Engineering; Aerospace engineering","score_opus":0.019291529902789862,"score_gpt":0.26600927166440475,"score_spread":0.24671774176161487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1629391210","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000036622378,0.00031859614,0.28401792,0.00008498576,0.0001927446,0.00056137796,0.00004680558,0.00014734195,0.71462655],"genre_scores_gemma":[0.00044017084,0.000012066231,0.07745967,0.00030544042,0.00001713622,0.000033558106,0.00008457533,0.000022425616,0.92162496],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969545,0.000061014445,0.00085488765,0.0009767441,0.00084978074,0.0003030923],"domain_scores_gemma":[0.9973455,0.00013374155,0.00048477913,0.0016396451,0.00023672802,0.00015957467],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011543706,0.0002838079,0.00059738045,0.0034468544,0.00011723494,0.00026755728,0.0013499178,0.000121883975,0.00024821595],"category_scores_gemma":[0.000013126322,0.00028728662,0.00028982497,0.00091451337,0.00065326336,0.00013115197,0.00024027296,0.0003387745,0.0000493165],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004236224,0.000113190974,0.00011099711,0.00016115449,0.0005255839,0.000051781226,0.001032658,0.0000091507245,0.003897841,0.5954654,0.043053765,0.3555742],"study_design_scores_gemma":[0.000244681,0.000024222962,0.00011925401,0.00021300708,0.00043232192,0.0000029141777,0.000012199142,0.0005453888,0.03889432,0.047521576,0.9111944,0.00079574686],"about_ca_topic_score_codex":0.0000051045154,"about_ca_topic_score_gemma":0.00034341915,"teacher_disagreement_score":0.8681406,"about_ca_system_score_codex":0.000108729975,"about_ca_system_score_gemma":0.00012496475,"threshold_uncertainty_score":0.9999579},"labels":[],"label_agreement":null},{"id":"W1641498739","doi":"10.1109/tmi.2014.2377694","title":"The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)","year":2014,"lang":"en","type":"review","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6521,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill Genome Centre; McGill University","funders":"National Center for Research Resources; National Institute of Biomedical Imaging and Bioengineering; Fundação para a Ciência e a Tecnologia; Technische Universität München; National Cancer Institute; Tekes; European Commission; Academy of Finland; National Institute for Health and Care Research; National Institutes of Health; Krebsliga Schweiz; Lundbeckfonden; National Science Foundation","keywords":"Artificial intelligence; Image segmentation; Benchmark (surveying); Computer science; Computer vision; Pattern recognition (psychology); Image (mathematics); Brain tumor; Segmentation; Medical imaging; Psychology","score_opus":0.016170612940573138,"score_gpt":0.3407134396215211,"score_spread":0.32454282668094797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1641498739","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.642656e-8,0.25275624,0.7426815,0.0015389626,0.0014396772,0.00076866825,0.000012275654,0.0005236868,0.00027896726],"genre_scores_gemma":[0.000006198673,0.9227251,0.07211212,0.003663571,0.00027962378,0.0007042722,0.000026769649,0.00008118126,0.00040117485],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99369097,0.0011563168,0.0013601204,0.0010115704,0.0020522308,0.00072881975],"domain_scores_gemma":[0.99417824,0.003314503,0.000508608,0.0012180133,0.00013834333,0.0006422894],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0019090376,0.0006520405,0.0010617393,0.00038729527,0.00081184006,0.0006976355,0.002619153,0.00022538677,0.00047293922],"category_scores_gemma":[0.00038502802,0.00044893325,0.00064745324,0.00081659557,0.00058741134,0.00072627974,0.000025861322,0.0018460714,0.00045482814],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017024377,0.00008121272,3.456995e-8,0.0008818388,0.000051304436,0.00006709265,0.000056132074,0.0000014502785,0.000012994885,0.00005311937,0.009805752,0.9889874],"study_design_scores_gemma":[0.00078294525,0.0000931596,3.0902777e-7,0.008788937,0.00030373226,0.00045080378,0.000060303195,0.045938455,0.0014813676,0.0003070145,0.9406905,0.0011024751],"about_ca_topic_score_codex":0.00003620107,"about_ca_topic_score_gemma":0.000011275048,"teacher_disagreement_score":0.9878849,"about_ca_system_score_codex":0.00030010866,"about_ca_system_score_gemma":0.0006463885,"threshold_uncertainty_score":0.9997963},"labels":[],"label_agreement":null},{"id":"W1656023708","doi":"10.1007/s10044-015-0492-0","title":"Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm","year":2015,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; Servier; Eisai; Northern California Institute for Research and Education; University of California, San Diego; Pfizer; Biogen; BioClinica; Alzheimer's Disease Neuroimaging Initiative; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Synarc; University of Southern California; Medpace; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; F. Hoffmann-La Roche; Alzheimer's Drug Discovery Foundation; University of California, San Francisco; Foundation for the National Institutes of Health","keywords":"Undersampling; Boosting (machine learning); Pattern recognition (psychology); Artificial intelligence; Segmentation; Computer science; Algorithm; Hippocampal formation; Random forest; Medicine","score_opus":0.03224919150146756,"score_gpt":0.3183575597015484,"score_spread":0.28610836820008084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1656023708","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039879684,0.000039346927,0.9952041,0.00017473937,0.0000068770323,0.00028906413,0.0000035419653,0.0002564341,0.00003794632],"genre_scores_gemma":[0.30150008,0.000012476124,0.69793195,0.00034122125,0.000024572715,0.00012733469,0.000048643866,0.0000068282784,0.000006894928],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989322,0.00006928346,0.00027427272,0.00032331757,0.00024974204,0.00015121326],"domain_scores_gemma":[0.9993424,0.00007455144,0.00013984986,0.00023570294,0.00008548939,0.00012204149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003903086,0.00010163343,0.00018558832,0.00030331776,0.00009400151,0.0001684409,0.00018218847,0.00003115457,0.0000073366505],"category_scores_gemma":[0.000009175912,0.00008866537,0.000030035306,0.0013198918,0.00004242488,0.00027375942,0.00006526636,0.0000755988,0.0000035532694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000047981166,0.00013124365,0.04564197,0.00001969518,0.00024797695,0.0000072401476,0.0014809988,0.008842665,0.0018856056,0.00006405346,0.00005172752,0.941622],"study_design_scores_gemma":[0.000649152,0.000013760909,0.0018217161,0.000013590132,0.00011200753,0.000004354361,0.0002980626,0.99490297,0.0018555962,0.00019596661,0.000013501565,0.00011932833],"about_ca_topic_score_codex":0.0004836211,"about_ca_topic_score_gemma":0.00009841025,"teacher_disagreement_score":0.9860603,"about_ca_system_score_codex":0.0000714194,"about_ca_system_score_gemma":0.00003971697,"threshold_uncertainty_score":0.3615669},"labels":[],"label_agreement":null},{"id":"W1658874752","doi":"10.1007/978-3-642-10546-3_18","title":"Link Shifting Based Pyramid Segmentation for Elongated Regions","year":2009,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Segmentation; Pyramid (geometry); Partition (number theory); Homogeneous; Computer science; Node (physics); Image segmentation; Image (mathematics); Artificial intelligence; Set (abstract data type); Pattern recognition (psychology); Computer vision; Minimum spanning tree-based segmentation; Mathematics; Scale-space segmentation; Combinatorics; Geometry; Physics","score_opus":0.050223721901259705,"score_gpt":0.33087449948334235,"score_spread":0.2806507775820827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1658874752","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000003036995,0.000103979735,0.96939737,0.0035774282,0.00014787554,0.0008866042,0.000011745283,0.00027709297,0.025594892],"genre_scores_gemma":[0.0019044194,0.0005902422,0.9918468,0.0045603313,0.000044593602,0.00010042671,0.00018879348,0.0000100847465,0.000754279],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99794114,0.000040263705,0.0008780345,0.0003456126,0.000529272,0.00026566695],"domain_scores_gemma":[0.9965663,0.00040882704,0.000522362,0.0017456535,0.00061527704,0.00014158916],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014850692,0.00023730943,0.00025023636,0.0011525248,0.000574513,0.0007648216,0.0028864911,0.00015708143,0.000007056351],"category_scores_gemma":[0.00014083816,0.00024812095,0.000063028834,0.00051869557,0.00069055724,0.006311838,0.00074051466,0.00038004734,0.000017608168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019777854,0.000013162805,0.0000032277492,0.000027958244,0.0000029246685,2.133145e-7,0.0007152007,0.0001151371,0.000024126955,0.17328915,0.0004551013,0.82535183],"study_design_scores_gemma":[0.00066147157,0.0001592378,0.00023214181,0.00039024575,0.000010178646,0.000008920981,0.00001714262,0.9323254,0.0006439725,0.027369235,0.037721563,0.00046046468],"about_ca_topic_score_codex":0.0000063298266,"about_ca_topic_score_gemma":0.000004095536,"teacher_disagreement_score":0.93221027,"about_ca_system_score_codex":0.00022022253,"about_ca_system_score_gemma":0.00041225692,"threshold_uncertainty_score":0.9999971},"labels":[],"label_agreement":null},{"id":"W1671521845","doi":"10.1023/a:1011897628647","title":"Deformable Pedal Curves and Surfaces: Hybrid Geometric Active Models for Shape Recovery","year":2001,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McGill University; University of Florida; National Institutes of Health; National Science Foundation","keywords":"Generator (circuit theory); Geometric shape; Active shape model; Geometric modeling; Curvature; Parameterized complexity; Object (grammar); Tangent; Computer science; Shape analysis (program analysis); Topology (electrical circuits); Point (geometry); Mathematics; Point cloud; Artificial intelligence; Function (biology); Set (abstract data type); Geometric mechanics; Geometry; Algorithm; Power (physics)","score_opus":0.02367164652924831,"score_gpt":0.3195842600305612,"score_spread":0.29591261350131287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1671521845","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037361056,0.0004617313,0.95956683,0.0013969732,0.00092302676,0.00013845868,0.0000070920396,0.00003772397,0.00010713532],"genre_scores_gemma":[0.30590022,0.0041029938,0.6865841,0.0027507963,0.00052157923,0.000006214639,0.000012123881,0.000014570156,0.00010739358],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984104,0.000049087008,0.0004779036,0.00018675496,0.0007245709,0.00015129497],"domain_scores_gemma":[0.9981649,0.00031726103,0.0004132181,0.00011956495,0.0008663199,0.00011872686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058319524,0.00011544372,0.00019428185,0.00048548597,0.00005019518,0.00023647351,0.0008786319,0.000032290016,0.000020457574],"category_scores_gemma":[0.0000736961,0.000095546275,0.00010423691,0.00019135796,0.000033733828,0.0028980048,0.00025707672,0.00014289055,0.0000023240732],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013302252,0.00011848446,0.00004598419,0.000022315471,0.000082933584,0.00006166502,0.00009790623,0.0026652636,0.00030611336,0.00027063632,0.011209829,0.9849858],"study_design_scores_gemma":[0.0011830758,0.0010201618,0.0010393992,0.0004974423,0.000013062563,0.001207254,0.000008744164,0.9722518,0.0077744867,0.012638855,0.0021857363,0.00017999217],"about_ca_topic_score_codex":0.000006061262,"about_ca_topic_score_gemma":3.8511456e-7,"teacher_disagreement_score":0.9848059,"about_ca_system_score_codex":0.00008815611,"about_ca_system_score_gemma":0.00006960511,"threshold_uncertainty_score":0.3896264},"labels":[],"label_agreement":null},{"id":"W167675073","doi":"10.1007/978-3-642-30618-1_13","title":"Fast and Robust Registration Based on Gradient Orientations: Case Study Matching Intra-operative Ultrasound to Pre-operative MRI in Neurosurgery","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Computer science; Image registration; Orientation (vector space); Neurosurgery; Matching (statistics); Magnetic resonance imaging; Context (archaeology); Artificial intelligence; Maximization; Computer vision; Ultrasound; Radiology; Medical physics; Medicine; Image (mathematics); Mathematics; Mathematical optimization","score_opus":0.023487593584918216,"score_gpt":0.30166211581189323,"score_spread":0.27817452222697503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W167675073","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0074469848,0.000038156526,0.9890657,0.0005668894,0.00071218424,0.0018566386,0.000008297704,0.00013021426,0.00017490803],"genre_scores_gemma":[0.5173873,0.000018428538,0.4791645,0.0030539364,0.00015589687,0.00012525698,0.0000094236775,0.000030200585,0.00005509511],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958727,0.0002439083,0.0007581383,0.0015596552,0.0010398711,0.0005257718],"domain_scores_gemma":[0.9970168,0.001302766,0.00027003008,0.0008775369,0.00022336862,0.0003095],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00161118,0.00052420935,0.00047543837,0.0011732416,0.00035169494,0.0009158602,0.0008936353,0.00015551291,0.000018608804],"category_scores_gemma":[0.00027015238,0.00047361787,0.000049452563,0.0008610892,0.0004181733,0.0011743585,0.00036821252,0.000828519,0.0000069482644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043038424,0.0007357648,0.0019746898,0.00007095227,0.000020773565,0.0025934968,0.07097694,0.40052223,0.0009339313,0.0030080788,0.00008079427,0.51903933],"study_design_scores_gemma":[0.0023623027,0.0046466137,0.005960426,0.0021748433,0.00005372552,0.0021420354,0.0002498774,0.9539658,0.012735721,0.012137692,0.00006296122,0.0035080463],"about_ca_topic_score_codex":0.00025183198,"about_ca_topic_score_gemma":0.00089405285,"teacher_disagreement_score":0.55344355,"about_ca_system_score_codex":0.00043082092,"about_ca_system_score_gemma":0.00034666673,"threshold_uncertainty_score":0.99977154},"labels":[],"label_agreement":null},{"id":"W1681294000","doi":"10.1007/978-3-642-33418-4_50","title":"Multi-Object Geodesic Active Contours (MOGAC)","year":2012,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Robarts Research Institute; Johns Hopkins University","keywords":"Computer science; Artificial intelligence; Pixel; Computer vision; Segmentation; Geodesic; Image segmentation; Object (grammar); Memory footprint; Graph; Dimension (graph theory); Segmentation-based object categorization; Scale-space segmentation; Pattern recognition (psychology); Theoretical computer science; Mathematics","score_opus":0.023352416777602844,"score_gpt":0.3070234742835281,"score_spread":0.28367105750592525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1681294000","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073285247,0.00012387511,0.9902225,0.00050262833,0.0011521837,0.00029731958,9.953661e-7,0.000335801,0.00003617137],"genre_scores_gemma":[0.48619655,0.0000038352323,0.5120589,0.0016097497,0.00011030055,0.000013908243,5.6809154e-7,0.0000049574437,0.0000012156347],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99733883,0.00013352491,0.00028619193,0.0006536965,0.0007380678,0.00084968965],"domain_scores_gemma":[0.9982672,0.00041966268,0.00012322048,0.0007413625,0.00013489308,0.00031369136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012104309,0.00022125797,0.0002266399,0.00038949182,0.00019596731,0.00027257414,0.0020535288,0.00008721731,0.0000221027],"category_scores_gemma":[0.0003839112,0.00018976102,0.000055899036,0.0017398853,0.00051571353,0.0022238132,0.0006773308,0.00034591302,0.00004764782],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030088647,0.0001400042,0.0021653462,0.000007328257,0.0000039222114,0.000011218743,0.002997884,0.00059060735,0.011885638,0.00015407325,0.00003684154,0.9820041],"study_design_scores_gemma":[0.0005586302,0.0001191129,0.022401107,0.000061124265,0.0000036329138,0.000051132974,0.0000032915514,0.4124874,0.56190443,0.0019028885,0.000058586927,0.00044867073],"about_ca_topic_score_codex":0.00006997357,"about_ca_topic_score_gemma":0.000019768979,"teacher_disagreement_score":0.98155546,"about_ca_system_score_codex":0.00023305521,"about_ca_system_score_gemma":0.00020299415,"threshold_uncertainty_score":0.77382296},"labels":[],"label_agreement":null},{"id":"W1705110471","doi":"10.1109/crv.2005.82","title":"Two-Stage Image Segmentation by Adaptive Thresholding and Gradient Watershed","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Thresholding; Image segmentation; Artificial intelligence; Stage (stratigraphy); Segmentation; Watershed; Computer science; Computer vision; Scale-space segmentation; Image (mathematics); Region growing; Pattern recognition (psychology); Segmentation-based object categorization; Balanced histogram thresholding; Histogram; Geology; Histogram matching","score_opus":0.016505792868158616,"score_gpt":0.2844222855725614,"score_spread":0.26791649270440276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1705110471","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011827461,0.00005437205,0.9826623,0.0011775681,0.000039304985,0.00023152401,0.000002794982,0.0003535098,0.0036511463],"genre_scores_gemma":[0.16619465,0.000034024157,0.8302067,0.0020479246,0.000028713826,0.00003051875,0.000010154904,0.000007648689,0.0014396735],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99895245,0.000048300957,0.00020097494,0.00032319562,0.00026606442,0.00020902805],"domain_scores_gemma":[0.99952406,0.00003879767,0.000060110295,0.00020382115,0.000040383744,0.00013280252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025642331,0.000113852955,0.00009778711,0.00007307528,0.00008590441,0.00018083514,0.0002570792,0.000025008098,0.00017155627],"category_scores_gemma":[0.0000122454785,0.00009424967,0.00002216286,0.00012413469,0.00007200093,0.0012443634,0.0001549222,0.00008056984,0.00003607241],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001060509,0.000119246186,0.00019886554,0.000015140107,0.000024608362,0.000014978157,0.0027848338,0.0000101906535,0.60766155,0.017504724,0.03392788,0.3377274],"study_design_scores_gemma":[0.0005572855,0.000084323954,0.000072732204,0.000008295709,0.0000038591315,0.000006010212,0.00027527346,0.036892932,0.96084106,0.0004608385,0.0006284672,0.00016892285],"about_ca_topic_score_codex":0.00006071423,"about_ca_topic_score_gemma":0.000007673125,"teacher_disagreement_score":0.3531795,"about_ca_system_score_codex":0.00006840845,"about_ca_system_score_gemma":0.000010732397,"threshold_uncertainty_score":0.384339},"labels":[],"label_agreement":null},{"id":"W1728159704","doi":"10.1109/crv.2005.27","title":"Combining Local and Global Features for Image Segmentation Using Iterative Classification and Region Merging","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Image segmentation; Artificial intelligence; Segmentation; Pattern recognition (psychology); Computer science; Image (mathematics); Feature (linguistics); Iterative method; Scale-space segmentation; Energy (signal processing); Function (biology); Segmentation-based object categorization; Contextual image classification; Enhanced Data Rates for GSM Evolution; Computer vision; Mathematics; Algorithm; Statistics","score_opus":0.032735343830324504,"score_gpt":0.33892348415631945,"score_spread":0.30618814032599495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1728159704","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010039266,0.0000826353,0.9877409,0.0013772629,0.000035407014,0.00028410403,0.0000011330384,0.0001414281,0.00029788938],"genre_scores_gemma":[0.363713,0.000014321436,0.63562196,0.00057413854,0.000020032612,0.000015472957,0.000005007364,0.0000029656483,0.000033092798],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992695,0.000046834393,0.00016091114,0.00026642604,0.00013400221,0.00012232181],"domain_scores_gemma":[0.99960196,0.00006711387,0.000082945015,0.00010361899,0.00007495402,0.000069412774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016690118,0.00008799483,0.00008590075,0.00004691315,0.00014407047,0.00023290084,0.000093919996,0.00003960502,0.000002391303],"category_scores_gemma":[0.000031467836,0.00008005077,0.000014271259,0.00011774095,0.00009616468,0.0011011562,0.00006444554,0.00004615557,4.995492e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000151104905,0.00003475433,0.000558525,0.000034806828,0.000014121775,0.000002787983,0.0017291538,0.000017180584,0.05577262,0.03630045,0.0016694112,0.9038511],"study_design_scores_gemma":[0.0010057422,0.00012057724,0.0041260496,0.00006046489,0.000017205168,0.00010769958,0.0010931507,0.8714735,0.117815144,0.0038371491,0.000101711434,0.00024162828],"about_ca_topic_score_codex":0.000018089542,"about_ca_topic_score_gemma":0.000007183175,"teacher_disagreement_score":0.90360945,"about_ca_system_score_codex":0.0000883936,"about_ca_system_score_gemma":0.000019201441,"threshold_uncertainty_score":0.32643756},"labels":[],"label_agreement":null},{"id":"W1734133568","doi":"10.1007/978-3-642-33718-5_56","title":"A Particle Filter Framework for Contour Detection","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Particle filter; Artificial intelligence; Computer science; Computer vision; Pattern recognition (psychology); Tracking (education); Segmentation; Detector; Bayesian probability; Active contour model; Filter (signal processing); Task (project management); Image segmentation; Engineering","score_opus":0.028614511097925316,"score_gpt":0.29546667637633833,"score_spread":0.26685216527841304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1734133568","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000014054269,0.00038942986,0.99654245,0.0005141658,0.0013812869,0.0006425731,0.0000035442342,0.0003036662,0.00020885104],"genre_scores_gemma":[0.06121904,0.00002409158,0.935077,0.0027962099,0.00063428946,0.00006324336,0.0000020115858,0.000025593763,0.00015848441],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99731725,0.000030529987,0.0004241428,0.00089599134,0.00069649454,0.0006356144],"domain_scores_gemma":[0.9975432,0.00082448847,0.0002380844,0.00094520097,0.00021221275,0.00023679402],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000981127,0.00032365843,0.0003335926,0.00027625333,0.0001843043,0.00037509925,0.0017095542,0.00032070713,0.000060179347],"category_scores_gemma":[0.0003465722,0.00029334484,0.000117144664,0.00030887907,0.00040535684,0.0007457728,0.0005335216,0.0005466039,0.0000443942],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043583714,0.00001862511,0.000010630948,0.00002378279,0.0000052939545,0.0000055371506,0.00038548472,0.000091139525,0.0009574784,0.013744876,0.000019828993,0.984733],"study_design_scores_gemma":[0.00032365022,0.00030814306,0.000071935,0.0003555805,0.000014044172,0.00003640902,2.0028016e-7,0.21783914,0.22399998,0.5546952,0.0016553632,0.0007003512],"about_ca_topic_score_codex":0.000007821584,"about_ca_topic_score_gemma":0.000016344276,"teacher_disagreement_score":0.98403263,"about_ca_system_score_codex":0.00021856633,"about_ca_system_score_gemma":0.00015742167,"threshold_uncertainty_score":0.99995184},"labels":[],"label_agreement":null},{"id":"W174545283","doi":"10.5220/0002138701750179","title":"BOUNDARY POINT DETECTION FOR ULTRASOUND IMAGE SEGMENTATION USING GUMBEL DISTRIBUTIONS","year":2007,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Boundary (topology); Context (archaeology); Artificial intelligence; Gumbel distribution; Segmentation; Computer vision; Computer science; Noise (video); Image segmentation; Intensity (physics); Contrast (vision); Point (geometry); Image (mathematics); Mathematics; Physics; Optics; Geometry; Geology; Statistics; Mathematical analysis; Extreme value theory","score_opus":0.019605638654597014,"score_gpt":0.32767701750804956,"score_spread":0.30807137885345254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W174545283","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008252872,0.0000128962765,0.98992527,0.00012574784,0.00026920982,0.00047992432,0.000010994117,0.00045851833,0.00046455907],"genre_scores_gemma":[0.13575502,0.00000383896,0.86359566,0.00039387814,0.000069532,0.000028312064,0.000035235164,0.000007935345,0.000110581],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988531,0.000030733114,0.0003156674,0.00027574922,0.00025117863,0.0002735933],"domain_scores_gemma":[0.9991235,0.0002347941,0.00010218301,0.00024967885,0.000174495,0.00011530866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079429464,0.00010533431,0.00008964144,0.00011060922,0.00031581294,0.0002386381,0.00024226597,0.000054348693,0.00005715675],"category_scores_gemma":[0.00020439454,0.0001027726,0.000065218446,0.0003272381,0.0000833333,0.0010954681,0.000054838463,0.00008052567,0.0000129974205],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000061270557,0.00004919004,0.00004057099,0.000012208175,0.000008144229,0.0000021056724,0.00012858985,8.898126e-7,0.88532346,0.0014737344,0.00078581186,0.112169154],"study_design_scores_gemma":[0.000311325,0.00007306282,0.0011910362,0.000008103095,0.000009587773,0.00004058004,0.00013124815,0.005763022,0.9863011,0.0058027366,0.00022509138,0.0001430837],"about_ca_topic_score_codex":0.00006314688,"about_ca_topic_score_gemma":0.00003056869,"teacher_disagreement_score":0.12750214,"about_ca_system_score_codex":0.00024639757,"about_ca_system_score_gemma":0.000052036416,"threshold_uncertainty_score":0.41909453},"labels":[],"label_agreement":null},{"id":"W1763141270","doi":"10.1007/978-3-540-74260-9_88","title":"Prostate Tissue Texture Feature Extraction for Cancer Recognition in TRUS Images Using Wavelet Decomposition","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Computer science; Wavelet; Feature extraction; Pattern recognition (psychology); Classifier (UML); Computer vision; Feature (linguistics)","score_opus":0.03669333981086926,"score_gpt":0.36317167447875975,"score_spread":0.3264783346678905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1763141270","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010849256,0.00050182093,0.9953399,0.00078557443,0.0012888085,0.001469204,0.000039997827,0.00020487826,0.00026134707],"genre_scores_gemma":[0.0037223764,0.0001640204,0.99354345,0.0016614598,0.0005169626,0.00007033324,0.00006435835,0.000043935775,0.00021308144],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9964671,0.00005154623,0.0005970181,0.0013869061,0.0008427528,0.00065466965],"domain_scores_gemma":[0.99797964,0.0004056498,0.00046185715,0.0005720366,0.00042167574,0.00015915753],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011793239,0.0004927823,0.0004712381,0.0012803356,0.00021644327,0.00046854172,0.001092523,0.000524873,0.00003309986],"category_scores_gemma":[0.00010606666,0.0004792653,0.000093430484,0.000718981,0.0003507084,0.001337419,0.0002807002,0.0009747441,0.0000066306116],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020514415,0.000030844072,0.000009271495,0.000085558255,0.0000051174147,0.00006471849,0.0003518454,0.00085533207,0.011317029,0.00010215084,0.00007588714,0.9870817],"study_design_scores_gemma":[0.0012060874,0.0004743146,0.00031879675,0.002824804,0.000036119214,0.0002909805,0.0000011556009,0.42624027,0.4495724,0.11577112,0.0016070638,0.0016568893],"about_ca_topic_score_codex":0.000099736164,"about_ca_topic_score_gemma":0.00024649128,"teacher_disagreement_score":0.9854248,"about_ca_system_score_codex":0.0008917931,"about_ca_system_score_gemma":0.00041157397,"threshold_uncertainty_score":0.9997659},"labels":[],"label_agreement":null},{"id":"W1781227130","doi":"10.1007/978-3-540-77046-6_77","title":"Deformable Object Tracking: A Kernel Density Estimation Approach Via Level Set Function Evolution","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Kernel density estimation; Probability density function; Kernel (algebra); Artificial intelligence; Video tracking; Divergence (linguistics); Density estimation; Partial differential equation; Computer vision; Boundary (topology); Tracking (education); Frame (networking); Mathematics; Constraint (computer-aided design); Computer science; Ordinary differential equation; Pattern recognition (psychology); Object (grammar); Differential equation; Mathematical analysis; Geometry; Statistics","score_opus":0.04895827984192361,"score_gpt":0.28633055353525677,"score_spread":0.23737227369333316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1781227130","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004009421,0.00011620162,0.99586284,0.00007278173,0.001038129,0.00065769546,0.0000036897843,0.00046596248,0.0017426269],"genre_scores_gemma":[0.13566004,0.00000833416,0.86281544,0.00097559136,0.00024919887,0.000014367623,0.00003824065,0.000028959314,0.00020981533],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954295,0.000059116886,0.00069877465,0.0014613988,0.0016707581,0.0006804405],"domain_scores_gemma":[0.9975288,0.00020728911,0.00045968543,0.0011248266,0.0004525424,0.00022686369],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0022886754,0.00051812653,0.000446015,0.0011446993,0.0003636165,0.0005030409,0.0018045362,0.00047379936,0.000014216873],"category_scores_gemma":[0.00017415496,0.00049521396,0.00013320394,0.00090507895,0.0005643472,0.0014395122,0.00072090287,0.0009225921,0.00004566604],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000137362595,0.000045697077,0.00004634205,0.0000680651,0.000011479598,0.000019049634,0.00048064327,0.021323202,0.00048753325,0.00353385,0.000057334637,0.9739131],"study_design_scores_gemma":[0.00024234985,0.0001884747,0.0007472123,0.00016789377,0.000014305858,0.00015157247,3.667366e-7,0.9090174,0.0072662923,0.08162992,0.00004419444,0.00053005444],"about_ca_topic_score_codex":0.00008744777,"about_ca_topic_score_gemma":0.000052733696,"teacher_disagreement_score":0.973383,"about_ca_system_score_codex":0.0009988436,"about_ca_system_score_gemma":0.0005335504,"threshold_uncertainty_score":0.99974996},"labels":[],"label_agreement":null},{"id":"W178856535","doi":"10.1007/978-3-319-10470-6_96","title":"Segmentation of Cerebrovascular Pathologies in Stroke Patients with Spatial and Shape Priors","year":2014,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; American Stroke Association; Natural Sciences and Engineering Research Council of Canada; BrightFocus Foundation; National Institute of Neurological Disorders and Stroke; American Health Assistance Foundation; National Institutes of Health; National Science Foundation","keywords":"Segmentation; Stroke (engine); Prior probability; Computer science; Artificial intelligence; Inference; Medicine; Pattern recognition (psychology); Bayesian probability","score_opus":0.005845584758022878,"score_gpt":0.2323503942599397,"score_spread":0.22650480950191682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W178856535","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3725891,0.000009487198,0.6270563,0.00006830823,0.00007176831,0.00016569975,4.6556394e-7,0.000036281566,0.0000025927636],"genre_scores_gemma":[0.55872184,0.0000030682306,0.44106773,0.00019063058,0.000008307635,0.0000052231203,7.9330545e-7,0.0000023037549,6.4820256e-8],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9983126,0.00010510174,0.0002604174,0.0004954296,0.0005836427,0.0002428186],"domain_scores_gemma":[0.9991516,0.00020712598,0.000119881915,0.00035562087,0.0001070724,0.000058701167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068652345,0.00012634156,0.0001831223,0.00033949382,0.000048565842,0.00010354733,0.00067857024,0.000046406458,0.0000032427147],"category_scores_gemma":[0.00019997908,0.000097820484,0.000016653761,0.0007450957,0.00040574578,0.00060483743,0.00034491206,0.00013897003,6.377875e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033866072,0.00005822429,0.16421428,0.000015382715,0.0000012168057,0.0000016273194,0.000897748,0.0008454404,0.0015020188,0.00003125367,3.6940384e-7,0.83242905],"study_design_scores_gemma":[0.0008951997,0.00052861363,0.49791875,0.00009832076,0.0000020499097,0.0000039218367,0.0000020588043,0.40116224,0.097882114,0.0013283374,8.394511e-7,0.0001775653],"about_ca_topic_score_codex":0.00006251504,"about_ca_topic_score_gemma":0.000040229224,"teacher_disagreement_score":0.8322515,"about_ca_system_score_codex":0.00005121128,"about_ca_system_score_gemma":0.00006083699,"threshold_uncertainty_score":0.39890036},"labels":[],"label_agreement":null},{"id":"W1794121648","doi":"10.1155/2015/813696","title":"MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans","year":2015,"lang":"en","type":"article","venue":"Computational Intelligence and Neuroscience","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":268,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"National Cancer Institute; National Institutes of Health; Universitair Medisch Centrum Utrecht; Technische Universiteit Eindhoven; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; China Scholarship Council; ZonMw","keywords":"Segmentation; Computer science; Artificial intelligence; White matter; Pattern recognition (psychology); Image segmentation; Machine learning; Medical physics; Magnetic resonance imaging; Medicine; Radiology","score_opus":0.1572354275397756,"score_gpt":0.4273993104421061,"score_spread":0.2701638829023305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1794121648","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019908475,0.0001066562,0.98958814,0.007061713,0.00035405273,0.00072172505,0.000011963378,0.00010797458,0.000056951776],"genre_scores_gemma":[0.3182187,0.000059594928,0.677547,0.003933483,0.000054125434,0.00012422445,0.00003008679,0.000009093966,0.000023711847],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99768007,0.00017856406,0.00040763017,0.0006383952,0.0008245226,0.00027079225],"domain_scores_gemma":[0.9983162,0.0007032625,0.00014791187,0.00021904062,0.00041030865,0.00020325427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013444921,0.00015471553,0.00014699706,0.00026128674,0.00012266584,0.00022542418,0.000605302,0.000061573846,0.000007693454],"category_scores_gemma":[0.0018793921,0.00015537758,0.000032959662,0.00074727845,0.0002486592,0.0012058105,0.00015960041,0.00016695852,0.000006833802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042317275,0.000801193,0.0007715628,0.00007663415,0.0000055210444,0.000023443992,0.0073300977,0.155745,0.0036714247,0.22003679,0.0025337588,0.60896224],"study_design_scores_gemma":[0.00017040122,0.00023317795,0.0007410176,0.000038001468,0.0000024584617,0.000013357688,0.00023509025,0.7297874,0.0055116517,0.2630518,0.000073699426,0.00014190855],"about_ca_topic_score_codex":0.000012456295,"about_ca_topic_score_gemma":0.000011180603,"teacher_disagreement_score":0.6088204,"about_ca_system_score_codex":0.00010361288,"about_ca_system_score_gemma":0.00024423897,"threshold_uncertainty_score":0.6336114},"labels":[],"label_agreement":null},{"id":"W1806220308","doi":"10.3968/j.ans.1715787020100302.006","title":"A Novel Method for 3D-Segmentation of Vascular Images","year":2010,"lang":"en","type":"article","venue":"Advances in natural science/Advances in natural sciences","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Octree; Segmentation; Computer science; Volume (thermodynamics); Computer vision; Artificial intelligence; Image segmentation; Scale-space segmentation; Region growing; Pattern recognition (psychology)","score_opus":0.009647105155649538,"score_gpt":0.37294501741840325,"score_spread":0.3632979122627537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1806220308","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067382096,0.014630588,0.971072,0.000516068,0.004459581,0.0012660739,0.000010396005,0.00017050207,0.0011365643],"genre_scores_gemma":[0.2346882,0.0010273424,0.76380587,0.0002582048,0.000066186585,0.00011818722,0.0000031819804,0.000007340734,0.00002550161],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9945737,0.00010015746,0.000956533,0.0014317026,0.0019072959,0.0010305708],"domain_scores_gemma":[0.997174,0.0011511609,0.0005640481,0.00054856145,0.00039478016,0.00016744537],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.005146086,0.00035976528,0.0005093919,0.0011376252,0.00042773224,0.00026884789,0.0041089966,0.00010964544,0.00001667438],"category_scores_gemma":[0.0022184676,0.00027322228,0.0001360026,0.006343028,0.0030999787,0.015307404,0.00049516774,0.0007314599,0.0000020328434],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013994961,0.00008456014,0.00089696853,0.00004719898,0.0000018237536,0.0000021299911,0.00023776076,0.00044936925,0.39907494,0.014029928,0.00000934039,0.585152],"study_design_scores_gemma":[0.002068025,0.00043220213,0.0049202293,0.0002513839,0.000010494757,0.000051167197,0.0005697959,0.13713671,0.80455196,0.04532096,0.00364661,0.0010404754],"about_ca_topic_score_codex":0.00011515046,"about_ca_topic_score_gemma":0.00066141493,"teacher_disagreement_score":0.5841115,"about_ca_system_score_codex":0.00018847028,"about_ca_system_score_gemma":0.00042689708,"threshold_uncertainty_score":0.999972},"labels":[],"label_agreement":null},{"id":"W1810327353","doi":"10.1007/978-3-642-02498-6_49","title":"Level Set Image Segmentation with a Statistical Overlap Constraint","year":2009,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"London Health Sciences Centre; CARE Canada","funders":"","keywords":"Segmentation; Artificial intelligence; Computer science; Bhattacharyya distance; Image segmentation; Pattern recognition (psychology); Statistical model; Gaussian; A priori and a posteriori; Algorithm; Constraint (computer-aided design); Mathematics","score_opus":0.02125545820288571,"score_gpt":0.3075554129049665,"score_spread":0.2862999547020808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1810327353","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002498348,0.000013959955,0.9953845,0.0013911879,0.00015634608,0.0003062667,0.000006466906,0.00020267039,0.000040246847],"genre_scores_gemma":[0.3923655,0.0000021295093,0.60487974,0.0027051144,0.000034131233,0.0000059972294,0.0000037658795,0.0000031578595,4.958448e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975415,0.00008315095,0.00029033507,0.0007374768,0.00088462717,0.00046290533],"domain_scores_gemma":[0.99879,0.00024846636,0.000097705764,0.00052007136,0.00015380378,0.00018989832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006688273,0.00019563793,0.00018347426,0.00022222885,0.00014197482,0.0004907503,0.0011089497,0.00005095743,0.000019048814],"category_scores_gemma":[0.00016823979,0.00015621263,0.000022271644,0.0010217383,0.00065814867,0.0008489216,0.00017602573,0.00024874762,0.000012719372],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007865738,0.00007061916,0.00031705265,0.0000070511796,0.0000023265197,0.00009100601,0.00092929,0.00089948333,0.02279077,0.0014044779,0.000073748226,0.9734063],"study_design_scores_gemma":[0.0011632981,0.0012920585,0.018258592,0.00013059801,0.000005693266,0.00028333286,0.0000058222695,0.585141,0.3589823,0.034090936,0.000014178303,0.0006322151],"about_ca_topic_score_codex":0.000025895977,"about_ca_topic_score_gemma":0.000013885981,"teacher_disagreement_score":0.9727741,"about_ca_system_score_codex":0.000144835,"about_ca_system_score_gemma":0.00033066573,"threshold_uncertainty_score":0.6370166},"labels":[],"label_agreement":null},{"id":"W1821156092","doi":"10.1109/camp.2003.1598168","title":"A New Moving Object Contour Detection Approach","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Artificial intelligence; Computer vision; Segmentation; Computer science; Edge detection; Image segmentation; Active contour model; Object detection; Object (grammar); Pixel; Boundary (topology); Noise (video); Motion detection; Motion (physics); Motion estimation; Pattern recognition (psychology); Image processing; Image (mathematics); Mathematics","score_opus":0.011006916556368073,"score_gpt":0.24426486573675632,"score_spread":0.23325794918038825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1821156092","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019825694,0.000020663,0.9555679,0.00009881851,0.000072352996,0.00011520167,6.063654e-8,0.00076118886,0.043165553],"genre_scores_gemma":[0.17623498,9.559828e-7,0.8191197,0.00035358508,0.00009772042,0.000010755727,8.6747303e-7,0.0000038832927,0.0041775913],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992657,0.00003208659,0.00014197055,0.00020736906,0.0002170743,0.00013583321],"domain_scores_gemma":[0.9996236,0.000027761771,0.00004024511,0.00021626083,0.00002824896,0.00006386434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015571594,0.000065249755,0.000068486836,0.000067803216,0.00004767021,0.00012941775,0.00029236174,0.000035402056,0.0000609676],"category_scores_gemma":[0.000024605308,0.00005582373,0.000030153167,0.0002210219,0.000011747525,0.00040478815,0.0000705948,0.00006716458,0.00003115803],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016147883,0.000043783493,0.00006870435,0.000008443791,0.0000045490483,0.0000049014266,0.00011052161,0.000019856328,0.051631335,0.011597249,0.018237863,0.9182712],"study_design_scores_gemma":[0.00045062177,0.00007327817,0.0017640018,0.0000080037225,0.000004026236,0.000033360237,0.000039806022,0.11276405,0.87401557,0.0096058985,0.0010031452,0.00023821487],"about_ca_topic_score_codex":0.0012219825,"about_ca_topic_score_gemma":0.000035773905,"teacher_disagreement_score":0.91803294,"about_ca_system_score_codex":0.00003583622,"about_ca_system_score_gemma":0.00003727964,"threshold_uncertainty_score":0.22764257},"labels":[],"label_agreement":null},{"id":"W1824514060","doi":"10.1109/icassp.1995.480069","title":"A segmentation criterion for digital image compression","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Lossy compression; Image segmentation; Color Cell Compression; Block (permutation group theory); Pixel; Segmentation; Pattern recognition (psychology); Image compression; Computer science; Scale-space segmentation; Range segmentation; Mathematics; Measure (data warehouse); Segmentation-based object categorization; Computer vision; Smoothness; Image (mathematics); Image processing; Data mining","score_opus":0.02849586564272617,"score_gpt":0.2967979571833998,"score_spread":0.26830209154067364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1824514060","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00038956353,0.000017697083,0.99354666,0.0007760529,0.00009762257,0.0003033663,0.0000036400409,0.0004482912,0.0044171116],"genre_scores_gemma":[0.04987156,0.000010135282,0.9470645,0.0010368499,0.00003727744,0.00008024616,0.000016060305,0.000006758409,0.0018765749],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993029,0.00001483108,0.00016204629,0.00020116531,0.00018946305,0.00012959246],"domain_scores_gemma":[0.9995575,0.000066543755,0.000045847843,0.00019623354,0.000064163294,0.00006969992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000070032555,0.00006988937,0.00006750483,0.00005726409,0.00006570054,0.00033746482,0.0002790803,0.000025607218,0.00030057854],"category_scores_gemma":[0.00004867782,0.000058316433,0.000037194255,0.0001028956,0.000027718022,0.0015758765,0.000083056046,0.000033896817,0.00008675721],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025870352,0.00010676224,0.000026719876,0.000023665953,0.0000039574893,0.0000030034905,0.00042167405,3.4964881e-7,0.11289361,0.0012743955,0.0931214,0.7921219],"study_design_scores_gemma":[0.0009952204,0.0002666736,0.00012741484,0.000042404812,0.0000041686367,0.000014794441,0.00008039677,0.267998,0.72238564,0.0037714208,0.0040320773,0.0002817571],"about_ca_topic_score_codex":0.0000017290746,"about_ca_topic_score_gemma":1.3452434e-7,"teacher_disagreement_score":0.79184014,"about_ca_system_score_codex":0.000022544955,"about_ca_system_score_gemma":0.0000033232361,"threshold_uncertainty_score":0.32911244},"labels":[],"label_agreement":null},{"id":"W1836993545","doi":"","title":"BAYESIAN DECISION THEORY IN SIMILARITY CRITERIA USED IN RANGE IMAGES SEGMENTATION","year":2007,"lang":"en","type":"article","venue":"Revista Ingenierías Universidad de Medellín","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Bayesian probability; Range (aeronautics); Pattern recognition (psychology); Similarity (geometry); Segmentation; Decision theory; Computer science; Mathematics; Statistics; Image (mathematics); Engineering","score_opus":0.014205244583216536,"score_gpt":0.3215990119019639,"score_spread":0.30739376731874735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1836993545","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07213724,0.0002560218,0.9252909,0.00017289139,0.00008910746,0.0004290361,0.0000029760042,0.00016148918,0.0014603373],"genre_scores_gemma":[0.64132905,0.00019271814,0.35762095,0.00062148715,0.0000356124,0.000007827048,0.000010086422,0.000020088524,0.00016220749],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99750674,0.00044191576,0.0005566666,0.0005111174,0.00048526304,0.0004982836],"domain_scores_gemma":[0.99836856,0.00067049905,0.00016209202,0.00050728355,0.00008092856,0.00021065354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037490116,0.00021345426,0.00029649303,0.0008811841,0.00008473423,0.00020165638,0.0007629817,0.00015133193,0.00015637866],"category_scores_gemma":[0.00037662237,0.0002295185,0.000079503065,0.0012746954,0.000109230416,0.0009786508,0.00022132251,0.00032890346,0.000015728987],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035348677,0.0005584617,0.057240967,0.0002595476,0.000036250287,0.002549637,0.011338672,0.000073250485,0.09031445,0.014580028,0.0033096615,0.8193856],"study_design_scores_gemma":[0.013995615,0.000646735,0.16404083,0.0025172264,0.0001142383,0.00016471387,0.007908764,0.03951927,0.71957713,0.040742476,0.0074526593,0.0033203082],"about_ca_topic_score_codex":0.0001088186,"about_ca_topic_score_gemma":0.00010573204,"teacher_disagreement_score":0.81606525,"about_ca_system_score_codex":0.00073400803,"about_ca_system_score_gemma":0.00012633714,"threshold_uncertainty_score":0.93594927},"labels":[],"label_agreement":null},{"id":"W1846168687","doi":"10.1007/s11760-015-0831-z","title":"Effect of image standardization on FLAIR MRI for brain extraction","year":2015,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Guelph","funders":"","keywords":"Fluid-attenuated inversion recovery; Standardization; Preprocessor; Computer science; Segmentation; Artificial intelligence; Feature extraction; Pipeline (software); Pattern recognition (psychology); Neuroimaging; Magnetic resonance imaging; Thresholding; Computer vision; Radiology; Medicine; Image (mathematics)","score_opus":0.01602109286850836,"score_gpt":0.3496054705403385,"score_spread":0.3335843776718302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1846168687","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018698534,0.00010619146,0.9964366,0.0005154996,0.000050920084,0.0003587563,0.0000039858533,0.00015130115,0.0005069225],"genre_scores_gemma":[0.43723932,0.000010817145,0.56170386,0.00064853864,0.00013041506,0.000083899235,0.00001777561,0.000022935326,0.00014242572],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988313,0.00015614198,0.0002426464,0.0002778484,0.00033635614,0.000155758],"domain_scores_gemma":[0.99894816,0.00034613413,0.00017461712,0.000131648,0.0002885073,0.00011091075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001764457,0.00012126753,0.00017549792,0.00011449078,0.000096735945,0.00023008036,0.00016410508,0.000050047915,0.0000064443484],"category_scores_gemma":[0.00054334267,0.0001001241,0.000036783942,0.00019777629,0.00008959108,0.0015320109,0.000046336278,0.000087585526,0.0000024866356],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000118159594,0.000029494047,0.000013424323,0.00030260388,0.000005332585,0.0000050719964,0.00047804808,0.000007837782,0.29945397,0.00008430822,0.005316394,0.6941854],"study_design_scores_gemma":[0.00081136706,0.0010278377,0.000016828406,0.00016504244,0.000012569237,0.000008542345,0.0000381917,0.030794254,0.96522135,0.0014742689,0.0003139175,0.00011580994],"about_ca_topic_score_codex":0.000004669237,"about_ca_topic_score_gemma":3.6184514e-7,"teacher_disagreement_score":0.69406956,"about_ca_system_score_codex":0.000040570638,"about_ca_system_score_gemma":0.00009171822,"threshold_uncertainty_score":0.40829426},"labels":[],"label_agreement":null},{"id":"W1847550145","doi":"10.1007/978-3-642-33718-5_42","title":"Segmentation with Non-linear Regional Constraints via Line-Search Cuts","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Unary operation; Maxima and minima; Computer science; Gradient descent; Mathematical optimization; Segmentation; Line (geometry); Submodular set function; Energy (signal processing); Line segment; Image segmentation; Class (philosophy); Line search; Minification; Algorithm; Descent direction; Mathematics; Artificial intelligence; Combinatorics; Artificial neural network; Geometry","score_opus":0.0314667443398586,"score_gpt":0.3019853726781258,"score_spread":0.27051862833826723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1847550145","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000331166,0.0001350215,0.9966157,0.0006566258,0.0005076558,0.00064970646,0.0000038906637,0.0002282029,0.0011700804],"genre_scores_gemma":[0.021155039,0.000056983947,0.9751283,0.0027832882,0.00050514203,0.000021391343,0.0000230842,0.000036355155,0.00029043952],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99556506,0.00005121174,0.0005341018,0.0012341145,0.0018969671,0.00071856665],"domain_scores_gemma":[0.9975818,0.00035436495,0.0002836409,0.0010052901,0.00041577136,0.00035913824],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010715909,0.00048774428,0.000429001,0.0007535108,0.00021303853,0.0002833701,0.0022624906,0.0002790362,0.0001292032],"category_scores_gemma":[0.000034207438,0.0004049643,0.000081833976,0.0005854123,0.0019098916,0.0010144078,0.0007075036,0.00091185904,0.000107429856],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008544584,0.000044490116,0.000050202074,0.000040989198,0.0000137951265,0.00007291199,0.00079469563,0.0014878485,0.0016773107,0.0010202255,0.000056956243,0.994732],"study_design_scores_gemma":[0.0018990242,0.0013606873,0.00032414155,0.0017121148,0.000044730343,0.00091053307,0.0000021246694,0.7781501,0.1784828,0.03387399,0.00068856945,0.0025511812],"about_ca_topic_score_codex":0.000023117802,"about_ca_topic_score_gemma":0.000015245729,"teacher_disagreement_score":0.9921808,"about_ca_system_score_codex":0.0003216501,"about_ca_system_score_gemma":0.00071826647,"threshold_uncertainty_score":0.9998402},"labels":[],"label_agreement":null},{"id":"W1849207675","doi":"10.1007/978-3-642-15745-5_79","title":"Hierarchical Multimodal Image Registration Based on Adaptive Local Mutual Information","year":2010,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Image registration; Computer science; Mutual information; Artificial intelligence; Robustness (evolution); Computer vision; Similarity measure; Metric (unit); Modalities; Pixel; Pattern recognition (psychology); Image (mathematics)","score_opus":0.008556847812374634,"score_gpt":0.26588120505634893,"score_spread":0.2573243572439743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1849207675","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021246676,0.000001113833,0.9948668,0.0016444757,0.0006867747,0.00029159815,0.0000019294944,0.00026944632,0.00011318837],"genre_scores_gemma":[0.49754864,1.625197e-7,0.5002507,0.002125238,0.000058475824,0.000010964806,0.0000031770408,0.0000024760618,1.6581355e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99759895,0.00008623178,0.0003658024,0.0005258501,0.0010274005,0.00039575816],"domain_scores_gemma":[0.99831647,0.0004573842,0.0001334298,0.0006960415,0.0002076964,0.0001889831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010522158,0.0001875865,0.000148195,0.00047990764,0.00018601266,0.00046440738,0.0014660698,0.000121720164,0.000019414663],"category_scores_gemma":[0.00055250106,0.00016196721,0.000044069056,0.0012231015,0.00093298225,0.0023627335,0.0002460797,0.0007541918,0.000047153368],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016215115,0.00006999749,0.000065419954,0.0000054849133,8.840146e-7,0.000012589695,0.00055361504,0.009556384,0.007214235,0.0014601815,0.000037454152,0.9810075],"study_design_scores_gemma":[0.0003244991,0.00025390254,0.0015427216,0.000022202003,8.257809e-7,0.000011575888,5.538886e-7,0.89391696,0.09881071,0.004928233,0.00001958657,0.00016819984],"about_ca_topic_score_codex":0.000043847078,"about_ca_topic_score_gemma":0.000036990725,"teacher_disagreement_score":0.9808393,"about_ca_system_score_codex":0.000111830464,"about_ca_system_score_gemma":0.0003966969,"threshold_uncertainty_score":0.6604831},"labels":[],"label_agreement":null},{"id":"W1853801442","doi":"10.1007/978-3-540-39903-2_111","title":"Tuning and Comparing Spatial Normalization Methods","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Normalization (sociology); Computer science; Spatial normalization; Algorithm; Artificial intelligence; Process (computing); Data mining","score_opus":0.02977655063255259,"score_gpt":0.3215934126039935,"score_spread":0.2918168619714409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1853801442","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000008726271,0.00029539742,0.9955363,0.00023007447,0.0008189632,0.00028127778,4.7119832e-7,0.0002145159,0.0026142227],"genre_scores_gemma":[0.0054001105,0.000058334997,0.9923125,0.0019705917,0.000119682634,0.0000064364626,0.0000036345066,0.00002053393,0.00010817693],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972384,0.000109211906,0.00048627047,0.001053704,0.00070127397,0.00041116722],"domain_scores_gemma":[0.99833983,0.00032387863,0.00026788295,0.00071237993,0.00016972727,0.00018629903],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015858868,0.00035173443,0.000428837,0.0006623076,0.00021483056,0.00054344843,0.0013896102,0.00021091981,0.000023461374],"category_scores_gemma":[0.00019617722,0.00034003457,0.000049243175,0.00040258092,0.0005825475,0.00069967756,0.00095501833,0.00056204695,0.0000057546454],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001079833,0.000007960811,0.00011671031,0.000030174264,0.0000044277776,0.000019750283,0.00047726216,0.00096902327,0.00050150463,0.004909817,0.000017658387,0.99294466],"study_design_scores_gemma":[0.0002171438,0.00009113253,0.00008970916,0.00030323962,0.0000075335192,0.00007770255,1.4715913e-7,0.9210583,0.016626475,0.060440067,0.00056952273,0.0005190439],"about_ca_topic_score_codex":0.000039684193,"about_ca_topic_score_gemma":0.000028351984,"teacher_disagreement_score":0.99242556,"about_ca_system_score_codex":0.00016805717,"about_ca_system_score_gemma":0.00017309752,"threshold_uncertainty_score":0.99990517},"labels":[],"label_agreement":null},{"id":"W1860889222","doi":"10.1007/3-540-45129-3_15","title":"On the Representation of Visual Information","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Entropy (arrow of time); Representation (politics); Isotropy; Computer vision; Extension (predicate logic); Variation (astronomy); Image (mathematics); Pattern recognition (psychology); Information theory; Mathematics; Optics; Statistics","score_opus":0.019394521685620565,"score_gpt":0.2974480177244668,"score_spread":0.2780534960388462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1860889222","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000048569782,0.000018020864,0.98950416,0.0009410851,0.00045823312,0.00038778264,0.0000014986039,0.000104891034,0.008535781],"genre_scores_gemma":[0.16870047,0.0001385584,0.818014,0.012243327,0.00036703894,0.00004700741,0.000027034479,0.000033684828,0.00042882803],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99755025,0.000044509197,0.0005192421,0.00043898943,0.0012103522,0.00023664196],"domain_scores_gemma":[0.9976121,0.000827677,0.0004115338,0.0008282542,0.00025338816,0.00006701334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075831724,0.00021934147,0.00024292246,0.00058701716,0.00011179085,0.0002463681,0.0018777943,0.00014243486,0.000071031485],"category_scores_gemma":[0.00036560954,0.00015553362,0.000073544754,0.0006571823,0.00052899134,0.0009899337,0.000500627,0.00043006917,0.00004286432],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003832356,0.000016961261,0.000008683258,0.000013163412,0.0000041425524,0.0000062184786,0.0005620221,0.0016836183,0.00013800865,0.045146104,0.00018084965,0.9522364],"study_design_scores_gemma":[0.00031241184,0.00054250995,0.000121597644,0.0005512539,0.0000075814028,0.000035523695,0.0000010769046,0.4415913,0.080035284,0.47564554,0.0006362285,0.000519695],"about_ca_topic_score_codex":0.000016907454,"about_ca_topic_score_gemma":0.000004413381,"teacher_disagreement_score":0.9517167,"about_ca_system_score_codex":0.00012868272,"about_ca_system_score_gemma":0.00020238076,"threshold_uncertainty_score":0.63424766},"labels":[],"label_agreement":null},{"id":"W1872492457","doi":"10.1007/978-3-642-40763-5_22","title":"An Automatic Multi-atlas Segmentation of the Prostate in Transrectal Ultrasound Images Using Pairwise Atlas Shape Similarity","year":2013,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Cancer Agency; University of British Columbia","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Institute for Computing, Information and Cognitive Systems","keywords":"Computer science; Segmentation; Prostate brachytherapy; Artificial intelligence; Atlas (anatomy); Computer vision; Image segmentation; Metric (unit); Similarity (geometry); Brachytherapy; Ultrasound; Image registration; Pattern recognition (psychology); Medicine; Image (mathematics); Radiology; Radiation therapy","score_opus":0.01726767781539539,"score_gpt":0.2920572531421354,"score_spread":0.27478957532674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1872492457","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39584193,0.000017433882,0.6031288,0.00020981343,0.00017045827,0.00054804777,0.0000014550435,0.000080989565,0.0000010898709],"genre_scores_gemma":[0.52299005,0.0000023160178,0.4766405,0.0003308906,0.000012908259,0.00001752387,8.408162e-7,0.0000047949484,2.0234725e-7],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973342,0.0003042914,0.00052492187,0.00065287267,0.00074067194,0.00044307558],"domain_scores_gemma":[0.99845594,0.0003952943,0.00020155359,0.0006629611,0.00016712055,0.00011711987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009649785,0.00021286577,0.00023531745,0.00030867447,0.00015853271,0.0003880786,0.0018768532,0.00007565373,0.00002934373],"category_scores_gemma":[0.00023940427,0.00015663108,0.000055715453,0.0020738041,0.0006640936,0.0021593317,0.00024140149,0.0003119346,0.000003120041],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018598146,0.00024471906,0.019293629,0.000055128337,0.0000029807472,0.0000063571933,0.005949305,0.013981867,0.53313226,0.000008025113,0.0000055252217,0.42731836],"study_design_scores_gemma":[0.0002409043,0.000058037655,0.04067826,0.00007410334,0.0000017028799,0.00001223468,0.0000040559535,0.6021766,0.35535648,0.001279246,7.0580036e-8,0.00011829883],"about_ca_topic_score_codex":0.0004278571,"about_ca_topic_score_gemma":0.000115778705,"teacher_disagreement_score":0.5881947,"about_ca_system_score_codex":0.00017736072,"about_ca_system_score_gemma":0.00024266432,"threshold_uncertainty_score":0.638723},"labels":[],"label_agreement":null},{"id":"W1874267771","doi":"10.1007/978-3-642-03641-5_1","title":"Multi-label Moves for MRFs with Truncated Convex Priors","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Prior probability; Piecewise; Computer science; Potts model; Regular polygon; Swap (finance); Cut; Algorithm; Range (aeronautics); Artificial intelligence; Mathematical optimization; Mathematics; Segmentation; Image segmentation; Statistical physics; Bayesian probability","score_opus":0.025363984385914636,"score_gpt":0.2878830357362171,"score_spread":0.26251905135030246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1874267771","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000017022307,0.00019925827,0.9962664,0.0008301536,0.0004842608,0.0012174082,0.0000072883086,0.00050458434,0.0004736242],"genre_scores_gemma":[0.0014349781,0.000029697363,0.992857,0.004563491,0.00015597294,0.000038121834,0.000011996847,0.000036862482,0.00087188795],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959578,0.000032013355,0.0005809688,0.0016367395,0.0010904738,0.0007020232],"domain_scores_gemma":[0.9972167,0.00046766584,0.00038974057,0.0012031113,0.00045388748,0.00026887254],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000772556,0.00058494916,0.00062007393,0.00071727444,0.00022593672,0.0005367282,0.0032131425,0.00032584797,0.000017068962],"category_scores_gemma":[0.00015821033,0.00046767254,0.000092398666,0.00058676506,0.0009494958,0.0007467085,0.00047490266,0.0006315774,0.000015759464],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011533025,0.00006078485,0.000007230541,0.000038732443,0.00001217969,0.000042821724,0.00047862408,0.0003120423,0.0012224503,0.0017917444,0.00007123913,0.99595064],"study_design_scores_gemma":[0.0017144517,0.0011421917,0.00010710895,0.0007010227,0.000020257787,0.00007481021,3.805749e-7,0.9135554,0.037925083,0.042692278,0.000857356,0.0012096927],"about_ca_topic_score_codex":0.000012138201,"about_ca_topic_score_gemma":0.000027905997,"teacher_disagreement_score":0.9947409,"about_ca_system_score_codex":0.00026215985,"about_ca_system_score_gemma":0.0006675517,"threshold_uncertainty_score":0.9997775},"labels":[],"label_agreement":null},{"id":"W1875662944","doi":"10.1007/978-3-642-31298-4_21","title":"Dental X-Ray Image Segmentation and Object Detection Based on Phase Congruency","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Phase congruency; Segmentation; Image segmentation; Translation (biology); Invariant (physics); Rotation (mathematics); Pattern recognition (psychology); Image (mathematics); Mathematics","score_opus":0.012936975064798273,"score_gpt":0.28433558803779246,"score_spread":0.27139861297299417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1875662944","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021129362,0.00015316652,0.99683654,0.0001400959,0.0010165357,0.0006472736,0.0000068838576,0.00028210925,0.00070610916],"genre_scores_gemma":[0.22813024,0.00004317679,0.7689994,0.0023878545,0.00029078338,0.000033922326,0.000015522242,0.000034245684,0.000064839296],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99668896,0.00007528176,0.0004721221,0.0011340732,0.0011307601,0.0004988084],"domain_scores_gemma":[0.99809533,0.0004159979,0.0003019914,0.0007567429,0.00016328055,0.00026664286],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00093729707,0.00045133522,0.00035033847,0.0008937637,0.00024445163,0.0005307944,0.0011003853,0.00023369146,0.00007102885],"category_scores_gemma":[0.000121300516,0.00041961894,0.00007855586,0.0004112953,0.00069289084,0.0011659615,0.0003727894,0.0006174895,0.000038177313],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010919382,0.00007141339,0.000022203785,0.000033161206,0.0000044685594,0.00003546217,0.00024021107,0.00024414228,0.018851021,0.00020891863,0.000011031624,0.98026705],"study_design_scores_gemma":[0.001639524,0.001089687,0.00019034072,0.00041083418,0.00002367733,0.00006647695,8.3067494e-7,0.618244,0.36807707,0.009160856,0.00012710033,0.0009695872],"about_ca_topic_score_codex":0.000022430162,"about_ca_topic_score_gemma":0.00003084228,"teacher_disagreement_score":0.97929746,"about_ca_system_score_codex":0.0003903134,"about_ca_system_score_gemma":0.00021816646,"threshold_uncertainty_score":0.99982554},"labels":[],"label_agreement":null},{"id":"W1886256801","doi":"10.1109/icip.2001.958504","title":"Image registration using the Hausdorff fraction and virtual circles","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Homothetic transformation; Hausdorff distance; Robustness (evolution); Computer vision; Computer science; Artificial intelligence; Similarity measure; Image registration; Rigid transformation; Transformation (genetics); Fraction (chemistry); Matrix similarity; Geometric transformation; Image (mathematics); Similarity (geometry); Edge detection; Mathematics; Image processing; Geometry","score_opus":0.03821826504743443,"score_gpt":0.29383884536624094,"score_spread":0.2556205803188065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1886256801","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054450585,0.000029553381,0.9907055,0.0014265854,0.000054038235,0.00009113318,1.5303104e-7,0.00017366926,0.0020743276],"genre_scores_gemma":[0.39953417,0.00005981798,0.59833676,0.0014340957,0.00006526051,0.0000066667667,7.023944e-7,0.000004547169,0.0005579996],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999419,0.00005005482,0.00011760891,0.00014740162,0.00018660884,0.00007936922],"domain_scores_gemma":[0.99960375,0.000062129715,0.000057370904,0.00020622392,0.00003398179,0.000036520578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019117873,0.000048465517,0.000043912998,0.000031094532,0.00012512812,0.00021457626,0.00016766776,0.000025448842,0.0001420155],"category_scores_gemma":[0.000062086016,0.000034399072,0.000012665571,0.000112590795,0.00008032428,0.0008418341,0.000051842253,0.00007062088,0.000014589571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019646548,0.000053447264,0.000075686,0.000007567347,0.000008621327,0.000009541426,0.0007482558,0.0000031134473,0.31050158,0.009832023,0.02029694,0.6584613],"study_design_scores_gemma":[0.00027222006,0.00010476114,0.0019490813,0.000018140776,0.000008741749,0.00008197113,0.0003414346,0.82225543,0.17088293,0.0030010436,0.00088308775,0.00020117058],"about_ca_topic_score_codex":0.000045565386,"about_ca_topic_score_gemma":0.0000033805509,"teacher_disagreement_score":0.8222523,"about_ca_system_score_codex":0.000019536743,"about_ca_system_score_gemma":0.000005767001,"threshold_uncertainty_score":0.20691638},"labels":[],"label_agreement":null},{"id":"W1888439455","doi":"10.1007/978-3-642-02611-9_73","title":"Brain MRI Segmentation Based on the Rényi’s Fractal Dimension","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; CancerCare Manitoba","funders":"","keywords":"Multifractal system; Computer science; Artificial intelligence; Fractal dimension; Segmentation; Pattern recognition (psychology); Magnetic resonance imaging; Entropy (arrow of time); Dimension (graph theory); Image segmentation; Fractal; Fractal analysis; Computer vision; Mathematics; Physics; Combinatorics","score_opus":0.014330198273255443,"score_gpt":0.26867622161654675,"score_spread":0.2543460233432913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1888439455","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000021115155,0.000040031875,0.97864884,0.016958937,0.00068269897,0.0007030154,0.0000027192752,0.00029989702,0.002642741],"genre_scores_gemma":[0.021447651,0.000017919097,0.9154711,0.062204324,0.00032007435,0.000025260511,0.000017390037,0.00003423848,0.00046207994],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957667,0.00011564368,0.00050939864,0.0012806216,0.0018290472,0.00049858564],"domain_scores_gemma":[0.99639106,0.0014906339,0.00033950488,0.001428592,0.00018099538,0.00016920062],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015263798,0.0004758462,0.00034298006,0.0006541854,0.00032787293,0.0005725815,0.0027182226,0.0002559182,0.0000867612],"category_scores_gemma":[0.00024983962,0.00033913908,0.0001246787,0.0005749449,0.00057055184,0.0005472041,0.00046570785,0.00092571933,0.00007422696],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006223852,0.000042245167,0.0000043088003,0.000009819491,0.0000035184896,0.000055993027,0.00030730423,0.006469717,0.0013003151,0.0039619636,0.0008974448,0.98694116],"study_design_scores_gemma":[0.0003383675,0.0005323752,0.0000836934,0.00049520493,0.0000064312826,0.000020166535,2.8810524e-7,0.8551886,0.06378038,0.0781516,0.0007888951,0.00061398064],"about_ca_topic_score_codex":0.000010305428,"about_ca_topic_score_gemma":0.000009283416,"teacher_disagreement_score":0.9863272,"about_ca_system_score_codex":0.00036754334,"about_ca_system_score_gemma":0.00039468473,"threshold_uncertainty_score":0.99990606},"labels":[],"label_agreement":null},{"id":"W18994745","doi":"10.1007/978-3-642-40763-5_6","title":"Random Walks with Efficient Search and Contextually Adapted Image Similarity for Deformable Registration","year":2013,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Artificial intelligence; Weighting; Probabilistic logic; Image registration; Random walk; Similarity (geometry); Image (mathematics); Pattern recognition (psychology); Computer vision; Data mining; Mathematics","score_opus":0.014048926270726639,"score_gpt":0.2701784047828632,"score_spread":0.2561294785121366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W18994745","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024850618,0.000032734053,0.9721067,0.0016665381,0.00010123654,0.0010649619,0.000001172342,0.00014992298,0.000026107062],"genre_scores_gemma":[0.47590512,0.0000021269768,0.52331626,0.0007061441,0.000019846024,0.00004445481,0.000001265086,0.0000035673963,0.0000012069996],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979599,0.00006872045,0.00027732394,0.00063409883,0.00060979335,0.00045012284],"domain_scores_gemma":[0.9984303,0.00045120312,0.000076820186,0.00045935914,0.0004164363,0.00016588783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012379212,0.00016326961,0.00019861027,0.00020985244,0.00024720613,0.0007280677,0.0008645608,0.000058907433,0.00000788286],"category_scores_gemma":[0.00022942816,0.00011937137,0.000025141439,0.0008061001,0.00058436085,0.0010401497,0.0002748893,0.00020392121,0.0000036958586],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053714306,0.00011734416,0.0002632194,0.000067484485,0.0000070372635,0.00001193835,0.0023754085,0.024066811,0.027508894,0.00062184816,0.00007954758,0.9448268],"study_design_scores_gemma":[0.0010648355,0.00026420483,0.0010099565,0.0000464076,0.000001755381,0.000024507008,0.000002666151,0.9060102,0.0896959,0.0017196336,0.0000048865495,0.00015500968],"about_ca_topic_score_codex":0.00016004388,"about_ca_topic_score_gemma":0.000051681374,"teacher_disagreement_score":0.94467175,"about_ca_system_score_codex":0.000083191146,"about_ca_system_score_gemma":0.00022175607,"threshold_uncertainty_score":0.7020774},"labels":[],"label_agreement":null},{"id":"W1901581672","doi":"10.1007/978-3-642-33418-4_66","title":"Global Assessment of Cardiac Function Using Image Statistics in MRI","year":2012,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CARE Canada; Robarts Clinical Trials; Western University","funders":"","keywords":"Bhattacharyya distance; Segmentation; Artificial intelligence; Computer science; Statistic; Pattern recognition (psychology); Similarity (geometry); Artificial neural network; Image (mathematics); Image segmentation; Statistics; Mathematics","score_opus":0.01658878769330017,"score_gpt":0.337025060007555,"score_spread":0.3204362723142548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1901581672","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00798253,0.000087291985,0.99052614,0.000061983854,0.0010635722,0.00019067644,0.000005777191,0.000057908077,0.000024119148],"genre_scores_gemma":[0.44507903,0.0000036056897,0.5546815,0.0001852787,0.000044659624,0.000002859689,9.3066393e-7,0.0000020630434,6.501869e-8],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800485,0.00013228145,0.00034710503,0.00037630426,0.00069980737,0.00043963178],"domain_scores_gemma":[0.99898666,0.00019038451,0.00013217669,0.00043912782,0.00013150493,0.00012012276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013665243,0.0001309228,0.0002076099,0.0002464484,0.000064088395,0.00011772494,0.000782489,0.000052759726,0.0000069028915],"category_scores_gemma":[0.000117106145,0.00012175323,0.000029034261,0.0019844358,0.00032975644,0.0011727024,0.0004456639,0.00015560303,0.0000016081294],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031728448,0.00020138062,0.10535359,0.000044898497,0.0000061141473,0.0000061618107,0.0007696731,0.010599123,0.02222089,0.0033461768,0.000044582368,0.85740423],"study_design_scores_gemma":[0.0001460538,0.00008531507,0.13725817,0.00005217324,0.0000043535897,0.0000069189773,0.0000014380565,0.8307086,0.023639262,0.007916525,0.000006296375,0.00017488567],"about_ca_topic_score_codex":0.00012379495,"about_ca_topic_score_gemma":0.00001348065,"teacher_disagreement_score":0.85722935,"about_ca_system_score_codex":0.0003278697,"about_ca_system_score_gemma":0.00027303142,"threshold_uncertainty_score":0.49649528},"labels":[],"label_agreement":null},{"id":"W1902154363","doi":"10.1002/hbm.22862","title":"Accurate cortical tissue classification on <scp>MRI</scp> by modeling cortical folding patterns","year":2015,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Pattern recognition (psychology); Computer science; Artificial intelligence; White matter; Gyrification; Cluster analysis; Grey matter; Magnetic resonance imaging; Anatomy; Neuroscience; Biology; Cerebral cortex; Medicine","score_opus":0.11577461155791825,"score_gpt":0.3486359898454369,"score_spread":0.23286137828751868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1902154363","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05084021,0.000023089458,0.94546837,0.0012477266,0.00017566737,0.00031202316,0.000004533384,0.00062027184,0.0013081334],"genre_scores_gemma":[0.95826596,0.0000058874143,0.03778311,0.003124048,0.00016874855,0.000067523084,0.000053411943,0.000029013061,0.00050231745],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997164,0.00033438113,0.00058626174,0.0006559037,0.0007599253,0.0004995127],"domain_scores_gemma":[0.99811685,0.0005424017,0.00016532812,0.0005982711,0.00014971409,0.00042745855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012433798,0.0002275393,0.00024320066,0.00018045882,0.0003311288,0.00040777843,0.00082094624,0.00013429488,0.000022717219],"category_scores_gemma":[0.0013158597,0.00023126497,0.00005266218,0.0002819865,0.0000779836,0.0006172562,0.000249015,0.00050034194,0.00013024724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000045908214,0.0003620311,0.00085311226,0.000096943215,0.00005146323,0.000087986526,0.0071131545,0.0006953943,0.75081277,0.061457664,0.15671952,0.021745348],"study_design_scores_gemma":[0.00070685084,0.00025209354,0.0021083588,0.00021363808,0.000010241924,0.000015399175,0.0011033863,0.97588605,0.011713603,0.0050016595,0.0027335552,0.00025514516],"about_ca_topic_score_codex":0.000022243972,"about_ca_topic_score_gemma":0.0000024975618,"teacher_disagreement_score":0.9751907,"about_ca_system_score_codex":0.00017689721,"about_ca_system_score_gemma":0.00006024473,"threshold_uncertainty_score":0.9430712},"labels":[],"label_agreement":null},{"id":"W1910418014","doi":"10.1118/1.4932366","title":"A framework for quantification and visualization of segmentation accuracy and variability in 3D lateral ventricle ultrasound images of preterm neonates","year":2015,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Research Grants Council, University Grants Committee; National Natural Science Foundation of China","keywords":"Visualization; Ultrasound; Segmentation; Ventricle; Medical imaging; Medicine; Artificial intelligence; Image segmentation; Radiology; Computer vision; Medical physics; Computer science; Internal medicine","score_opus":0.03346282816051722,"score_gpt":0.3584056510324061,"score_spread":0.3249428228718889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1910418014","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06530401,0.000040887226,0.9340879,0.00017967696,0.000049307768,0.0002983006,0.0000052295018,0.000026398162,0.000008314729],"genre_scores_gemma":[0.86660403,0.00003820503,0.13321835,0.0000691943,0.000020382016,0.000026179825,0.000018585031,0.000003830943,0.0000012558494],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99894047,0.00012495597,0.00031249525,0.00019040058,0.00034410367,0.000087569606],"domain_scores_gemma":[0.998509,0.00094847695,0.00017605019,0.00014759599,0.00013149309,0.00008739785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009879931,0.00006447683,0.00014513232,0.000038075894,0.00001636237,0.000025826597,0.00014596159,0.00006391141,0.000004001226],"category_scores_gemma":[0.003139225,0.000059190435,0.000014142089,0.00022110272,0.00021244308,0.00040214253,0.00005713776,0.00006185602,1.6951063e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009141645,0.0007250101,0.06341685,0.00080955843,0.00002891137,0.0000019142276,0.009936224,0.000012762933,0.037008684,0.05074345,0.00024707714,0.83697814],"study_design_scores_gemma":[0.0010628194,0.00021497803,0.022096913,0.00018200226,0.000014246493,0.000002812939,0.000067312845,0.027892143,0.63734704,0.3109789,0.000005677449,0.00013517906],"about_ca_topic_score_codex":0.000027391401,"about_ca_topic_score_gemma":6.8699234e-7,"teacher_disagreement_score":0.83684295,"about_ca_system_score_codex":0.000021222017,"about_ca_system_score_gemma":0.000060052073,"threshold_uncertainty_score":0.37581724},"labels":[],"label_agreement":null},{"id":"W1917482396","doi":"10.1109/icassp.1987.1169608","title":"Characterizing &amp;#8711;&amp;gt;sup&amp;lt;2&amp;gt;/sup&amp;lt;G filtered images by their zero crossings","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Nabla symbol; Zero (linguistics); Algorithm; Discrete mathematics; Computer science; Artificial intelligence; Combinatorics; Mathematics; Physics; Philosophy; Quantum mechanics","score_opus":0.03377088949801319,"score_gpt":0.2999973663627506,"score_spread":0.2662264768647374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1917482396","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043706458,0.00060068927,0.93686783,0.007088124,0.00047728443,0.00088676397,0.000184202,0.0030912366,0.007097393],"genre_scores_gemma":[0.044751573,0.00033673397,0.8694948,0.0142612485,0.00071390613,0.00021672547,0.0008220375,0.00018113565,0.06922187],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9920262,0.0004908334,0.0017871475,0.002106276,0.0017142077,0.0018753055],"domain_scores_gemma":[0.9940524,0.0006182962,0.0007365173,0.0030680732,0.00052330893,0.0010014237],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0016500141,0.0011533336,0.00108996,0.0005348219,0.0008864031,0.0027482624,0.0035032746,0.0005018392,0.005200864],"category_scores_gemma":[0.00073307357,0.0010145659,0.00047275604,0.0011383526,0.0007903248,0.0038531013,0.0013931883,0.0009540992,0.0032705336],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027220569,0.0003523176,0.00012432401,0.000065135224,0.000078088626,0.000005303441,0.0038854296,0.0000069238686,0.5011817,0.0002068482,0.35393935,0.14012736],"study_design_scores_gemma":[0.0011028424,0.000079674595,0.0004841133,0.00024213103,0.00004135865,0.00012385401,0.00003509777,0.00053518265,0.14389607,0.0008450385,0.8511355,0.0014791086],"about_ca_topic_score_codex":0.00015620423,"about_ca_topic_score_gemma":0.0001509985,"teacher_disagreement_score":0.49719617,"about_ca_system_score_codex":0.0003647089,"about_ca_system_score_gemma":0.00023287862,"threshold_uncertainty_score":0.99923044},"labels":[],"label_agreement":null},{"id":"W1922796982","doi":"10.1109/nafips.2005.1548506","title":"Non-Rigid Registration using Free-Form Deformation for Prostate Images","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Image registration; Free-form deformation; Computer vision; Computer science; Artificial intelligence; Prostate; Similarity (geometry); Prostate brachytherapy; Mutual information; Deformation (meteorology); Similarity measure; Brachytherapy; Image (mathematics); Medicine; Radiology; Radiation therapy; Geography","score_opus":0.022088603748653075,"score_gpt":0.3066149625590769,"score_spread":0.2845263588104238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1922796982","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018431505,0.000006428534,0.9931182,0.0014225077,0.00006571471,0.00053829345,0.0000031948473,0.0003168318,0.0026856652],"genre_scores_gemma":[0.051254336,0.000006777996,0.94696933,0.00083957886,0.00007863873,0.000053970372,0.000014313476,0.000005979467,0.00077708275],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991148,0.00000996053,0.00028678562,0.0001780564,0.00023595424,0.00017444984],"domain_scores_gemma":[0.9993053,0.000027374826,0.00013371505,0.00033771622,0.00013250251,0.00006340626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036186934,0.000084848725,0.00007777126,0.00008004297,0.0001069665,0.00019955884,0.00037894305,0.00003807515,0.000023415669],"category_scores_gemma":[0.00006395856,0.000071492424,0.000035873258,0.00014198091,0.000030257648,0.0024632122,0.000071430644,0.00004853605,0.00001590353],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016162578,0.00011632432,0.00011361603,0.00011545845,0.000014704267,0.000001775811,0.0017999135,0.00014894022,0.13830507,0.010996086,0.071148,0.77722394],"study_design_scores_gemma":[0.00036515444,0.00006836627,0.00013314902,0.000016303005,0.0000034778088,0.000009010271,0.00003068943,0.3043976,0.6898835,0.004132058,0.00083843066,0.0001223013],"about_ca_topic_score_codex":0.000038628827,"about_ca_topic_score_gemma":0.000013492244,"teacher_disagreement_score":0.77710164,"about_ca_system_score_codex":0.00008744491,"about_ca_system_score_gemma":0.00005875545,"threshold_uncertainty_score":0.29153764},"labels":[],"label_agreement":null},{"id":"W1926518666","doi":"10.1007/978-3-642-15711-0_17","title":"Nonlocal Patch-Based Label Fusion for Hippocampus Segmentation","year":2010,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Pattern recognition (psychology); Image warping; Market segmentation; Fusion; Prior probability; Computer vision","score_opus":0.014041845707215032,"score_gpt":0.2984307847783678,"score_spread":0.2843889390711528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1926518666","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023595423,0.000011401881,0.97236973,0.0015804303,0.0015791281,0.00056668924,0.0000021207923,0.0002892049,0.000005895178],"genre_scores_gemma":[0.36085695,8.7459017e-7,0.63620824,0.0027716004,0.000104967075,0.000046646986,0.0000041177423,0.0000062187432,3.9576227e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976753,0.00005345374,0.0003277396,0.00078246876,0.00068285,0.00047818114],"domain_scores_gemma":[0.9982602,0.000541161,0.00012550721,0.0006650004,0.00023081244,0.00017734138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011752719,0.00019110195,0.00017318595,0.0003602915,0.00024473906,0.00036639496,0.0016323905,0.00011445868,0.000015919042],"category_scores_gemma":[0.0003434021,0.00016763163,0.000047145146,0.001309827,0.00041475217,0.0007698219,0.00030122296,0.00034476578,0.00001029587],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005440696,0.000074524854,0.00030168376,0.000013406221,8.0462974e-7,0.0000045523902,0.00022450533,0.0005569311,0.13978799,0.00013802105,0.000026145426,0.858866],"study_design_scores_gemma":[0.00044588858,0.00016278846,0.00021868211,0.000020168069,0.0000012232273,0.0000060024468,2.8207648e-7,0.6409053,0.34907323,0.009012374,0.000013113729,0.00014093552],"about_ca_topic_score_codex":0.000037506663,"about_ca_topic_score_gemma":0.00008988372,"teacher_disagreement_score":0.8587251,"about_ca_system_score_codex":0.000089359586,"about_ca_system_score_gemma":0.00035973004,"threshold_uncertainty_score":0.68358195},"labels":[],"label_agreement":null},{"id":"W1926902435","doi":"10.1109/icpr.1998.711179","title":"A system for segmenting ultrasound images","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Speckle noise; Artificial intelligence; Computer vision; Computer science; Initialization; Speckle pattern; Noise (video); Feature (linguistics); Image segmentation; Segmentation; Multiplicative noise; Maxima and minima; Pattern recognition (psychology); Image (mathematics); Mathematics","score_opus":0.02310597272929742,"score_gpt":0.2661428922748501,"score_spread":0.24303691954555268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1926902435","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006419375,0.00004463462,0.98351866,0.00025710472,0.00012508173,0.00024094574,0.0000013056846,0.00090841093,0.014839635],"genre_scores_gemma":[0.08919961,0.000005872592,0.9067329,0.00049811223,0.000049469767,0.00008025817,0.0000011162476,0.0000061142227,0.0034265467],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99924,0.00002348545,0.0001702501,0.00021019469,0.00017622468,0.00017987625],"domain_scores_gemma":[0.999379,0.0001952612,0.000051475075,0.00025079603,0.000056114655,0.00006739075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023185239,0.00006793488,0.00008166276,0.000049416933,0.00008522098,0.00017530669,0.00039810143,0.000024148603,0.00012726951],"category_scores_gemma":[0.00009261134,0.000057103436,0.000041738665,0.00013177205,0.000021520756,0.00039083092,0.000055945573,0.000036046258,0.00007802821],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016950706,0.00014843702,0.0002537809,0.0003311434,0.000042581014,0.00001971788,0.0010279953,0.0000025392405,0.38599765,0.045234427,0.2839726,0.28296745],"study_design_scores_gemma":[0.00031981568,0.000054496955,0.000039742365,0.000036321482,0.000004999233,0.000039694514,0.00014736479,0.013038404,0.985165,0.00032857235,0.0006649309,0.00016067528],"about_ca_topic_score_codex":0.000005841441,"about_ca_topic_score_gemma":2.7708754e-7,"teacher_disagreement_score":0.59916735,"about_ca_system_score_codex":0.000033988883,"about_ca_system_score_gemma":0.000004355204,"threshold_uncertainty_score":0.23286106},"labels":[],"label_agreement":null},{"id":"W1929760380","doi":"10.1109/ictta.2004.1307578","title":"Data reduction in machine vision and remote sensing applications","year":2004,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Artificial intelligence; Machine vision; Artificial neural network; Computer vision; Reduction (mathematics); Artificial vision","score_opus":0.02797899918917735,"score_gpt":0.34279901054642875,"score_spread":0.3148200113572514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1929760380","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003006375,0.000041970423,0.99631846,0.0024021093,0.000022642766,0.00015398064,0.000001021688,0.00017000796,0.0005891858],"genre_scores_gemma":[0.025196178,0.000055324825,0.9744075,0.00025893043,0.000016433662,4.5553261e-7,0.000016200296,0.0000023980624,0.00004658322],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943256,0.000018285831,0.00012077433,0.00025219723,0.00010883053,0.00006733786],"domain_scores_gemma":[0.9993987,0.000013489186,0.000025995418,0.0005057318,0.000015521178,0.00004054785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002382976,0.00004105092,0.000046025787,0.0000732158,0.000036253085,0.00005553916,0.0002561054,0.000020807327,0.0000041625076],"category_scores_gemma":[0.000021486574,0.000035455483,0.000003774363,0.00022801214,0.000030655458,0.00050873344,0.00026948767,0.000063749365,0.000007541073],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.008032e-7,0.000008967319,0.0000014975382,0.0000032115765,5.2461695e-7,0.0000016256522,0.000047055535,0.000002772658,0.008812688,0.001231356,0.00016056738,0.98972934],"study_design_scores_gemma":[0.0013727029,0.00011455864,0.0013259806,0.0001585687,0.0000073311685,0.000352596,0.00010850347,0.68292564,0.175297,0.13352463,0.004375526,0.00043693505],"about_ca_topic_score_codex":0.00027445503,"about_ca_topic_score_gemma":0.000043737025,"teacher_disagreement_score":0.9892924,"about_ca_system_score_codex":0.000025933128,"about_ca_system_score_gemma":0.00001793756,"threshold_uncertainty_score":0.14458327},"labels":[],"label_agreement":null},{"id":"W1943028203","doi":"10.1515/itit-2015-0011","title":"Model-based analysis of cerebrovascular diseases combining 3D and 4D MRA datasets","year":2015,"lang":"en","type":"article","venue":"it - Information Technology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Visualization; Stroke (engine); Segmentation; Computer science; Cerebral blood flow; Blood flow; High resolution; Medicine; Artificial intelligence; Radiology; Cardiology","score_opus":0.019727600634490333,"score_gpt":0.283388406725437,"score_spread":0.26366080609094666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1943028203","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040009283,0.00004185346,0.9944061,0.0007792192,0.000027867285,0.00013457212,0.000073592055,0.00041380143,0.00012209093],"genre_scores_gemma":[0.55569243,0.00001342401,0.44257,0.0012031567,0.0000023836494,0.00003710602,0.00047524518,0.0000033676515,0.0000029024195],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989366,0.000027324624,0.0004239214,0.00013959566,0.00032991133,0.0001426422],"domain_scores_gemma":[0.99882853,0.000039795137,0.00023869252,0.00061680895,0.00017347025,0.00010269246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002775753,0.00009693842,0.0002349938,0.0012267166,0.00005220262,0.00007771815,0.00056350953,0.00010806832,0.000012436341],"category_scores_gemma":[0.00032215888,0.000092506256,0.000043027347,0.0014233724,0.00022030761,0.0014364922,0.00025341334,0.0000976652,0.000008374743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032359072,0.00035171348,0.0129568195,0.00029404098,0.0010654974,0.00000829399,0.0034241062,0.042281136,0.0005109009,0.11161797,0.028872369,0.7985848],"study_design_scores_gemma":[0.00045666995,0.000065093154,0.00016265629,0.00001334043,0.00011363988,0.0000019455006,0.00015321217,0.98885745,0.007209922,0.002175007,0.00068362337,0.00010741988],"about_ca_topic_score_codex":0.000019661495,"about_ca_topic_score_gemma":0.0000034118323,"teacher_disagreement_score":0.94657636,"about_ca_system_score_codex":0.000036323054,"about_ca_system_score_gemma":0.00011559353,"threshold_uncertainty_score":0.37722957},"labels":[],"label_agreement":null},{"id":"W194567576","doi":"10.1007/978-3-642-33415-3_66","title":"Rotational-Slice-Based Prostate Segmentation Using Level Set with Shape Constraint for 3D End-Firing TRUS Guided Biopsy","year":2012,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials","funders":"","keywords":"3D ultrasound; Prostate biopsy; Segmentation; Computer science; Prostate; Ultrasound; Artificial intelligence; Image segmentation; Computer vision; Medicine; Radiology","score_opus":0.06620918864385694,"score_gpt":0.3353622447775685,"score_spread":0.26915305613371154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W194567576","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03191798,0.000033996927,0.9660329,0.00038750787,0.00037249294,0.0010662763,0.000013059037,0.00017025869,0.000005475903],"genre_scores_gemma":[0.3930768,4.757424e-7,0.60557306,0.0011678911,0.00007778228,0.000084819,0.000009844363,0.000008947109,3.88607e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973727,0.00008630616,0.00041616333,0.00065711804,0.00080770545,0.0006600149],"domain_scores_gemma":[0.99840325,0.00042985042,0.00023863667,0.00042308684,0.00029806764,0.0002071006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013705549,0.00024407882,0.00020473177,0.00041141507,0.00028964705,0.00037545728,0.00085250975,0.000062820975,0.00001677444],"category_scores_gemma":[0.00019300586,0.00020701719,0.00004245654,0.0013299148,0.000503784,0.0012403626,0.00018729111,0.00016046026,0.0000028706722],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016521099,0.00008824835,0.004615573,0.000051216884,0.000008223581,0.000007131086,0.0022025343,0.032067884,0.04783611,0.00020977348,0.000010390822,0.9128864],"study_design_scores_gemma":[0.00066695386,0.00011515072,0.002349029,0.000093596886,0.000005078334,0.00006858132,0.0000031350098,0.7924107,0.20337331,0.000654521,0.0000072834464,0.00025267206],"about_ca_topic_score_codex":0.000034057914,"about_ca_topic_score_gemma":0.000009755748,"teacher_disagreement_score":0.9126337,"about_ca_system_score_codex":0.00027071327,"about_ca_system_score_gemma":0.0005616721,"threshold_uncertainty_score":0.8441916},"labels":[],"label_agreement":null},{"id":"W1953071312","doi":"10.1109/imtc.2000.848676","title":"A framework for distributed, image-based measurement systems","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Independence (probability theory); Client–server model; Image (mathematics); Architecture; Distributed computing; Property (philosophy); Fat client; Server; Real-time computing; Computer vision; Operating system; Mathematics","score_opus":0.0773616353308668,"score_gpt":0.30225098036336867,"score_spread":0.22488934503250185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1953071312","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000003899013,0.00012317076,0.99561584,0.0021577307,0.00023895892,0.0005360904,0.0000080728,0.00067004195,0.00064622326],"genre_scores_gemma":[0.0380818,0.0000038171293,0.96029776,0.0011462205,0.000054837303,0.0002883108,0.000004199578,0.0000074511054,0.00011562812],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986495,0.000048603873,0.00023400555,0.00026774718,0.0005721486,0.0002280199],"domain_scores_gemma":[0.99894494,0.00014466367,0.00007076757,0.0004511099,0.0002592469,0.0001292865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046649354,0.00010035251,0.0001215069,0.000051471507,0.000074780815,0.00024094134,0.0005467756,0.000056755187,0.00015251007],"category_scores_gemma":[0.00042193822,0.00008280815,0.00005963066,0.0002154042,0.000035372886,0.00023477325,0.00004602702,0.00007861695,0.00007744458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000098620985,0.0006457424,0.000075108255,0.0003260613,0.00006036982,0.000024410088,0.00024153382,0.00007007156,0.00813799,0.26496258,0.5760451,0.14940116],"study_design_scores_gemma":[0.00096858124,0.0002850424,0.000042760767,0.00020072603,0.000015154563,0.000006418961,0.00004489837,0.84117484,0.13334903,0.010558916,0.012909184,0.00044446814],"about_ca_topic_score_codex":0.000011614112,"about_ca_topic_score_gemma":4.820185e-7,"teacher_disagreement_score":0.84110475,"about_ca_system_score_codex":0.000102496895,"about_ca_system_score_gemma":0.000021998629,"threshold_uncertainty_score":0.3376818},"labels":[],"label_agreement":null},{"id":"W1953328384","doi":"10.1109/robot.1995.525295","title":"WHERE and WHAT: object perception for autonomous robots","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Robot; Object (grammar); Artificial intelligence; Computer vision; Computer science; Enhanced Data Rates for GSM Evolution; Deep-sky object; Graph; Set (abstract data type); Perception; Scale (ratio); Cognitive neuroscience of visual object recognition; Image (mathematics); Pattern recognition (psychology); Theoretical computer science; Geography","score_opus":0.024576373937012393,"score_gpt":0.2790960451491033,"score_spread":0.2545196712120909,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1953328384","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00042156503,0.0003471599,0.9955908,0.001837233,0.00009744969,0.0002131748,1.9502411e-7,0.00034305474,0.0011493864],"genre_scores_gemma":[0.022540703,0.0009477043,0.9708862,0.0018250373,0.00004521229,0.000058146255,0.000001206827,0.000005955065,0.0036898814],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999458,0.000018047653,0.00010613106,0.00019929073,0.00010301416,0.00011549947],"domain_scores_gemma":[0.9996611,0.00004610635,0.000025385401,0.00016671269,0.000033903736,0.000066800334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011200397,0.00005868045,0.000065224944,0.000040305305,0.00005736696,0.00036102385,0.0001759278,0.00003467048,0.00041124923],"category_scores_gemma":[0.000020381733,0.000050022147,0.000021385003,0.00006723669,0.000028572853,0.0014713154,0.00005531656,0.00003633449,0.00004096572],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.57641e-7,0.000017985327,0.000011916412,0.0000144163905,0.0000020248426,8.931975e-7,0.0005622092,0.0000010394238,0.0022939516,0.0009611501,0.0135528445,0.9825812],"study_design_scores_gemma":[0.0015159141,0.00082028355,0.0029154099,0.00016351549,0.00001726816,0.000085176194,0.001255606,0.93224955,0.04049948,0.0060499865,0.013675501,0.00075233803],"about_ca_topic_score_codex":0.000008731589,"about_ca_topic_score_gemma":0.000005724466,"teacher_disagreement_score":0.98182887,"about_ca_system_score_codex":0.000021313683,"about_ca_system_score_gemma":0.0000058337528,"threshold_uncertainty_score":0.45028907},"labels":[],"label_agreement":null},{"id":"W1957456020","doi":"10.1007/978-3-642-40760-4_24","title":"A Variational Formulation for Discrete Registration","year":2013,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Minification; Finite element method; Regularization (linguistics); Gaussian; Computer science; Energy minimization; Markov random field; Variational method; Energy functional; Random field; Mathematical optimization; Markov chain; Algorithm; Applied mathematics; Mathematics; Artificial intelligence; Mathematical analysis; Image segmentation; Segmentation; Physics","score_opus":0.015830043516821206,"score_gpt":0.29145217853284,"score_spread":0.2756221350160188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1957456020","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008003886,0.000008455831,0.9950711,0.0030116395,0.00032678584,0.0006065247,7.7597167e-7,0.00015242885,0.0000219051],"genre_scores_gemma":[0.42886975,5.7487927e-7,0.5699542,0.0010317867,0.000067780944,0.000068448695,0.00000366711,0.0000024452895,0.0000012970644],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984567,0.000031202202,0.00027378756,0.00047188316,0.0004856641,0.000280746],"domain_scores_gemma":[0.9988498,0.00033665943,0.000114098526,0.00037896482,0.00023634703,0.00008412309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006431828,0.00010631547,0.00009976533,0.00021742689,0.00015744797,0.00048949715,0.00086288655,0.000051330513,0.00001635323],"category_scores_gemma":[0.00033847225,0.000091594135,0.000032878335,0.0008429939,0.000087162945,0.0020485776,0.0001549929,0.000094712545,0.000011432029],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020525256,0.000032700198,0.00043188626,0.000013313466,0.000001967906,8.258706e-7,0.00062058476,0.0056958133,0.011883368,0.012718963,0.00012508366,0.96847343],"study_design_scores_gemma":[0.0001700028,0.00007076797,0.0040871613,0.000013190903,6.7560933e-7,0.0000045252277,1.95575e-7,0.8412072,0.031060606,0.12327061,0.000011155717,0.00010391566],"about_ca_topic_score_codex":0.000047045207,"about_ca_topic_score_gemma":0.000010419894,"teacher_disagreement_score":0.96836954,"about_ca_system_score_codex":0.00010559139,"about_ca_system_score_gemma":0.00015449672,"threshold_uncertainty_score":0.47202325},"labels":[],"label_agreement":null},{"id":"W1958165731","doi":"10.1007/978-3-642-40811-3_67","title":"Joint Segmentation of 3D Femoral Lumen and Outer Wall Surfaces from MR Images","year":2013,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"Canadian Institutes of Health Research; Canada Research Chairs; City University of Hong Kong","keywords":"Segmentation; Regular polygon; Lumen (anatomy); Computer science; Relaxation (psychology); Algorithm; Convex optimization; Artificial intelligence; Geometry; Mathematics","score_opus":0.015196312255101476,"score_gpt":0.2638009538905236,"score_spread":0.24860464163542212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1958165731","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17841451,0.00008087929,0.8197645,0.0011356686,0.00022542205,0.00027408797,0.0000016899289,0.00009397223,0.000009257493],"genre_scores_gemma":[0.45732877,0.000009818554,0.541786,0.000836598,0.000022993016,0.000009824652,0.0000015166153,0.0000033564174,0.0000011341822],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99801195,0.00009088442,0.00037622626,0.0006225638,0.00058462925,0.0003137748],"domain_scores_gemma":[0.9988547,0.0002310152,0.00016132335,0.00046615457,0.00016321405,0.00012359099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048838696,0.00016882627,0.0002304797,0.00023855551,0.000085005166,0.00041969257,0.0009297053,0.00005785862,0.000051303396],"category_scores_gemma":[0.00009088643,0.00013908345,0.000025988806,0.0006494194,0.000503262,0.0016150627,0.00059191097,0.00016054914,0.0000139404965],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012811023,0.000044360302,0.006083642,0.00001677623,0.0000046281125,0.000006567917,0.0021967946,0.0007096664,0.15439533,0.000016919797,0.00008065445,0.83644336],"study_design_scores_gemma":[0.00025040787,0.00009944629,0.029589446,0.000053534455,0.000002248137,0.000006015274,0.000002754855,0.38510832,0.5768514,0.0078624375,0.0000017606549,0.00017221605],"about_ca_topic_score_codex":0.0008037904,"about_ca_topic_score_gemma":0.000019328112,"teacher_disagreement_score":0.83627117,"about_ca_system_score_codex":0.00006186267,"about_ca_system_score_gemma":0.00007713448,"threshold_uncertainty_score":0.56716585},"labels":[],"label_agreement":null},{"id":"W196093984","doi":"10.1007/978-3-319-10443-0_14","title":"Optimized PatchMatch for Near Real Time and Accurate Label Fusion","year":2014,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Segmentation; Dice; Artificial intelligence; Computer science; Pattern recognition (psychology); Fusion; Sørensen–Dice coefficient; Computation; Image segmentation; Computer vision; Mathematics; Algorithm; Statistics","score_opus":0.014225481475771589,"score_gpt":0.2902908348874141,"score_spread":0.2760653534116425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W196093984","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012880311,0.000014703936,0.9848982,0.001304598,0.0002773706,0.00035603458,0.0000011961033,0.00024749624,0.000020060277],"genre_scores_gemma":[0.07199199,0.000013259378,0.9260988,0.0017873794,0.00007581883,0.000021226566,0.0000020956732,0.0000070857463,0.0000023183889],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998143,0.000090691,0.0002667904,0.00068956485,0.00041182342,0.00039813505],"domain_scores_gemma":[0.99837744,0.00071868324,0.0001025062,0.00050939765,0.00013624082,0.00015574429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013938226,0.00016171517,0.00021866795,0.00014983841,0.00024870018,0.000557329,0.0011638086,0.00007310831,0.0000072825655],"category_scores_gemma":[0.00034326097,0.0001336475,0.000026784486,0.0007653081,0.00038263694,0.0007065408,0.00060910196,0.00013838575,0.0000097406555],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010176048,0.000034580564,0.000054393582,0.000014226084,0.0000014907764,0.0000025618588,0.00062735594,0.0025364622,0.020168401,0.00016679353,0.00005985292,0.9763237],"study_design_scores_gemma":[0.0006044268,0.00021811399,0.00029339228,0.000035967823,0.000001531128,0.000008792337,1.3091025e-7,0.94880193,0.041946087,0.007905726,0.000022137692,0.0001617613],"about_ca_topic_score_codex":0.00004770736,"about_ca_topic_score_gemma":0.0000034466186,"teacher_disagreement_score":0.97616196,"about_ca_system_score_codex":0.00004574275,"about_ca_system_score_gemma":0.00010778335,"threshold_uncertainty_score":0.5449987},"labels":[],"label_agreement":null},{"id":"W1964858048","doi":"10.1167/11.11.1038","title":"Spatial properties of texture-surround suppression of contour-shape coding","year":2011,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Texture (cosmology); Coding (social sciences); Artificial intelligence; Computer vision; Computer science; Pattern recognition (psychology); Mathematics; Image (mathematics)","score_opus":0.04695231602663091,"score_gpt":0.300672543714625,"score_spread":0.25372022768799407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964858048","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16316217,0.00025283755,0.83576566,0.00009892847,0.00032024604,0.00009336648,6.2925284e-7,0.000016290116,0.00028987628],"genre_scores_gemma":[0.92760587,0.000059113463,0.07221848,0.000048837897,0.00004412359,4.7181427e-7,1.3604564e-7,0.0000041866438,0.000018793675],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985232,0.000103943705,0.0006283066,0.00008928842,0.00056095637,0.00009430364],"domain_scores_gemma":[0.99850065,0.00004768061,0.0008047479,0.00017776048,0.00039249132,0.00007670356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066151086,0.00007434886,0.00024013962,0.00014495573,0.00002819196,0.000020541464,0.00051950244,0.00005406639,0.000108165776],"category_scores_gemma":[0.00020710351,0.00004812576,0.00008413404,0.00010771894,0.00008243175,0.0006652076,0.00012941926,0.0001435659,0.0000013818085],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000085180036,0.00016536705,0.00041779454,0.00006128798,0.000012780176,0.000010727622,0.0018266581,6.3275996e-7,0.87050456,0.0001480533,0.0009804067,0.12578656],"study_design_scores_gemma":[0.0004430553,0.0008007865,0.0075961202,0.00079550553,0.000010293387,0.000028210843,0.00008270231,0.004259864,0.98547775,0.00039584946,0.000049092236,0.00006077997],"about_ca_topic_score_codex":0.000037922942,"about_ca_topic_score_gemma":0.0000011080456,"teacher_disagreement_score":0.7644437,"about_ca_system_score_codex":0.000019870025,"about_ca_system_score_gemma":0.00006511402,"threshold_uncertainty_score":0.19625115},"labels":[],"label_agreement":null},{"id":"W1965492743","doi":"10.1117/12.480867","title":"Similarity metrics based on nonadditive entropies for 2D-3D multimodal biomedical image registration","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McGill University","keywords":"Mutual information; Parameterized complexity; Computer science; Entropy (arrow of time); Artificial intelligence; Boltzmann machine; Image registration; Similarity (geometry); Information theory; Pattern recognition (psychology); Joint entropy; Data mining; Machine learning; Mathematics; Algorithm; Image (mathematics); Principle of maximum entropy; Statistics; Artificial neural network","score_opus":0.01328546589741226,"score_gpt":0.2568215274567775,"score_spread":0.24353606155936525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965492743","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4796579,0.000039049602,0.5085278,0.0057488154,0.00053273235,0.0017749935,0.00018418064,0.000341034,0.0031935044],"genre_scores_gemma":[0.038182322,0.00003554502,0.9606231,0.00047017913,0.00020203929,0.00034156404,0.000026426784,0.00003683439,0.00008197612],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99699813,7.155478e-8,0.0007655827,0.00055921735,0.0012443879,0.0004325855],"domain_scores_gemma":[0.99642324,0.0006526334,0.00048967166,0.00010486286,0.0021289738,0.00020061016],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001204048,0.0003260795,0.00039116133,0.0002229542,0.00012713241,0.00022699725,0.0012622288,0.0002291746,0.000019494106],"category_scores_gemma":[0.0046565123,0.00027634372,0.00053641543,0.0006060841,0.0003612122,0.0008481796,0.00010980591,0.00033768438,0.0000013257103],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008538995,0.00049937575,0.00006363892,0.00047608008,0.00021221634,3.265077e-7,0.0001914007,0.00004640247,0.33257696,0.64926344,0.012796212,0.0037885779],"study_design_scores_gemma":[0.002215315,0.0010465785,0.00022682754,0.00025431535,0.00010118258,0.000008764105,0.00041060493,0.43312022,0.5507578,0.0040378287,0.007330085,0.0004904911],"about_ca_topic_score_codex":0.000005790932,"about_ca_topic_score_gemma":9.236393e-8,"teacher_disagreement_score":0.6452256,"about_ca_system_score_codex":0.000251764,"about_ca_system_score_gemma":0.00010226358,"threshold_uncertainty_score":0.9999689},"labels":[],"label_agreement":null},{"id":"W1966445443","doi":"10.1117/12.878068","title":"A novel hybrid model for deformable image registration in abdominal procedures","year":2011,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal University Hospital; Hospital for Sick Children; University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Fuzzy logic; Feature (linguistics); Transformation (genetics); Deformation (meteorology); Image (mathematics); Image registration; Domain (mathematical analysis); Boundary (topology); Algorithm; Physics; Mathematics; Mathematical analysis","score_opus":0.021565896323083044,"score_gpt":0.2510997217948549,"score_spread":0.22953382547177187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966445443","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6560292,0.000021779197,0.3405725,0.0008300868,0.000091426955,0.0008898452,0.000026845282,0.0001297807,0.0014085226],"genre_scores_gemma":[0.11542431,0.00002932472,0.88362986,0.00016437641,0.000092763934,0.0004728937,0.0000056893646,0.000031220494,0.0001495539],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976927,1.2032286e-8,0.00081189553,0.00044642648,0.00063611317,0.00041285978],"domain_scores_gemma":[0.997806,0.00008671755,0.00045649486,0.00008204644,0.0014479896,0.000120741315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093347905,0.0002688369,0.00033255984,0.00015220241,0.00006890575,0.00014012124,0.0014315238,0.00012166725,0.0000043859613],"category_scores_gemma":[0.00086161826,0.000231812,0.00038908698,0.0002957669,0.00019669943,0.0017470247,0.00019517208,0.00023511112,6.628472e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009933991,0.00022860798,0.000047178983,0.00057969044,0.00008521385,1.09234385e-7,0.00063905015,0.0000825645,0.599348,0.39516175,0.0029944603,0.00073406444],"study_design_scores_gemma":[0.0008911266,0.00019971172,0.0001318111,0.00019184404,0.000030355966,0.000019079163,0.00023774886,0.5970576,0.39383504,0.0071400614,0.00004755084,0.00021810508],"about_ca_topic_score_codex":0.000018091398,"about_ca_topic_score_gemma":6.222295e-7,"teacher_disagreement_score":0.596975,"about_ca_system_score_codex":0.00016308637,"about_ca_system_score_gemma":0.00010188726,"threshold_uncertainty_score":0.9453019},"labels":[],"label_agreement":null},{"id":"W1967200547","doi":"10.1109/iembs.2011.6091967","title":"A feature-based approach for refinement of Model-based segmentation of low contrast structures","year":2011,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Philips (Canada); Princess Margaret Cancer Centre","funders":"","keywords":"Artificial intelligence; Segmentation; Computer science; Robustness (evolution); Pattern recognition (psychology); Weighting; Voxel; Image segmentation; Contrast (vision); Feature (linguistics); Scale-space segmentation; Segmentation-based object categorization; Feature extraction; Computer vision; Prior probability; Feature selection; Bayesian probability","score_opus":0.04051969198868334,"score_gpt":0.28532190763522447,"score_spread":0.24480221564654114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967200547","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00055140967,0.000011606256,0.9977354,0.000081586535,0.000029478739,0.0006581909,0.000014815858,0.00009390335,0.0008236466],"genre_scores_gemma":[0.32599694,4.0964952e-7,0.6734761,0.0003814216,0.00000400594,0.00007905295,0.000023726656,0.000004736063,0.00003358799],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990056,0.000037482725,0.0002736609,0.000232857,0.00031473624,0.00013562378],"domain_scores_gemma":[0.9991244,0.000053446085,0.00022250453,0.00033049917,0.00020769321,0.000061453415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024681035,0.000108294145,0.00018392477,0.00011908769,0.00002430639,0.000012450377,0.000406378,0.00005569926,0.00003944365],"category_scores_gemma":[0.000036283214,0.00008636565,0.000074575,0.00014948935,0.00008226994,0.00013458876,0.000028956527,0.000046288103,1.7685015e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000524476,0.0020034574,0.0005340577,0.0021310255,0.00013922676,0.0000018964889,0.0021128564,0.013999793,0.69382995,0.09655653,0.014588249,0.17357852],"study_design_scores_gemma":[0.00063615816,0.00013114225,0.000060920338,0.000010135438,0.000006355369,1.0900059e-7,0.000016634707,0.41472948,0.5834321,0.00092280004,0.0000011923735,0.000053018615],"about_ca_topic_score_codex":0.000022184231,"about_ca_topic_score_gemma":0.0000018752393,"teacher_disagreement_score":0.4007297,"about_ca_system_score_codex":0.000024614372,"about_ca_system_score_gemma":0.000115281735,"threshold_uncertainty_score":0.35218892},"labels":[],"label_agreement":null},{"id":"W1967866963","doi":"10.1109/icip.2014.7025002","title":"Cross modality label fusion in multi-atlas segmentation","year":2014,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Computer science; Atlas (anatomy); Segmentation; Image fusion; Pattern recognition (psychology); Computer vision; Image segmentation; Fusion; Wavelet transform; Image (mathematics); Wavelet","score_opus":0.039125114637997525,"score_gpt":0.3588219203947126,"score_spread":0.3196968057567151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967866963","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04965657,0.0000050429,0.94821966,0.00044583177,0.00010566166,0.00016878516,4.2920468e-7,0.00029639664,0.0011015965],"genre_scores_gemma":[0.2628726,0.0000063859493,0.7351903,0.0011836631,0.000014111214,0.000027705737,0.0000054423,0.000004247707,0.00069555466],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99888504,0.00012869562,0.00025523832,0.0002853263,0.0002780077,0.00016769767],"domain_scores_gemma":[0.9994219,0.000074479525,0.000059149097,0.00030992448,0.00005635717,0.00007817561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006557088,0.00008170292,0.00009271385,0.000083972445,0.000049266397,0.00012808928,0.00040472738,0.000050908806,0.00012158419],"category_scores_gemma":[0.00012000995,0.00007080582,0.000018357428,0.0002519127,0.000043287168,0.00063770293,0.0001754221,0.00009040055,0.00008558203],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009487754,0.00049808406,0.021778751,0.00004201906,0.0000044624594,0.000007128919,0.0011579675,0.000042583823,0.18551153,0.011885121,0.0010810876,0.77798176],"study_design_scores_gemma":[0.0015578641,0.00009950345,0.049943656,0.00002065739,0.000001170863,0.0000024182687,0.000026103387,0.45317474,0.49161395,0.003222181,0.00012788881,0.00020985481],"about_ca_topic_score_codex":0.00022569903,"about_ca_topic_score_gemma":0.000044452954,"teacher_disagreement_score":0.7777719,"about_ca_system_score_codex":0.000052218184,"about_ca_system_score_gemma":0.000017943608,"threshold_uncertainty_score":0.28873777},"labels":[],"label_agreement":null},{"id":"W1968206046","doi":"10.1007/s10439-006-9168-7","title":"Evaluation of Three-dimensional Image Registration Methodologies for In Vivo Micro-computed Tomography","year":2006,"lang":"en","type":"article","venue":"Annals of Biomedical Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Image registration; Artificial intelligence; Computer vision; Fiducial marker; Computer science; Similarity measure; Similarity (geometry); Scanner; Image resolution; Gold standard (test); Tomography; Mutual information; Pattern recognition (psychology); Image (mathematics); Medicine; Radiology","score_opus":0.0874831392830568,"score_gpt":0.3638740105880612,"score_spread":0.2763908713050044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968206046","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018668063,0.0002362387,0.9799852,0.00059172744,0.00010907042,0.00028409035,0.00000926498,0.00009089116,0.000025452497],"genre_scores_gemma":[0.13116685,0.000003814137,0.86867815,0.00005062195,0.00003706292,0.000039437215,0.00001622307,0.000005848236,0.0000020108],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982526,0.00008502997,0.0005225196,0.00020715574,0.0007582215,0.00017450217],"domain_scores_gemma":[0.9987025,0.00045500344,0.00016510526,0.0001974845,0.00043381884,0.00004608579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037312047,0.000096865486,0.00020861898,0.0004014863,0.000012689216,0.000013682651,0.00029264734,0.00008534256,0.0000118548905],"category_scores_gemma":[0.0008759478,0.000092064096,0.00008557399,0.0005833001,0.0000993282,0.00023154142,0.000057204146,0.00006846877,2.3097851e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009122008,0.00012239136,0.000029311752,0.00012504689,0.000025956444,0.0000018961649,0.000037257625,0.0017725813,0.9333963,0.0018863785,0.0029293771,0.05966442],"study_design_scores_gemma":[0.0003167065,0.000087292516,0.0018832237,0.000083939885,0.000007991306,0.0000015477432,0.0000023208222,0.31168112,0.6789962,0.006825549,0.00003975408,0.00007437293],"about_ca_topic_score_codex":0.00008035584,"about_ca_topic_score_gemma":0.0000055607393,"teacher_disagreement_score":0.30990854,"about_ca_system_score_codex":0.000015299169,"about_ca_system_score_gemma":0.000073273244,"threshold_uncertainty_score":0.3754265},"labels":[],"label_agreement":null},{"id":"W1968747769","doi":"10.1155/2011/410912","title":"Unsupervised 3D Prostate Segmentation Based on Diffusion-Weighted Imaging MRI Using Active Contour Models with a Shape Prior","year":2011,"lang":"en","type":"article","venue":"Journal of Electrical and Computer Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Health Network; Mount Sinai Hospital","funders":"","keywords":"Segmentation; Artificial intelligence; Computer science; Magnetic resonance imaging; Pattern recognition (psychology); Diffusion MRI; Modality (human–computer interaction); Computer vision; Effective diffusion coefficient; Active contour model; Prostate; Image segmentation; Active shape model; Medicine; Radiology","score_opus":0.012761404108284977,"score_gpt":0.21674337159659623,"score_spread":0.20398196748831127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968747769","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05492304,0.000046178124,0.94461507,0.000075323136,0.00006791061,0.00017411636,4.7332063e-7,0.00007779542,0.000020104377],"genre_scores_gemma":[0.3601575,0.000014101886,0.6394372,0.00031901288,0.000054912893,0.0000038233757,5.058611e-7,0.000011498947,0.0000014428507],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987674,0.000047198344,0.00034440693,0.00021017039,0.00039305713,0.0002377731],"domain_scores_gemma":[0.9992248,0.0000978467,0.0001933879,0.00011838734,0.00018004488,0.00018557771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016800404,0.00017471764,0.00023651263,0.00031316857,0.00006325939,0.00009464298,0.00025811972,0.00003418541,0.000007355169],"category_scores_gemma":[0.000008768199,0.00012782901,0.000049001843,0.0003574523,0.000019031046,0.0007541581,0.000052668147,0.0002729875,3.1135025e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005889391,0.0006691126,0.00053213857,0.00009236663,0.00015254342,0.00057285675,0.0037753556,0.025480965,0.034885433,0.0011527134,0.000073869036,0.9320237],"study_design_scores_gemma":[0.0011419181,0.0005583388,0.00070780306,0.00015351223,0.000018332485,0.0001098928,0.000006894135,0.98064727,0.016313704,0.00017969632,0.000003401356,0.00015926614],"about_ca_topic_score_codex":0.000006041841,"about_ca_topic_score_gemma":9.073012e-8,"teacher_disagreement_score":0.9551663,"about_ca_system_score_codex":0.000088750196,"about_ca_system_score_gemma":0.00007274424,"threshold_uncertainty_score":0.5212716},"labels":[],"label_agreement":null},{"id":"W1969200843","doi":"10.1142/s0218001414550027","title":"AN UNSUPERVISED COLOR-TEXTURE SEGMENTATION USING TWO-STAGE FUZZY c-MEANS ALGORITHM","year":2014,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Initialization; Artificial intelligence; Computer science; Image segmentation; Segmentation; Pattern recognition (psychology); Scale-space segmentation; Pixel; Centroid; Segmentation-based object categorization; Cluster analysis; Fuzzy logic; Feature (linguistics); Algorithm; Computer vision","score_opus":0.07579680939473107,"score_gpt":0.359721079520662,"score_spread":0.2839242701259309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969200843","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08217462,0.000016407022,0.9163272,0.0004148501,0.00083646463,0.0001048338,0.000022405009,0.000047392314,0.000055830336],"genre_scores_gemma":[0.68362164,0.000070945425,0.31362003,0.002011843,0.0006126458,0.00000483873,0.00003482917,0.0000130834105,0.000010169011],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980024,0.00023477168,0.00070551975,0.00024868004,0.0006500715,0.00015853078],"domain_scores_gemma":[0.998307,0.00012903949,0.00046276156,0.00013426201,0.0007823425,0.00018461386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076874736,0.00014796111,0.0001715505,0.00030544418,0.00008775105,0.00042820355,0.0006364807,0.00006223901,0.00022147356],"category_scores_gemma":[0.00010282526,0.0001382288,0.00007434241,0.00014920995,0.00009236858,0.0013383572,0.00006950571,0.00022273419,0.000031969324],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014153839,0.00011779982,0.00013017512,0.0000051593947,0.000026570568,0.00003123721,0.00059709814,0.00017613482,0.032897606,0.00025565646,0.000012402061,0.96573603],"study_design_scores_gemma":[0.00025978737,0.00039674787,0.00015006137,0.00016175039,0.000021917025,0.00021628248,0.000765504,0.6963848,0.2769753,0.02431117,0.00008500481,0.00027167113],"about_ca_topic_score_codex":0.000060749597,"about_ca_topic_score_gemma":0.000017278104,"teacher_disagreement_score":0.96546435,"about_ca_system_score_codex":0.00006948364,"about_ca_system_score_gemma":0.00004915867,"threshold_uncertainty_score":0.5636807},"labels":[],"label_agreement":null},{"id":"W1969576961","doi":"10.1016/j.neuroimage.2004.12.052","title":"Cortical thickness analysis in autism with heat kernel smoothing","year":2005,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":331,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Mental Health; University of Wisconsin-Madison","keywords":"Smoothing; Geodesic; Heat kernel; Kernel (algebra); Mathematics; Kernel smoother; Euclidean distance; Noise (video); Computer science; Artificial intelligence; Pattern recognition (psychology); Kernel method; Mathematical analysis; Statistics; Combinatorics; Radial basis function kernel; Support vector machine","score_opus":0.01527490330966017,"score_gpt":0.2802562558993492,"score_spread":0.264981352589689,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969576961","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040985625,0.000020892989,0.9536896,0.0032566271,0.000037482372,0.00012526588,7.857436e-7,0.00033896056,0.0015447828],"genre_scores_gemma":[0.7676068,0.00000920399,0.22877769,0.0034004422,0.000021048894,0.000015081972,0.0000021851545,0.000009708358,0.00015784921],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837023,0.0001669232,0.00027134008,0.00045347612,0.00045634128,0.00028171044],"domain_scores_gemma":[0.9991221,0.00014749376,0.000040908242,0.00052671134,0.000030256586,0.00013253356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003553421,0.00012739889,0.00020915085,0.00028881594,0.0000622633,0.00016739558,0.000584527,0.000047906393,0.0001005935],"category_scores_gemma":[0.00008687751,0.00010687379,0.00005464045,0.0012375971,0.000095114236,0.00072649034,0.00015683104,0.0003702524,0.000043848766],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013385531,0.002384166,0.1285929,0.00015442469,0.00044774648,0.0052242675,0.012361596,0.006021319,0.20889212,0.038193084,0.006835493,0.59075904],"study_design_scores_gemma":[0.0012494993,0.00022732116,0.47196418,0.00005393811,0.000119962584,0.00010462405,0.000038868868,0.48725212,0.036880206,0.0005718433,0.0009289376,0.0006085083],"about_ca_topic_score_codex":0.00007797518,"about_ca_topic_score_gemma":0.000070758266,"teacher_disagreement_score":0.72662115,"about_ca_system_score_codex":0.000048168138,"about_ca_system_score_gemma":0.000043171716,"threshold_uncertainty_score":0.43581867},"labels":[],"label_agreement":null},{"id":"W1969811245","doi":"10.1117/12.595972","title":"Localization of perfusion abnormalities in brain SPECT imaging (Honorable Mention Poster Award)","year":2005,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Atlas (anatomy); Centroid; Artificial intelligence; Computer science; Brain atlas; Image registration; Computer vision; Histogram; Single-photon emission computed tomography; Spect imaging; Cerebral blood flow; Pattern recognition (psychology); Nuclear medicine; Medicine; Image (mathematics)","score_opus":0.008404414403388526,"score_gpt":0.24031141838118114,"score_spread":0.23190700397779263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969811245","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96431965,0.0001380423,0.027741542,0.0054770024,0.0001516133,0.00051576266,0.000010735165,0.00012280054,0.0015228757],"genre_scores_gemma":[0.515619,0.00017716397,0.48271447,0.0007465226,0.00028197342,0.00012553677,0.000014152183,0.000048950948,0.00027226246],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99775916,5.6494322e-8,0.00085257244,0.0003529231,0.00071992504,0.00031535086],"domain_scores_gemma":[0.99825233,0.00012486849,0.0004311625,0.00007451811,0.0010337444,0.00008335932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010928737,0.00022481362,0.00031904047,0.00021534499,0.000055704095,0.00012822878,0.0010332545,0.00009278867,0.000019370638],"category_scores_gemma":[0.00043710132,0.0002003683,0.0003035168,0.00045779682,0.0001764135,0.0016790307,0.00026552702,0.00021058398,0.0000012955576],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057403828,0.0002744259,0.0019600599,0.000687248,0.00010290929,1.97208e-7,0.0014042999,0.00042658183,0.6624327,0.31472647,0.008791356,0.009136396],"study_design_scores_gemma":[0.0010302496,0.00019160997,0.0005953233,0.0005894379,0.000032396183,0.000016341124,0.0009546775,0.23279032,0.75970507,0.0020322665,0.0017673483,0.00029498414],"about_ca_topic_score_codex":0.000031691736,"about_ca_topic_score_gemma":5.270105e-7,"teacher_disagreement_score":0.45497292,"about_ca_system_score_codex":0.00022543865,"about_ca_system_score_gemma":0.000037655547,"threshold_uncertainty_score":0.81707823},"labels":[],"label_agreement":null},{"id":"W1970044037","doi":"10.1016/j.cviu.2013.06.006","title":"Special issue on Shape Modeling in Medical Image Analysis","year":2013,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Hausdorff distance; Artificial intelligence; Image registration; Maxima and minima; Regularization (linguistics); Computer science; Similarity (geometry); Dice; Computer vision; Pattern recognition (psychology); Segmentation; Signed distance function; Mathematics; Image (mathematics); Statistics","score_opus":0.04090815132542817,"score_gpt":0.31780670060763627,"score_spread":0.2768985492822081,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970044037","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019932692,0.000012767354,0.990926,0.0025063113,0.00030146,0.00021777951,5.8693456e-7,0.00018668442,0.0038551337],"genre_scores_gemma":[0.22623852,0.00015639687,0.76527274,0.005839072,0.0023268864,0.00002082022,0.000013008987,0.000028264032,0.00010427561],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977483,0.0001377354,0.00044848345,0.0005957139,0.00072932214,0.00034043984],"domain_scores_gemma":[0.9990176,0.00018398941,0.0000744142,0.00034938086,0.00006179855,0.00031279877],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00057122554,0.00020546356,0.00032980388,0.00075098575,0.00014156116,0.0007679943,0.0005792894,0.00010318446,0.0018888101],"category_scores_gemma":[0.00006700398,0.00017445027,0.00009470391,0.0009025726,0.00011860037,0.0012765093,0.00045254437,0.00030567872,0.00011652612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019391737,0.00028611915,0.00014077805,0.000053967397,0.000109192704,0.0003210676,0.001907278,0.00012428028,0.0013936803,0.008042221,0.06217055,0.9254315],"study_design_scores_gemma":[0.00047469191,0.000118253076,0.00016612714,0.000091262096,0.0000105940835,0.000008605806,0.00013936231,0.9946524,0.00025658886,0.0037712452,0.000111459514,0.00019942224],"about_ca_topic_score_codex":0.00003456495,"about_ca_topic_score_gemma":0.0000067635638,"teacher_disagreement_score":0.9945281,"about_ca_system_score_codex":0.00016967337,"about_ca_system_score_gemma":0.00003324036,"threshold_uncertainty_score":0.9990236},"labels":[],"label_agreement":null},{"id":"W1970950289","doi":"10.1109/icip.2012.6466852","title":"Convex relaxation for image segmentation by kernel mapping","year":2012,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Electric (Canada); Robarts Clinical Trials; University of Alberta","funders":"","keywords":"Image segmentation; Kernel (algebra); Computer vision; Regular polygon; Artificial intelligence; Segmentation; Computer science; Scale-space segmentation; Image (mathematics); Segmentation-based object categorization; Region growing; Pattern recognition (psychology); Mathematics; Combinatorics; Geometry","score_opus":0.022095965833544725,"score_gpt":0.29756368257756133,"score_spread":0.2754677167440166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970950289","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00074550376,0.000047224938,0.9953658,0.00069941033,0.0002085202,0.00040938245,0.0000026297416,0.00039539617,0.0021261207],"genre_scores_gemma":[0.036503863,0.000011009477,0.9597896,0.0018659902,0.00006646006,0.00014428628,0.000043730866,0.000007985982,0.0015671236],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99916315,0.000038033086,0.00020749326,0.00016489004,0.0002089887,0.0002174473],"domain_scores_gemma":[0.9994284,0.000099299315,0.000107850945,0.00018399669,0.00007380155,0.00010663145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045593976,0.00007724518,0.000076885895,0.000057174882,0.00007163674,0.0000905343,0.00023146239,0.000040695784,0.00012341437],"category_scores_gemma":[0.00009555737,0.000070754315,0.000031803156,0.00014026376,0.000027191641,0.0016432854,0.000054980846,0.000045972814,0.00007535484],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027297135,0.000107102445,0.00060762744,0.000042138134,0.000013961371,3.2748525e-7,0.0015839221,1.889019e-7,0.5218578,0.011109435,0.25534362,0.20933117],"study_design_scores_gemma":[0.00045985996,0.000044087254,0.0006205475,0.000010546433,0.0000045239976,0.0000042044344,0.00021809402,0.009320569,0.98333865,0.0014784737,0.00432211,0.00017831412],"about_ca_topic_score_codex":0.000011664762,"about_ca_topic_score_gemma":1.981248e-7,"teacher_disagreement_score":0.4614809,"about_ca_system_score_codex":0.000056150115,"about_ca_system_score_gemma":0.000013940711,"threshold_uncertainty_score":0.28852773},"labels":[],"label_agreement":null},{"id":"W1971184062","doi":"10.1007/s11760-010-0178-4","title":"Geometric image registration under arbitrarily-shaped locally variant illuminations","year":2010,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Pixel; Computer vision; Convergence (economics); Artificial intelligence; Image registration; Image (mathematics); Computer science; Shading; Rate of convergence; Mathematics; Algorithm; Key (lock); Computer graphics (images)","score_opus":0.014817270518095822,"score_gpt":0.2845762983336264,"score_spread":0.26975902781553057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971184062","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014065908,0.00012225342,0.99320716,0.0014459584,0.00008949747,0.0002053986,0.000002361563,0.00035581252,0.0031649487],"genre_scores_gemma":[0.46459606,0.000016440092,0.5337336,0.0012393807,0.00010609587,0.00002855799,0.000010266421,0.000014501707,0.00025512752],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99825037,0.00006742981,0.00039595034,0.0005085286,0.00046733068,0.00031037253],"domain_scores_gemma":[0.9987466,0.00016271092,0.0002142151,0.00029637755,0.0003767562,0.00020333809],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008112123,0.00018943699,0.00017399566,0.00034914422,0.00035648537,0.0010767786,0.00046223734,0.00011247469,0.00013708037],"category_scores_gemma":[0.00034239018,0.00017463793,0.000044628803,0.0009459752,0.00037828958,0.0029669844,0.00013955694,0.00043811006,0.00002398971],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000040021555,0.000092063034,0.000011601583,0.00007463472,0.000008286393,0.00006219916,0.0003786219,5.4307935e-7,0.6641407,0.0026631197,0.0007691422,0.33179507],"study_design_scores_gemma":[0.0016055829,0.00032393486,0.0028124584,0.0002570542,0.00008680604,0.00060698466,0.0005718808,0.2085244,0.7327192,0.050318185,0.0008947908,0.0012787501],"about_ca_topic_score_codex":0.00003643434,"about_ca_topic_score_gemma":0.0000120508,"teacher_disagreement_score":0.46318945,"about_ca_system_score_codex":0.000025491328,"about_ca_system_score_gemma":0.00023938136,"threshold_uncertainty_score":0.9999602},"labels":[],"label_agreement":null},{"id":"W1971784657","doi":"10.1117/12.410795","title":"&lt;title&gt;Shading- and highlight-invariant color image segmentation using the MPC algorithm&lt;/title&gt;","year":2000,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"RGB color model; Pixel; Segmentation; Invariant (physics); Artificial intelligence; Algorithm; Image segmentation; Shading; Computer science; Color space; Mathematics; Computer vision; Matrix similarity; Pattern recognition (psychology); Image (mathematics); Computer graphics (images)","score_opus":0.01059983592411176,"score_gpt":0.2427153952229786,"score_spread":0.23211555929886685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971784657","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.915228,0.0002925854,0.049842898,0.00499181,0.0005471904,0.0010450692,0.000045078392,0.00035789187,0.027649451],"genre_scores_gemma":[0.00921802,0.00023462868,0.9886786,0.00032789438,0.00032505882,0.000066987166,0.000005384091,0.000034668257,0.0011088077],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987321,4.3504187e-8,0.0003325768,0.00025135558,0.00047683148,0.00020707567],"domain_scores_gemma":[0.9992174,0.00007436302,0.00015296135,0.000056419867,0.00042218599,0.0000766801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003907107,0.00016019319,0.00017155666,0.00005988066,0.00008473745,0.0001725283,0.00066906045,0.000090319336,0.00015982696],"category_scores_gemma":[0.00010982706,0.00011793237,0.00015602297,0.00023917783,0.0001688054,0.0004866557,0.00011796583,0.00015235758,0.000012889359],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000099984345,0.000053460386,0.00000830688,0.0001140265,0.00013334525,3.1727208e-7,0.00023101765,0.0000075055937,0.62530506,0.33651328,0.022006722,0.015616994],"study_design_scores_gemma":[0.0011068524,0.0002901141,0.000200405,0.0003309098,0.00016548319,0.00007048354,0.0003121751,0.52021945,0.43841133,0.0048061674,0.03347875,0.000607876],"about_ca_topic_score_codex":0.0000032737632,"about_ca_topic_score_gemma":3.1130327e-8,"teacher_disagreement_score":0.9388357,"about_ca_system_score_codex":0.00009065722,"about_ca_system_score_gemma":0.000027107531,"threshold_uncertainty_score":0.48091426},"labels":[],"label_agreement":null},{"id":"W1972154060","doi":"10.1117/12.2043135","title":"Motion and deformation compensation for freehand prostate biopsies","year":2014,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Computer vision; Computer science; Artificial intelligence; Image registration; Volume (thermodynamics); Motion compensation; Prostate biopsy; Free-form deformation; Prostate; Deformation (meteorology); Medicine; Geology; Image (mathematics)","score_opus":0.010555662953752883,"score_gpt":0.23317052319949697,"score_spread":0.2226148602457441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972154060","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87090784,0.000024092611,0.12540832,0.0022734466,0.0001438111,0.00074858154,0.000015293332,0.00013955729,0.0003390791],"genre_scores_gemma":[0.28031012,0.000043432774,0.7189562,0.0001805137,0.00016843993,0.00024693334,0.000015225698,0.000023783074,0.00005537094],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984192,2.3779362e-8,0.00054485793,0.0003005129,0.00049611187,0.00023927503],"domain_scores_gemma":[0.9979392,0.00017383942,0.00038244322,0.000054037406,0.0013592055,0.000091229056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095284905,0.00019156541,0.00024730182,0.00010715308,0.00010171818,0.00021223267,0.000645655,0.00011186161,0.0000020658056],"category_scores_gemma":[0.0007188401,0.00015829636,0.00020447902,0.00021018417,0.00016930167,0.0012510066,0.00014789628,0.00013024926,5.9960377e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029032824,0.00005217891,0.00017227075,0.00051591336,0.00008238233,8.507911e-9,0.0004397297,0.000027821308,0.40317288,0.5821796,0.0012872656,0.01204094],"study_design_scores_gemma":[0.0013107102,0.00048351387,0.0015720166,0.00021398887,0.00006413388,0.000011449745,0.00041214755,0.4431688,0.5392168,0.012223726,0.0010202987,0.00030242853],"about_ca_topic_score_codex":0.0000050558842,"about_ca_topic_score_gemma":9.521568e-8,"teacher_disagreement_score":0.5935478,"about_ca_system_score_codex":0.00007988531,"about_ca_system_score_gemma":0.000013535207,"threshold_uncertainty_score":0.6455138},"labels":[],"label_agreement":null},{"id":"W1972239682","doi":"10.1016/j.ijleo.2014.06.176","title":"Medical image ensemble registration based on Gaussian mixture model and color component regularization","year":2014,"lang":"en","type":"article","venue":"Optik","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Linyi University; China Scholarship Council; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Computer vision; Image registration; Regularization (linguistics); Gaussian; Pattern recognition (psychology); Component (thermodynamics); Color image; Mixture model; Image (mathematics); Image processing; Physics","score_opus":0.009589075351714455,"score_gpt":0.2584697169958836,"score_spread":0.24888064164416915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972239682","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00096370635,0.0000048526617,0.98499304,0.006510345,0.000058817168,0.00017130365,0.0000010214995,0.00020089469,0.007096028],"genre_scores_gemma":[0.2971135,0.00000807547,0.6987619,0.0035743907,0.000046777954,0.000025978165,0.000033931,0.000009452601,0.00042599562],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986324,0.000114119415,0.00019883034,0.00031044023,0.00060225313,0.00014201399],"domain_scores_gemma":[0.9992222,0.0000848193,0.00009012598,0.00036299217,0.00005796618,0.000181848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005685284,0.00010424489,0.0001147219,0.00006952978,0.0000914754,0.00013214732,0.00029119372,0.00011004053,0.000028388193],"category_scores_gemma":[0.00023246791,0.00009124096,0.000022458451,0.00011905323,0.00008651928,0.00026327634,0.000057669633,0.00013301529,0.0000114097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009318738,0.00070591137,0.00009800968,0.0001998561,0.000021206188,0.00006646424,0.0008272706,0.0036214634,0.21691437,0.41283277,0.054567136,0.31005237],"study_design_scores_gemma":[0.0003387822,0.00011256436,0.00014358164,0.000044712273,0.0000030423882,0.0000050602234,0.0000028355848,0.96863806,0.027443888,0.0029378382,0.00023080289,0.00009881692],"about_ca_topic_score_codex":0.0000061299656,"about_ca_topic_score_gemma":0.0000038405988,"teacher_disagreement_score":0.9650166,"about_ca_system_score_codex":0.000032210137,"about_ca_system_score_gemma":0.000069511516,"threshold_uncertainty_score":0.37206984},"labels":[],"label_agreement":null},{"id":"W1972519108","doi":"10.1016/s0895-6111(00)00075-6","title":"A multiscale optimization approach for the dynamic contour-based boundary detection issue","year":2001,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Active contour model; Computer science; Segmentation; Image segmentation; Energy minimization; Context (archaeology); Boundary (topology); Minification; Mathematical optimization; Artificial intelligence; Algorithm; Mathematics","score_opus":0.01143591766278341,"score_gpt":0.2803534942312576,"score_spread":0.2689175765684742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972519108","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011024747,0.0006291906,0.9923386,0.0054839603,0.00042648034,0.000519247,0.0000021724577,0.00045890134,0.000031173706],"genre_scores_gemma":[0.03907436,0.00051616924,0.9524718,0.0074918126,0.00017136506,0.00018013407,0.00003622969,0.000020478219,0.000037666392],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998345,0.00012938214,0.00031371642,0.0004257391,0.00049884286,0.00028731037],"domain_scores_gemma":[0.99862367,0.00052458205,0.00010965734,0.0003627233,0.00013696833,0.00024238188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008512908,0.00017181538,0.00020041082,0.00014289464,0.00042859465,0.00031800425,0.0005939521,0.00009434905,0.0000136570525],"category_scores_gemma":[0.00025180486,0.000127509,0.00008488125,0.00040822232,0.00043528544,0.00028738444,0.00014365817,0.00026905054,6.2367747e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003070689,0.00012939064,0.00007540231,0.000062464984,0.000024804769,0.0000154762,0.00017435844,0.00066904386,0.0004643988,0.00017843377,0.0009442616,0.99723125],"study_design_scores_gemma":[0.0014022411,0.00004572977,0.00012686018,0.00004069572,0.000017011618,0.000069209804,0.000021929342,0.9945812,0.00034005285,0.00025765327,0.002943263,0.00015415032],"about_ca_topic_score_codex":0.000026253309,"about_ca_topic_score_gemma":0.0000033322078,"teacher_disagreement_score":0.9970771,"about_ca_system_score_codex":0.00002485345,"about_ca_system_score_gemma":0.000083579835,"threshold_uncertainty_score":0.5199666},"labels":[],"label_agreement":null},{"id":"W1972540344","doi":"10.1109/tmi.2011.2162528","title":"Medial-Based Deformable Models in Nonconvex Shape-Spaces for Medical Image Segmentation","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Initialization; Segmentation; Maxima and minima; Artificial intelligence; Computer science; Image segmentation; Gradient descent; Pattern recognition (psychology); Mathematics; Algorithm; Computer vision; Artificial neural network","score_opus":0.029207129129201568,"score_gpt":0.30027982469702436,"score_spread":0.27107269556782276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972540344","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00088327745,0.000035378693,0.99281967,0.003591807,0.000765235,0.0006234735,0.000008725144,0.0005185829,0.0007538391],"genre_scores_gemma":[0.4028302,0.000112769376,0.5870792,0.008911257,0.00009947111,0.00082701753,0.000014363801,0.00005146894,0.000074189666],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960051,0.0001830435,0.0007648924,0.00062003284,0.0018185242,0.00060841057],"domain_scores_gemma":[0.9980419,0.00053655275,0.00013936755,0.0004329797,0.00014797862,0.00070119585],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0017803116,0.00028069312,0.00033404934,0.0005106103,0.00018118572,0.000117170544,0.0011229014,0.00019615445,0.0016474846],"category_scores_gemma":[0.00016681521,0.00025759303,0.00015571596,0.00057478424,0.00034299024,0.0017479009,0.0000106191,0.00062955054,0.000047521284],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000108887165,0.0010322499,0.000045764955,0.00015733748,0.000034352208,0.0002433785,0.0028942225,0.00035200146,0.002545838,0.00051318243,0.0016553884,0.9904174],"study_design_scores_gemma":[0.0018856426,0.00008419501,0.000016902968,0.00019802772,0.000014506817,0.00002242558,0.00015590242,0.8521508,0.14337191,0.0018064961,0.00003933459,0.0002538399],"about_ca_topic_score_codex":0.00018181883,"about_ca_topic_score_gemma":0.00008716592,"teacher_disagreement_score":0.99016356,"about_ca_system_score_codex":0.00017235609,"about_ca_system_score_gemma":0.0005859597,"threshold_uncertainty_score":0.9999876},"labels":[],"label_agreement":null},{"id":"W1972617227","doi":"10.1002/mrm.10436","title":"Automatic segmentation of the brain and intracranial cerebrospinal fluid in <i>T</i><sub>1</sub>‐weighted volume MRI scans of the head, and its application to serial cerebral and intracranial volumetry","year":2003,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"University of Glasgow","keywords":"Cerebrospinal fluid; Nuclear medicine; Segmentation; White matter; Brain size; Partial volume; Medicine; Magnetic resonance imaging; Context (archaeology); Epilepsy; Reproducibility; Radiology; Chemistry; Pathology; Computer science; Artificial intelligence","score_opus":0.0060153902309422365,"score_gpt":0.2463838372885456,"score_spread":0.24036844705760335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972617227","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9467582,0.0010624101,0.0472856,0.0034475334,0.00014935371,0.001234125,0.0000059487147,0.000022651375,0.000034168148],"genre_scores_gemma":[0.978798,0.00016383806,0.020075537,0.0007722585,0.00005642057,0.00009292525,0.0000017218795,0.000011733113,0.000027565216],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9978707,0.00034096345,0.0006635908,0.00038630143,0.0005117194,0.00022676721],"domain_scores_gemma":[0.99914753,0.00015516348,0.00019413937,0.00033706747,0.0000739834,0.00009211687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009246707,0.00016704974,0.0003230693,0.00014796338,0.000062312094,0.000022685994,0.00036593887,0.00008899659,0.00003391254],"category_scores_gemma":[0.0004996867,0.00011726989,0.000018704104,0.0009241713,0.00037600988,0.00018452635,0.00015367674,0.00020070325,0.0000010845552],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054346205,0.00009212035,0.027037987,0.00020115422,0.0000033628685,0.000003840567,0.0021574986,0.0000066525695,0.31946504,0.00053525355,0.00051166053,0.6499311],"study_design_scores_gemma":[0.0040040016,0.00091197557,0.78496957,0.0008032057,0.00002122016,0.000077115445,0.00018882702,0.12512602,0.08185389,0.0016353762,0.00017421899,0.00023457495],"about_ca_topic_score_codex":0.00018773036,"about_ca_topic_score_gemma":0.00022146586,"teacher_disagreement_score":0.7579316,"about_ca_system_score_codex":0.00005350942,"about_ca_system_score_gemma":0.000075187476,"threshold_uncertainty_score":0.47821274},"labels":[],"label_agreement":null},{"id":"W1973185594","doi":"10.4236/jbise.2014.73017","title":"Extracting and smoothing contours in mammograms using Fourier descriptors","year":2014,"lang":"en","type":"article","venue":"Journal of Biomedical Science and Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Agence Universitaire de la Francophonie","keywords":"Smoothing; Artificial intelligence; Segmentation; Computer science; Computer vision; Pattern recognition (psychology); Pixel; Noise (video); Boundary (topology); Image segmentation; Edge detection; Fourier transform; Image processing; Mathematics; Image (mathematics)","score_opus":0.011487006328964244,"score_gpt":0.2539558400589104,"score_spread":0.24246883372994618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973185594","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27294272,0.000087336775,0.7264367,0.00025536303,0.00021886111,0.000023200302,3.3561033e-8,0.000016148264,0.00001966601],"genre_scores_gemma":[0.67085564,0.000021216234,0.32895386,0.00010019188,0.00006530973,3.336883e-7,1.8907716e-8,0.0000025093948,9.493542e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986564,0.000019507164,0.00030727155,0.00013916566,0.00063872663,0.00023894494],"domain_scores_gemma":[0.99929947,0.00012134213,0.000113622766,0.00007472766,0.00007550256,0.00031534708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032825135,0.00007044851,0.00014191109,0.00038275236,0.00006851233,0.00019527582,0.00031752686,0.000039788294,0.0000019857343],"category_scores_gemma":[0.0011884888,0.000055727156,0.000015832804,0.00060153415,0.00022740972,0.0011553547,0.00011023063,0.00022141615,1.1119155e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019773083,0.00002563501,0.0011700384,0.000031358915,0.0000041015282,0.00004716725,0.0009890365,0.00005776551,0.23618966,0.0005618314,0.000030381327,0.760891],"study_design_scores_gemma":[0.0005742973,0.00020599652,0.0066263173,0.0004867661,0.0000074831555,0.0005367617,0.00032754464,0.97712207,0.012421491,0.00044668705,0.0010248206,0.00021974045],"about_ca_topic_score_codex":0.000021250202,"about_ca_topic_score_gemma":5.3800125e-7,"teacher_disagreement_score":0.9770643,"about_ca_system_score_codex":0.00006623756,"about_ca_system_score_gemma":0.00007067078,"threshold_uncertainty_score":0.22724876},"labels":[],"label_agreement":null},{"id":"W1975612142","doi":"10.1016/j.imavis.2006.10.009","title":"Watershed segmentation using prior shape and appearance knowledge","year":2006,"lang":"en","type":"article","venue":"Image and Vision Computing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":154,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Watershed; Segmentation; Artificial intelligence; Image segmentation; Scale-space segmentation; Computer science; Computer vision; Pattern recognition (psychology); Cluster analysis; Segmentation-based object categorization; Region growing; Histogram; Noise (video); Transformation (genetics); Image (mathematics)","score_opus":0.015843906942410446,"score_gpt":0.3366024530884319,"score_spread":0.32075854614602145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975612142","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25801566,0.00037258127,0.741015,0.00010767567,0.00006110423,0.00012744771,2.8510686e-7,0.00015878491,0.00014141877],"genre_scores_gemma":[0.5594302,0.0000136444905,0.44027817,0.00015946473,0.00007498972,0.0000011867758,0.0000022971258,0.0000069835164,0.00003307505],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989666,0.00007543554,0.00025590233,0.00035025273,0.00015867814,0.00019317488],"domain_scores_gemma":[0.9995655,0.000060593975,0.00009276246,0.00014389728,0.000069241745,0.000068019675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035610158,0.00011779823,0.00012996489,0.000095409014,0.0002426341,0.00035363194,0.00015757288,0.000036931302,0.000006531068],"category_scores_gemma":[0.000019603189,0.00010601659,0.000020175457,0.0001758852,0.00008120225,0.0006410131,0.00028352832,0.00008667546,0.000006736769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027775495,0.000040402876,0.0005378879,0.00006268339,0.0000030347314,0.000011039065,0.00064715993,0.0000065648064,0.25266355,0.00015500443,0.00038949004,0.7454804],"study_design_scores_gemma":[0.0005806232,0.000064177264,0.007028908,0.00017175754,0.0000062167187,0.000041658113,0.000056306522,0.8853844,0.10578523,0.0005685394,0.000107468375,0.00020476364],"about_ca_topic_score_codex":0.000036252706,"about_ca_topic_score_gemma":0.0000011118291,"teacher_disagreement_score":0.88537776,"about_ca_system_score_codex":0.00002134583,"about_ca_system_score_gemma":0.00001537689,"threshold_uncertainty_score":0.4323231},"labels":[],"label_agreement":null},{"id":"W1976879160","doi":"10.1007/s11548-008-0265-y","title":"Myocardium tracking via matching distributions","year":2008,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Joseph’s Healthcare Hamilton; St Joseph's Health Care; London Health Sciences Centre; General Electric (Canada)","funders":"","keywords":"Bhattacharyya distance; Similarity (geometry); Matching (statistics); Segmentation; Tracking (education); Boundary (topology); Level set (data structures); Similarity measure; Mathematics; Computer science; Maximization; Measure (data warehouse); Image segmentation; Algorithm; Pattern recognition (psychology); Artificial intelligence; Mathematical optimization; Statistics; Mathematical analysis; Image (mathematics); Data mining","score_opus":0.026243462212492675,"score_gpt":0.2808055804134438,"score_spread":0.2545621182009511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976879160","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051878303,0.00023840224,0.94374603,0.0019637614,0.0020552427,0.000026979196,0.0000018857988,0.000051863768,0.000037539303],"genre_scores_gemma":[0.84109724,0.00026952365,0.15687194,0.0011995959,0.0005382431,0.0000016726586,0.00000769291,0.0000047711687,0.000009344783],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9985941,0.00020482368,0.0005716024,0.00015179883,0.00033132438,0.00014636257],"domain_scores_gemma":[0.9982927,0.00074234145,0.000353687,0.00011602561,0.00037977708,0.00011548734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006340008,0.00010293827,0.00028447935,0.00029998578,0.00011359408,0.00006734664,0.0005094511,0.000075636985,0.00001277689],"category_scores_gemma":[0.00007694693,0.00008850784,0.00016793654,0.00011632169,0.00015063572,0.0006097925,0.00010826187,0.00025862406,0.000002789796],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007371115,0.00030113338,0.03262951,0.000014077095,0.0009025286,0.0047295555,0.0012190093,0.00012701146,0.005021351,0.0029369383,0.03797714,0.91406804],"study_design_scores_gemma":[0.0016565222,0.00028696447,0.7911635,0.00033866393,0.00005565579,0.16035487,0.0000361613,0.015255786,0.011073183,0.012765668,0.0062509575,0.000762093],"about_ca_topic_score_codex":0.0000038788785,"about_ca_topic_score_gemma":2.0020366e-7,"teacher_disagreement_score":0.91330594,"about_ca_system_score_codex":0.000051954252,"about_ca_system_score_gemma":0.000100775185,"threshold_uncertainty_score":0.3609245},"labels":[],"label_agreement":null},{"id":"W1976993630","doi":"10.1016/j.laa.2009.03.020","title":"A computational framework for image-based constrained registration","year":2009,"lang":"en","type":"article","venue":"Linear Algebra and its Applications","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Image registration; Transformation (genetics); Image (mathematics); Reliability (semiconductor); Uniqueness; Mathematics; Mathematical optimization; Algorithm; Computer science; Artificial intelligence; Computer vision","score_opus":0.017172494856256524,"score_gpt":0.31594819834415405,"score_spread":0.2987757034878975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976993630","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013781863,0.000055580596,0.99012256,0.008303685,0.00001614525,0.0007521981,0.000017737048,0.0002898788,0.00030440345],"genre_scores_gemma":[0.08388485,0.0000071737795,0.9124927,0.003156175,0.0000806738,0.00024684254,0.00008083601,0.0000047230105,0.000046001616],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99918795,0.000018697127,0.00022606888,0.0002793242,0.00015985512,0.00012810335],"domain_scores_gemma":[0.99914396,0.00027165544,0.00009745799,0.00020977014,0.00017474046,0.00010241269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014901879,0.000090973976,0.00009534205,0.00006183238,0.00016863912,0.000109220564,0.00023219181,0.00006363717,0.000017043762],"category_scores_gemma":[0.00012014692,0.000090208065,0.000035859885,0.00022973861,0.0000571432,0.00023210724,0.000015076822,0.00008533598,0.000013918854],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031803459,0.00007631747,0.000002211029,0.000014328236,0.0000040123864,4.0047112e-7,0.000053594853,0.00003424872,0.0020656032,0.92803794,0.0008274125,0.06888076],"study_design_scores_gemma":[0.00051755726,0.00017521353,0.0002912347,0.000025528856,0.0000122769925,0.00000835144,0.000015081699,0.41688523,0.02049093,0.55882233,0.002545213,0.0002110708],"about_ca_topic_score_codex":6.281528e-7,"about_ca_topic_score_gemma":1.5484657e-7,"teacher_disagreement_score":0.41685098,"about_ca_system_score_codex":0.000012239352,"about_ca_system_score_gemma":0.00008085525,"threshold_uncertainty_score":0.3678578},"labels":[],"label_agreement":null},{"id":"W1977764052","doi":"10.1016/j.cageo.2006.12.007","title":"An artificial neural net assisted approach to editing edges in petrographic images collected with the rotating polarizer stage","year":2007,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Artificial neural network; Petrography; Pattern recognition (psychology); Stage (stratigraphy); Segmentation; Net (polyhedron); Enhanced Data Rates for GSM Evolution; Texture (cosmology); Computer vision; Image (mathematics); Geology; Mathematics; Geometry; Mineralogy","score_opus":0.024429171924858672,"score_gpt":0.28485339856444925,"score_spread":0.2604242266395906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977764052","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20237145,0.000019126328,0.79563224,0.0007305798,0.00029362706,0.00041217468,0.000002114472,0.00030134444,0.00023734734],"genre_scores_gemma":[0.53211147,4.596439e-7,0.46639106,0.0013109036,0.00012328428,0.000025758609,0.0000044517133,0.000007737399,0.000024878567],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99701416,0.00021800853,0.00042331545,0.00076778757,0.0008769864,0.0006997616],"domain_scores_gemma":[0.9984623,0.00044470956,0.00020656665,0.0004782305,0.00014664272,0.0002615307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002270356,0.00022327132,0.0002243766,0.00053975364,0.00059189176,0.00096872763,0.002011466,0.000050768365,0.0000037812044],"category_scores_gemma":[0.00014499504,0.00015150498,0.000044051503,0.0038313193,0.0005154607,0.0010225759,0.00026749066,0.00031160886,0.000001907727],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053321186,0.0007521035,0.031940244,0.000043469463,0.000023549945,0.00014441289,0.014911787,0.002073334,0.030895736,0.002650622,0.0026945362,0.91381687],"study_design_scores_gemma":[0.0005981128,0.0012582123,0.3589307,0.000101550686,0.00001171764,0.00007314858,0.0054234485,0.61007863,0.022271555,0.0002469489,0.00019776062,0.0008081858],"about_ca_topic_score_codex":0.00032791615,"about_ca_topic_score_gemma":0.00027913222,"teacher_disagreement_score":0.9130087,"about_ca_system_score_codex":0.00004236173,"about_ca_system_score_gemma":0.000103278566,"threshold_uncertainty_score":0.9341463},"labels":[],"label_agreement":null},{"id":"W1977967056","doi":"10.1016/j.jneumeth.2014.12.003","title":"Threshold segmentation algorithm for automatic extraction of cerebral vessels from brain magnetic resonance angiography images","year":2014,"lang":"en","type":"article","venue":"Journal of Neuroscience Methods","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Nautical Research Society","funders":"National Natural Science Foundation of China","keywords":"Segmentation; Artificial intelligence; Similarity (geometry); Computer science; Magnetic resonance imaging; Pattern recognition (psychology); Image segmentation; Region growing; Sørensen–Dice coefficient; Scale-space segmentation; Medicine; Radiology; Image (mathematics)","score_opus":0.03149620817135802,"score_gpt":0.3815189369955056,"score_spread":0.3500227288241476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977967056","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042675827,0.00041745394,0.99344414,0.0006154488,0.0009393921,0.00022838674,0.00000680161,0.000054008677,0.00002680907],"genre_scores_gemma":[0.0061382153,0.00003902162,0.9927385,0.0009267286,0.00010154657,0.000013246704,6.4079467e-7,0.0000098065175,0.00003231143],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99743956,0.0006096125,0.0007549304,0.00030918192,0.000660402,0.00022629726],"domain_scores_gemma":[0.99714255,0.0012739123,0.0008596035,0.00032790974,0.00025622986,0.00013982103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031735343,0.00014003192,0.00030670292,0.00040921874,0.00009788964,0.00015404803,0.00094970275,0.000052460553,0.000011132697],"category_scores_gemma":[0.0011005446,0.00011877494,0.00023570933,0.00081421115,0.00020886607,0.0014534381,0.000079004305,0.00017606054,4.2090758e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002375748,0.000042968317,0.0000838503,0.000008428033,0.000001516035,0.0000018947482,0.0000961419,0.0000060445277,0.43676963,0.000059721588,0.000535089,0.56239235],"study_design_scores_gemma":[0.00058721553,0.0010346497,0.023729013,0.0000833112,0.000026757161,0.000056916295,0.000033906363,0.24888709,0.7145558,0.01022586,0.00063199556,0.00014747643],"about_ca_topic_score_codex":0.0000066991215,"about_ca_topic_score_gemma":1.8913026e-7,"teacher_disagreement_score":0.5622449,"about_ca_system_score_codex":0.000027419232,"about_ca_system_score_gemma":0.00006811155,"threshold_uncertainty_score":0.48435012},"labels":[],"label_agreement":null},{"id":"W1978305040","doi":"10.1118/1.2965263","title":"Accuracy and sensitivity of finite element model‐based deformable registration of the prostate","year":2008,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Princess Margaret Cancer Centre; BC Cancer Agency; University of Toronto; University of British Columbia; University Health Network","funders":"National Cancer Institute; Institute for Prostate Cancer Research; Varian Medical Systems","keywords":"Image registration; Residual; Sensitivity (control systems); Voxel; Mathematics; Magnetic resonance imaging; Nuclear medicine; Artificial intelligence; Algorithm; Computer science; Computer vision; Image (mathematics); Medicine; Radiology","score_opus":0.026887548424196477,"score_gpt":0.2818847707807032,"score_spread":0.2549972223565067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978305040","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019083805,0.000010038061,0.9797639,0.0007487161,0.000026018075,0.0001629021,0.000003784653,0.000030316918,0.00017047414],"genre_scores_gemma":[0.97451156,0.000033535245,0.024763625,0.0006360344,0.000013943333,0.000007186618,0.0000031948418,0.0000028633692,0.000028047263],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986933,0.000084451334,0.00025474757,0.00012408257,0.00074134744,0.000102058766],"domain_scores_gemma":[0.99911016,0.00023194899,0.00020308515,0.00029017494,0.0000937931,0.000070855975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005702887,0.00006254476,0.00011794457,0.000013294122,0.00005532866,0.0000064204387,0.00016986224,0.000031680276,0.0000037508912],"category_scores_gemma":[0.00044170333,0.000042446343,0.000034081753,0.00017142372,0.0002805113,0.00023396377,0.0001225576,0.00011084013,4.8446594e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005011049,0.0011122685,0.003939089,0.00061794644,0.00005005157,0.000050287334,0.0046904483,0.007987525,0.027452748,0.00936115,0.007128232,0.93756014],"study_design_scores_gemma":[0.00019445507,0.000030069712,0.00028593536,0.00003953629,0.0000026765733,0.0000026970488,0.000002513999,0.60855055,0.38826102,0.002581853,0.000012373515,0.000036340964],"about_ca_topic_score_codex":0.000038951966,"about_ca_topic_score_gemma":0.0000032989794,"teacher_disagreement_score":0.95542777,"about_ca_system_score_codex":0.000013294389,"about_ca_system_score_gemma":0.00024803818,"threshold_uncertainty_score":0.17309116},"labels":[],"label_agreement":null},{"id":"W1978756306","doi":"10.1016/j.optlaseng.2010.06.011","title":"Medical image registration using stochastic optimization","year":2010,"lang":"en","type":"article","venue":"Optics and Lasers in Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Convexity; Image registration; Divergence (linguistics); Tsallis entropy; Artificial intelligence; Entropy (arrow of time); Degenerate energy levels; Thresholding; Image (mathematics); Gaussian; Kullback–Leibler divergence; Computer vision; Algorithm; Pattern recognition (psychology); Physics","score_opus":0.0063806745546963455,"score_gpt":0.24693219327761518,"score_spread":0.24055151872291883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978756306","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008393526,0.000006365293,0.9910612,0.00011321396,0.0001708617,0.000057794794,3.166768e-7,0.00007977187,0.000116953786],"genre_scores_gemma":[0.20299661,0.000010507475,0.79690576,0.000042125634,0.000030307745,0.000003561146,0.0000019646714,0.0000051829575,0.0000039792003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944144,0.0000059121066,0.00013713639,0.0001261963,0.00018516579,0.00010417061],"domain_scores_gemma":[0.99969554,0.000045490324,0.000026979169,0.00012124179,0.000022917728,0.00008782543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024744926,0.00005909269,0.00006078944,0.00008437382,0.000021478487,0.00007678398,0.00014002748,0.00006073282,0.000014839533],"category_scores_gemma":[0.00030001142,0.00006162766,0.000008421773,0.00013754627,0.000026407703,0.00026724313,0.000048994716,0.000181545,4.0630215e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003038589,0.000072993986,0.00009502069,0.000104038554,0.000009950235,0.00009235901,0.00046289753,0.87924993,0.07375596,0.019291922,0.000050609222,0.026811264],"study_design_scores_gemma":[0.00010993815,0.000008598547,0.00004531201,0.000030853123,0.0000012865576,0.000016182037,0.000005688384,0.9970035,0.002657594,0.000052566935,0.0000025943482,0.000065893415],"about_ca_topic_score_codex":0.000009122391,"about_ca_topic_score_gemma":0.000005737563,"teacher_disagreement_score":0.19460309,"about_ca_system_score_codex":0.000009652144,"about_ca_system_score_gemma":0.000028478504,"threshold_uncertainty_score":0.2513103},"labels":[],"label_agreement":null},{"id":"W1978998928","doi":"10.1111/j.1365-2818.2006.01600.x","title":"An automatic method for identifying appropriate gradient magnitude for 3D boundary detection of confocal image stacks","year":2006,"lang":"en","type":"article","venue":"Journal of Microscopy","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Divergence theorem; Boundary (topology); Magnitude (astronomy); Edge detection; Measure (data warehouse); Divergence (linguistics); Computer science; Function (biology); Image (mathematics); Image processing; Artificial intelligence; Algorithm; Computer vision; Mathematics; Physics; Mathematical analysis; Data mining","score_opus":0.017508824842147466,"score_gpt":0.3681596609886048,"score_spread":0.35065083614645737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978998928","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02710338,0.00013549555,0.97168875,0.00007547069,0.00043664096,0.00048108638,0.000011315597,0.00005605071,0.0000118340595],"genre_scores_gemma":[0.02104599,0.000008329238,0.97863674,0.00011601502,0.00012480213,0.000024438032,0.000003560317,0.000015185913,0.000024946305],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982598,0.00013057594,0.00086278986,0.00018802975,0.00033296805,0.00022582717],"domain_scores_gemma":[0.9980587,0.00020160085,0.0008863696,0.00022485713,0.0005316236,0.000096845804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018238069,0.00012790569,0.00031842678,0.00023493501,0.000120245975,0.00024136405,0.00052536675,0.00006764654,0.000010545131],"category_scores_gemma":[0.00012792599,0.00011242132,0.0001693595,0.00015169645,0.00008444027,0.00085685484,0.000041756615,0.00014497513,8.729917e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038120925,0.00012518404,0.0000071018094,0.00018622524,0.000019477384,0.0000033904576,0.00022994698,0.000006783466,0.86923814,0.000071552175,0.00038280146,0.12969129],"study_design_scores_gemma":[0.0010613075,0.0008034064,0.00020446291,0.0000965825,0.00004384287,0.00006502206,0.00006235779,0.055465214,0.935245,0.0065641394,0.00028147834,0.000107234344],"about_ca_topic_score_codex":0.000028158267,"about_ca_topic_score_gemma":0.0000070923925,"teacher_disagreement_score":0.12958404,"about_ca_system_score_codex":0.00010347497,"about_ca_system_score_gemma":0.00012188459,"threshold_uncertainty_score":0.45844084},"labels":[],"label_agreement":null},{"id":"W1979447612","doi":"10.1016/j.neuroimage.2015.02.065","title":"Bayesian segmentation of brainstem structures in MRI","year":2015,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":292,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"NIH Blueprint for Neuroscience Research; National Institute of Mental Health; National Center for Complementary and Integrative Health; National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; University of California, San Diego; Genentech; National Institutes of Health; Diputación Foral de Gipuzkoa; National Institute of Neurological Disorders and Stroke; IXICO; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; Harvard Catalyst; Tekes; Synarc; University of Southern California; Medpace; Novartis Pharmaceuticals Corporation; Harvard University; National Center for Research Resources; F. Hoffmann-La Roche; Ellison Medical Foundation; Alzheimer's Drug Discovery Foundation; U.S. Department of Defense; Eli Lilly and Company; Tau Consortium; Bristol-Myers Squibb; Foundation for the National Institutes of Health; Alzheimer's Disease Neuroimaging Initiative; National Center for Complementary and Alternative Medicine; Meso Scale Diagnostics","keywords":"Brainstem; Pons; Segmentation; Computer science; Artificial intelligence; Midbrain; Neuroimaging; Pattern recognition (psychology); Neuroscience; Anatomy; Medicine; Psychology; Central nervous system","score_opus":0.0310069286934055,"score_gpt":0.30374621188559847,"score_spread":0.272739283192193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979447612","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0137276845,0.000021576812,0.9829032,0.00041913608,0.0001538085,0.0001852274,0.0000015005746,0.0001296536,0.0024582178],"genre_scores_gemma":[0.65263844,0.0000053798635,0.3463316,0.0009159426,0.000021150694,0.000009772209,0.0000036710105,0.000007996782,0.00006606918],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99892735,0.00013163815,0.00025107525,0.00021954069,0.00034320645,0.00012720775],"domain_scores_gemma":[0.99940026,0.000048420115,0.000094802555,0.00030680696,0.000055668865,0.00009406177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025844428,0.0000767904,0.000112385365,0.00014664252,0.000013277163,0.000042386422,0.0004191096,0.000029578494,0.000016605854],"category_scores_gemma":[0.00011973296,0.000072718656,0.000022039967,0.00032100806,0.000053376247,0.00049148203,0.00010915926,0.00009441766,0.0000067916235],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039943992,0.0002990061,0.011693219,0.00015912925,0.000014382094,0.00035935213,0.009679735,0.00031287398,0.42635354,0.013564128,0.029795347,0.50772935],"study_design_scores_gemma":[0.001605187,0.00031537376,0.018838199,0.000035523964,0.000004109378,0.000032067197,0.000267978,0.02518864,0.93943024,0.013672015,0.00034262135,0.00026804095],"about_ca_topic_score_codex":0.000031210864,"about_ca_topic_score_gemma":0.0000062511112,"teacher_disagreement_score":0.6389107,"about_ca_system_score_codex":0.000030510002,"about_ca_system_score_gemma":0.000056095567,"threshold_uncertainty_score":0.29653808},"labels":[],"label_agreement":null},{"id":"W1979901286","doi":"10.1109/tvcg.2013.240","title":"An Evaluation of Depth Enhancing Perceptual Cues for Vascular Volume Visualization in Neurosurgery","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":85,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research","keywords":"Depth perception; Perception; Computer science; Artificial intelligence; Computer vision; Visualization; Stereopsis; Contrast (vision); Psychology; Neuroscience","score_opus":0.03329687405101511,"score_gpt":0.32657872375988267,"score_spread":0.29328184970886756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979901286","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07451295,0.000017258568,0.9241408,0.000017484803,0.0002977246,0.00082428876,0.0000028993697,0.00018071337,0.000005853413],"genre_scores_gemma":[0.98546076,0.000104628554,0.01350293,0.00061191927,0.000031441545,0.00023609084,0.000025289377,0.000021018428,0.0000059446],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99776775,0.00043109007,0.0005531325,0.0004345183,0.0006234253,0.00019006548],"domain_scores_gemma":[0.998669,0.00012416924,0.00015805005,0.00030956627,0.00062858785,0.00011058012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009479209,0.00017295651,0.00022080232,0.0006614109,0.00013363016,0.00016850917,0.0002295814,0.000116203046,0.000046812933],"category_scores_gemma":[0.000018822575,0.00018312168,0.00008327638,0.0007907737,0.00007903045,0.0011747815,0.00000382762,0.00009510454,0.0000025859179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043025524,0.003073776,0.0028205626,0.00046262573,0.0001347268,0.0000017419203,0.013784643,0.012797048,0.015181031,0.1026034,0.00077428966,0.8483231],"study_design_scores_gemma":[0.0005771841,0.00030859973,0.0051032566,0.000056314184,0.000025374165,0.0000018777723,0.00008203427,0.9656635,0.027409472,0.0005813948,0.000013387265,0.00017763764],"about_ca_topic_score_codex":0.0000618249,"about_ca_topic_score_gemma":0.00006102819,"teacher_disagreement_score":0.95286644,"about_ca_system_score_codex":0.00003907554,"about_ca_system_score_gemma":0.000073404124,"threshold_uncertainty_score":0.74674857},"labels":[],"label_agreement":null},{"id":"W1980012993","doi":"10.1118/1.4734931","title":"SU‐E‐J‐95: Towards Optimum Boundary Conditions for Biomechanical Model Based Deformable Registration Using Intensity Based Image Matching for Prostate Correlative Pathology","year":2012,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Princess Margaret Cancer Centre; Toronto General Hospital; University of Toronto","funders":"","keywords":"Image registration; Magnetic resonance imaging; Ex vivo; Artificial intelligence; Computer science; Computer vision; Nuclear medicine; Biomedical engineering; In vivo; Medicine; Radiology; Image (mathematics)","score_opus":0.04456014033257506,"score_gpt":0.3391828645894667,"score_spread":0.2946227242568917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980012993","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030133154,0.000014323549,0.9938795,0.0012344007,0.0004240432,0.000995644,0.0001259346,0.0002747043,0.00003813188],"genre_scores_gemma":[0.31269935,0.000001685848,0.68345684,0.0030945926,0.00015730059,0.00023372826,0.0003160568,0.000022622333,0.000017825236],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979234,0.000090288806,0.0004633295,0.00037572297,0.0005965719,0.0005506899],"domain_scores_gemma":[0.998299,0.00033452048,0.000252916,0.00035931723,0.0003820579,0.00037220883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013585947,0.00020934106,0.0003189341,0.000063404004,0.0003535179,0.000120773744,0.00042047264,0.00017923176,0.000018379804],"category_scores_gemma":[0.00059745024,0.00019213185,0.00015490997,0.00024020027,0.00033025313,0.0011879433,0.0001278008,0.00029652865,0.000004897366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015493572,0.008795038,0.00033970023,0.0030443184,0.0003973442,0.00015078986,0.010357163,0.01488533,0.4001712,0.111543655,0.061050635,0.3877155],"study_design_scores_gemma":[0.0008661561,0.0001473272,0.000009285597,0.00006232508,0.000027275863,0.000008547926,0.00002378706,0.8412107,0.113128416,0.044291727,0.0000366838,0.00018774741],"about_ca_topic_score_codex":0.000021467025,"about_ca_topic_score_gemma":0.000001187278,"teacher_disagreement_score":0.8263254,"about_ca_system_score_codex":0.00018129993,"about_ca_system_score_gemma":0.0007556289,"threshold_uncertainty_score":0.78349096},"labels":[],"label_agreement":null},{"id":"W1981429050","doi":"10.1117/12.594574","title":"JESS: Java extensible snakes system","year":2005,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Java; Computer science; Preprocessor; Extensibility; Segmentation; Class (philosophy); Image segmentation; Java applet; Java annotation; Graphical user interface; Software; Artificial intelligence; Computer graphics (images); Programming language","score_opus":0.01226291664619395,"score_gpt":0.24258657878755366,"score_spread":0.23032366214135971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981429050","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9702443,0.00017125867,0.017202547,0.005223053,0.00034116153,0.0007004921,0.000018212444,0.00056087464,0.0055381185],"genre_scores_gemma":[0.13646585,0.00007598229,0.86190027,0.00037643843,0.00055755815,0.00019398879,0.0000041820917,0.00004866011,0.0003771035],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9970733,1.9365604e-8,0.0008607127,0.0005181988,0.0010918642,0.00045591377],"domain_scores_gemma":[0.9971534,0.00015993821,0.00045664483,0.00011710087,0.001919943,0.00019298632],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00093310483,0.00032040078,0.0004269861,0.00014713495,0.00010403385,0.00025581618,0.0021926628,0.00018394366,0.0000139929],"category_scores_gemma":[0.00049756136,0.00026369453,0.00052741467,0.0004724997,0.00022308528,0.0014436137,0.00036005548,0.00031705337,0.0000075631156],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017276243,0.00009848974,0.00005660715,0.00041851937,0.00017061255,1.6781593e-7,0.00024286393,0.00003585599,0.35011032,0.6333384,0.011707631,0.0038032343],"study_design_scores_gemma":[0.0011196819,0.00032619204,0.00028279153,0.00066383835,0.000099047356,0.000059153062,0.001365821,0.14390399,0.8431917,0.0012270717,0.0072096386,0.0005511114],"about_ca_topic_score_codex":0.000008016127,"about_ca_topic_score_gemma":9.3443305e-8,"teacher_disagreement_score":0.8446977,"about_ca_system_score_codex":0.0002621285,"about_ca_system_score_gemma":0.000049159855,"threshold_uncertainty_score":0.9999815},"labels":[],"label_agreement":null},{"id":"W1981665992","doi":"10.5244/c.24.101","title":"TV-Based Multi-Label Image Segmentation with Label Cost Prior","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Minimum description length; Segmentation; Image segmentation; Computer science; Regular polygon; Convex optimization; Regularization (linguistics); Pattern recognition (psychology); Artificial intelligence; Energy functional; Mathematical optimization; Mathematics; Algorithm","score_opus":0.02474284707216573,"score_gpt":0.32032341213872145,"score_spread":0.2955805650665557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981665992","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057710325,0.000003055122,0.9906638,0.0010244615,0.00013060172,0.0006196123,0.000003130333,0.0007674005,0.0010168672],"genre_scores_gemma":[0.009249853,0.000001653299,0.98674643,0.0029772671,0.000025539352,0.00012264395,0.000014550944,0.000014459081,0.00084761024],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99863756,0.000048717025,0.00022858533,0.00038452874,0.0004464571,0.00025417103],"domain_scores_gemma":[0.9989052,0.00009658937,0.00010839049,0.0005108218,0.00019139216,0.00018762807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003083786,0.00015435455,0.00012431321,0.0001091832,0.000114974864,0.00024453332,0.00060102687,0.00006505214,0.0004077406],"category_scores_gemma":[0.00007998157,0.00011677557,0.000019287232,0.000327131,0.00014028292,0.0008509707,0.000088170236,0.00024313445,0.00015668427],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010283772,0.00041689724,0.00039367692,0.000019382658,0.000009214437,0.000022635459,0.00017134899,7.5061496e-7,0.59470314,0.0010478649,0.0035928416,0.39961195],"study_design_scores_gemma":[0.0026042068,0.00015437327,0.00071548484,0.000015124292,0.000007041263,0.000008620328,0.000028637469,0.187981,0.8080307,0.000059552942,0.00016747192,0.0002277907],"about_ca_topic_score_codex":0.00006828686,"about_ca_topic_score_gemma":0.00009480784,"teacher_disagreement_score":0.39938414,"about_ca_system_score_codex":0.00003397134,"about_ca_system_score_gemma":0.00012923231,"threshold_uncertainty_score":0.47619697},"labels":[],"label_agreement":null},{"id":"W1982010659","doi":"10.1016/j.oceaneng.2010.03.003","title":"Sonar image segmentation based on GMRF and level-set models","year":2010,"lang":"en","type":"article","venue":"Ocean Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Gallipoli Medical Research Foundation","keywords":"Sonar; Artificial intelligence; Level set (data structures); Segmentation; Computer science; Initialization; Level set method; Pattern recognition (psychology); Image segmentation; Set (abstract data type); Energy (signal processing); Computer vision; Mathematics; Statistics","score_opus":0.01785714966511417,"score_gpt":0.24606932965488904,"score_spread":0.22821217998977486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982010659","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013130179,0.0000052310415,0.9858823,0.00020222795,0.00012126221,0.00010959198,0.0000043362797,0.00032558604,0.00021931677],"genre_scores_gemma":[0.34228736,0.0000027117428,0.6573599,0.0002638168,0.000032095068,0.000005180671,0.000006728021,0.000010899708,0.00003129348],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929816,0.000010305947,0.00011767504,0.00021358489,0.00021304592,0.00014726134],"domain_scores_gemma":[0.9995608,0.00005085303,0.00002839696,0.00022999114,0.000027575947,0.00010238884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019440448,0.00010173089,0.00007304361,0.00010838265,0.000036529455,0.00010081591,0.00021457419,0.000040277886,0.000014429226],"category_scores_gemma":[0.000036792662,0.000103351995,0.00001836155,0.00011355775,0.000019623429,0.00039231664,0.00005254059,0.00016391395,0.0000060937573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009617839,0.00011125108,0.00023415526,0.0001670335,0.00002180559,0.00006623126,0.0013156467,0.026841935,0.74499565,0.01594507,0.008051719,0.2022399],"study_design_scores_gemma":[0.00018097518,0.00003252,0.00031695902,0.000016420008,0.0000016658697,0.0000047363196,0.0000058141973,0.8691954,0.12982579,0.00022702232,0.000080001715,0.000112724796],"about_ca_topic_score_codex":0.000003434835,"about_ca_topic_score_gemma":4.860664e-7,"teacher_disagreement_score":0.84235346,"about_ca_system_score_codex":0.000015776248,"about_ca_system_score_gemma":0.000016650649,"threshold_uncertainty_score":0.4214572},"labels":[],"label_agreement":null},{"id":"W1982634576","doi":"10.1080/10255810390445274","title":"Shape Registration Using Deformable Self-Organizing Feature Maps","year":2003,"lang":"en","type":"article","venue":"International Journal of Smart Engineering System Design","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Topology (electrical circuits); Surface (topology); Computer vision; Lattice (music); Artificial intelligence; Matching (statistics); Feature (linguistics); Coordinate system; Algorithm; Pattern recognition (psychology); Geometry; Mathematics; Combinatorics","score_opus":0.017568092113663968,"score_gpt":0.24140777382130874,"score_spread":0.22383968170764476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982634576","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010588834,0.00012488349,0.99662066,0.00013402799,0.0016193221,0.00010557775,7.540524e-7,0.00018214884,0.0001537471],"genre_scores_gemma":[0.29569727,0.000009890653,0.704034,0.000059617763,0.00015336627,0.000002243444,5.790181e-7,0.000013034235,0.000030052906],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838793,0.000089842906,0.0004973898,0.00013363434,0.00072932796,0.000161869],"domain_scores_gemma":[0.99871343,0.000092745424,0.00040462794,0.0001533889,0.0005288647,0.00010695063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011957288,0.00013148916,0.00017143926,0.00029585738,0.00004374409,0.0002639755,0.0006885877,0.00007086132,0.000007768079],"category_scores_gemma":[0.00021679843,0.00012115608,0.000072895025,0.0002210324,0.000007835136,0.0010987564,0.00003413293,0.00020332843,0.0000065831837],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011326893,0.00048750217,0.0012482164,0.00083898817,0.0021034058,0.003382395,0.004449555,0.20454758,0.61154485,0.12541701,0.02988003,0.015987203],"study_design_scores_gemma":[0.00085222936,0.00018061907,0.00008665005,0.0009549366,0.000036650148,0.006754242,0.00014480346,0.6947898,0.29254764,0.00017190629,0.0031267311,0.00035375782],"about_ca_topic_score_codex":0.0000027886756,"about_ca_topic_score_gemma":8.916143e-8,"teacher_disagreement_score":0.49024224,"about_ca_system_score_codex":0.00046251933,"about_ca_system_score_gemma":0.0001624361,"threshold_uncertainty_score":0.49406016},"labels":[],"label_agreement":null},{"id":"W1982668309","doi":"10.1118/1.4871620","title":"Vision 20/20: Perspectives on automated image segmentation for radiotherapy","year":2014,"lang":"en","type":"review","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":376,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Philips (Canada)","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health","keywords":"Computer vision; Image segmentation; Medical imaging; Radiation therapy; Artificial intelligence; Image-guided radiation therapy; Computer science; Segmentation; Medical physics; Image processing; Image (mathematics); Medicine; Radiology","score_opus":0.025774957337371448,"score_gpt":0.4028835782834476,"score_spread":0.37710862094607617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982668309","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[5.168173e-8,0.29363376,0.70345014,0.00028065173,0.00031347448,0.0010410369,0.000017869179,0.0010722239,0.000190771],"genre_scores_gemma":[7.1844164e-7,0.851567,0.14563182,0.0009253823,0.00089011533,0.0004473633,0.00027795637,0.000070436916,0.00018924968],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.996203,0.0003857242,0.00072331174,0.0008911805,0.0014019109,0.00039486476],"domain_scores_gemma":[0.9972801,0.0009437598,0.00049688347,0.0007788153,0.00015612044,0.00034427995],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00093240355,0.0004863004,0.0012723887,0.00015089755,0.00014024027,0.00020836321,0.0014300469,0.00036732288,0.000122028105],"category_scores_gemma":[0.0004011134,0.00037135897,0.0004892991,0.0004932703,0.00023615597,0.0003738951,0.00014184546,0.00052408763,0.000116226984],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000022125248,0.00019480416,2.541164e-8,0.0020731743,0.000059116042,0.000005970891,0.00023260328,2.3628418e-7,0.0000141659575,0.0007629709,0.041029997,0.9556247],"study_design_scores_gemma":[0.0014858438,0.001267882,4.2227117e-7,0.011674985,0.00020481757,0.000020179217,0.000032503052,0.028106121,0.0014668279,0.0025143777,0.95207703,0.0011490159],"about_ca_topic_score_codex":0.0000026135037,"about_ca_topic_score_gemma":2.951664e-7,"teacher_disagreement_score":0.9544757,"about_ca_system_score_codex":0.00033829678,"about_ca_system_score_gemma":0.00037700444,"threshold_uncertainty_score":0.9998738},"labels":[],"label_agreement":null},{"id":"W1983019361","doi":"10.1371/journal.pone.0060344","title":"aBEAT: A Toolbox for Consistent Analysis of Longitudinal Adult Brain MRI","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; National Key Research and Development Program of China; University of California, San Diego; National Institutes of Health; Genentech; IXICO; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; University of California, Los Angeles; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; Alzheimer's Association; Amorfix Life Sciences; F. Hoffmann-La Roche; Medpace; AstraZeneca; Eli Lilly and Company; Bristol-Myers Squibb; Novartis Pharmaceuticals Corporation; Synarc; Bayer HealthCare; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Foundation for the National Institutes of Health","keywords":"Computer science; Segmentation; Toolbox; Image warping; Artificial intelligence; Image segmentation; Pattern recognition (psychology); Image processing; Computer vision; Image (mathematics)","score_opus":0.04666778301999156,"score_gpt":0.27403583043375757,"score_spread":0.227368047413766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983019361","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0381463,0.000027191929,0.957141,0.0036670095,0.000015176475,0.0005116991,0.000010676241,0.00013019591,0.00035075666],"genre_scores_gemma":[0.22251093,0.000011484333,0.7750061,0.0016120585,0.00002184718,0.00028335984,0.000017675191,0.0000063155685,0.0005302114],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988258,0.000037929476,0.00030826987,0.00025909615,0.00040080203,0.00016809683],"domain_scores_gemma":[0.99861914,0.0002733116,0.00013358919,0.00041799323,0.0004571034,0.00009884536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018693879,0.00008313794,0.0002939032,0.00021389769,0.000034311633,0.00006328432,0.000416908,0.00003826751,0.00023032821],"category_scores_gemma":[0.00036516853,0.0000749529,0.00011921195,0.0005738454,0.000068234025,0.00028397763,0.00007391651,0.00005221472,0.000026384349],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059364054,0.011028245,0.0486639,0.0012872753,0.017942052,0.000022201562,0.0043015387,0.000042675074,0.6459164,0.04287819,0.09990495,0.12795322],"study_design_scores_gemma":[0.0008658358,0.00052887044,0.025513738,0.00017213968,0.0012132376,0.0000011848628,0.000078383724,0.22025472,0.74870074,0.0022767312,0.000033638244,0.00036078616],"about_ca_topic_score_codex":0.00008775514,"about_ca_topic_score_gemma":0.000013495393,"teacher_disagreement_score":0.22021204,"about_ca_system_score_codex":0.000026112362,"about_ca_system_score_gemma":0.00002858612,"threshold_uncertainty_score":0.30564904},"labels":[],"label_agreement":null},{"id":"W1984289039","doi":"10.1007/s11263-009-0241-1","title":"Simplex Mesh Diffusion Snakes: Integrating 2D and 3D Deformable Models and Statistical Shape Knowledge in a Variational Framework","year":2009,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital","funders":"","keywords":"Segmentation; Artificial intelligence; Computer science; Computer vision; Pixel; Simplex; Image segmentation; Tracking (education); Level set (data structures); Process (computing); Pattern recognition (psychology); Mathematics; Geometry","score_opus":0.015874682765809445,"score_gpt":0.3394505231651412,"score_spread":0.32357584039933174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984289039","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021697206,0.00013217956,0.97647476,0.0010816737,0.0003431906,0.00008441354,0.0000029622452,0.000030074376,0.00015352627],"genre_scores_gemma":[0.44753236,0.000083286126,0.55160475,0.00062476215,0.00014373819,8.619245e-7,0.0000033362614,0.0000034988796,0.0000034217028],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981839,0.00011652595,0.00066957244,0.00022549172,0.0006463927,0.0001580901],"domain_scores_gemma":[0.9985906,0.0004986164,0.00028344334,0.00010479366,0.0003754416,0.00014713114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067792326,0.00014094956,0.00022231668,0.00038148812,0.000055152155,0.00035471653,0.0005436691,0.000082598104,0.000036131893],"category_scores_gemma":[0.000110424146,0.000113902104,0.00003716231,0.0001424953,0.000049376406,0.0014616149,0.00025884775,0.00035749274,0.0000017532108],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004702163,0.00019873379,0.00013207863,0.0000072767843,0.000017988497,0.00007876341,0.0013937694,0.00036035056,0.00039456107,0.07263775,0.00056741945,0.9241643],"study_design_scores_gemma":[0.00061017484,0.00038303778,0.007168602,0.00035934107,0.0000037582981,0.00023167026,0.000021015789,0.90507823,0.00011114065,0.085795045,0.00013093177,0.00010702932],"about_ca_topic_score_codex":0.000006400672,"about_ca_topic_score_gemma":0.000001677902,"teacher_disagreement_score":0.92405725,"about_ca_system_score_codex":0.000097428325,"about_ca_system_score_gemma":0.00008186803,"threshold_uncertainty_score":0.4644793},"labels":[],"label_agreement":null},{"id":"W1984514956","doi":"10.1109/ccece.2014.6901112","title":"Image co-registration by minimizing cumulative distortion","year":2014,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Image registration; Computer science; Distortion (music); Image (mathematics); A priori and a posteriori; Artificial intelligence; Computer vision; Image restoration; Set (abstract data type); Algorithm; Image processing","score_opus":0.014678839345453604,"score_gpt":0.3079866566747851,"score_spread":0.2933078173293315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984514956","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00058306986,0.000004929777,0.97593546,0.00064856594,0.00006412996,0.000103831495,9.469959e-7,0.0003982813,0.022260763],"genre_scores_gemma":[0.22168343,0.0000033628698,0.77558917,0.00097372016,0.000041767984,0.000020042096,0.00003427934,0.000006027288,0.0016482075],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99910665,0.000079975885,0.00019892599,0.00022222036,0.00027449662,0.000117722746],"domain_scores_gemma":[0.9994487,0.00008031421,0.00009684085,0.00024494386,0.000054084816,0.00007510091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032747452,0.00007318001,0.00007540936,0.000038408438,0.000067095665,0.00012034452,0.00026189018,0.000033037373,0.00009688687],"category_scores_gemma":[0.00013419663,0.000065037915,0.000024981966,0.000105796,0.00005556646,0.0007951734,0.00003434432,0.000058813162,0.000081697974],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046540713,0.00012194741,0.0002276261,0.000022811131,0.000010382004,0.0000023882214,0.000849964,0.000001671168,0.28357208,0.018515581,0.38063437,0.31603652],"study_design_scores_gemma":[0.00038854947,0.00014984074,0.000833337,0.000016986929,0.000004418177,0.0000037881616,0.000043103722,0.13074431,0.85639447,0.0038993638,0.0072499746,0.0002718494],"about_ca_topic_score_codex":0.000037158017,"about_ca_topic_score_gemma":0.0000024770814,"teacher_disagreement_score":0.5728224,"about_ca_system_score_codex":0.000040949355,"about_ca_system_score_gemma":0.000012948786,"threshold_uncertainty_score":0.26521692},"labels":[],"label_agreement":null},{"id":"W1984682938","doi":"10.1049/iet-cvi.2013.0149","title":"Bhattacharyya distance‐based irregular pyramid method for image segmentation","year":2014,"lang":"en","type":"article","venue":"IET Computer Vision","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Bhattacharyya distance; Robustness (evolution); Artificial intelligence; Computer science; Segmentation; Image segmentation; Pattern recognition (psychology); Pyramid (geometry); Similarity (geometry); Computer vision; Algorithm; Image (mathematics); Mathematics","score_opus":0.012115235619187698,"score_gpt":0.3454734067000522,"score_spread":0.3333581710808645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984682938","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011810413,0.000018897135,0.99604046,0.0017947932,0.00061751134,0.00064231176,0.0000065494223,0.000661399,0.00009998525],"genre_scores_gemma":[0.007089705,0.0000028379084,0.98901826,0.0034037705,0.00023701436,0.00008948446,0.00007152585,0.00002367413,0.00006373938],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99779475,0.000281832,0.00041454603,0.0006648138,0.0005157049,0.00032836132],"domain_scores_gemma":[0.99828756,0.00044825798,0.00019491269,0.0007034136,0.00019531099,0.00017052214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010851444,0.00022511357,0.0002595368,0.00015929274,0.00017003114,0.00036847507,0.00083179935,0.00008643302,0.000029325196],"category_scores_gemma":[0.00005084921,0.0002064697,0.00013715608,0.00028453185,0.000066243534,0.00087039516,0.00019760692,0.000121974816,0.000039327166],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002217878,0.00014147286,0.000013976912,0.00009446749,0.000011952612,0.0000058129053,0.00018026237,0.00018721822,0.044221498,0.003429128,0.028345754,0.9233463],"study_design_scores_gemma":[0.0008568781,0.00045054394,0.00018836267,0.00006006757,0.000008123577,0.0000047043627,0.0000031199595,0.83115005,0.15713117,0.003945175,0.00597693,0.00022490506],"about_ca_topic_score_codex":0.0000055165297,"about_ca_topic_score_gemma":0.0000010393846,"teacher_disagreement_score":0.9231214,"about_ca_system_score_codex":0.00007870959,"about_ca_system_score_gemma":0.000039445167,"threshold_uncertainty_score":0.841959},"labels":[],"label_agreement":null},{"id":"W1984690585","doi":"10.1002/jmri.23612","title":"A novel MRI‐compatible brain ventricle phantom for validation of segmentation and volumetry methods","year":2012,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lawson Health Research Institute; Western University","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute on Aging","keywords":"Imaging phantom; Ventricle; Segmentation; Nuclear medicine; Magnetic resonance imaging; Medicine; Cerebral ventricle; Biomedical engineering; Voxel; Computer science; Radiology; Artificial intelligence; Anatomy; Internal medicine","score_opus":0.024487209034942614,"score_gpt":0.36180507372611553,"score_spread":0.3373178646911729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984690585","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0125828935,0.008232008,0.9778584,0.0008331166,0.00023463093,0.0002053563,0.0000022388333,0.000017692402,0.000033637752],"genre_scores_gemma":[0.05211019,0.00009887413,0.94734514,0.00029211465,0.000093921335,0.000008780001,8.6156064e-7,0.000008091915,0.000042023566],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986298,0.00013420156,0.00055962574,0.000121765,0.00034987857,0.00020469693],"domain_scores_gemma":[0.998451,0.0004817546,0.0005598961,0.00014600687,0.00025037478,0.00011097566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023055677,0.00009157177,0.00020736664,0.00022941842,0.000049235416,0.0000727758,0.00027964445,0.000022903041,0.000020720869],"category_scores_gemma":[0.00049003435,0.00008413864,0.00006126131,0.00031307744,0.000060456587,0.0012644681,0.00007007648,0.00009497045,4.860393e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000124862145,0.0000838151,0.005369643,0.00003969827,0.000003726647,8.445477e-7,0.0006574867,0.000003736432,0.21491791,0.00014106945,0.001350335,0.77741927],"study_design_scores_gemma":[0.001976075,0.00032232882,0.027309276,0.0001792635,0.000038507133,0.00020149826,0.00023396434,0.04587151,0.9195651,0.0011567908,0.0029692403,0.0001764534],"about_ca_topic_score_codex":0.00000890511,"about_ca_topic_score_gemma":1.0135608e-7,"teacher_disagreement_score":0.7772428,"about_ca_system_score_codex":0.0000448017,"about_ca_system_score_gemma":0.000046993813,"threshold_uncertainty_score":0.34310743},"labels":[],"label_agreement":null},{"id":"W1985179035","doi":"10.1109/cjece.2009.5291208","title":"Thickness analysis and reconstruction of trabecular bone and bone substitute microstructure based on fuzzy distance map using both ridge and thinning skeletonization","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Skeletonization; Ridge; Computer science; Distance transform; Facet (psychology); Materials science; Biomedical engineering; Artificial intelligence; Computer vision; Mathematics; Geology; Image (mathematics); Medicine","score_opus":0.0044132657682480255,"score_gpt":0.19435442071719877,"score_spread":0.18994115494895075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985179035","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2077538,0.0011680276,0.79082733,0.00015033083,0.000048120204,0.000039611605,0.0000014651516,0.000010164725,0.0000011278005],"genre_scores_gemma":[0.8019486,0.000043781536,0.19781262,0.00015215573,0.000037179034,1.990858e-7,0.0000011217824,0.0000036332385,6.803055e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992494,0.00002738125,0.00027797345,0.00016588141,0.00012258708,0.00015680025],"domain_scores_gemma":[0.9993847,0.000050926243,0.00013425955,0.000077726705,0.00007268611,0.00027969223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018776322,0.00011094603,0.00025860616,0.0005070696,0.00007514089,0.00012790498,0.00007356259,0.000059755446,7.4224766e-7],"category_scores_gemma":[0.000030096593,0.00010319431,0.000031963136,0.00050668954,0.00004801153,0.00025789178,0.000008459672,0.0001903101,8.794865e-9],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049181148,0.000043874068,0.017527243,0.00030116248,0.0003134511,0.00054171204,0.0012966606,0.037345916,0.07115273,0.0070214914,0.0000875157,0.8643191],"study_design_scores_gemma":[0.000351273,0.00018202992,0.034104872,0.00015776427,0.00007076057,0.0004595883,0.0000029081489,0.9600461,0.004100811,0.00034451453,0.00002492529,0.00015441843],"about_ca_topic_score_codex":0.00006650796,"about_ca_topic_score_gemma":0.000026775904,"teacher_disagreement_score":0.9227002,"about_ca_system_score_codex":0.000043875694,"about_ca_system_score_gemma":0.00007778162,"threshold_uncertainty_score":0.4208142},"labels":[],"label_agreement":null},{"id":"W1985978695","doi":"10.1117/12.811139","title":"Improving an affine and non-linear image registration and/or segmentation task by incorporating characteristics of the displacement field","year":2009,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Image registration; Computer vision; Affine transformation; Artificial intelligence; Displacement field; Curl (programming language); Displacement (psychology); Field (mathematics); Image segmentation; Segmentation; Image processing; Image (mathematics); Task (project management); Mathematics; Finite element method; Geometry","score_opus":0.008075719598415064,"score_gpt":0.24656500721418884,"score_spread":0.23848928761577376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985978695","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95723414,0.00001505951,0.03892777,0.0030955605,0.00007103239,0.00048491728,0.000017664814,0.00004667016,0.000107178326],"genre_scores_gemma":[0.46936268,0.00005070127,0.53005064,0.00031231702,0.000102116945,0.000042814812,0.000008746286,0.000013447527,0.000056556673],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985059,5.18684e-8,0.00058543246,0.00028600026,0.00045542337,0.00016719002],"domain_scores_gemma":[0.998535,0.000093478404,0.00059733115,0.00006733224,0.0006267227,0.00008018058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057426543,0.0001769625,0.0002241323,0.000045199427,0.000083396204,0.00014026786,0.00061590027,0.000091092574,0.000002327108],"category_scores_gemma":[0.00047801298,0.00012548012,0.00010492355,0.0001949077,0.00014343645,0.001002986,0.00016058373,0.00017831854,6.391219e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038004007,0.000079679376,0.0002013655,0.00022181551,0.00003497962,4.8098226e-8,0.00028177426,0.0000016955772,0.95535934,0.03486588,0.0007578193,0.0081576165],"study_design_scores_gemma":[0.0006855994,0.00086545246,0.001630335,0.00021532175,0.00005268978,0.000009360425,0.0005243023,0.13677752,0.85821706,0.0007834088,0.00003737311,0.0002015894],"about_ca_topic_score_codex":0.000014428298,"about_ca_topic_score_gemma":1.9081824e-7,"teacher_disagreement_score":0.49112284,"about_ca_system_score_codex":0.000058653357,"about_ca_system_score_gemma":0.000027616674,"threshold_uncertainty_score":0.51169306},"labels":[],"label_agreement":null},{"id":"W1986167634","doi":"10.1007/s10278-013-9667-7","title":"SimITK: Visual Programming of the ITK Image-Processing Library within Simulink","year":2014,"lang":"en","type":"article","venue":"Journal of Digital Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of British Columbia; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Computer science; Workflow; Software; Class (philosophy); Visualization; Usability; Schematic; Block (permutation group theory); Segmentation; Programming language; Software engineering; Human–computer interaction; Artificial intelligence; Database","score_opus":0.008146449892474657,"score_gpt":0.2711526759938627,"score_spread":0.2630062261013881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986167634","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025707407,0.000096964344,0.9704496,0.0017754519,0.00024073596,0.00009002228,6.363472e-7,0.00010386528,0.0015353042],"genre_scores_gemma":[0.7811206,0.000001768731,0.21804363,0.0006430862,0.00011567355,7.759152e-7,3.5894683e-7,0.000014192498,0.000059954767],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99822646,0.00007328572,0.000706679,0.00015588419,0.0006310336,0.00020668283],"domain_scores_gemma":[0.9983691,0.000152985,0.0009082396,0.00022374956,0.00021100888,0.00013493064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048705665,0.00012988073,0.00021733831,0.00014551356,0.00008339354,0.0009938383,0.0010741364,0.00003579525,0.000007964778],"category_scores_gemma":[0.0005222367,0.000086239874,0.00014848322,0.00042437858,0.0002000848,0.006542662,0.0003755811,0.00040340627,0.0000023126008],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008132728,0.0001428116,0.0045866272,0.00008746156,0.00001742601,0.000020911277,0.0011452257,0.00002652277,0.0141171925,0.00026152158,0.0005758774,0.9790103],"study_design_scores_gemma":[0.0013208796,0.0003050246,0.002210746,0.0014075559,0.00004980246,0.00051393354,0.0006727973,0.21038342,0.7653731,0.014485169,0.002742123,0.0005354561],"about_ca_topic_score_codex":5.4801467e-7,"about_ca_topic_score_gemma":4.073184e-8,"teacher_disagreement_score":0.97847486,"about_ca_system_score_codex":0.000021117616,"about_ca_system_score_gemma":0.00016222773,"threshold_uncertainty_score":0.9583606},"labels":[],"label_agreement":null},{"id":"W1986477076","doi":"10.1186/2193-9772-2-2","title":"High resolution micrograph synthesis using a parametric texture model and a particle filter","year":2013,"lang":"en","type":"article","venue":"Integrating materials and manufacturing innovation","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec","funders":"Division of Civil, Mechanical and Manufacturing Innovation; National Science Foundation","keywords":"Micrograph; Pixel; Artificial intelligence; Image resolution; Resolution (logic); Parametric statistics; Computer science; Mathematics; Optics; Statistics; Physics","score_opus":0.023623079681436322,"score_gpt":0.2569707174811151,"score_spread":0.2333476377996788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986477076","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5686626,0.00001001781,0.4308426,0.00017361905,0.000058460948,0.00014646868,0.0000022305258,0.00009899334,0.000005014838],"genre_scores_gemma":[0.6897759,0.000009223706,0.30985886,0.00026170086,0.000022929697,0.000050458977,0.00000351481,0.000006504119,0.000010942876],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989615,0.00006158344,0.00036977424,0.0002796813,0.0001431749,0.00018430228],"domain_scores_gemma":[0.999444,0.000063812295,0.00018225954,0.00017480105,0.00009686666,0.00003825062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036915863,0.00013517532,0.00015495048,0.00022106273,0.00013745295,0.0006114443,0.00012454798,0.0000768423,0.00003261883],"category_scores_gemma":[0.00018657488,0.00010863645,0.000011510015,0.00027203257,0.000046096862,0.00077769347,0.00011670157,0.00008019186,0.0000026113512],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029498833,0.000014477848,0.000029997404,0.000040713585,0.000007794876,8.36753e-7,0.00016545232,0.000051388575,0.90784806,0.0029539326,0.00012847173,0.088755906],"study_design_scores_gemma":[0.00009143958,0.00002207385,0.0015318681,0.00007826374,0.000005886554,0.000008798963,0.00003166346,0.063244715,0.92827845,0.0065787756,0.0000041469325,0.00012392465],"about_ca_topic_score_codex":0.0005560558,"about_ca_topic_score_gemma":0.0000019932597,"teacher_disagreement_score":0.121113285,"about_ca_system_score_codex":0.000027137454,"about_ca_system_score_gemma":0.000012693071,"threshold_uncertainty_score":0.58961713},"labels":[],"label_agreement":null},{"id":"W1986509777","doi":"10.1007/s11517-009-0489-1","title":"Self-organizing map-based multi-thresholding on neural stem cells images","year":2009,"lang":"en","type":"article","venue":"Medical & Biological Engineering & Computing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Stem Cell Network","keywords":"Thresholding; Artificial intelligence; Segmentation; Pattern recognition (psychology); Image segmentation; Computer science; Computer vision; Feature (linguistics); Tracking (education); Artificial neural network; Scale-space segmentation; Image (mathematics)","score_opus":0.022343891226318403,"score_gpt":0.2642198350315336,"score_spread":0.2418759438052152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986509777","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021979019,0.00015948861,0.9729598,0.0013060694,0.00057453546,0.0002464589,0.0000011035999,0.0027073016,0.00006619688],"genre_scores_gemma":[0.6530396,0.000007971422,0.34320137,0.003537233,0.00018378216,0.000004212988,0.0000032896157,0.00001426142,0.000008328317],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996919,0.00014155041,0.0006150989,0.0007721339,0.0007934238,0.00075883494],"domain_scores_gemma":[0.998118,0.0006883468,0.00012971704,0.00043996586,0.00006835098,0.0005556298],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010311337,0.000375109,0.00042058039,0.00017598998,0.00016995521,0.00018143692,0.0014112265,0.00027777388,0.00007100026],"category_scores_gemma":[0.00034655514,0.00029437314,0.00013758193,0.0004998589,0.00006886845,0.000157434,0.00031085676,0.00085183606,0.00009240724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002256903,0.0013933047,0.001451255,0.00023403043,0.000064352644,0.0011981651,0.0005045392,0.022414705,0.1318472,0.0027803353,0.0025512606,0.83553827],"study_design_scores_gemma":[0.00053189945,0.00035314955,0.0017217795,0.00019719375,0.0000042077195,0.000017033897,0.000008098349,0.90414894,0.09226896,0.000019092702,0.00033882592,0.00039085367],"about_ca_topic_score_codex":0.0000031525,"about_ca_topic_score_gemma":4.762662e-8,"teacher_disagreement_score":0.8817342,"about_ca_system_score_codex":0.000119612436,"about_ca_system_score_gemma":0.000051436575,"threshold_uncertainty_score":0.9999508},"labels":[],"label_agreement":null},{"id":"W1986809342","doi":"10.1109/prni.2014.6858519","title":"Improved method for automatic cerebrovascular labelling using stochastic tunnelling","year":2014,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Labelling; Simulated annealing; Computer science; Graph; Artificial intelligence; Algorithm; Enhanced Data Rates for GSM Evolution; Quantum tunnelling; Computer vision; Pattern recognition (psychology); Theoretical computer science; Materials science; Chemistry","score_opus":0.0282787387213091,"score_gpt":0.32399994908744295,"score_spread":0.29572121036613386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986809342","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024255116,0.000019109266,0.9981954,0.000098779434,0.00017652649,0.0004625709,3.6512597e-7,0.00073260005,0.000072082985],"genre_scores_gemma":[0.010325112,6.673918e-7,0.9887224,0.0007236798,0.000071954004,0.000044492856,0.0000015392859,0.000016913807,0.000093285125],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986193,0.00012562034,0.00033781558,0.00038046658,0.00023651829,0.0003002363],"domain_scores_gemma":[0.9985994,0.00057825714,0.000116305746,0.00046301607,0.000117603224,0.00012543393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015405961,0.00013870255,0.00021397998,0.00011344596,0.00013629146,0.00016111309,0.00055260083,0.000060685677,0.000043191896],"category_scores_gemma":[0.00037592123,0.00012197314,0.00010149675,0.00022561279,0.000024033901,0.00034742858,0.00010986501,0.00008994866,0.00000792882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027233395,0.00009783463,0.000007097385,0.00031163174,0.000095301075,7.67104e-7,0.00074884226,0.008659905,0.22529553,0.022003405,0.00034462052,0.74243236],"study_design_scores_gemma":[0.0002822618,0.000050923205,0.0000016979861,0.00003767268,0.00001742271,0.0000050868116,0.000014521959,0.89940023,0.09430145,0.0057173073,0.000025891073,0.00014553667],"about_ca_topic_score_codex":0.000045714358,"about_ca_topic_score_gemma":0.0000014370256,"teacher_disagreement_score":0.89074033,"about_ca_system_score_codex":0.000044446053,"about_ca_system_score_gemma":0.000040717157,"threshold_uncertainty_score":0.49739206},"labels":[],"label_agreement":null},{"id":"W1987891135","doi":"10.1016/j.cmpb.2013.04.011","title":"Validation study of a fast, accurate, and precise brain tumor volume measurement","year":2013,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Volume (thermodynamics)","score_opus":0.0833055216989577,"score_gpt":0.37911273396197903,"score_spread":0.2958072122630213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987891135","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08577647,0.0002192708,0.911662,0.00079769053,0.00015393336,0.0012873074,2.2235962e-7,0.000088612585,0.0000144835585],"genre_scores_gemma":[0.12882355,0.000015139389,0.87064433,0.00028278778,0.000049131264,0.00016242029,0.0000024352348,0.0000064939286,0.000013688133],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786735,0.0006013595,0.0005131629,0.0004101778,0.0004150296,0.00019291585],"domain_scores_gemma":[0.9990059,0.00015956588,0.00017090068,0.00032174404,0.00019747243,0.000144384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002861759,0.00015545386,0.00032871595,0.0002476909,0.000036885333,0.000111909576,0.0003188366,0.000052341064,0.000012568523],"category_scores_gemma":[0.00010488225,0.00011465426,0.000017312806,0.00057070435,0.00014685607,0.00034782215,0.00032317,0.00015304006,9.664122e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033057524,0.00030980172,0.0031892548,0.000059257334,0.000014763559,0.0000042995994,0.0023075985,3.004186e-7,0.004752929,0.000021735803,0.00049182674,0.98884493],"study_design_scores_gemma":[0.016292326,0.025872767,0.35316843,0.0020570701,0.00012808997,0.00020973785,0.003346501,0.52934843,0.048599467,0.014982726,0.0041822796,0.001812154],"about_ca_topic_score_codex":0.00017777208,"about_ca_topic_score_gemma":0.000002849945,"teacher_disagreement_score":0.9870328,"about_ca_system_score_codex":0.000023642047,"about_ca_system_score_gemma":0.000021594928,"threshold_uncertainty_score":0.46754652},"labels":[],"label_agreement":null},{"id":"W1988479807","doi":"10.1117/12.428064","title":"&lt;title&gt;Correlation of preoperative MRI and intraoperative 3D ultrasound to measure brain tissue shift&lt;/title&gt;","year":2001,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Ultrasound; 3D ultrasound; Image warping; Computer science; Interpolation (computer graphics); Artificial intelligence; Computer vision; Volume (thermodynamics); Medicine; Radiology; Image (mathematics); Physics","score_opus":0.009987792451747364,"score_gpt":0.25026790786417147,"score_spread":0.2402801154124241,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988479807","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85172224,0.00021253285,0.10825673,0.0055965977,0.00035938266,0.0010625493,0.000032139742,0.00019058751,0.032567248],"genre_scores_gemma":[0.25458658,0.00013267479,0.7429574,0.00039330314,0.00028920776,0.000106131716,0.000007840593,0.000040072,0.001486782],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99879175,5.403949e-8,0.00033626688,0.00024726766,0.00046205812,0.00016258076],"domain_scores_gemma":[0.9988682,0.00012616435,0.00013950115,0.00004823517,0.0007283982,0.000089466885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039130243,0.00015057118,0.00020011503,0.00007627125,0.00004061205,0.00008029485,0.000499197,0.00010077143,0.00007528139],"category_scores_gemma":[0.0005783422,0.00012569482,0.00009878186,0.00027203676,0.00012274442,0.00042722418,0.000107211046,0.00014935617,0.000008898035],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001656126,0.000057030986,0.00009651045,0.00010430016,0.0001154687,1.4130137e-7,0.0009144719,0.000027864042,0.4513129,0.52331215,0.019367352,0.0046752426],"study_design_scores_gemma":[0.001517168,0.0012713182,0.0029961497,0.00094290666,0.0001495571,0.00006649698,0.0007127374,0.06893357,0.8786822,0.007971524,0.03574796,0.001008436],"about_ca_topic_score_codex":0.0000024228664,"about_ca_topic_score_gemma":1.6043822e-7,"teacher_disagreement_score":0.6347007,"about_ca_system_score_codex":0.000070256916,"about_ca_system_score_gemma":0.000028732502,"threshold_uncertainty_score":0.5125686},"labels":[],"label_agreement":null},{"id":"W1988594964","doi":"10.1109/tsmcb.2012.2215849","title":"A Nonsymmetric Mixture Model for Unsupervised Image Segmentation","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Artificial intelligence; Segmentation; Image segmentation; Computer science; Image (mathematics); Computer vision; Pattern recognition (psychology)","score_opus":0.028405115880937654,"score_gpt":0.29426126368202754,"score_spread":0.2658561478010899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988594964","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007120521,0.000044340417,0.99717903,0.00030591246,0.0004614278,0.0006168606,0.000034568875,0.0003576994,0.0002881189],"genre_scores_gemma":[0.30357382,0.000048341542,0.694477,0.0008455988,0.000044525474,0.00021385924,0.000006182769,0.000020935233,0.0007697348],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862915,0.00005456263,0.0002873685,0.0002735221,0.00038228947,0.0003730852],"domain_scores_gemma":[0.9989596,0.00019793701,0.000074848,0.00041012064,0.00013274331,0.00022476981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027058637,0.00017960012,0.00014745814,0.0002787128,0.00014839167,0.00009715493,0.00040933024,0.0001114389,0.000046449193],"category_scores_gemma":[0.000016891689,0.00017595306,0.00011875134,0.0005476488,0.000053247848,0.0007001421,0.0000029769155,0.00019005586,0.00006183935],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057707664,0.0019281536,0.000022016005,0.00014011441,0.000116386305,0.0000030440897,0.00626489,0.007482419,0.12478459,0.0019179164,0.012415582,0.84486717],"study_design_scores_gemma":[0.0005808404,0.00010258128,0.00002444151,0.000013070676,0.000034980032,0.000005760509,0.000033903267,0.5413937,0.45702353,0.00047525455,0.000104910934,0.00020701054],"about_ca_topic_score_codex":0.0000074213845,"about_ca_topic_score_gemma":0.000003230986,"teacher_disagreement_score":0.84466016,"about_ca_system_score_codex":0.00010138772,"about_ca_system_score_gemma":0.000050584924,"threshold_uncertainty_score":0.71751577},"labels":[],"label_agreement":null},{"id":"W1989287407","doi":"10.1109/icip.2011.6116064","title":"Temporal registration of partial data using particle filtering","year":2011,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Center for Research Resources; National Institute of Biomedical Imaging and Bioengineering","keywords":"Computer vision; Artificial intelligence; Clutter; Computer science; Image registration; Segmentation; Image segmentation; Process (computing); Noise (video); Particle filter; Magnetic resonance imaging; Image (mathematics); Pattern recognition (psychology); Filter (signal processing); Radar","score_opus":0.30246815164169266,"score_gpt":0.3642559106012709,"score_spread":0.06178775895957822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989287407","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020597845,0.000004942641,0.97796273,0.000039273196,0.000054088803,0.00006023647,0.0000011429224,0.00011883889,0.0011609044],"genre_scores_gemma":[0.49723288,7.2011267e-7,0.5026716,0.0000506263,0.00000958663,9.520648e-7,0.000002319751,0.000001450357,0.000029851439],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99931836,0.000031687225,0.0002201439,0.000172082,0.00016591135,0.0000918025],"domain_scores_gemma":[0.99917275,0.000012602587,0.00008649806,0.0006519723,0.000029368988,0.000046788067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031975497,0.000040476858,0.00005958566,0.000018167617,0.000022373235,0.000024314599,0.00061431987,0.000017437656,0.00014921557],"category_scores_gemma":[0.000052735442,0.000035896002,0.000010339178,0.00010905134,0.000043106615,0.0009198967,0.0002861477,0.000028463126,0.000004774195],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030198273,0.0004866646,0.01223873,0.0000838422,0.000040568444,0.000049757455,0.0036534097,0.000010079237,0.7082923,0.049364816,0.0056719123,0.22007772],"study_design_scores_gemma":[0.000075868906,0.000035049245,0.00037857745,0.000008417592,0.000002392226,0.0000036043266,0.00002253628,0.18700837,0.81190133,0.0004773964,0.000038501257,0.000047951708],"about_ca_topic_score_codex":0.00031384247,"about_ca_topic_score_gemma":0.0000074454433,"teacher_disagreement_score":0.47663504,"about_ca_system_score_codex":0.000006440813,"about_ca_system_score_gemma":0.000031118,"threshold_uncertainty_score":0.1633806},"labels":[],"label_agreement":null},{"id":"W1989708985","doi":"10.1109/tpami.2010.83","title":"Decoupled Active Contour (DAC) for Boundary Detection","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":107,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Initialization; Computer science; Active contour model; Artificial intelligence; Viterbi algorithm; Boundary (topology); Image segmentation; Noise (video); Curvature; Segmentation; Maxima and minima; Computer vision; Energy (signal processing); Algorithm; Pattern recognition (psychology); Hidden Markov model; Image (mathematics); Mathematics","score_opus":0.016066986892304243,"score_gpt":0.29726433529736235,"score_spread":0.2811973484050581,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989708985","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004254168,0.000014697463,0.99455404,0.00029814977,0.00036425373,0.00027538594,0.00003546566,0.0001742325,0.000029608076],"genre_scores_gemma":[0.97667795,0.00007397796,0.022291822,0.0006677659,0.000029145158,0.00012251524,0.0000051326338,0.000009961009,0.00012170316],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987193,0.00004656225,0.0003208974,0.00047197737,0.00023543167,0.00020587127],"domain_scores_gemma":[0.99894744,0.00024774022,0.00011666373,0.00039736409,0.00013621662,0.00015457223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002907597,0.0001762563,0.00024446176,0.00043010645,0.00027621372,0.00018383635,0.00036409954,0.00008743521,0.00021209892],"category_scores_gemma":[0.000019748732,0.00015705205,0.0002216792,0.00059423415,0.00009550197,0.00036164676,0.0000042847437,0.0003473955,0.000012171472],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000146579105,0.000088848916,0.000028334214,0.000008625762,0.0001978466,0.0000017789571,0.00017436947,0.00011261645,0.017029386,0.00001974401,0.000007962267,0.98231584],"study_design_scores_gemma":[0.0001116031,0.00013832643,0.00039704217,0.000005891829,0.00018982936,0.000007054995,0.00002868943,0.14955881,0.8487898,0.00049776613,0.00010789239,0.00016731407],"about_ca_topic_score_codex":0.00057917,"about_ca_topic_score_gemma":0.0052524786,"teacher_disagreement_score":0.9821485,"about_ca_system_score_codex":0.000027746677,"about_ca_system_score_gemma":0.000026733464,"threshold_uncertainty_score":0.6404397},"labels":[],"label_agreement":null},{"id":"W1990994043","doi":"10.1118/1.2409238","title":"Subvoxel precise skeletons of volumetric data based on fast marching methods","year":2007,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":123,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Terahertz Technology Solutions (Canada)","funders":"National Institutes of Health","keywords":"Voxel; Computer science; Skeletonization; Smoothing; Artificial intelligence; Computer vision; Representation (politics); Field (mathematics); Algorithm; Fast marching method; Object (grammar); Distance transform; Mathematics; Image (mathematics)","score_opus":0.058618486734657006,"score_gpt":0.4142345303478467,"score_spread":0.35561604361318966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990994043","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003756979,0.000027749535,0.9968488,0.00042566698,0.00023671878,0.00016598446,0.0000093931485,0.00019341042,0.0017165438],"genre_scores_gemma":[0.121922724,0.0000063873217,0.87648106,0.0012778958,0.00019221699,0.0000060512284,0.000036430818,0.0000150781525,0.00006214637],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99694157,0.00024620275,0.0004509254,0.00047207176,0.0015574993,0.00033170913],"domain_scores_gemma":[0.9958616,0.0019878193,0.00016285181,0.0015188984,0.000082655424,0.0003861726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043197377,0.00014099028,0.00025838794,0.00018697511,0.00006141923,0.000033738066,0.0026747973,0.000089622925,0.000086501626],"category_scores_gemma":[0.0024193653,0.0001227308,0.00006470118,0.0012819405,0.00021528029,0.00024111362,0.0007309257,0.00043712737,0.00001839102],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048701927,0.00024540946,0.00013414769,0.000029243933,0.0000072243984,0.0000145423655,0.00006322742,0.000004377346,0.00039458662,0.0003655526,0.002359416,0.9963774],"study_design_scores_gemma":[0.0011278978,0.0003841744,0.0025577848,0.00028902304,0.000029681327,0.000008934586,0.000020436919,0.5505537,0.43490425,0.008527536,0.0011436502,0.00045291617],"about_ca_topic_score_codex":0.000039348328,"about_ca_topic_score_gemma":0.0000026948458,"teacher_disagreement_score":0.9959245,"about_ca_system_score_codex":0.000042829113,"about_ca_system_score_gemma":0.00021276397,"threshold_uncertainty_score":0.50048167},"labels":[],"label_agreement":null},{"id":"W1991087923","doi":"10.1117/12.477141","title":"Linear cost reconstruction of vascular trees from intensity volume angiograms","year":2002,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Voxel; Computer science; Artificial intelligence; Adjacency list; Computer vision; Volume (thermodynamics); Algorithm; Graph; Line (geometry); Pattern recognition (psychology); Mathematics; Theoretical computer science","score_opus":0.015058485675591973,"score_gpt":0.2279180627257386,"score_spread":0.21285957705014663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991087923","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97659487,0.000118407675,0.020307582,0.001443784,0.00030603894,0.0005029614,0.00003227985,0.00018332843,0.00051078235],"genre_scores_gemma":[0.19724113,0.0002148428,0.8018427,0.00014945697,0.00029693634,0.00009669441,0.000008947616,0.000034838413,0.00011448354],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976429,3.1810913e-8,0.00078370713,0.00043453765,0.0008367014,0.00030216452],"domain_scores_gemma":[0.99717134,0.00011241038,0.00050524814,0.00011125396,0.001958089,0.00014165799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005287579,0.00025776812,0.00045031152,0.00013326152,0.00006253261,0.00009886368,0.001418988,0.00018695422,0.00004617028],"category_scores_gemma":[0.0006129823,0.00022425034,0.00071206375,0.0004858135,0.0003449004,0.000960654,0.0002681451,0.0002860953,0.0000033163653],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037376638,0.00029613657,0.0018272743,0.00026225898,0.0007352408,2.3389372e-7,0.0006236695,0.000048512455,0.8850299,0.078373976,0.009383685,0.023381716],"study_design_scores_gemma":[0.0012674847,0.0004410732,0.0035740763,0.0005237531,0.00017760218,0.000024083132,0.0007956618,0.36722946,0.62148184,0.002590323,0.0013980804,0.00049656833],"about_ca_topic_score_codex":0.00004245077,"about_ca_topic_score_gemma":1.4163753e-7,"teacher_disagreement_score":0.7815351,"about_ca_system_score_codex":0.000115097995,"about_ca_system_score_gemma":0.000015301674,"threshold_uncertainty_score":0.9144663},"labels":[],"label_agreement":null},{"id":"W1991258631","doi":"10.1006/nimg.1999.0534","title":"Automated 3-D Extraction of Inner and Outer Surfaces of Cerebral Cortex from MRI","year":2000,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":859,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Surface (topology); Artificial intelligence; Computer science; Computer vision; Partial volume; Pattern recognition (psychology); Geometry; Mathematics","score_opus":0.013843960206264693,"score_gpt":0.2810957862788266,"score_spread":0.26725182607256187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991258631","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8947194,0.00003478729,0.10323706,0.00024391511,0.00009414099,0.00012544339,0.000015686726,0.00040355252,0.0011259809],"genre_scores_gemma":[0.88417274,0.00004855814,0.115219586,0.0002951438,0.0000128969705,0.0000026999487,0.000006033838,0.000007303066,0.0002350159],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99911475,0.000080189864,0.00025781995,0.00022833476,0.00022289938,0.00009602805],"domain_scores_gemma":[0.9994276,0.000091003014,0.00011022899,0.00027609,0.000045025103,0.000050033592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009454481,0.000077409044,0.0001407599,0.00005345081,0.000019569068,0.00003363139,0.00023256503,0.000038329752,0.0004186341],"category_scores_gemma":[0.000024893387,0.00007027095,0.000024213927,0.0001462287,0.00010136859,0.0005025145,0.000055425407,0.000080849415,0.000012960016],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018796978,0.00014471087,0.003236816,0.000033114593,0.000014388211,0.000039277187,0.00063916575,0.000013231047,0.8569158,0.00007933421,0.0073140184,0.13155136],"study_design_scores_gemma":[0.00050916744,0.00016402609,0.32512325,0.000035044905,0.000011243624,0.000016255968,0.00001378695,0.08974893,0.5833812,0.000424875,0.00042814785,0.0001441168],"about_ca_topic_score_codex":0.00016953071,"about_ca_topic_score_gemma":0.0000030058795,"teacher_disagreement_score":0.32188645,"about_ca_system_score_codex":0.0000050363533,"about_ca_system_score_gemma":0.000015246856,"threshold_uncertainty_score":0.45837498},"labels":[],"label_agreement":null},{"id":"W1991456242","doi":"10.1016/j.neuroimage.2010.04.193","title":"Towards accurate, automatic segmentation of the hippocampus and amygdala from MRI by augmenting ANIMAL with a template library and label fusion","year":2010,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":226,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; Douglas Mental Health University Institute; McGill University","funders":"Canadian Institutes of Health Research","keywords":"Segmentation; Artificial intelligence; Pattern recognition (psychology); Computer science; Amygdala; Template; Gold standard (test); Dice; Hippocampus; Fusion; Transformation (genetics); Atlas (anatomy); Set (abstract data type); Mathematics; Medicine; Neuroscience; Biology; Anatomy; Radiology","score_opus":0.00907286689020323,"score_gpt":0.2452359640651315,"score_spread":0.23616309717492828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991456242","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95149815,0.000035269666,0.04659823,0.0010274668,0.00008953983,0.0003020253,0.00001528961,0.00019586703,0.00023814586],"genre_scores_gemma":[0.7736891,0.00004060643,0.22536114,0.00081722037,0.000016964741,0.000016210193,0.000008741769,0.00001556148,0.000034465767],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989266,0.000102708815,0.00022579703,0.00032229035,0.00028015324,0.00014246453],"domain_scores_gemma":[0.999275,0.000116979885,0.00019694751,0.0003095546,0.000019370962,0.00008210813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011446647,0.00012856089,0.00013153064,0.000047199766,0.00011154128,0.00022111283,0.00034988608,0.000045712146,0.000039854378],"category_scores_gemma":[0.000037493402,0.00008749282,0.000014613899,0.0002185089,0.00016365512,0.0014398402,0.00040645857,0.00021420352,0.0000011158897],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008421027,0.00003497091,0.0025671278,0.000030374882,0.00000568666,0.0000074561663,0.0003526265,6.361845e-8,0.86574453,0.000056117555,0.0009015148,0.1302911],"study_design_scores_gemma":[0.0011138857,0.00032274227,0.09648005,0.00009878125,0.000025302788,0.00004315689,0.00005597163,0.030363776,0.8691783,0.0020393678,0.00006327194,0.00021542185],"about_ca_topic_score_codex":0.000022987972,"about_ca_topic_score_gemma":0.0000018351136,"teacher_disagreement_score":0.1787629,"about_ca_system_score_codex":0.0000030664592,"about_ca_system_score_gemma":0.00003653006,"threshold_uncertainty_score":0.3567854},"labels":[],"label_agreement":null},{"id":"W1992534760","doi":"10.1016/s0167-8655(01)00112-x","title":"Multi-resolution image registration using multi-class Hausdorff fraction","year":2002,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Image registration; Class (philosophy); Hausdorff distance; Hausdorff space; Fraction (chemistry); Artificial intelligence; Resolution (logic); Computer vision; Mathematics; Image (mathematics); Computer science; Pattern recognition (psychology); Combinatorics","score_opus":0.09427159185282867,"score_gpt":0.30569666933955225,"score_spread":0.21142507748672357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992534760","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017705146,0.000025198262,0.97787935,0.0029072477,0.000414243,0.0004006312,0.000012092737,0.00059254066,0.000063581174],"genre_scores_gemma":[0.11254272,0.000050349183,0.8770272,0.009900249,0.00020971932,0.00008192449,0.000093697934,0.000030758172,0.00006334221],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979386,0.00023860682,0.0004734427,0.0005483273,0.00046190326,0.0003391608],"domain_scores_gemma":[0.9989002,0.000068854715,0.00035271855,0.00039398146,0.0001474579,0.00013683943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032715886,0.00021767883,0.00016949444,0.00025340283,0.00021517243,0.00030104152,0.0003337788,0.000108439686,0.00039265785],"category_scores_gemma":[0.00012645771,0.00023993642,0.00009628487,0.00030013802,0.000097565986,0.0020247886,0.00007022333,0.0002787451,0.00060562766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000531934,0.0003041138,0.000357479,0.000049803675,0.000025550004,0.000058321144,0.00035250862,0.0000470148,0.54867566,0.0000012761684,0.006892783,0.4432302],"study_design_scores_gemma":[0.0011129006,0.00004020014,0.0018636674,0.00010247231,0.000021508642,0.000057232563,0.000032772376,0.91510886,0.080991216,0.000027697191,0.00024664073,0.00039481267],"about_ca_topic_score_codex":0.0001659518,"about_ca_topic_score_gemma":0.000015187695,"teacher_disagreement_score":0.9150619,"about_ca_system_score_codex":0.00023435315,"about_ca_system_score_gemma":0.000010561268,"threshold_uncertainty_score":0.9784323},"labels":[],"label_agreement":null},{"id":"W1992982408","doi":"10.1016/j.neunet.2008.08.002","title":"LAGO on the unit sphere","year":2008,"lang":"en","type":"article","venue":"Neural Networks","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Unit sphere; Kernel (algebra); Computer science; Domain (mathematical analysis); Euclidean space; Euclidean geometry; Space (punctuation); Algorithm; Mathematics; Artificial intelligence; Pure mathematics; Geometry; Mathematical analysis","score_opus":0.04366619269067086,"score_gpt":0.26535637446051047,"score_spread":0.22169018176983962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992982408","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014189097,0.00007259965,0.97538185,0.0037922275,0.0003472915,0.00017210502,2.385536e-7,0.00044017984,0.0056044236],"genre_scores_gemma":[0.96915585,0.000064110754,0.011997117,0.017342892,0.00024404748,0.00002185401,0.0000018391569,0.000009409754,0.0011629063],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991877,0.00009854442,0.00011552657,0.00017328434,0.00023952175,0.00018543038],"domain_scores_gemma":[0.99924797,0.00020622223,0.000042148462,0.0004055351,0.000028403694,0.0000697166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011203837,0.00008185952,0.00006840929,0.000014952276,0.0001671019,0.000047234254,0.0006938016,0.000042636155,0.00017099585],"category_scores_gemma":[0.000035090772,0.000050538205,0.000035987494,0.0002572985,0.0000690298,0.00015044281,0.0001146665,0.0002618449,0.000057029425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000117813115,0.00009280169,0.0012349737,0.000003930582,0.000014374383,0.00026241934,0.00040003905,0.0036877096,0.00023986635,0.018559208,0.39170292,0.58378994],"study_design_scores_gemma":[0.0002780616,0.00024499546,0.0045304783,0.000026923117,0.0000033518197,0.00009839543,0.000021000082,0.9784091,0.0060558426,0.0012399796,0.008824133,0.0002677421],"about_ca_topic_score_codex":0.000007927742,"about_ca_topic_score_gemma":0.000002198834,"teacher_disagreement_score":0.9747214,"about_ca_system_score_codex":0.000008691637,"about_ca_system_score_gemma":0.0000110429655,"threshold_uncertainty_score":0.20608883},"labels":[],"label_agreement":null},{"id":"W1993037306","doi":"10.1109/iembs.2010.5627557","title":"Registering a non-rigid multi-sensor ensemble of images","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Singular value decomposition; Cluster analysis; Regularization (linguistics); Computer science; Artificial intelligence; Computer vision; Image registration; Process (computing); Spectral clustering; Algorithm; Image (mathematics)","score_opus":0.022650308633483763,"score_gpt":0.3090434397991917,"score_spread":0.2863931311657079,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993037306","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064169844,0.0000031964084,0.9845059,0.00025818867,0.00017496913,0.00011635781,5.507314e-7,0.00023712353,0.008286761],"genre_scores_gemma":[0.17057462,0.0000038146488,0.8264235,0.00026798848,0.000019025938,0.000008737247,4.0699754e-7,0.000004862939,0.0026970338],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992226,0.0000159178,0.0002113716,0.00020795756,0.00019832105,0.00014384482],"domain_scores_gemma":[0.99917763,0.000056803645,0.00008305305,0.0005124682,0.00008574221,0.0000843022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002151859,0.00007541679,0.0001143448,0.00007380764,0.000028726374,0.00005331318,0.00054025376,0.000042272357,0.00010598725],"category_scores_gemma":[0.000109017856,0.00006242665,0.000039015515,0.0001368069,0.00008175737,0.0003256731,0.0001853351,0.0001259041,0.000038410286],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.531699e-7,0.000034557062,0.00012862025,0.000013777803,0.0000025376671,0.000006422168,0.00013680276,1.7898645e-7,0.9601751,0.0005197126,0.0020024602,0.036979076],"study_design_scores_gemma":[0.00018144914,0.0000291071,0.0010040152,0.00001087492,0.0000012860571,0.00001186062,0.000016277918,0.0041580675,0.99409986,0.00013410524,0.00027428014,0.00007882105],"about_ca_topic_score_codex":0.00009121443,"about_ca_topic_score_gemma":0.000013205286,"teacher_disagreement_score":0.16415764,"about_ca_system_score_codex":0.000005494692,"about_ca_system_score_gemma":0.000026410187,"threshold_uncertainty_score":0.2545685},"labels":[],"label_agreement":null},{"id":"W1993150907","doi":"10.1117/12.2082599","title":"3D active shape models of human brain structures: application to patient-specific mesh generation","year":2015,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"European Commission","keywords":"Computer science; Artificial intelligence; Point distribution model; Computer vision; Set (abstract data type); Brain atlas; Mesh generation; Pattern recognition (psychology); Algorithm; Finite element method","score_opus":0.024170744077062756,"score_gpt":0.2651103743204735,"score_spread":0.24093963024341075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993150907","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90749735,0.000032690605,0.089021064,0.0015627946,0.00013569444,0.00085150835,0.000026210111,0.00012434176,0.0007483235],"genre_scores_gemma":[0.3755818,0.000016376865,0.62353534,0.00029577082,0.00023212995,0.0002548133,0.000017329952,0.000033541724,0.00003291483],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9973979,5.0772606e-8,0.00076207204,0.00047114445,0.0010902497,0.00027856245],"domain_scores_gemma":[0.9966172,0.00008115678,0.00049857376,0.00011077641,0.0024838855,0.00020838989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005719977,0.00025294576,0.00034006132,0.00015905638,0.00006769335,0.00011993565,0.0013846128,0.00015307503,0.0000066596085],"category_scores_gemma":[0.000352466,0.00022218534,0.00026050105,0.0004690502,0.00014849429,0.0011410762,0.00030496434,0.00021211314,0.0000010985376],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021001792,0.00006353963,0.000008187652,0.00006595794,0.000068922534,2.6128058e-8,0.0009270543,0.000217525,0.586047,0.40082204,0.005503847,0.0062548956],"study_design_scores_gemma":[0.0006358523,0.0005511089,0.00009097899,0.000096178126,0.000028242579,0.0000050803305,0.0008636823,0.23814829,0.74854946,0.010175036,0.00057159807,0.00028451707],"about_ca_topic_score_codex":0.000012404803,"about_ca_topic_score_gemma":1.5654908e-7,"teacher_disagreement_score":0.53451425,"about_ca_system_score_codex":0.00022164648,"about_ca_system_score_gemma":0.00004781828,"threshold_uncertainty_score":0.9060455},"labels":[],"label_agreement":null},{"id":"W1993156157","doi":"10.3791/50887","title":"Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly","year":2014,"lang":"en","type":"article","venue":"Journal of Visualized Experiments","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Heart and Stroke Foundation; Sunnybrook Health Science Centre","funders":"Canadian Institutes of Health Research; Sunnybrook Research Institute; Alzheimer Society; Heart and Stroke Foundation of Canada","keywords":"Computer science; Segmentation; Pipeline (software); Artificial intelligence; Lesion; Neuroimaging; Medicine; Neuroscience; Pathology; Psychology","score_opus":0.07901929831273287,"score_gpt":0.4600877371353995,"score_spread":0.38106843882266667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993156157","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0141615225,0.00028023357,0.96800965,0.00020901927,0.00010858224,0.01715841,0.000002295102,0.000048331665,0.00002195928],"genre_scores_gemma":[0.032061756,0.00018842162,0.93734366,0.0009419275,0.00015468923,0.029226612,0.000003243992,0.000049921568,0.000029784142],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99772036,0.00021507511,0.0008589836,0.00029802014,0.0006245957,0.0002829642],"domain_scores_gemma":[0.99830407,0.00017461016,0.00059942994,0.00023111023,0.00028532642,0.0004054401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013675581,0.00019675924,0.00041261906,0.00038630838,0.000109154586,0.00027613004,0.00038077912,0.000067911926,0.00001781101],"category_scores_gemma":[0.00049918605,0.00016337108,0.000076961005,0.00028976487,0.00007697126,0.0013420307,0.0001955609,0.0001446455,8.8787266e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.010508179,0.002693007,0.0043171006,0.0006707783,0.00056100637,0.00026339942,0.009669507,0.00007028354,0.29286817,0.0015130125,0.06339716,0.6134684],"study_design_scores_gemma":[0.039216377,0.0042173085,0.0015056795,0.0009350505,0.0000943576,0.00006569973,0.00031457,0.057672787,0.8815108,0.003353601,0.010358377,0.00075536786],"about_ca_topic_score_codex":0.0000090916865,"about_ca_topic_score_gemma":2.9202744e-7,"teacher_disagreement_score":0.61271304,"about_ca_system_score_codex":0.00006778643,"about_ca_system_score_gemma":0.0001301105,"threshold_uncertainty_score":0.6662079},"labels":[],"label_agreement":null},{"id":"W1993947467","doi":"10.1016/s1076-6332(03)00671-8","title":"Statistical validation of image segmentation quality based on a spatial overlap index1","year":2004,"lang":"en","type":"article","venue":"Academic Radiology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1857,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Center for Research Resources; U.S. National Library of Medicine; National Cancer Institute; National Institute on Aging; Agency for Healthcare Research and Quality","keywords":"Reproducibility; Segmentation; Nuclear medicine; Artificial intelligence; Medicine; Pattern recognition (psychology); Computer science; Image quality; Metric (unit); Magnetic resonance imaging; Radiology; Mathematics; Statistics; Image (mathematics)","score_opus":0.02476122096801544,"score_gpt":0.35561046427766924,"score_spread":0.3308492433096538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993947467","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0508836,0.000008855728,0.9470442,0.0011805354,0.00017035603,0.00025573,0.000015569505,0.0001374518,0.00030373628],"genre_scores_gemma":[0.6693523,0.000024422277,0.32885426,0.0015999055,0.000057842764,0.00003323042,0.00006422739,0.0000069203875,0.000006918845],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99804354,0.00041140517,0.00057190907,0.00036052466,0.0003962347,0.00021641375],"domain_scores_gemma":[0.99871945,0.00051109534,0.0002889228,0.00031279924,0.00007032347,0.00009741698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008804516,0.00012472342,0.00023722411,0.00031683943,0.000041328058,0.000012627576,0.00046815746,0.0002119115,0.00006627597],"category_scores_gemma":[0.00069161825,0.00011794744,0.000039858223,0.00036414355,0.00021874989,0.00029060128,0.000058214864,0.00037972836,0.000029877669],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001854782,0.00030324105,0.007667132,0.00011584119,0.000042690375,0.00003313021,0.0015488921,0.00166182,0.7413548,0.13003382,0.003671098,0.11338204],"study_design_scores_gemma":[0.0018544359,0.0005590403,0.02228048,0.00003729708,0.000011531633,0.000017638336,0.000035551224,0.005928565,0.94296753,0.026040388,0.000045453846,0.00022206652],"about_ca_topic_score_codex":0.00020139226,"about_ca_topic_score_gemma":0.0000014637169,"teacher_disagreement_score":0.6184687,"about_ca_system_score_codex":0.00012789964,"about_ca_system_score_gemma":0.0001427729,"threshold_uncertainty_score":0.48097572},"labels":[],"label_agreement":null},{"id":"W1994343730","doi":"10.1002/sim.1723","title":"Three validation metrics for automated probabilistic image segmentation of brain tumours","year":2004,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":95,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Center for Research Resources; U.S. National Library of Medicine; National Cancer Institute; Agency for Healthcare Research and Quality; National Institutes of Health","keywords":"Segmentation; Computer science; Artificial intelligence; Markov random field; Metric (unit); Similarity (geometry); Pixel; Pattern recognition (psychology); Gold standard (test); Image segmentation; Image (mathematics); Mathematics; Statistics","score_opus":0.027769904315747322,"score_gpt":0.3596305153886217,"score_spread":0.33186061107287435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994343730","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008811906,0.000029982446,0.9960353,0.0015424445,0.00022018215,0.00089352403,0.000057578254,0.00025957433,0.000080272854],"genre_scores_gemma":[0.029939307,0.0000137503675,0.9693884,0.00031796467,0.000037205828,0.00009663254,0.00018312185,0.000012416726,0.00001115267],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99825937,0.00004889233,0.00064025685,0.00028374768,0.0005632891,0.00020444216],"domain_scores_gemma":[0.9980875,0.000911828,0.00028986722,0.00028450694,0.00034488065,0.00008141777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012266673,0.00012930755,0.00026501244,0.00040698203,0.00003938193,0.000022842263,0.0003824552,0.000050070714,0.000025699159],"category_scores_gemma":[0.0050761476,0.00011573169,0.000018367971,0.0008944515,0.00021700333,0.00023928132,0.000052412062,0.0001040133,0.000003156427],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008487922,0.0008168269,0.0013540104,0.0017881902,0.00006842419,0.00010267505,0.006617204,0.0018507957,0.16305736,0.53760445,0.07506582,0.21158937],"study_design_scores_gemma":[0.0058462657,0.0014938184,0.006550688,0.0004421081,0.00005873552,0.000013111318,0.00024977204,0.19803026,0.13669865,0.6502268,0.000029628436,0.0003601898],"about_ca_topic_score_codex":0.00013789615,"about_ca_topic_score_gemma":0.00004582826,"teacher_disagreement_score":0.21122918,"about_ca_system_score_codex":0.00019028907,"about_ca_system_score_gemma":0.00013326004,"threshold_uncertainty_score":0.607699},"labels":[],"label_agreement":null},{"id":"W1994363864","doi":"10.1109/tip.2013.2295752","title":"Enhanced Decoupled Active Contour Using Structural and Textural Variation Energy Functionals","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Active contour model; Boundary (topology); Artificial intelligence; Parametric statistics; Computer vision; Computer science; Object detection; Image segmentation; Segmentation; Pattern recognition (psychology); Computation; Image texture; Mathematics; Algorithm","score_opus":0.013403651853249082,"score_gpt":0.27960202217320607,"score_spread":0.26619837031995697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994363864","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00869558,0.000027782658,0.9902964,0.00015226936,0.00027002554,0.00011613232,0.0000032066293,0.00030732763,0.00013123127],"genre_scores_gemma":[0.75486076,0.0000055868486,0.24452633,0.00044465568,0.00005465356,0.000024696372,0.0000015826416,0.000012261057,0.00006946457],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859893,0.00010577815,0.000283067,0.0004332086,0.00034139206,0.00023761645],"domain_scores_gemma":[0.9991387,0.00013874966,0.00017365557,0.0001950419,0.00023187236,0.00012196291],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018615977,0.00018893083,0.00018449595,0.00018721481,0.0004431202,0.0003514355,0.00021351721,0.00007955101,0.00006370897],"category_scores_gemma":[0.000029499528,0.00017683182,0.000048024092,0.00030638595,0.00010143267,0.0019512876,0.000004413337,0.00018225932,0.0000035373537],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002354936,0.000025429257,4.986378e-7,0.000024159286,0.000015225415,0.000001242312,0.00050470035,0.0002471637,0.3632415,0.00012154772,0.000005908951,0.6357891],"study_design_scores_gemma":[0.0004192462,0.00005614692,0.0001581646,0.000052909527,0.000018567227,0.000026293596,0.000037971764,0.5102957,0.4873741,0.001378636,0.0000057484426,0.00017654138],"about_ca_topic_score_codex":0.000063535015,"about_ca_topic_score_gemma":0.000013824098,"teacher_disagreement_score":0.7461652,"about_ca_system_score_codex":0.000098873024,"about_ca_system_score_gemma":0.000085619286,"threshold_uncertainty_score":0.72109926},"labels":[],"label_agreement":null},{"id":"W1994804254","doi":"10.1016/j.neuroimage.2011.06.054","title":"Appearance-based modeling for segmentation of hippocampus and amygdala using multi-contrast MR imaging","year":2011,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Segmentation; Artificial intelligence; Sørensen–Dice coefficient; Contrast (vision); Pattern recognition (psychology); Active appearance model; Computer science; Weighting; Image segmentation; Voxel; Partial volume; Computer vision; Mathematics; Image (mathematics); Physics","score_opus":0.07852845862408922,"score_gpt":0.3065548098609314,"score_spread":0.2280263512368422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994804254","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.102326445,0.000078790064,0.8968999,0.000041503106,0.00009872522,0.00038375426,0.0000039588167,0.00013628477,0.000030687082],"genre_scores_gemma":[0.5098947,0.000004026102,0.4897732,0.0002916164,0.000009106157,0.00001478239,0.000001277463,0.00000939095,0.0000018332277],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892056,0.00006577859,0.00029433085,0.00033782126,0.00018105106,0.00020044183],"domain_scores_gemma":[0.99936694,0.00004736127,0.00014188586,0.00025184493,0.00011444433,0.000077501456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027421257,0.00012137437,0.00015185788,0.00012259734,0.000077256635,0.00005502121,0.00026220028,0.000025983547,0.0000045682295],"category_scores_gemma":[0.00007861923,0.00012451262,0.000046403104,0.000131434,0.00008317751,0.0005806186,0.00006594983,0.000078282865,8.4684564e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032112723,0.00014643486,0.001977147,0.00017697812,0.0000081913695,0.000014830441,0.0010618826,0.0003089547,0.7947281,0.00015132371,0.000020825635,0.20137319],"study_design_scores_gemma":[0.00074176246,0.000037203685,0.00039654376,0.00004590274,0.000008944348,0.0000051422758,0.00002953984,0.7800299,0.21795392,0.0006588546,7.118632e-7,0.00009159968],"about_ca_topic_score_codex":0.00005316423,"about_ca_topic_score_gemma":0.0000013693507,"teacher_disagreement_score":0.7797209,"about_ca_system_score_codex":0.000018756804,"about_ca_system_score_gemma":0.00004184518,"threshold_uncertainty_score":0.5077477},"labels":[],"label_agreement":null},{"id":"W1994846734","doi":"10.1117/12.652446","title":"An ITK framework for deterministic global optimization for medical image registration","year":2006,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Robarts Clinical Trials","funders":"U.S. Air Force","keywords":"Computer science; Image registration; Metric (unit); Speedup; Medical imaging; Mutual information; Range (aeronautics); Similarity (geometry); Locality; Artificial intelligence; Computer vision; Image (mathematics)","score_opus":0.009776366040109646,"score_gpt":0.27450768595134245,"score_spread":0.2647313199112328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994846734","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23511793,0.000022016795,0.7601864,0.0027850666,0.000240665,0.0010134921,0.00006490104,0.00021209706,0.00035746797],"genre_scores_gemma":[0.031980593,0.000019138639,0.96649355,0.00026353137,0.0005793184,0.0005481157,0.00004245964,0.00003575246,0.00003751791],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971343,3.8670475e-8,0.0009057488,0.00053609716,0.0010179953,0.000405781],"domain_scores_gemma":[0.9966726,0.00035606936,0.0005404796,0.000106823245,0.002140194,0.00018386956],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010659926,0.00028732893,0.00035413002,0.0000731294,0.0001295906,0.00033421925,0.0017410174,0.00030947285,0.000012088127],"category_scores_gemma":[0.0022468534,0.00025685277,0.00046088026,0.00033349017,0.00025269875,0.0012120458,0.00012221822,0.00019356389,4.535353e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006533617,0.00020259935,0.000049082955,0.00043411858,0.000083886676,1.150829e-7,0.00005609086,0.00023034432,0.068834044,0.923319,0.0046929787,0.0020324318],"study_design_scores_gemma":[0.001204941,0.00074140506,0.00015115163,0.00030534758,0.000095537376,0.00001920919,0.00018564089,0.8395899,0.11226006,0.04457872,0.00047640214,0.0003916929],"about_ca_topic_score_codex":0.00000961154,"about_ca_topic_score_gemma":2.9175146e-7,"teacher_disagreement_score":0.87874025,"about_ca_system_score_codex":0.00019624468,"about_ca_system_score_gemma":0.00009880048,"threshold_uncertainty_score":0.9999884},"labels":[],"label_agreement":null},{"id":"W1994966878","doi":"10.1016/j.neuroimage.2014.03.044","title":"Automatic anatomical labeling of the complete cerebral vasculature in mouse models","year":2014,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Segmentation; Computer science; Artificial intelligence; High resolution; Pattern recognition (psychology); Graph; Computer vision; Geology","score_opus":0.023034728160458495,"score_gpt":0.2555506732591921,"score_spread":0.23251594509873363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994966878","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23532483,0.000012712039,0.7627899,0.00070646795,0.000110576526,0.0002278932,0.0000029241858,0.00021741036,0.0006073027],"genre_scores_gemma":[0.8389507,0.0000020287605,0.15945537,0.00152497,0.000013889672,0.000006843579,8.851152e-7,0.00000965119,0.00003569245],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986787,0.00026029747,0.00027646497,0.00024751172,0.0003600439,0.0001769738],"domain_scores_gemma":[0.99903613,0.00012435518,0.000091716676,0.00065391243,0.00004009005,0.000053813055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032976922,0.00009453031,0.00016400914,0.00007405502,0.000036633362,0.00005303676,0.00094432395,0.00004507146,0.000015646228],"category_scores_gemma":[0.0001820931,0.00006899035,0.00006720408,0.00030700353,0.00009192875,0.00036273943,0.00031624667,0.00025276022,0.000006102263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000938356,0.000511424,0.00406477,0.0003637612,0.000029916524,0.00003099386,0.0027760575,0.0026002943,0.7154573,0.09145285,0.0049656336,0.17773758],"study_design_scores_gemma":[0.00031716106,0.000020786523,0.0032274933,0.000028908766,0.0000024618064,0.000004781283,0.000003499142,0.94239944,0.047679577,0.00620526,0.000030145791,0.00008045332],"about_ca_topic_score_codex":0.000024989073,"about_ca_topic_score_gemma":0.000005612206,"teacher_disagreement_score":0.9397992,"about_ca_system_score_codex":0.000018244542,"about_ca_system_score_gemma":0.00002363378,"threshold_uncertainty_score":0.28133452},"labels":[],"label_agreement":null},{"id":"W1995083187","doi":"10.1016/j.media.2008.04.002","title":"Area-preserving flattening maps of 3D ultrasound carotid arteries images","year":2008,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thrombosis and Atherosclerosis Research Institute; Western University; Robarts Clinical Trials","funders":"Canadian Institutes of Health Research; Canada Research Chairs","keywords":"Flattening; Computer vision; Artificial intelligence; Carotid arteries; Ultrasound; Computer science; Anatomy; Radiology; Medicine; Surgery; Physics","score_opus":0.0155384322519146,"score_gpt":0.2721831698747432,"score_spread":0.25664473762282863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995083187","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010861117,0.00012083516,0.9855629,0.0007936458,0.00006872956,0.00010830377,0.000013717582,0.00030459545,0.0021661527],"genre_scores_gemma":[0.36797103,0.00038816655,0.62923956,0.0012198953,0.00011615816,0.0000456728,0.00009781042,0.000023217572,0.0008984862],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99624705,0.00023692234,0.0007692538,0.0005590598,0.0017676033,0.0004201192],"domain_scores_gemma":[0.9974518,0.00058279786,0.0002683022,0.0009851669,0.00030454874,0.00040741774],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008874317,0.00023148628,0.00064467243,0.00056002394,0.0001895329,0.00010184716,0.001633513,0.00012316446,0.0022999644],"category_scores_gemma":[0.001745748,0.00019951446,0.00031584303,0.0021549757,0.0007168648,0.0009867585,0.00037051804,0.00032507876,0.00003678153],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004046408,0.0013860659,0.2333604,0.000495958,0.005855411,0.004008095,0.014902854,0.00011391512,0.4032928,0.00045567917,0.23316331,0.10292505],"study_design_scores_gemma":[0.0015167113,0.00027332405,0.044907235,0.00028056058,0.0013239264,0.0004933642,0.0006855055,0.043802477,0.9028224,0.0013037625,0.001167992,0.0014227878],"about_ca_topic_score_codex":0.0003016266,"about_ca_topic_score_gemma":0.000027762497,"teacher_disagreement_score":0.49952954,"about_ca_system_score_codex":0.0000385946,"about_ca_system_score_gemma":0.00014519891,"threshold_uncertainty_score":0.99861205},"labels":[],"label_agreement":null},{"id":"W1995085281","doi":"10.1118/1.4742056","title":"Experimental validation of an intrasubject elastic registration algorithm for dynamic‐3D ultrasound images","year":2012,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vancouver General Hospital; Queen's University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Image registration; Feature (linguistics); Computer science; Artificial intelligence; Computer vision; 3D ultrasound; Visualization; Matching (statistics); Ultrasound; Algorithm; Image (mathematics); Mathematics; Radiology; Medicine","score_opus":0.015835910093755057,"score_gpt":0.32064602765716443,"score_spread":0.30481011756340937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995085281","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064946883,0.00005187547,0.99252,0.000063463485,0.00031600715,0.00027163586,0.000010298631,0.00015105384,0.00012102519],"genre_scores_gemma":[0.63690597,0.0000049207542,0.36251467,0.00014740149,0.00024499782,0.00005779257,0.00009435499,0.000010259205,0.000019622566],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983856,0.000084329426,0.00031981786,0.00022131196,0.00074498507,0.0002439409],"domain_scores_gemma":[0.9989106,0.00030017443,0.00016310888,0.00029433038,0.00008919803,0.00024257599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005340806,0.00012120058,0.00016266345,0.000035169105,0.00006267185,0.000045652763,0.00042415116,0.00008094717,0.000052391333],"category_scores_gemma":[0.00033530692,0.00011186388,0.000052191546,0.00017038314,0.00016813523,0.0011461283,0.000052548534,0.00011623691,0.000006320519],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005784508,0.00091737544,0.00005546482,0.00004314422,0.000021143895,0.00000164288,0.0009245047,0.000002874257,0.07932784,0.0006255484,0.0009785081,0.9170962],"study_design_scores_gemma":[0.00032189465,0.00021232186,0.00017964761,0.00002407401,0.000009688481,0.000009056496,0.000041505427,0.0105616655,0.98678035,0.0017217115,0.000016353933,0.00012171347],"about_ca_topic_score_codex":0.000010623382,"about_ca_topic_score_gemma":2.6043512e-7,"teacher_disagreement_score":0.9169745,"about_ca_system_score_codex":0.000061429506,"about_ca_system_score_gemma":0.00008141333,"threshold_uncertainty_score":0.4561677},"labels":[],"label_agreement":null},{"id":"W1995291210","doi":"10.1109/icassp.2013.6638103","title":"A generic demosaicing algorithm based on a diffusion model","year":2013,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Demosaicing; Color filter array; RGB color model; Computer science; Computer vision; Artificial intelligence; Algorithm; Enhanced Data Rates for GSM Evolution; Color gel; Bayer filter; Image (mathematics); Image processing; Color image","score_opus":0.016899432165354153,"score_gpt":0.25294961250000686,"score_spread":0.2360501803346527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995291210","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00066148327,0.000003219301,0.9943921,0.0009775361,0.0000429804,0.00020339138,2.515691e-7,0.00048219817,0.003236853],"genre_scores_gemma":[0.020374248,0.0000024159967,0.9678752,0.010918478,0.000016392696,0.000063068444,0.0000017395731,0.0000060453735,0.00074242],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907976,0.000033629178,0.00014325623,0.00024660278,0.00032666387,0.0001700621],"domain_scores_gemma":[0.9993876,0.000049324648,0.00003591708,0.00035479592,0.000051114534,0.00012123785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011061047,0.000086974724,0.00007949317,0.00008985197,0.000058498492,0.00010711628,0.0004036136,0.000035761164,0.0002309485],"category_scores_gemma":[0.000025199728,0.0000653421,0.000031155385,0.00019895368,0.000019689462,0.00031853456,0.000106951775,0.00007548997,0.00017273286],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.9544796e-7,0.000065740205,0.000012266387,0.0000030989172,0.0000011279533,0.000002512514,0.000058233276,0.0005450347,0.012694913,0.0004623809,0.012344292,0.9738101],"study_design_scores_gemma":[0.00013927363,0.000045493645,0.00007183367,0.00000984856,7.553297e-7,8.7926634e-7,0.000003866766,0.95610255,0.042127818,0.0013943665,0.000020137653,0.00008315007],"about_ca_topic_score_codex":0.00008228303,"about_ca_topic_score_gemma":5.4957695e-7,"teacher_disagreement_score":0.9737269,"about_ca_system_score_codex":0.000035063298,"about_ca_system_score_gemma":0.00003479398,"threshold_uncertainty_score":0.26645738},"labels":[],"label_agreement":null},{"id":"W1995389184","doi":"10.1109/icassp.2010.5495336","title":"A robust morphological gradient estimator and edge detector for color images","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Image gradient; Artificial intelligence; Estimator; Noise (video); Computer vision; Pixel; Morphological gradient; Computer science; Robustness (evolution); Mathematics; Edge detection; Image (mathematics); Pattern recognition (psychology); Image processing; Statistics","score_opus":0.029759986916612486,"score_gpt":0.2839621504045179,"score_spread":0.2542021634879054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995389184","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029265236,0.000012702282,0.96837443,0.0009867068,0.00021948919,0.00039983663,0.000004039856,0.00042598814,0.00031158348],"genre_scores_gemma":[0.09600247,0.0000032921782,0.9027848,0.00075196265,0.00003838095,0.00015458265,0.0000014122734,0.000005400573,0.00025764678],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991586,0.00001893959,0.00015919913,0.00032358183,0.00013550345,0.00020416704],"domain_scores_gemma":[0.9992399,0.00021000647,0.00004420443,0.00025387955,0.000071181756,0.00018081225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027515602,0.00009911377,0.00012129035,0.000052693318,0.00009583259,0.00013669707,0.00038540503,0.00006802155,0.00011458501],"category_scores_gemma":[0.00035525433,0.00007185439,0.000033934608,0.000093090675,0.00015567473,0.0002456457,0.00017560493,0.00012464872,0.000013630376],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017577084,0.00020599298,0.0005154501,0.00004735786,0.000014809435,0.000056122582,0.00014878255,0.0000021368387,0.76824063,0.028042205,0.051278137,0.15143079],"study_design_scores_gemma":[0.00086151995,0.0005325956,0.0038813155,0.000009536361,0.000010598588,0.00018480614,0.000023433342,0.06638204,0.92156035,0.0048083332,0.0014050932,0.00034036217],"about_ca_topic_score_codex":0.000008780436,"about_ca_topic_score_gemma":0.0000052205833,"teacher_disagreement_score":0.15331972,"about_ca_system_score_codex":0.000009660861,"about_ca_system_score_gemma":0.000024461327,"threshold_uncertainty_score":0.2930137},"labels":[],"label_agreement":null},{"id":"W1995395399","doi":"10.1109/icip.2006.312755","title":"Weighted Voting-Based Robust Image Thresholding","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Thresholding; Artificial intelligence; Robustness (evolution); Balanced histogram thresholding; Computer science; Image (mathematics); Pattern recognition (psychology); Computer vision; Voting; Image processing","score_opus":0.01348844265725141,"score_gpt":0.25107651914455037,"score_spread":0.23758807648729896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995395399","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00073116703,0.000015801697,0.97925645,0.0011334702,0.00008445993,0.00011852396,4.675723e-7,0.0011966578,0.017462973],"genre_scores_gemma":[0.12374918,4.4269444e-7,0.87397134,0.001535133,0.000055614648,0.000012462383,0.000005528497,0.0000074932946,0.0006627768],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99890876,0.00003667307,0.0002228565,0.00028073037,0.0003249424,0.00022600974],"domain_scores_gemma":[0.999411,0.0000760328,0.00006289697,0.00030496382,0.00007621458,0.00006891795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025035546,0.000100459634,0.00009502578,0.00010627838,0.00008329328,0.00020548665,0.0005573111,0.000039758084,0.00035599124],"category_scores_gemma":[0.00002647791,0.00008453978,0.000043940123,0.00033270314,0.000055864253,0.00047590426,0.000099033714,0.00009313059,0.00009429456],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000905069,0.0006586736,0.004053189,0.00009020576,0.000020419604,0.000265029,0.00011337821,0.00048512657,0.20596616,0.2825943,0.3968379,0.108906575],"study_design_scores_gemma":[0.0002794204,0.000027059288,0.0006382332,0.000016885002,0.0000022804904,0.000003411582,0.0000036526915,0.36833242,0.6281444,0.001969802,0.00041626507,0.0001662095],"about_ca_topic_score_codex":0.00011202368,"about_ca_topic_score_gemma":0.000010023689,"teacher_disagreement_score":0.4221782,"about_ca_system_score_codex":0.00003596572,"about_ca_system_score_gemma":0.000038111935,"threshold_uncertainty_score":0.38978544},"labels":[],"label_agreement":null},{"id":"W1995730340","doi":"10.1109/isbi.2013.6556625","title":"Vessel Walker: Coronary arteries segmentation using random walks and hessian-based vesselness filter","year":2013,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Hessian matrix; Segmentation; Artificial intelligence; Random walker algorithm; Computer science; Computer vision; Image segmentation; Filter (signal processing); Pattern recognition (psychology); Coronary arteries; Random walk; Mathematics; Pixel; Artery; Medicine; Statistics; Cardiology","score_opus":0.017466834064069514,"score_gpt":0.2634608490284216,"score_spread":0.24599401496435208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995730340","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07694468,0.000045540884,0.9209581,0.0007775993,0.00014800955,0.0004442971,0.0000011932615,0.0003175303,0.00036305448],"genre_scores_gemma":[0.5654375,0.00001131959,0.4318042,0.0023848887,0.000027683918,0.00010392275,0.0000116620495,0.000011798055,0.00020701702],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99870473,0.00012324013,0.00029156188,0.00034509652,0.00030516178,0.000230236],"domain_scores_gemma":[0.9991953,0.00015989998,0.00009764592,0.00031117842,0.000091983566,0.00014398723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020349471,0.00016145383,0.00017907206,0.000105317726,0.00014343913,0.0003721254,0.00030051396,0.00006296589,0.000669733],"category_scores_gemma":[0.000022723,0.00013292169,0.00003233793,0.0001730759,0.0001403556,0.0014487219,0.00010303927,0.0000922023,0.000043689168],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004350847,0.00020223417,0.0030801669,0.00017854084,0.000039710663,0.000034808938,0.0018402075,0.00005218176,0.3177329,0.00079217495,0.013451799,0.66255176],"study_design_scores_gemma":[0.0032035536,0.00015540492,0.010043404,0.000109606306,0.000020833108,0.000046659865,0.00045584427,0.3366043,0.6452775,0.0032989136,0.00022244536,0.00056155253],"about_ca_topic_score_codex":0.000111978465,"about_ca_topic_score_gemma":0.000001629648,"teacher_disagreement_score":0.6619902,"about_ca_system_score_codex":0.000035461304,"about_ca_system_score_gemma":0.000054467055,"threshold_uncertainty_score":0.73331064},"labels":[],"label_agreement":null},{"id":"W1996113760","doi":"10.1190/1.1817388","title":"The key practical aspects of 3D tomography: Data picking and model representation","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Key (lock); Computer science; Representation (politics); Tomography; Data modeling; Solid modeling; Artificial intelligence; Computer vision; Software engineering; Computer security; Radiology; Medicine","score_opus":0.10896819549550173,"score_gpt":0.3638864799070378,"score_spread":0.2549182844115361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996113760","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028113974,0.000075178315,0.98475915,0.0045441682,0.000028850322,0.000105770276,8.0278386e-7,0.00010161629,0.0101033235],"genre_scores_gemma":[0.20587753,0.00017166212,0.7933008,0.0004733354,0.000012903638,0.0000049100895,0.0000020389905,0.0000030006327,0.00015379905],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991362,0.000057234574,0.00017275567,0.00023499595,0.0002995355,0.00009926518],"domain_scores_gemma":[0.9986643,0.00040841993,0.000076290366,0.0007513297,0.00005001635,0.00004959538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034032454,0.00004762544,0.0000613215,0.000039849543,0.00007720308,0.00011271853,0.0005056122,0.000021508064,0.000025838404],"category_scores_gemma":[0.00046339174,0.000031967003,0.000010299577,0.00019284956,0.00010236954,0.00089008687,0.00041613213,0.0000750898,0.0000027538802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005072657,0.00017932693,0.0009188435,0.000024792162,0.000046656936,0.00001935211,0.0018059332,0.000034842546,0.00525312,0.32720646,0.045402065,0.61910355],"study_design_scores_gemma":[0.00009781725,0.000024195,0.0002021827,0.0000067499336,0.000004782124,0.000010788921,0.000038476323,0.97460085,0.014944925,0.009904399,0.000114849645,0.000050002673],"about_ca_topic_score_codex":0.000026137224,"about_ca_topic_score_gemma":0.000008811246,"teacher_disagreement_score":0.974566,"about_ca_system_score_codex":0.0000046281234,"about_ca_system_score_gemma":0.000013376816,"threshold_uncertainty_score":0.13035767},"labels":[],"label_agreement":null},{"id":"W1996380367","doi":"10.1007/s11548-011-0620-2","title":"Comparing two approaches to rigid registration of three-dimensional ultrasound and magnetic resonance images for neurosurgery","year":2011,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"Canadian Institutes of Health Research","keywords":"Neurosurgery; Magnetic resonance imaging; Ultrasound; Image registration; Computer science; Medical physics; Radiology; Medicine; Computer vision; Artificial intelligence; Image (mathematics)","score_opus":0.11275969748187674,"score_gpt":0.2797306583284756,"score_spread":0.16697096084659885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996380367","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20577441,0.0006777905,0.7921655,0.00047722275,0.00074740057,0.00009349325,0.000003579373,0.000018827923,0.00004179194],"genre_scores_gemma":[0.71693003,0.000048905604,0.28237098,0.00047881837,0.00014721093,0.0000060528855,0.0000035668393,0.0000054342822,0.000008977408],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9985249,0.00014043383,0.0007067957,0.00022132856,0.0002698578,0.00013670034],"domain_scores_gemma":[0.9975269,0.0015011475,0.00044383277,0.00012623273,0.00029199073,0.00010993371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010596191,0.00011921744,0.0003571397,0.00033557945,0.000047362053,0.00005485261,0.00034282,0.000053574317,0.0000052740074],"category_scores_gemma":[0.00019857624,0.00010424476,0.00009691303,0.00009691683,0.00019119531,0.00036590346,0.000090050475,0.00012661377,2.7013903e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008341287,0.00044251196,0.30687824,0.000073574294,0.00029536182,0.00028045045,0.0008022315,0.00012857199,0.007506919,0.007978788,0.021276765,0.65350246],"study_design_scores_gemma":[0.0009872881,0.00047626384,0.9595446,0.00025391264,0.000031659776,0.0048291874,0.000010711169,0.01667522,0.010290636,0.006110917,0.0005061416,0.00028344602],"about_ca_topic_score_codex":0.000012804118,"about_ca_topic_score_gemma":0.0000032392177,"teacher_disagreement_score":0.653219,"about_ca_system_score_codex":0.000017823015,"about_ca_system_score_gemma":0.00007399882,"threshold_uncertainty_score":0.42509782},"labels":[],"label_agreement":null},{"id":"W1996515728","doi":"10.1016/j.jneumeth.2006.03.016","title":"Registration of in vivo magnetic resonance T1-weighted brain images to triphenyltetrazolium chloride stained sections in small animals","year":2006,"lang":"en","type":"article","venue":"Journal of Neuroscience Methods","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health","keywords":"Magnetic resonance imaging; In vivo; Fixation (population genetics); Image registration; Histology; Pathology; Biomedical engineering; Landmark; Nuclear medicine; Artificial intelligence; Nuclear magnetic resonance; Computer science; Medicine; Biology; Radiology; Physics; Image (mathematics)","score_opus":0.03273635456833894,"score_gpt":0.35997117312551086,"score_spread":0.3272348185571719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996515728","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13165024,0.00014855804,0.8654839,0.0018795772,0.00034019747,0.00022833887,0.0000021461126,0.000028011837,0.00023908187],"genre_scores_gemma":[0.1279665,0.000042742995,0.87085927,0.00069763,0.00006374431,0.00001196945,8.5546695e-8,0.000008844825,0.00034920181],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99682003,0.00078839995,0.0011814163,0.0003549074,0.00053856627,0.00031666923],"domain_scores_gemma":[0.998107,0.0006540797,0.00055008894,0.00031975916,0.00023942502,0.00012961241],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046812957,0.0001436238,0.00034349118,0.0009962427,0.000050533497,0.00011800448,0.0009836411,0.00005468858,0.000008240428],"category_scores_gemma":[0.0028363778,0.00013091478,0.000075933414,0.0030240377,0.00015261362,0.0006641453,0.00010243527,0.0003123498,2.8631106e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036660607,0.0001300601,0.00051957165,0.000012179811,2.7328048e-7,0.00008124648,0.00017503276,0.00018104432,0.9732992,0.00062934787,0.00052979536,0.024405593],"study_design_scores_gemma":[0.000518608,0.00086333405,0.09859791,0.000085900276,0.0000027570884,0.00010236788,0.00003354762,0.0068582306,0.886826,0.005167759,0.00080508925,0.0001385013],"about_ca_topic_score_codex":0.00017179752,"about_ca_topic_score_gemma":0.00006694724,"teacher_disagreement_score":0.09807833,"about_ca_system_score_codex":0.00012621081,"about_ca_system_score_gemma":0.00023758694,"threshold_uncertainty_score":0.53385496},"labels":[],"label_agreement":null},{"id":"W1996595747","doi":"10.1016/j.media.2010.12.003","title":"Evaluating intensity normalization on MRIs of human brain with multiple sclerosis","year":2010,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":178,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University Health Centre; McGill University; NeuroRx Research (Canada)","funders":"","keywords":"Artificial intelligence; Computer science; Segmentation; Normalization (sociology); Pattern recognition (psychology); Bayesian probability; Voxel; Computer vision","score_opus":0.04306000652787775,"score_gpt":0.35168841171107285,"score_spread":0.3086284051831951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996595747","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21611398,0.0000025039149,0.782203,0.0011598082,0.0000363387,0.00010572862,0.0000018770884,0.00014262267,0.00023412936],"genre_scores_gemma":[0.7854133,0.0000030612973,0.21280742,0.0016164164,0.000041307296,0.000017438733,0.000032803964,0.000008598955,0.000059602964],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99699473,0.00017881018,0.0004890453,0.00043643537,0.0016732335,0.00022774091],"domain_scores_gemma":[0.99787617,0.00036871218,0.00026837442,0.0007281877,0.00048160547,0.00027694376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018948644,0.00014993199,0.0003830196,0.00043475121,0.00013757756,0.00008445158,0.0007992762,0.00011021259,0.0008655478],"category_scores_gemma":[0.0031127285,0.00011446024,0.00014903123,0.0018300958,0.00032286512,0.00039998954,0.00020306928,0.00038807065,0.000017004782],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036391517,0.00066464866,0.03552946,0.00005424882,0.0007016203,0.00005905701,0.0013212416,0.000054958884,0.7398499,0.0006838215,0.003769066,0.21727557],"study_design_scores_gemma":[0.0009948574,0.0005307461,0.050164696,0.00009743339,0.0003629905,0.0000056148874,0.0000702946,0.43904027,0.50819504,0.00020935298,0.000019980518,0.0003087119],"about_ca_topic_score_codex":0.00038332795,"about_ca_topic_score_gemma":0.0003589897,"teacher_disagreement_score":0.5693956,"about_ca_system_score_codex":0.000023371607,"about_ca_system_score_gemma":0.00006992091,"threshold_uncertainty_score":0.94771415},"labels":[],"label_agreement":null},{"id":"W1996738877","doi":"10.1016/j.asoc.2006.12.003","title":"A reinforcement agent for threshold fusion","year":2007,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Reinforcement; Reinforcement learning; Fusion; Artificial intelligence; Mathematical optimization; Mathematics; Materials science; Composite material","score_opus":0.023702627435903692,"score_gpt":0.29983505916022457,"score_spread":0.2761324317243209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996738877","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019311827,0.000020336181,0.99111205,0.00012724228,0.0002202104,0.000632523,2.0526294e-7,0.00061515655,0.0053411014],"genre_scores_gemma":[0.46055776,0.0000014937638,0.5368704,0.0023861262,0.000088814,0.00001580655,0.0000056538083,0.000008821105,0.00006510165],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984666,0.0000065094505,0.00040041283,0.00035418983,0.00036758534,0.00040469997],"domain_scores_gemma":[0.999071,0.00024739117,0.00014688683,0.0003619786,0.00006217266,0.00011056187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012686582,0.00013191787,0.00014106455,0.00010035858,0.00021553977,0.00009603909,0.0005815566,0.00005782212,0.000011799311],"category_scores_gemma":[0.00003765163,0.00012698564,0.00005698495,0.0002317559,0.00003376101,0.000101025944,0.0003768041,0.000113092196,0.000024852896],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014503497,0.00005018138,0.0000388151,0.000047756435,0.000014158662,0.000007018347,0.00116128,0.00045881106,0.027308179,0.06751961,0.005246634,0.89813304],"study_design_scores_gemma":[0.0020434097,0.00025556088,0.000499956,0.000118614866,0.000017260321,0.000014177469,0.00028975538,0.46997583,0.5030161,0.012550761,0.01042448,0.00079405453],"about_ca_topic_score_codex":0.0000042659003,"about_ca_topic_score_gemma":8.575268e-7,"teacher_disagreement_score":0.897339,"about_ca_system_score_codex":0.00007572231,"about_ca_system_score_gemma":0.000029685949,"threshold_uncertainty_score":0.51783246},"labels":[],"label_agreement":null},{"id":"W1996766943","doi":"10.1007/s13173-011-0032-8","title":"Detection of point landmarks in 3D medical images via phase congruency model","year":2011,"lang":"en","type":"article","venue":"Journal of the Brazilian Computer Society","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Philips (Canada); Princess Margaret Cancer Centre; University of Toronto","funders":"Universidade Federal de Uberlândia","keywords":"Phase congruency; Artificial intelligence; Computer science; Computer vision; Point (geometry); Pattern recognition (psychology); Feature (linguistics); Image (mathematics); Mathematics; Geometry","score_opus":0.015298963223126294,"score_gpt":0.2755091249199168,"score_spread":0.2602101616967905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996766943","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01632576,0.00005527182,0.98224026,0.00062737963,0.00051881216,0.00011421795,0.0000011507582,0.000029629606,0.00008749921],"genre_scores_gemma":[0.48475835,0.000051936073,0.51379734,0.0012545661,0.00010706752,0.0000020288721,1.8809575e-7,0.000007794113,0.000020751353],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800736,0.00017533377,0.0007378256,0.00016470355,0.00072699744,0.00018775978],"domain_scores_gemma":[0.9986351,0.000086537504,0.0005479515,0.00035270661,0.00022106954,0.00015664802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013840721,0.00012480174,0.0002745098,0.000074710784,0.00005394357,0.000032526077,0.001441927,0.00011803047,0.00004015655],"category_scores_gemma":[0.00006842985,0.00008449546,0.00030786174,0.0003053798,0.0001715387,0.00061695033,0.00034132577,0.00050354167,0.0000014371143],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007605974,0.0017263133,0.0015267585,0.00013703262,0.00020447468,0.00009781875,0.014877097,0.00025734518,0.03930183,0.00020423603,0.021503948,0.9200871],"study_design_scores_gemma":[0.002230556,0.00037971747,0.0028250332,0.00021298125,0.000018810417,0.00024946904,0.000040782765,0.827051,0.1627988,0.003993307,0.00003394731,0.00016561623],"about_ca_topic_score_codex":0.00002837036,"about_ca_topic_score_gemma":0.00000667528,"teacher_disagreement_score":0.91992146,"about_ca_system_score_codex":0.0000814111,"about_ca_system_score_gemma":0.00019389512,"threshold_uncertainty_score":0.3445625},"labels":[],"label_agreement":null},{"id":"W1997430748","doi":"10.1155/2013/205494","title":"Robust Diffeomorphic Mapping via Geodesically Controlled Active Shapes","year":2013,"lang":"en","type":"article","venue":"International Journal of Biomedical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; National Institute on Aging; University of California, San Diego; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; University of California, Los Angeles; National Institutes of Health; Servier; Genentech; Innogenetics; Eisai; Natural Sciences and Engineering Research Council of Canada; Pfizer; Biogen; BioClinica; Canadian HIV Trials Network, Canadian Institutes of Health Research; Northern California Institute for Research and Education; Alzheimer's Association; Amorfix Life Sciences; National Center for Research Resources; F. Hoffmann-La Roche; Medpace; AstraZeneca; Eli Lilly and Company; Bristol-Myers Squibb; National Heart, Lung, and Blood Institute; Novartis Pharmaceuticals Corporation; Synarc; Bayer HealthCare; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Foundation for the National Institutes of Health","keywords":"Diffeomorphism; Geodesic; Computer science; Metric (unit); Algorithm; Landmark; Artificial intelligence; Mathematics; Geometry; Mathematical analysis","score_opus":0.01639820500168484,"score_gpt":0.26538728743108564,"score_spread":0.2489890824294008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997430748","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004315088,0.00009288076,0.9742886,0.019653937,0.0010323186,0.00019062485,0.0000017743884,0.00008855292,0.0003362648],"genre_scores_gemma":[0.6758432,0.0000522075,0.31829968,0.004911182,0.0007579868,0.000025483716,0.000005057304,0.000015648619,0.000089574845],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99672997,0.00014575716,0.00095086853,0.00023768385,0.0016527813,0.00028295358],"domain_scores_gemma":[0.99723256,0.000463989,0.00068957824,0.00014066565,0.0011057811,0.00036740414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007167059,0.00017208113,0.00037534026,0.0005917396,0.000060212707,0.00040309542,0.0018885734,0.00005652794,0.0006812396],"category_scores_gemma":[0.0007491686,0.0001299503,0.0002162523,0.0002461253,0.000265939,0.0016743318,0.0003107737,0.00041904446,0.000076164324],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050153143,0.00031072268,0.00027924034,0.000007636959,0.00030908236,0.00045419513,0.00042261303,0.000009177293,0.10987379,0.00038167293,0.007988738,0.879913],"study_design_scores_gemma":[0.02312562,0.00049791235,0.027462613,0.0012256537,0.00010295152,0.0045943186,0.00066174,0.85545707,0.042599462,0.037590496,0.0054318076,0.0012503765],"about_ca_topic_score_codex":0.000040697974,"about_ca_topic_score_gemma":2.9617343e-7,"teacher_disagreement_score":0.8786626,"about_ca_system_score_codex":0.00016935817,"about_ca_system_score_gemma":0.00013874384,"threshold_uncertainty_score":0.7459096},"labels":[],"label_agreement":null},{"id":"W1998076850","doi":"10.5244/c.22.16","title":"Parameter Selection for Graph Cut Based Image Segmentation","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Cut; Segmentation-based object categorization; Segmentation; Image segmentation; Scale-space segmentation; Artificial intelligence; Computer science; Pattern recognition (psychology); AdaBoost; Graph; Minimum spanning tree-based segmentation; Feature selection; Classifier (UML); Computer vision; Theoretical computer science","score_opus":0.026948132178639676,"score_gpt":0.2984128017541642,"score_spread":0.2714646695755245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998076850","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002271716,0.0000034162413,0.99574053,0.00043797708,0.00009491997,0.00042330447,0.0000011535209,0.00057055865,0.0004564423],"genre_scores_gemma":[0.021051895,0.0000051602037,0.9759052,0.002345841,0.000026282296,0.00018254931,0.00001312775,0.0000070251067,0.0004629321],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99912494,0.000044155586,0.00018462432,0.0002535885,0.00022946527,0.00016320651],"domain_scores_gemma":[0.99941945,0.0001514559,0.00006140654,0.00017297782,0.0001221923,0.00007250626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017061931,0.00008570208,0.000082142855,0.00012890394,0.0001309665,0.000060038557,0.00022636287,0.000038072216,0.00011913197],"category_scores_gemma":[0.00008106994,0.00007621691,0.00006259136,0.00029550033,0.000049907034,0.00065689924,0.000022488231,0.00005098534,0.000025282905],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048820002,0.00050353474,0.002731544,0.00009551618,0.000049546787,0.000013232226,0.00081614807,0.00003707896,0.61001223,0.0050109997,0.18772733,0.19295402],"study_design_scores_gemma":[0.00041945023,0.00013858493,0.00032842238,0.0000037335678,0.0000034222153,0.000010204184,0.0000072163484,0.0677821,0.92926866,0.001735655,0.00019189545,0.00011063424],"about_ca_topic_score_codex":0.000022933888,"about_ca_topic_score_gemma":0.000004358075,"teacher_disagreement_score":0.31925645,"about_ca_system_score_codex":0.000036725145,"about_ca_system_score_gemma":0.000047867583,"threshold_uncertainty_score":0.31080353},"labels":[],"label_agreement":null},{"id":"W1998150108","doi":"10.1109/icdsp.2013.6622693","title":"A multi-steps segmentation approach for 3D ultrasound images using the combination of 3D-Snake and Level-Set","year":2013,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Defence Research and Development Canada","funders":"","keywords":"Segmentation; Level set (data structures); 3D ultrasound; Artificial intelligence; Computer science; Level set method; Image segmentation; Computer vision; Speckle noise; Pattern recognition (psychology); Noise (video); Speckle pattern; Set (abstract data type); Scale-space segmentation; Ultrasound; Image (mathematics)","score_opus":0.07789226950764165,"score_gpt":0.32906418468288107,"score_spread":0.2511719151752394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998150108","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005256067,0.000026267147,0.993219,0.00016359208,0.00004229784,0.0010709276,0.000007974653,0.000073530486,0.00014031772],"genre_scores_gemma":[0.09373706,0.000008124318,0.90558565,0.00030108038,0.000010614187,0.00012210825,0.000022582319,0.0000064392434,0.00020632346],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990775,0.000083415485,0.00024984864,0.0002241984,0.00023303754,0.00013198539],"domain_scores_gemma":[0.99917066,0.00022099624,0.00014343899,0.00022841076,0.00019231385,0.000044183715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004178491,0.00009496333,0.000107425825,0.00006866092,0.000111055095,0.0001652875,0.00027903612,0.000039799907,0.000023168213],"category_scores_gemma":[0.000116356816,0.00006556999,0.00002374103,0.00015412997,0.00012181253,0.00063416344,0.00008027286,0.000052346986,0.0000012045961],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000057613647,0.0003085303,0.0012403291,0.00017951707,0.000047357957,2.4413762e-7,0.0031502603,0.000045637884,0.8432118,0.0019193531,0.0053133657,0.14457783],"study_design_scores_gemma":[0.001195787,0.00013421102,0.006238388,0.000018736406,0.000019867422,0.000016733535,0.0008915924,0.54556316,0.44486442,0.00084779767,0.000016152026,0.00019315326],"about_ca_topic_score_codex":0.00015699798,"about_ca_topic_score_gemma":0.00000274064,"teacher_disagreement_score":0.5455175,"about_ca_system_score_codex":0.000024317667,"about_ca_system_score_gemma":0.000024485598,"threshold_uncertainty_score":0.26738665},"labels":[],"label_agreement":null},{"id":"W1998816291","doi":"10.4236/ojrad.2012.21001","title":"Color Fusion of Magnetic Resonance Images Improves Intracranial Volume Measurement in Studies of Aging","year":2012,"lang":"en","type":"article","venue":"Open Journal of Radiology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Sciences Centre","funders":"Economic and Social Research Council; Biotechnology and Biological Sciences Research Council; Centre for Cognitive Ageing and Cognitive Epidemiology; Medical Research Council; Engineering and Physical Sciences Research Council; Age UK; Mrs Gladys Row Fogo Charitable Trust","keywords":"Thresholding; Segmentation; Magnetic resonance imaging; Artificial intelligence; Medicine; Fusion; Gold standard (test); Computer vision; Nuclear medicine; Computer science; Pattern recognition (psychology); Radiology; Image (mathematics)","score_opus":0.043808366587980097,"score_gpt":0.33236244871537485,"score_spread":0.28855408212739475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998816291","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6763844,0.05423472,0.26494968,0.0022706923,0.0012196164,0.0006400854,0.0000028583036,0.000015110904,0.00028283594],"genre_scores_gemma":[0.7682624,0.0012867061,0.23020504,0.00012888198,0.00007228385,0.000006079665,1.07014415e-7,0.0000044536764,0.00003407857],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982776,0.00036208564,0.0007427372,0.000107636275,0.00031485182,0.00019507219],"domain_scores_gemma":[0.9986381,0.00013330323,0.0006009947,0.00017163335,0.00038767824,0.00006829873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00339879,0.00008425415,0.00045454287,0.00015806367,0.000024381425,0.000015025108,0.0009443451,0.000041701544,0.00002958536],"category_scores_gemma":[0.0006100191,0.00006555901,0.000044147808,0.00018227778,0.00020791007,0.0006438565,0.00033287134,0.00015698401,0.0000010454597],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000092210765,0.00021800646,0.0127267325,0.0000673516,0.000034707144,0.000039165272,0.0026057716,0.0000043209902,0.55949706,0.00030208373,0.0045004836,0.4199121],"study_design_scores_gemma":[0.0049258033,0.005755192,0.30893463,0.0011446835,0.00007200771,0.0014294314,0.0015779244,0.000825423,0.67044556,0.0028684791,0.00159316,0.00042769386],"about_ca_topic_score_codex":0.000026582267,"about_ca_topic_score_gemma":0.0000047450917,"teacher_disagreement_score":0.4194844,"about_ca_system_score_codex":0.00007972849,"about_ca_system_score_gemma":0.000102892416,"threshold_uncertainty_score":0.26734188},"labels":[],"label_agreement":null},{"id":"W1999063347","doi":"10.1088/0031-9155/53/19/003","title":"Subject-specific models for image-guided cardiac surgery","year":2008,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Robarts Clinical Trials; Grand River Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Heart and Stroke Foundation of Canada","keywords":"Computer science; Artificial intelligence; Visualization; Surgical planning; Computer vision; Image quality; Subject (documents); Image-guided surgery; Noise (video); Image (mathematics); Medicine; Radiology","score_opus":0.3320716253737013,"score_gpt":0.40121179305652305,"score_spread":0.06914016768282177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999063347","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050568,0.0006741219,0.99190843,0.0012115715,0.00026848642,0.00020272445,0.0000034762345,0.00007817707,0.0005962145],"genre_scores_gemma":[0.61746496,0.0129261715,0.36199725,0.0053343317,0.0016764846,0.00030811512,0.00013367941,0.00002867193,0.00013036598],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99907005,0.00008990152,0.00025569642,0.00029290296,0.000082991726,0.00020845601],"domain_scores_gemma":[0.9989266,0.0006454515,0.000063910054,0.00022310327,0.0000782029,0.00006272815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050965184,0.00010091785,0.00034219516,0.00008989514,0.000055654808,0.0000059018475,0.00019850841,0.00005338324,0.000004692102],"category_scores_gemma":[0.00008747407,0.00007644561,0.0000449505,0.00022198405,0.00033946376,0.00019095991,0.00007043047,0.00009630456,0.0000019562494],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002305136,0.00013175167,0.0029221678,0.00008833992,0.000039729002,0.000035142115,0.0025264586,0.000005850639,0.06595602,0.09203684,0.27543175,0.5608029],"study_design_scores_gemma":[0.0021434461,0.0007374903,0.001791641,0.00019415037,0.00002416247,0.00009032635,0.0002507759,0.042379033,0.12774777,0.80953765,0.014152604,0.00095094333],"about_ca_topic_score_codex":0.00004190225,"about_ca_topic_score_gemma":5.07818e-7,"teacher_disagreement_score":0.7175008,"about_ca_system_score_codex":0.000016162621,"about_ca_system_score_gemma":0.000038294744,"threshold_uncertainty_score":0.31173617},"labels":[],"label_agreement":null},{"id":"W1999120491","doi":"10.1109/icip.2011.6116025","title":"Multi-scale 3D representation via volumetric quasi-random scale space","year":2011,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scale (ratio); Scale space; Representation (politics); Estimator; Computer science; Gaussian; Space (punctuation); Nonlinear system; Mathematics; Algorithm; Artificial intelligence; Statistics; Physics; Image processing","score_opus":0.04063419127269936,"score_gpt":0.29764379474213465,"score_spread":0.2570096034694353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999120491","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013345211,0.000036239453,0.9898119,0.00017592567,0.0002592177,0.000354271,6.800257e-7,0.0007831898,0.007244065],"genre_scores_gemma":[0.045152735,0.000021845135,0.9501479,0.0005045398,0.000034539382,0.000048013775,0.0000036062866,0.000011196655,0.0040756054],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983215,0.00014563318,0.00033228673,0.0004808227,0.000452103,0.00026769805],"domain_scores_gemma":[0.9987513,0.000104045605,0.00012360867,0.00068470556,0.00013897404,0.00019740238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043634573,0.0001375209,0.00018578774,0.00027549392,0.00009144247,0.000090544054,0.00072315015,0.00007704981,0.00068207557],"category_scores_gemma":[0.00013692943,0.00012107896,0.000073948606,0.0010168198,0.00009160712,0.00090316625,0.0002244293,0.000111948764,0.000331168],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039764796,0.0012083313,0.01946414,0.000034711484,0.00003450773,0.000036049587,0.0059725945,0.0000024276135,0.044657376,0.00033903364,0.019831173,0.9083799],"study_design_scores_gemma":[0.0025074976,0.00029025672,0.025171716,0.000022471193,0.000020383868,0.000030601932,0.00021894653,0.3022121,0.6675156,0.0011069928,0.0004126741,0.0004907939],"about_ca_topic_score_codex":0.0008114885,"about_ca_topic_score_gemma":0.0000833857,"teacher_disagreement_score":0.90788907,"about_ca_system_score_codex":0.00004209468,"about_ca_system_score_gemma":0.000028311144,"threshold_uncertainty_score":0.7468249},"labels":[],"label_agreement":null},{"id":"W1999142088","doi":"10.1111/j.1552-6569.2012.00713.x","title":"Generating a Minimal Set of Templates for the Hippocampal Region in MR Neuroimages","year":2012,"lang":"en","type":"article","venue":"Journal of Neuroimaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; Canadian Institutes of Health Research","keywords":"Template; Hippocampal formation; Metric (unit); Pattern recognition (psychology); Set (abstract data type); Neuroimaging; Artificial intelligence; Neuropathology; Population; Computer science; Magnetic resonance imaging; Medicine; Similarity (geometry); Position (finance); Pathology; Radiology; Image (mathematics); Disease","score_opus":0.06826859314498233,"score_gpt":0.33640600350091826,"score_spread":0.2681374103559359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999142088","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19148737,0.0004900893,0.8038353,0.0035417117,0.00042840457,0.00017082415,8.0865794e-7,0.000022675962,0.000022843471],"genre_scores_gemma":[0.844638,0.000050253366,0.1541326,0.0008587596,0.00029521834,0.000005167164,2.216733e-7,0.000010881386,0.000008930806],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998554,0.00015244135,0.0006047414,0.00011602451,0.0003197739,0.0002530491],"domain_scores_gemma":[0.9981939,0.00080257904,0.00056582986,0.00021092685,0.00014462195,0.00008215079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010989556,0.00010199117,0.00019914338,0.00019698463,0.000067841735,0.00008436224,0.0006204693,0.00002116449,0.0000027442552],"category_scores_gemma":[0.00063562894,0.000071084934,0.000100683465,0.00025175326,0.00006806247,0.0010817066,0.00011457293,0.0002521116,4.8817714e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006978728,0.00018602468,0.040581863,0.00013566251,0.000036137117,0.00015311553,0.005880085,0.00040737036,0.6331276,0.00045507675,0.011287974,0.3076793],"study_design_scores_gemma":[0.0038038802,0.0012758011,0.08297218,0.0006690965,0.00011874758,0.005170992,0.0014297313,0.3252558,0.5729219,0.0019205582,0.0037106695,0.00075064076],"about_ca_topic_score_codex":0.000005846436,"about_ca_topic_score_gemma":4.2161324e-7,"teacher_disagreement_score":0.6531506,"about_ca_system_score_codex":0.000022156342,"about_ca_system_score_gemma":0.00005071049,"threshold_uncertainty_score":0.28987595},"labels":[],"label_agreement":null},{"id":"W1999363260","doi":"10.1016/j.cviu.2003.10.003","title":"Detection and characterization of junctions in a 2D image","year":2003,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Curvature; Characterization (materials science); Process (computing); Position (finance); Feature (linguistics); Artificial intelligence; Image (mathematics); Computer science; Pattern recognition (psychology); Constant (computer programming); Computer vision; Algorithm; Mathematics; Geometry; Physics; Optics","score_opus":0.024697541015888284,"score_gpt":0.27651847325959,"score_spread":0.2518209322437017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999363260","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018480295,0.000017055185,0.98067397,0.00016242056,0.00016729624,0.00015116834,6.8023206e-7,0.00007799138,0.00026911544],"genre_scores_gemma":[0.73321205,0.00010373929,0.2664135,0.00022177864,0.000014619022,0.0000047125586,0.0000021405672,0.0000070540027,0.000020436075],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99912184,0.00010504395,0.00024304139,0.00026003004,0.00014463009,0.00012543204],"domain_scores_gemma":[0.9995884,0.000054773256,0.000089301284,0.00014524163,0.00005099541,0.00007133265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032207914,0.00009752145,0.00013726455,0.00028545316,0.00009216755,0.00017292974,0.000086838096,0.000042350945,0.000012679565],"category_scores_gemma":[0.000038890164,0.00009114037,0.000019923615,0.0003289467,0.00010007439,0.0009831236,0.00008760924,0.00009338015,0.0000012609426],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007324584,0.000065750195,0.0001440971,0.000062164,0.000005976126,0.000010287854,0.00083348143,2.6648974e-7,0.9082249,0.011424833,0.00009927596,0.07912168],"study_design_scores_gemma":[0.0038740782,0.0011055042,0.021490421,0.0006194204,0.000024619052,0.00025139772,0.00093427533,0.35425487,0.5849411,0.031097809,0.00054306135,0.0008634713],"about_ca_topic_score_codex":0.0000039231227,"about_ca_topic_score_gemma":0.0000022239715,"teacher_disagreement_score":0.71473175,"about_ca_system_score_codex":0.00006321897,"about_ca_system_score_gemma":0.000014520663,"threshold_uncertainty_score":0.37165964},"labels":[],"label_agreement":null},{"id":"W1999478155","doi":"10.1023/b:visi.0000022288.19776.77","title":"Efficient Graph-Based Image Segmentation","year":2004,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6198,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Science Foundation","keywords":"Image segmentation; Range segmentation; Segmentation; Pattern recognition (psychology); Artificial intelligence; Graph; Segmentation-based object categorization; Computer science; Minimum spanning tree-based segmentation; Scale-space segmentation; Connected-component labeling; Mathematics; Algorithm; Theoretical computer science","score_opus":0.008245873275068125,"score_gpt":0.31508200023711325,"score_spread":0.3068361269620451,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999478155","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020320764,0.000029149372,0.97463375,0.0030291032,0.0017531107,0.000091102,0.0000013215429,0.000077361256,0.000064330474],"genre_scores_gemma":[0.26141328,0.000007249773,0.7370275,0.0013020129,0.00023578925,0.0000015537386,0.0000030966696,0.000006135914,0.0000033669407],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99783564,0.00006860991,0.00056811643,0.00017725774,0.0012199576,0.00013041818],"domain_scores_gemma":[0.9983392,0.000080421465,0.0004566414,0.00017855696,0.00082029036,0.00012493007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048500314,0.00011836925,0.00014210206,0.0004844327,0.000043982192,0.00028785295,0.0012086534,0.000040286366,0.000030749135],"category_scores_gemma":[0.000034517256,0.00009951668,0.00014991518,0.00021292332,0.00005841059,0.0005082383,0.00014944354,0.00016731712,0.000028628352],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016766039,0.0015895015,0.000115674935,0.000025583693,0.00017893813,0.0012317059,0.0011244128,0.09481878,0.19179195,0.008023568,0.004821243,0.69611096],"study_design_scores_gemma":[0.0072843614,0.0019061271,0.0038686695,0.0007229853,0.000023008708,0.0007605377,0.000029556779,0.19859943,0.7700717,0.015623064,0.00062909455,0.00048150224],"about_ca_topic_score_codex":0.000005364852,"about_ca_topic_score_gemma":3.0314567e-7,"teacher_disagreement_score":0.6956295,"about_ca_system_score_codex":0.0001904897,"about_ca_system_score_gemma":0.00013851434,"threshold_uncertainty_score":0.40581724},"labels":[],"label_agreement":null},{"id":"W1999893542","doi":"10.1371/journal.pone.0077810","title":"Knowledge-Guided Robust MRI Brain Extraction for Diverse Large-Scale Neuroimaging Studies on Humans and Non-Human Primates","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":94,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Mental Health; National Institute on Aging; University of California, San Diego; University of North Carolina at Chapel Hill; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; University of California, Los Angeles; National Institutes of Health; Servier; National Natural Science Foundation of China; Eisai; Genentech; IXICO; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; National Center for Research Resources; F. Hoffmann-La Roche; National Research Foundation; Medpace; Novartis Pharmaceuticals Corporation; Eli Lilly and Company; Bristol-Myers Squibb; Dana Foundation; Synarc; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Hill's Pet Nutrition; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Neuroimaging; Population; Macaque; Human brain; Computer science; Set (abstract data type); Artificial intelligence; Biology; Neuroscience; Medicine","score_opus":0.10240019006152762,"score_gpt":0.36230287466677497,"score_spread":0.25990268460524735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999893542","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.118627496,0.00005674108,0.87794477,0.0015906285,0.00010804584,0.00054931606,0.000003669632,0.00039553494,0.0007238124],"genre_scores_gemma":[0.2668127,0.000106288324,0.7274187,0.0031300993,0.0003713844,0.00028804704,0.000021522188,0.00004649596,0.0018047139],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987895,0.000080636564,0.0002473747,0.00040714684,0.00024638348,0.00022895767],"domain_scores_gemma":[0.9989783,0.00034743492,0.00011684499,0.0003114874,0.0001703214,0.00007556779],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062003813,0.00013745864,0.00022117703,0.0001261952,0.0003220971,0.0001173805,0.00027371684,0.000036103913,0.000009076577],"category_scores_gemma":[0.00034386193,0.00013072169,0.00003353309,0.00010975994,0.000082693085,0.000449748,0.00020336379,0.00012290945,0.000017802044],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032529137,0.005878545,0.0059476593,0.0021462736,0.00038745842,0.000013434512,0.020690683,0.00007044842,0.8455524,0.007578354,0.08507348,0.026628766],"study_design_scores_gemma":[0.0022560172,0.0009620885,0.0055585997,0.000680766,0.00013466299,0.0000030452657,0.0005449856,0.21222608,0.7724707,0.004000343,0.0005401584,0.0006225428],"about_ca_topic_score_codex":0.0000036471056,"about_ca_topic_score_gemma":0.000014259666,"teacher_disagreement_score":0.21215564,"about_ca_system_score_codex":0.00004350622,"about_ca_system_score_gemma":0.000010740148,"threshold_uncertainty_score":0.5330676},"labels":[],"label_agreement":null},{"id":"W2000388550","doi":"10.1109/embc.2012.6346434","title":"Extraction of liver vessel centerlines under guidance of patient-specific models","year":2012,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University; Hospital for Sick Children; SickKids Foundation; University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Image registration; Image (mathematics); Pattern recognition (psychology)","score_opus":0.03998434313551659,"score_gpt":0.29358777813388676,"score_spread":0.2536034349983702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000388550","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010184651,0.0004355465,0.98724365,0.000052579202,0.00016427909,0.00009599121,9.737032e-7,0.000077532226,0.0017447844],"genre_scores_gemma":[0.6715452,0.00009983175,0.32813135,0.000111849855,0.000017569115,0.0000045084435,7.992179e-7,0.000003014681,0.00008584592],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991338,0.000043405846,0.00029511328,0.000117816395,0.0002822425,0.0001276066],"domain_scores_gemma":[0.9993093,0.000055848766,0.0001636802,0.000279345,0.0001303614,0.00006145169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000148394,0.00006354471,0.00010059296,0.00006662937,0.000014901089,0.000010013268,0.00024902247,0.000033719905,0.00010407588],"category_scores_gemma":[0.000010785458,0.000053215517,0.0000356739,0.00014887439,0.000050961142,0.0011579829,0.0000790206,0.000043826414,0.000008149847],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019128642,0.0010523912,0.0011416714,0.00012845207,0.000031182644,0.000001906277,0.0030770386,0.00027522037,0.24176,0.23571508,0.014473936,0.502324],"study_design_scores_gemma":[0.00015650786,0.000071376235,0.0031610485,0.000043926266,0.000003165984,0.0000053000226,0.000095013405,0.023384128,0.96981376,0.0025268537,0.000629977,0.000108946864],"about_ca_topic_score_codex":0.000029297924,"about_ca_topic_score_gemma":5.846448e-7,"teacher_disagreement_score":0.72805375,"about_ca_system_score_codex":0.000013683227,"about_ca_system_score_gemma":0.000011081392,"threshold_uncertainty_score":0.21700658},"labels":[],"label_agreement":null},{"id":"W2000714473","doi":"10.1016/j.media.2012.04.008","title":"Surface-based multi-template automated hippocampal segmentation: Application to temporal lobe epilepsy","year":2012,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Canadian Institutes of Health Research","keywords":"Epilepsy; Temporal lobe; Segmentation; Artificial intelligence; Hippocampal formation; Computer science; Pattern recognition (psychology); Surface (topology); Computer vision; Neuroscience; Psychology; Mathematics","score_opus":0.018889320166164552,"score_gpt":0.3355391048080844,"score_spread":0.31664978464191984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000714473","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012537478,0.00005602076,0.9825048,0.0024255903,0.0001381535,0.00046455814,0.0000102218555,0.0017681542,0.00009506515],"genre_scores_gemma":[0.4214274,0.000004978207,0.5746041,0.003494605,0.000081678045,0.000108468004,0.00017629839,0.000015811183,0.00008668967],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958236,0.0003999651,0.0007926428,0.0006374328,0.0016976871,0.0006486709],"domain_scores_gemma":[0.9970669,0.00022703853,0.0002477831,0.0009123496,0.00024379852,0.0013021444],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001990165,0.00028845778,0.0004902153,0.00048323045,0.00017503646,0.00019840282,0.001161965,0.00017994377,0.0012713725],"category_scores_gemma":[0.00046263065,0.00025984662,0.00025972558,0.0036141847,0.00015879,0.000952476,0.00025429617,0.00026509608,0.0008053412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008700893,0.0047515463,0.4071015,0.00025946513,0.0026078713,0.0003173152,0.004298133,0.0017609372,0.12142933,0.00046372638,0.07958089,0.37734225],"study_design_scores_gemma":[0.0008543869,0.00006887605,0.016292516,0.000024305691,0.00031552455,0.000007038761,0.0000688754,0.90035784,0.08088289,0.000028758417,0.00062499853,0.0004740103],"about_ca_topic_score_codex":0.00032196107,"about_ca_topic_score_gemma":0.00004132718,"teacher_disagreement_score":0.8985969,"about_ca_system_score_codex":0.00017263852,"about_ca_system_score_gemma":0.00015192378,"threshold_uncertainty_score":0.9999854},"labels":[],"label_agreement":null},{"id":"W2001180178","doi":"10.1118/1.4810968","title":"Three‐dimensional prostate segmentation using level set with shape constraint based on rotational slices for 3D end‐firing TRUS guided biopsy","year":2013,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"Canadian Institutes of Health Research","keywords":"Prostate; Segmentation; Constraint (computer-aided design); Prostate biopsy; Medical imaging; Image segmentation; Set (abstract data type); Level set (data structures); Biopsy; Artificial intelligence; Computer vision; Medicine; Computer science; Nuclear medicine; Biomedical engineering; Radiology; Mathematics; Geometry","score_opus":0.06883677741839982,"score_gpt":0.323870545994812,"score_spread":0.2550337685764122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001180178","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020937663,0.00000540887,0.976466,0.0010524238,0.00012809571,0.0011289759,0.000033751814,0.00016622127,0.000081453494],"genre_scores_gemma":[0.17339721,5.418646e-7,0.8223257,0.0034997938,0.00016603766,0.0004318123,0.0001469831,0.000021610838,0.000010285457],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972955,0.000054370495,0.00038565623,0.00046670332,0.0014717104,0.00032604687],"domain_scores_gemma":[0.99858415,0.00038171205,0.00020845258,0.0002593589,0.000289558,0.00027679626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043758785,0.00021476952,0.00020310354,0.00007495173,0.00017671422,0.00013769946,0.0003867179,0.000076765406,0.000322421],"category_scores_gemma":[0.00017469432,0.00017014483,0.00005846371,0.00026123587,0.00028197424,0.00045863786,0.00007732171,0.00018368625,0.000022943454],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044591925,0.00032284565,0.0006807315,0.00010510179,0.000057436522,0.000021513933,0.00028483948,0.0015101826,0.010282069,0.0025396978,0.0027253043,0.9814257],"study_design_scores_gemma":[0.0014907275,0.00024742453,0.00111414,0.00019170162,0.000013479023,0.000017724926,0.000015710777,0.9494752,0.042480826,0.0046695042,0.000023960989,0.00025957],"about_ca_topic_score_codex":0.00006569628,"about_ca_topic_score_gemma":0.000004848077,"teacher_disagreement_score":0.9811661,"about_ca_system_score_codex":0.00009622261,"about_ca_system_score_gemma":0.00047715093,"threshold_uncertainty_score":0.6938305},"labels":[],"label_agreement":null},{"id":"W2001410840","doi":"10.1016/j.cviu.2009.07.003","title":"Dynamic edge tracing: Boundary identification in medical images","year":2009,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"McGill University; Massachusetts General Hospital","keywords":"Tracing; Computer vision; Artificial intelligence; Boundary (topology); Identification (biology); Computer science; Enhanced Data Rates for GSM Evolution; Edge detection; Computer graphics (images); Pattern recognition (psychology); Image processing; Mathematics; Image (mathematics); Mathematical analysis; Biology","score_opus":0.021992406637427554,"score_gpt":0.3258861672043246,"score_spread":0.30389376056689704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001410840","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001690919,0.00019317908,0.9923314,0.0045284317,0.00024979695,0.00018351467,7.4708726e-7,0.00029938403,0.00052263524],"genre_scores_gemma":[0.7553111,0.00020085013,0.24250399,0.0018734256,0.00003860933,0.0000034525792,0.000008035,0.000008752971,0.00005181731],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980716,0.00012414654,0.00045818466,0.0005122877,0.0005522293,0.00028152572],"domain_scores_gemma":[0.99918985,0.00012710756,0.000104001585,0.00032328605,0.000040632647,0.00021512918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008704593,0.00017019431,0.00020887032,0.0003805775,0.00018168235,0.0007542719,0.00052586105,0.00009463506,0.000039706676],"category_scores_gemma":[0.000070129805,0.00015620777,0.00004671281,0.0004180273,0.00015928852,0.0014731368,0.00018546045,0.00026946963,0.000013344013],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014299753,0.00021928916,0.00005438033,0.00004975792,0.0000075928474,0.00029388856,0.0012315051,0.000002209773,0.024380412,0.010992051,0.0073345946,0.95542],"study_design_scores_gemma":[0.0021028647,0.00055690686,0.015065069,0.0006769418,0.000009248452,0.0002911813,0.00033786407,0.88278043,0.010947672,0.08630378,0.0002218089,0.000706258],"about_ca_topic_score_codex":0.0000026598757,"about_ca_topic_score_gemma":0.0000022465422,"teacher_disagreement_score":0.95471376,"about_ca_system_score_codex":0.00021395524,"about_ca_system_score_gemma":0.00006203025,"threshold_uncertainty_score":0.7273462},"labels":[],"label_agreement":null},{"id":"W2001586684","doi":"10.1109/icip.2011.6116627","title":"Clump splitting via bottleneck detection","year":2011,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Bottleneck; Image segmentation; Line (geometry); Segmentation; Regular polygon; Image (mathematics); Computer science; Algorithm; Computer vision; Artificial intelligence; Line segment; Mathematics; Geometry","score_opus":0.029663989704131158,"score_gpt":0.24393205089702025,"score_spread":0.2142680611928891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001586684","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013518714,0.000006351592,0.97601885,0.000044881606,0.00015241887,0.00007604777,4.2560252e-8,0.00070221495,0.0216473],"genre_scores_gemma":[0.36507887,0.0000024674039,0.63388896,0.0006330946,0.000023663624,0.000010382261,1.761062e-7,0.0000034858456,0.00035891886],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99934036,0.000032209377,0.00014741758,0.0001873506,0.0001599538,0.00013270412],"domain_scores_gemma":[0.9995565,0.000021290974,0.00004468235,0.00026519425,0.0000392038,0.00007314621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002400721,0.000056437104,0.0000542823,0.00005584442,0.000051557017,0.000034230583,0.0003434447,0.000033581015,0.00032620825],"category_scores_gemma":[0.00003830755,0.00004903746,0.000025703845,0.00016432429,0.000024100926,0.00043705263,0.00011529385,0.00006745195,0.00018364307],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.3315623e-7,0.00002750494,0.00013577807,0.000004336675,0.0000032400985,0.0000048986026,0.000401508,4.3179647e-8,0.035249393,0.0018823777,0.0003239384,0.96196604],"study_design_scores_gemma":[0.00007188247,0.00005521695,0.0014181196,0.000004282925,0.0000013507012,0.000010781094,0.000016300057,0.009130876,0.98404354,0.005027377,0.00013894154,0.000081324084],"about_ca_topic_score_codex":0.00009908235,"about_ca_topic_score_gemma":0.0000069936646,"teacher_disagreement_score":0.96188474,"about_ca_system_score_codex":0.000019402762,"about_ca_system_score_gemma":0.0000095066225,"threshold_uncertainty_score":0.35717517},"labels":[],"label_agreement":null},{"id":"W2001929039","doi":"10.1155/2010/836753","title":"Optical Flow Active Contours with Primitive Shape Priors for Echocardiography","year":2009,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vector flow; Active contour model; Computer science; Artificial intelligence; Optical flow; Computer vision; Prior probability; Sensitivity (control systems); Flow (mathematics); Pattern recognition (psychology); Image (mathematics); Image segmentation; Mathematics","score_opus":0.012296890739466038,"score_gpt":0.3106295995619595,"score_spread":0.29833270882249346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001929039","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024891952,0.0007798468,0.99368787,0.00073214027,0.00008237229,0.00037743512,0.0000016864489,0.00012868381,0.001720776],"genre_scores_gemma":[0.49570262,0.00017067899,0.50248027,0.0014678407,0.00013743463,0.000019935453,0.0000011529929,0.00001252175,0.0000075395],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99788,0.00010375189,0.00043215565,0.00043028992,0.0007052815,0.00044853528],"domain_scores_gemma":[0.9986321,0.00034879558,0.00033943442,0.00013752164,0.0003060323,0.00023611556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005785305,0.00024364324,0.0003303438,0.00037843033,0.00023790494,0.0003488062,0.0006262043,0.00006633544,0.000010187103],"category_scores_gemma":[0.00015951264,0.00018341347,0.00011295296,0.00066792965,0.00014842975,0.0029234765,0.000026712589,0.00060425285,0.0000017506435],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002668875,0.00012389927,0.000116268355,0.000018822217,0.000010553999,0.00010776193,0.00046789172,0.0006866611,0.0010133617,0.00037839988,0.00003857898,0.9967709],"study_design_scores_gemma":[0.017326904,0.024528718,0.026946742,0.01037359,0.00016931306,0.0017988395,0.0034428667,0.23766436,0.58528334,0.08528113,0.0032578276,0.0039263708],"about_ca_topic_score_codex":1.0563314e-7,"about_ca_topic_score_gemma":4.810802e-7,"teacher_disagreement_score":0.9928445,"about_ca_system_score_codex":0.00014001249,"about_ca_system_score_gemma":0.00021241634,"threshold_uncertainty_score":0.7479384},"labels":[],"label_agreement":null},{"id":"W2002053178","doi":"10.1109/tip.2003.809019","title":"Guiding ziplock snakes with a priori information","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"A priori and a posteriori; Artificial intelligence; Computer science; Computation; Computer vision; Noise (video); Pattern recognition (psychology); Image (mathematics); Algorithm","score_opus":0.01761290563369538,"score_gpt":0.26277836371588814,"score_spread":0.24516545808219276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002053178","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003588693,0.000022423937,0.9955031,0.00021691891,0.00011872427,0.00021821151,0.0000012838736,0.0006452334,0.0029151984],"genre_scores_gemma":[0.34344572,0.000010073007,0.6557724,0.0005808313,0.000008695607,0.00006324008,7.1057394e-7,0.0000109312405,0.000107409884],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99871397,0.000058444748,0.00030051396,0.0002352953,0.00043697585,0.0002547706],"domain_scores_gemma":[0.99921536,0.000044897282,0.00013238173,0.00028141774,0.00021356458,0.00011236249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027259334,0.0001625941,0.00012784317,0.0002457288,0.00034979222,0.0005486371,0.00030450613,0.00005301059,0.000038425263],"category_scores_gemma":[0.000025141524,0.0001375952,0.000038201542,0.0006502778,0.00007785191,0.004488595,0.0000014131218,0.00017093802,0.000055869074],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018320043,0.00015742125,0.000008471882,0.00016013031,0.000020701253,0.000012913634,0.0026922447,0.00038248362,0.02087874,0.00031454238,0.0003219881,0.97503203],"study_design_scores_gemma":[0.00053694606,0.00014181608,0.000014841437,0.00018905236,0.000017647533,0.00008097375,0.00025271668,0.01568201,0.98155427,0.00033808404,0.0008913443,0.00030028232],"about_ca_topic_score_codex":0.0000054396232,"about_ca_topic_score_gemma":0.000002133031,"teacher_disagreement_score":0.97473174,"about_ca_system_score_codex":0.00008006873,"about_ca_system_score_gemma":0.00018371342,"threshold_uncertainty_score":0.56109697},"labels":[],"label_agreement":null},{"id":"W2002532790","doi":"10.1007/s10278-015-9782-8","title":"Robust Intensity Standardization in Brain Magnetic Resonance Images","year":2015,"lang":"en","type":"article","venue":"Journal of Digital Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Mental Health; National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; University of California, Los Angeles; Canadian Institutes of Health Research; Eli Lilly and Company; U.S. Public Health Service; National Institutes of Health; Genentech; IXICO; Servier; Eisai; Northern California Institute for Research and Education; University of California, San Diego; Pfizer; Biogen; BioClinica; National Center for Research Resources; F. Hoffmann-La Roche; Medpace; Bristol-Myers Squibb; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Synarc; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Artificial intelligence; Segmentation; Computer science; Pattern recognition (psychology); Magnetic resonance imaging; Smoothing; Standardization; Computer vision; Robustness (evolution); Medicine; Radiology","score_opus":0.028606944475711826,"score_gpt":0.27703970932362215,"score_spread":0.24843276484791033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002532790","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031540764,0.0009530385,0.9907474,0.0034575488,0.00019876163,0.000059513815,0.0000021020628,0.00004665487,0.001380925],"genre_scores_gemma":[0.7435529,0.00003170448,0.2546995,0.0013680178,0.000112642105,0.0000014640924,0.0000016209874,0.000013854919,0.00021826093],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986933,0.000044130316,0.0004447123,0.00013432438,0.00051936886,0.00016417545],"domain_scores_gemma":[0.99888474,0.00007165857,0.00021465159,0.00016031043,0.0005151845,0.00015344548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076900295,0.00008724067,0.00017563034,0.00023495349,0.000018253133,0.00048237728,0.0004360137,0.000017388295,0.00000432238],"category_scores_gemma":[0.0011729978,0.00007721186,0.000044357344,0.0003654588,0.00007132696,0.0035867535,0.00014714905,0.00018698737,0.00000499966],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003813688,0.0001170528,0.027767964,0.000012287209,0.000004068772,0.00061834324,0.0012062868,0.00019240846,0.001434679,0.00017618081,0.05082515,0.9176074],"study_design_scores_gemma":[0.024151977,0.0042333,0.17357364,0.0045749857,0.000079661564,0.01188102,0.006388608,0.24329728,0.29484034,0.16512834,0.067740686,0.004110156],"about_ca_topic_score_codex":0.0000036924148,"about_ca_topic_score_gemma":6.1482444e-7,"teacher_disagreement_score":0.91349727,"about_ca_system_score_codex":0.00014481167,"about_ca_system_score_gemma":0.00014483434,"threshold_uncertainty_score":0.4651575},"labels":[],"label_agreement":null},{"id":"W2003245850","doi":"10.1117/12.771862","title":"Pyramidal flux in an anisotropic diffusion scheme for enhancing structures in 3D images","year":2008,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials","funders":"","keywords":"Pyramid (geometry); Voxel; Computer science; Computation; Anisotropic diffusion; Speckle pattern; Artificial intelligence; Computer vision; Noise (video); Diffusion; Algorithm; Speckle noise; Intensity (physics); Anisotropy; Image (mathematics); Mathematics; Optics; Geometry; Physics","score_opus":0.012669446908935943,"score_gpt":0.25460453823591606,"score_spread":0.2419350913269801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003245850","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98491704,0.0000466738,0.0131818205,0.0007186754,0.00014283917,0.00068329717,0.000012020105,0.00012650501,0.00017115266],"genre_scores_gemma":[0.33507606,0.000044393255,0.66434443,0.00012749666,0.0001464797,0.00017432006,0.0000066001953,0.000028174107,0.000052021493],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99766463,3.5653283e-8,0.0007617099,0.0004873492,0.0006683731,0.00041790595],"domain_scores_gemma":[0.99852103,0.00014662367,0.00028691662,0.000081051745,0.00084348023,0.0001208804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005437349,0.00026870242,0.00038239904,0.00020949243,0.00007914019,0.00011482009,0.0014155165,0.000173574,0.000008619595],"category_scores_gemma":[0.00064444495,0.00023420138,0.0002612522,0.0004243448,0.00021663685,0.0013310285,0.0002539085,0.00031035746,4.5645905e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036422905,0.00013987647,0.0017590481,0.00025508815,0.000035429977,3.762186e-7,0.000556256,0.00003534975,0.8803425,0.11511127,0.00056798314,0.001160435],"study_design_scores_gemma":[0.0018556432,0.0004740478,0.01155649,0.0002979558,0.00001849434,0.000020333198,0.00069391006,0.1114246,0.86843276,0.0046577607,0.00014772755,0.00042027293],"about_ca_topic_score_codex":0.00001922543,"about_ca_topic_score_gemma":0.0000012958604,"teacher_disagreement_score":0.6511626,"about_ca_system_score_codex":0.00020366383,"about_ca_system_score_gemma":0.00005266978,"threshold_uncertainty_score":0.9550455},"labels":[],"label_agreement":null},{"id":"W2003251747","doi":"10.1109/tmi.2012.2218116","title":"Multi-Modal Image Registration Based on Gradient Orientations of Minimal Uncertainty","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital; McGill Genome Centre; McGill University Health Centre","funders":"Canadian Institutes of Health Research","keywords":"Image registration; Artificial intelligence; Computer science; Robustness (evolution); Computer vision; Mutual information; Orientation (vector space); Resampling; Context (archaeology); Pixel; Modal; Image (mathematics); Mathematics; Geometry","score_opus":0.02089883991814954,"score_gpt":0.31503924085178187,"score_spread":0.2941404009336323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003251747","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016382007,0.000013902129,0.99421674,0.002260157,0.0008466234,0.00027880594,0.0000151546155,0.0002969795,0.00043341643],"genre_scores_gemma":[0.73330283,0.000008711433,0.26497078,0.001505993,0.00005577394,0.000074749056,0.000008254742,0.000014121075,0.000058783302],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971523,0.00021036534,0.0005574484,0.00037324626,0.0012866838,0.00041994726],"domain_scores_gemma":[0.9982552,0.00040262958,0.00017321757,0.0005186864,0.00014887138,0.00050140964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008639254,0.00020373326,0.00021269236,0.00031786572,0.00019047048,0.000058636157,0.0005294544,0.00008216862,0.00034768812],"category_scores_gemma":[0.00017103364,0.00018734849,0.00014271465,0.0005199048,0.00033444876,0.0007714677,0.000004429552,0.00043475506,0.000044224336],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016357726,0.008023506,0.00056141865,0.00021380684,0.00009368189,0.00009972223,0.0043104608,0.0037982145,0.077316016,0.002051822,0.0057162065,0.89765155],"study_design_scores_gemma":[0.0014682955,0.00016817584,0.0006878552,0.00018177113,0.000033866505,0.000023924951,0.00021901097,0.7758007,0.22085421,0.00006612494,0.00019432024,0.00030172753],"about_ca_topic_score_codex":0.00008582915,"about_ca_topic_score_gemma":0.000010436582,"teacher_disagreement_score":0.89734983,"about_ca_system_score_codex":0.00013897184,"about_ca_system_score_gemma":0.00020089617,"threshold_uncertainty_score":0.763985},"labels":[],"label_agreement":null},{"id":"W2003521715","doi":"10.1109/tfuzz.2013.2246761","title":"EFIS—Evolving Fuzzy Image Segmentation","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Thresholding; Computer science; Segmentation; Artificial intelligence; Image segmentation; Scale-space segmentation; Pattern recognition (psychology); Computer vision; Fuzzy logic; Image (mathematics)","score_opus":0.015149140976801515,"score_gpt":0.25877367392079986,"score_spread":0.24362453294399836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003521715","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012102535,0.000059411625,0.98922825,0.00039887914,0.0017216069,0.0010220514,0.000008460598,0.0010108517,0.0053402185],"genre_scores_gemma":[0.8582157,0.00003742752,0.13788,0.0005567743,0.000113050184,0.00090742455,0.0000052999126,0.000036671863,0.0022476586],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976782,0.00022427904,0.0005373268,0.0005068173,0.0006750194,0.00037835288],"domain_scores_gemma":[0.99849266,0.0001703004,0.00016433641,0.0006856611,0.00024441155,0.00024260287],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00035065325,0.00024023106,0.0002451188,0.00031247266,0.00026931378,0.0006499759,0.0006405159,0.000113835194,0.00021335526],"category_scores_gemma":[0.0000121018575,0.00022563033,0.00011446209,0.00052777055,0.00007453659,0.0020794678,0.000004537811,0.00027638828,0.0017004928],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014061439,0.0008063948,0.000049389855,0.0004207057,0.00020897419,0.000050251136,0.0031465695,0.0018961853,0.5356707,0.0015003044,0.05935137,0.39688513],"study_design_scores_gemma":[0.0020698395,0.0006915503,0.00056301075,0.00050530216,0.00007051423,0.00016904707,0.0015915963,0.112183005,0.87809944,0.0021015252,0.00048103728,0.0014741013],"about_ca_topic_score_codex":0.0006267422,"about_ca_topic_score_gemma":0.000007177117,"teacher_disagreement_score":0.8570054,"about_ca_system_score_codex":0.00019462516,"about_ca_system_score_gemma":0.000057205507,"threshold_uncertainty_score":0.9990768},"labels":[],"label_agreement":null},{"id":"W2003899288","doi":"10.1090/s0033-569x-2011-01192-4","title":"GRID macroscopic growth law and its application to image inference","year":2011,"lang":"en","type":"article","venue":"Quarterly of Applied Mathematics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Diffeomorphism; Grid; Mathematics; Iterated function; Applied mathematics; Mathematical optimization; Inference; Optimal control; Function (biology); Mathematical analysis; Computer science; Geometry; Artificial intelligence","score_opus":0.017565905254795414,"score_gpt":0.27392000041569853,"score_spread":0.25635409516090313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003899288","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0103988275,0.0000053194776,0.97643137,0.000048702408,0.0000296591,0.0005301393,0.0000033716003,0.00018237089,0.012370214],"genre_scores_gemma":[0.28910708,0.000003609063,0.7104873,0.00026140845,0.000011933435,0.00011142239,0.0000014518948,0.000008142285,0.000007676471],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989765,0.0000080886875,0.00035206927,0.00025329783,0.00024622053,0.00016382082],"domain_scores_gemma":[0.999155,0.000069560294,0.00015839355,0.0003911023,0.00009826585,0.00012771998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020823402,0.00012573527,0.0001989869,0.00007229079,0.000043761323,0.000043029682,0.0005273196,0.000049556846,0.000021110889],"category_scores_gemma":[0.000011672032,0.00011774944,0.000019081175,0.00018432988,0.00007600584,0.00024057922,0.00006239244,0.000072480056,0.000083102175],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031102677,0.00013601019,0.0000017206139,0.00026860242,0.0000074030045,0.0000010284164,0.014934346,4.3692754e-8,0.12913658,0.8342341,0.00013000079,0.021147022],"study_design_scores_gemma":[0.00021309494,0.0004168351,0.000054635628,0.000047638765,0.000010604706,0.0000032172675,0.00031129163,0.0021497363,0.76908857,0.22748332,0.000021320227,0.00019976794],"about_ca_topic_score_codex":0.000015507863,"about_ca_topic_score_gemma":0.0000036274068,"teacher_disagreement_score":0.63995194,"about_ca_system_score_codex":0.00001184524,"about_ca_system_score_gemma":0.000014412624,"threshold_uncertainty_score":0.48016828},"labels":[],"label_agreement":null},{"id":"W2003954396","doi":"10.1088/0031-9155/59/22/6891","title":"Non-rigid registration of medical images based on estimation of deformation states","year":2014,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; McMaster University","funders":"","keywords":"Image registration; Computer vision; Artificial intelligence; Computer science; Medical imaging; Voxel; Process (computing); Image-guided surgery; Image (mathematics)","score_opus":0.07715135975112745,"score_gpt":0.39754431287102493,"score_spread":0.32039295311989746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003954396","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01686408,0.00000909345,0.9802748,0.0022348876,0.000045780387,0.00007849575,8.249221e-7,0.00001646037,0.0004756024],"genre_scores_gemma":[0.967351,0.000055498484,0.031838745,0.00066805736,0.000036997302,0.000006587393,0.00003995627,0.0000016626612,0.0000014602524],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992082,0.00007630587,0.00030983306,0.00011432376,0.00022294995,0.00006836169],"domain_scores_gemma":[0.9993006,0.0002848425,0.00018228871,0.00014439995,0.00005472569,0.0000331229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009341428,0.0000567657,0.000168285,0.00008554837,0.000011812404,0.000002983974,0.00017254094,0.000052822434,0.000007598909],"category_scores_gemma":[0.0003311776,0.0000397163,0.000010690034,0.00015336502,0.00022475069,0.00011176269,0.000026023343,0.00007783868,5.259247e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019558714,0.0001427329,0.0014333101,0.00023071376,0.0000058427618,6.542835e-7,0.00079189613,0.00061269995,0.01289018,0.056455273,0.001288753,0.9261284],"study_design_scores_gemma":[0.00058877224,0.0006349577,0.0014459664,0.00018558057,0.0000032583926,7.4783236e-7,0.000029767683,0.88453233,0.0725187,0.03999496,0.000017776598,0.000047206955],"about_ca_topic_score_codex":0.000099721656,"about_ca_topic_score_gemma":0.000002433324,"teacher_disagreement_score":0.95048696,"about_ca_system_score_codex":0.000007704408,"about_ca_system_score_gemma":0.000028203991,"threshold_uncertainty_score":0.16195837},"labels":[],"label_agreement":null},{"id":"W2004004650","doi":"10.1016/j.neuroimage.2005.09.041","title":"The creation of a brain atlas for image guided neurosurgery using serial histological data","year":2006,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":315,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Voxel; Computer science; Atlas (anatomy); Artificial intelligence; Coronal plane; Sagittal plane; Computer vision; Data set; Transverse plane; Anatomy; Medicine","score_opus":0.08365469786089165,"score_gpt":0.3528050130432245,"score_spread":0.26915031518233284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004004650","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0140658,0.00002758797,0.98218584,0.0022073705,0.00039238323,0.00040446525,0.000029668598,0.00018613454,0.00050071994],"genre_scores_gemma":[0.06430738,0.000020229447,0.931848,0.0022794066,0.00050290104,0.000057706453,0.00015144705,0.000043138403,0.00078981166],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983072,0.00021989859,0.00047671897,0.00044168837,0.00031800906,0.00023644588],"domain_scores_gemma":[0.9975141,0.0009922783,0.00026442818,0.0010579675,0.00012485519,0.000046371057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078706874,0.00011732198,0.000168229,0.00005854261,0.00018202262,0.00018752356,0.0012916737,0.000045122313,0.000010004151],"category_scores_gemma":[0.0016985388,0.00008828074,0.000062876,0.00022451452,0.00023314943,0.000656004,0.00046224814,0.000094866715,0.0000029334458],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002564603,0.00008033098,0.000093235925,0.000022740034,0.000003504697,0.000032298638,0.000019000694,0.000010339421,0.81356806,0.002233619,0.16992728,0.01398396],"study_design_scores_gemma":[0.001791587,0.00038408407,0.007463837,0.00004315891,0.00005076628,0.00020512161,0.000013361667,0.40309066,0.531886,0.010422559,0.044073083,0.0005758175],"about_ca_topic_score_codex":0.0000801246,"about_ca_topic_score_gemma":0.0000051469274,"teacher_disagreement_score":0.4030803,"about_ca_system_score_codex":0.00002366144,"about_ca_system_score_gemma":0.00007713234,"threshold_uncertainty_score":0.35999843},"labels":[],"label_agreement":null},{"id":"W2004859876","doi":"10.1117/12.2007008","title":"Registration of whole-mount histology and tomography of the prostate using particle filtering","year":2013,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research","keywords":"Computer vision; Artificial intelligence; Computer science; Segmentation; Image registration; Particle filter; Digital pathology; Image segmentation; Pattern recognition (psychology); Kalman filter; Image (mathematics)","score_opus":0.013059377208353705,"score_gpt":0.23973038214735864,"score_spread":0.22667100493900494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004859876","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99130946,0.00006255455,0.005835452,0.0019627465,0.00009892977,0.00051124534,0.000008453003,0.000042999174,0.00016814734],"genre_scores_gemma":[0.67790383,0.000020003283,0.32186553,0.000067409004,0.000036663572,0.000061264094,8.153517e-7,0.000013036166,0.00003145011],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99836165,4.7508614e-8,0.0006648876,0.00024604972,0.0005164673,0.0002109119],"domain_scores_gemma":[0.99810004,0.00008949275,0.00056417636,0.00007957073,0.001099243,0.00006748398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047506666,0.00015492231,0.00025944618,0.00006449888,0.000052404328,0.00006983048,0.0008454028,0.0000875566,0.0000045022957],"category_scores_gemma":[0.00035117954,0.000114318966,0.00023795877,0.00033315076,0.00040378756,0.00075158506,0.00025827927,0.00014995856,1.6550624e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000902119,0.000049168983,0.00049002445,0.0002902265,0.00006990181,1.9381517e-8,0.00032566066,0.00002700334,0.81785935,0.17965074,0.00033396177,0.00089492294],"study_design_scores_gemma":[0.0003830533,0.00020583892,0.0029653963,0.00025974266,0.000043684286,0.000012126862,0.00046426975,0.095457554,0.8962502,0.003713268,0.00009975369,0.00014514117],"about_ca_topic_score_codex":0.00004034766,"about_ca_topic_score_gemma":1.3762205e-7,"teacher_disagreement_score":0.31603009,"about_ca_system_score_codex":0.00006975055,"about_ca_system_score_gemma":0.000027156784,"threshold_uncertainty_score":0.46617922},"labels":[],"label_agreement":null},{"id":"W2005171018","doi":"10.1117/12.2043300","title":"Prostate segmentation in MRI using fused T2-weighted and elastography images","year":2014,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; British Columbia Innovation Council","keywords":"Contouring; Artificial intelligence; Segmentation; Computer science; Computer vision; Image segmentation; Magnetic resonance imaging; Consistency (knowledge bases); Pattern recognition (psychology); Magnetic resonance elastography; Reproducibility; Elastography; Mathematics; Medicine; Ultrasound; Radiology; Statistics","score_opus":0.008869174478253316,"score_gpt":0.24003881632480298,"score_spread":0.23116964184654967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005171018","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9636107,0.00005488652,0.033892415,0.0012784036,0.00011733767,0.0005911987,0.000009324972,0.00013083115,0.00031488176],"genre_scores_gemma":[0.16472994,0.000097473785,0.83467764,0.00021424687,0.00010341167,0.00011337793,0.0000044971935,0.00003114339,0.000028294779],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979321,5.0813224e-8,0.0006477761,0.00043324163,0.00065671786,0.0003301644],"domain_scores_gemma":[0.9983041,0.00014720445,0.00036570753,0.00006422465,0.000998452,0.00012028863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089766714,0.00025065578,0.00031873008,0.00023096474,0.00007427084,0.00020174631,0.000840044,0.0001201776,0.0000039936403],"category_scores_gemma":[0.0003301648,0.00021598833,0.00022666543,0.0005551059,0.0002521178,0.0011894287,0.00023035015,0.00024383001,4.4050162e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029902516,0.00008424874,0.0014980695,0.00033024643,0.00010158939,9.8345396e-8,0.00057467626,0.000032163523,0.8776584,0.11567903,0.00067942136,0.0033321022],"study_design_scores_gemma":[0.0017172501,0.00036047926,0.0025211642,0.00041114647,0.000060613336,0.000016458249,0.0008596958,0.22948469,0.7588766,0.0050255605,0.00024687996,0.00041944836],"about_ca_topic_score_codex":0.000014322645,"about_ca_topic_score_gemma":1.3789314e-7,"teacher_disagreement_score":0.8007852,"about_ca_system_score_codex":0.00009848367,"about_ca_system_score_gemma":0.000023489447,"threshold_uncertainty_score":0.88077486},"labels":[],"label_agreement":null},{"id":"W2007149632","doi":"10.1016/j.ultrasmedbio.2005.07.005","title":"Ultrasound image segmentation using spectral clustering","year":2005,"lang":"en","type":"article","venue":"Ultrasound in Medicine & Biology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Center for Research Resources; Centre National de la Recherche Scientifique","keywords":"Segmentation; Artificial intelligence; Image segmentation; Computer science; Segmentation-based object categorization; Scale-space segmentation; Computer vision; Cluster analysis; Region growing; Pattern recognition (psychology); Spectral clustering; Ultrasound; Medicine; Radiology","score_opus":0.025841853185103195,"score_gpt":0.3412579028722762,"score_spread":0.315416049687173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007149632","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07769676,0.00020763636,0.9180943,0.0010321145,0.00040361317,0.00028591845,0.000002340076,0.00023658293,0.0020407287],"genre_scores_gemma":[0.3711086,0.00024930018,0.6252031,0.0027209867,0.00056444114,0.000022073562,0.00004310683,0.000015993364,0.000072414165],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978726,0.00023013857,0.0006277622,0.00052576006,0.00025156562,0.0004921793],"domain_scores_gemma":[0.9984365,0.0007683224,0.00017575697,0.00041185957,0.00006474467,0.00014279973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008599173,0.0002262179,0.00033423363,0.0003270812,0.000090176436,0.00005069727,0.0006575124,0.00011192241,0.00051082746],"category_scores_gemma":[0.00082590844,0.0001925057,0.000043175387,0.0004969377,0.0003924781,0.000669211,0.000086024986,0.00033920706,0.00004282297],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000071690333,0.000056818615,0.0035493106,0.000014207976,0.000011005527,0.000011446559,0.0014806014,0.000039636114,0.9376165,0.0008274873,0.0012505604,0.0551352],"study_design_scores_gemma":[0.012051651,0.00298476,0.047511782,0.00084841106,0.00013602074,0.0037744471,0.0031257095,0.07439554,0.8032337,0.035112735,0.01346249,0.0033627572],"about_ca_topic_score_codex":0.00023957065,"about_ca_topic_score_gemma":0.000111383575,"teacher_disagreement_score":0.29341182,"about_ca_system_score_codex":0.00026635214,"about_ca_system_score_gemma":0.0000599634,"threshold_uncertainty_score":0.78501546},"labels":[],"label_agreement":null},{"id":"W2008437871","doi":"10.1109/embc.2012.6346134","title":"Two solutions for registration of ultrasound to MRI for image-guided prostate interventions","year":2012,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Center for Research Resources; National Institute of Biomedical Imaging and Bioengineering; National Cancer Institute","keywords":"Image registration; Computer science; Ultrasound; Segmentation; Artificial intelligence; Computer vision; Modalities; Prostate; Image segmentation; Prostate gland; Medicine; Medical physics; Radiology; Image (mathematics)","score_opus":0.08301715854857426,"score_gpt":0.4008010885742414,"score_spread":0.3177839300256671,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008437871","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017055984,0.00002111192,0.99539024,0.0015076621,0.00017374125,0.0014525686,0.00002847889,0.00017298802,0.0010826744],"genre_scores_gemma":[0.046671852,0.0000037098728,0.9503267,0.00036333373,0.00005175971,0.0006439928,0.000030818606,0.0000066450807,0.0019012081],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99897015,0.000028629916,0.00040071062,0.00016528895,0.00015557292,0.00027963184],"domain_scores_gemma":[0.99892205,0.00023658636,0.00013323576,0.00028866978,0.00029434028,0.0001251424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000994194,0.00007268376,0.00010410964,0.00009633121,0.00010617741,0.00006444428,0.00029108458,0.000022632845,0.00004365244],"category_scores_gemma":[0.0005867877,0.00006681341,0.00012681162,0.0001842141,0.000046559835,0.0008000115,0.000067176,0.000029418015,0.000008435047],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013599605,0.00044397573,0.00008520673,0.00028288845,0.000036459795,1.3237427e-7,0.0012661249,0.0000139527265,0.20092349,0.3348304,0.4368057,0.025298076],"study_design_scores_gemma":[0.0013022878,0.00058833946,0.0005809297,0.00013829432,0.000045806828,0.000017708719,0.0002828971,0.0055649886,0.9542407,0.03057821,0.0063274973,0.00033235576],"about_ca_topic_score_codex":0.00003605141,"about_ca_topic_score_gemma":0.000018551706,"teacher_disagreement_score":0.7533172,"about_ca_system_score_codex":0.00003777114,"about_ca_system_score_gemma":0.000039415518,"threshold_uncertainty_score":0.27245718},"labels":[],"label_agreement":null},{"id":"W2009192051","doi":"10.1088/0031-9155/46/1/318","title":"Application of anisotropic diffusion to digital enhancement of portal images","year":2000,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Northeast Cancer Centre","funders":"","keywords":"Anisotropic diffusion; Diffusion; Anisotropy; Contrast (vision); Computer science; Noise (video); Filter (signal processing); Homogeneous; Image enhancement; Computer vision; Digital image; Artificial intelligence; Image (mathematics); Image processing; Optics; Physics; Statistical physics","score_opus":0.049477555040456346,"score_gpt":0.3621166810096426,"score_spread":0.31263912596918625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009192051","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08731993,0.000039255956,0.91100484,0.0003611654,0.000015865142,0.000115069575,0.000005506158,0.000011490667,0.0011268633],"genre_scores_gemma":[0.9814697,0.00011450467,0.018029284,0.00027913967,0.000036480298,0.000015278963,0.000026082029,0.0000014688247,0.000028068238],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995346,0.000013974404,0.00018836083,0.00013258339,0.00006526019,0.000065194734],"domain_scores_gemma":[0.99971545,0.00004956316,0.0000548422,0.00012592398,0.000026243259,0.000027998498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006206204,0.000044809374,0.00013559422,0.000036127043,0.0000070304445,0.0000018972128,0.00013581332,0.000018704955,0.000083677565],"category_scores_gemma":[0.000021048956,0.00003267732,0.000008529781,0.00014881032,0.0001154413,0.000066337074,0.000048137943,0.0000319181,0.0000020265727],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034429174,0.000057680278,0.0011335068,0.000011573033,0.0000019987017,3.3035255e-7,0.00020739071,3.1760908e-7,0.14913298,0.0025034978,0.0003929124,0.84655434],"study_design_scores_gemma":[0.0018380038,0.003551084,0.016433641,0.00027981636,0.000014533766,0.0000070064325,0.00023399499,0.004161216,0.88239163,0.086648,0.0041112257,0.00032984317],"about_ca_topic_score_codex":0.000035020315,"about_ca_topic_score_gemma":6.6553594e-7,"teacher_disagreement_score":0.8941498,"about_ca_system_score_codex":0.000004163474,"about_ca_system_score_gemma":0.000007065191,"threshold_uncertainty_score":0.13325423},"labels":[],"label_agreement":null},{"id":"W2009298542","doi":"10.1109/embc.2012.6345963","title":"Automatic brain tumor extraction from T1-weighted coronal MRI using fast bounding box and dynamic snake","year":2012,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Massachusetts General Hospital","keywords":"Minimum bounding box; Computer science; Preprocessor; Artificial intelligence; Segmentation; Hausdorff distance; Pattern recognition (psychology); Computer vision; Voxel; Coronal plane; Image segmentation; Bounding overwatch; Image (mathematics)","score_opus":0.018362523058190177,"score_gpt":0.3137670481151659,"score_spread":0.29540452505697573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009298542","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25806776,0.00005890676,0.7406135,0.0002700397,0.00024345725,0.00013344755,0.0000020062973,0.00040781387,0.0002030784],"genre_scores_gemma":[0.42266747,0.00000285042,0.5765294,0.00063896575,0.00004925403,0.0000067614415,0.0000074599097,0.000007864773,0.000089985275],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987268,0.00010735515,0.00026923313,0.0002605303,0.00034168025,0.00029439875],"domain_scores_gemma":[0.99917316,0.0002467823,0.00012949826,0.00022630008,0.000030650197,0.00019357738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039860664,0.00013228388,0.00014462003,0.00011548052,0.00015192303,0.00023602591,0.0002301143,0.000047345726,0.0005393439],"category_scores_gemma":[0.000047406946,0.000118007236,0.000028416285,0.00018945089,0.00006978362,0.0016160945,0.00013483662,0.00013474083,0.000029559718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000072772286,0.00024436848,0.0042972416,0.00006189953,0.000055747765,0.000032442575,0.0025325618,0.0000025154768,0.4340393,0.0031174542,0.002708751,0.55290043],"study_design_scores_gemma":[0.00031197938,0.000042538726,0.015062958,0.00006945687,0.000014498969,0.00011166041,0.00015298362,0.9384564,0.04379723,0.0015694275,0.00015894059,0.00025193585],"about_ca_topic_score_codex":0.000153212,"about_ca_topic_score_gemma":0.000015741838,"teacher_disagreement_score":0.93845385,"about_ca_system_score_codex":0.00014053125,"about_ca_system_score_gemma":0.00004290094,"threshold_uncertainty_score":0.5905438},"labels":[],"label_agreement":null},{"id":"W2010139045","doi":"10.1016/j.media.2006.09.001","title":"3D prostate model formation from non-parallel 2D ultrasound biopsy images","year":2006,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"London Health Sciences Centre; Robarts Clinical Trials; Western University","funders":"Canadian Institutes of Health Research; Prostate Cancer Foundation","keywords":"Biopsy; 3D ultrasound; Prostate biopsy; Prostate cancer; Prostate; Ultrasound; Radiology; Medicine; Gold standard (test); Computer science; Cancer; Internal medicine","score_opus":0.006800600494576521,"score_gpt":0.2638776791929591,"score_spread":0.2570770786983826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010139045","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004473694,0.00009037352,0.99142885,0.0013318053,0.000050332506,0.00020823104,0.00003710876,0.00045827444,0.001921314],"genre_scores_gemma":[0.11890415,0.00012381523,0.8777218,0.0013892815,0.00012584277,0.000082612874,0.0005038432,0.000017887009,0.0011308005],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99626446,0.00013933014,0.00080651115,0.00064564974,0.0016773045,0.00046675315],"domain_scores_gemma":[0.998076,0.0002451689,0.0002666834,0.0008235938,0.00023127222,0.00035723276],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007570112,0.00027036443,0.00047143246,0.00047028618,0.0001657213,0.00043730627,0.0012029674,0.00015663488,0.00095919723],"category_scores_gemma":[0.00037654984,0.00022887294,0.00028560768,0.0017135674,0.00026344584,0.0018744619,0.000250026,0.0003093907,0.0002057584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007838844,0.002542622,0.005567618,0.00019206991,0.0025482082,0.0016703283,0.0041720597,0.009150767,0.31472245,0.001211404,0.3092454,0.34889868],"study_design_scores_gemma":[0.00048726884,0.000021486201,0.0014223332,0.000021844242,0.00030262058,0.0000115607645,0.000029779547,0.9574968,0.036151923,0.0036603925,0.00008109686,0.00031285197],"about_ca_topic_score_codex":0.0010847114,"about_ca_topic_score_gemma":0.00006555163,"teacher_disagreement_score":0.9483461,"about_ca_system_score_codex":0.000086960354,"about_ca_system_score_gemma":0.00010477333,"threshold_uncertainty_score":0.99995404},"labels":[],"label_agreement":null},{"id":"W2010587020","doi":"10.1016/j.neuroimage.2010.09.018","title":"Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation","year":2010,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":697,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; Douglas Mental Health University Institute; McGill University","funders":"Canadian Institutes of Health Research","keywords":"Segmentation; Artificial intelligence; Computer science; Pattern recognition (psychology); Prior probability; Image warping; Computer vision","score_opus":0.016306739797189707,"score_gpt":0.3085012917549075,"score_spread":0.29219455195771776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010587020","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36186653,0.000010934178,0.6368185,0.00043150567,0.00013954706,0.00047977435,0.0000017604659,0.00022246032,0.000028965398],"genre_scores_gemma":[0.4608059,0.0000049295213,0.5362575,0.0027462377,0.00005906549,0.000094898074,0.0000101814285,0.000015099724,0.0000062045588],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985684,0.000085725704,0.00026249717,0.00048984546,0.0003779847,0.00021554534],"domain_scores_gemma":[0.9990769,0.00008072772,0.00012479025,0.00043532145,0.00008645497,0.00019581424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024950426,0.00014496499,0.00011141543,0.00017240173,0.00013758746,0.00022449,0.00032800814,0.00005587297,0.000026845497],"category_scores_gemma":[0.00009059261,0.00015245208,0.00002821493,0.00040914275,0.000054131284,0.000684324,0.00011703366,0.00016357448,0.000029432334],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037132604,0.000039044065,0.0006397928,0.0000072533408,0.0000012295878,0.0000042083457,0.00034978674,0.000010205495,0.7238604,0.00006039459,0.000138495,0.27488548],"study_design_scores_gemma":[0.0005046987,0.00010739855,0.0042264587,0.000012209352,0.000006393687,0.000016459062,0.00006924412,0.09185363,0.9019323,0.0007870205,0.000267626,0.0002165782],"about_ca_topic_score_codex":0.00010140556,"about_ca_topic_score_gemma":0.000012363261,"teacher_disagreement_score":0.2746689,"about_ca_system_score_codex":0.00004741464,"about_ca_system_score_gemma":0.000049656315,"threshold_uncertainty_score":0.6216815},"labels":[],"label_agreement":null},{"id":"W2010792042","doi":"10.1006/cviu.1999.0822","title":"Watershed-Based Segmentation and Region Merging","year":2000,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":311,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Watershed; Computer science; Mathematical morphology; Image (mathematics); Image segmentation; Replicate; Process (computing); Segmentation; Artificial intelligence; Computer vision; Image processing; Mathematics; Statistics","score_opus":0.030879798709862405,"score_gpt":0.2844545071042837,"score_spread":0.2535747083944213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010792042","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052533373,0.000057277426,0.9922966,0.0014476023,0.00007852902,0.00016395091,3.2566493e-7,0.00028985433,0.0004125523],"genre_scores_gemma":[0.35040775,0.00015936523,0.64709896,0.0021532045,0.00005018978,0.000005538684,0.0000079771835,0.000012813504,0.00010422261],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988765,0.0000894535,0.00021132261,0.00039716187,0.00022472402,0.00020084628],"domain_scores_gemma":[0.99949574,0.00008255705,0.000052993037,0.00020027398,0.00002323015,0.00014518038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022663326,0.00014552684,0.00013704589,0.00016124596,0.00024732412,0.000544465,0.00017472608,0.000043130924,0.00006360621],"category_scores_gemma":[0.0000055266437,0.00012518394,0.000028176839,0.00017559504,0.00012115968,0.0010179261,0.000097199845,0.00009643126,0.000008000183],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000249379,0.000049516337,0.00012206342,0.00007212637,0.000012881144,0.000085181666,0.0014486096,0.000015758513,0.013394093,0.003378913,0.0062198658,0.97517604],"study_design_scores_gemma":[0.0022045837,0.00052881596,0.00056073716,0.00030486227,0.00001713746,0.00013785741,0.00031087495,0.9482954,0.036762554,0.009533539,0.000778512,0.0005650823],"about_ca_topic_score_codex":0.0000053390163,"about_ca_topic_score_gemma":4.094941e-7,"teacher_disagreement_score":0.974611,"about_ca_system_score_codex":0.000082738065,"about_ca_system_score_gemma":0.000012950777,"threshold_uncertainty_score":0.5250288},"labels":[],"label_agreement":null},{"id":"W2011082917","doi":"10.1016/s1053-8119(02)00017-4","title":"Deformation-based surface morphometry applied to gray matter deformation","year":2003,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":277,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Flattening; Gaussian curvature; Smoothing; Brain morphometry; Curvature; Surface (topology); Surface reconstruction; Geometry; Mean curvature; Mathematics; Artificial intelligence; Physics; Computer vision; Geology; Computer science; Magnetic resonance imaging","score_opus":0.013115893010689272,"score_gpt":0.25215282566652353,"score_spread":0.23903693265583426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011082917","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027967677,0.000004369979,0.95950145,0.0006406895,0.00018023238,0.00040685752,0.0000025221009,0.0004037013,0.010892489],"genre_scores_gemma":[0.6073856,9.72587e-7,0.3781437,0.014256739,0.000011003096,0.00003588971,0.00001101,0.000015095802,0.000140035],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984628,0.000110523986,0.0003390536,0.0003135601,0.0004838614,0.00029018766],"domain_scores_gemma":[0.9989253,0.00009524844,0.00010728596,0.0006042229,0.000080710946,0.00018721071],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004205907,0.00015923208,0.00013990983,0.0002018271,0.0001110676,0.0002310756,0.00053601566,0.000048793583,0.00029157632],"category_scores_gemma":[0.00011385852,0.00014738952,0.000045381483,0.0007694496,0.00003229102,0.00073770864,0.000071403534,0.00015236957,0.0019348498],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044618027,0.0006932786,0.010182067,0.00039012227,0.00003071527,0.000075363176,0.002653321,0.005502859,0.6740378,0.018178748,0.21039154,0.07781956],"study_design_scores_gemma":[0.00064335135,0.00008978445,0.00948307,0.000018142515,0.0000065812687,0.000021799615,0.00003523443,0.011853691,0.9721919,0.0007624708,0.004462619,0.00043137695],"about_ca_topic_score_codex":0.0000071329287,"about_ca_topic_score_gemma":6.4560527e-7,"teacher_disagreement_score":0.5813578,"about_ca_system_score_codex":0.000061316714,"about_ca_system_score_gemma":0.000051662788,"threshold_uncertainty_score":0.99884224},"labels":[],"label_agreement":null},{"id":"W2011278558","doi":"10.1109/icip.2006.312607","title":"Variational Textured Image Decomposition with Improved Edge Segregation","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Inpainting; Image texture; Artificial intelligence; Morphological gradient; Pattern recognition (psychology); Piecewise; Computer vision; Image segmentation; Image (mathematics); Decomposition; Mathematics; Computer science; Image processing","score_opus":0.004098543800979271,"score_gpt":0.24704635441475697,"score_spread":0.2429478106137777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011278558","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005844023,0.000006742965,0.9895119,0.0009790438,0.00005670812,0.00021082336,0.0000015418254,0.0005164536,0.00813239],"genre_scores_gemma":[0.081061274,7.9047925e-7,0.9174627,0.0006260362,0.00006158598,0.000035677047,0.000059512127,0.0000061538753,0.0006862374],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991098,0.000041496674,0.00018406245,0.00024799697,0.00027885006,0.00013780015],"domain_scores_gemma":[0.9994362,0.000048529066,0.00008770725,0.00022214124,0.00015516799,0.000050231243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015316186,0.000093333,0.00007450763,0.00008199072,0.0000824275,0.00018755588,0.00025123672,0.00004163387,0.00013903063],"category_scores_gemma":[0.000012118975,0.000072246425,0.000022576474,0.0002502509,0.000038344628,0.0009386588,0.00004601321,0.00006910408,0.000038790862],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028100156,0.00036500976,0.00036141375,0.000025055822,0.000025637693,0.000019401145,0.0001652754,0.00002869378,0.6091088,0.23900424,0.027946867,0.12292147],"study_design_scores_gemma":[0.0011193256,0.00019657309,0.016263884,0.000020003148,0.000011020019,0.00004393179,0.000010473077,0.20222649,0.7472253,0.032206427,0.0003340211,0.0003425377],"about_ca_topic_score_codex":0.00010549792,"about_ca_topic_score_gemma":0.000020351099,"teacher_disagreement_score":0.20679781,"about_ca_system_score_codex":0.000055013337,"about_ca_system_score_gemma":0.000054986085,"threshold_uncertainty_score":0.29461238},"labels":[],"label_agreement":null},{"id":"W2011376662","doi":"10.1109/icip.2006.312518","title":"Increasing Object Recognition Rate using Reinforced Segmentation","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Computer science; Cognitive neuroscience of visual object recognition; Segmentation; Object (grammar); 3D single-object recognition; Image segmentation; Pattern recognition (psychology); Feature extraction; Computer vision","score_opus":0.027817266341789795,"score_gpt":0.2854419706423717,"score_spread":0.25762470430058193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011376662","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08279987,0.0000057034617,0.91243666,0.00006792427,0.000090859525,0.00018112517,6.0259725e-7,0.0005104853,0.0039067776],"genre_scores_gemma":[0.12820289,0.0000037352343,0.870612,0.0007445537,0.000064382715,0.000011070859,0.00003169857,0.0000070125043,0.0003226289],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998977,0.00015485974,0.00026998494,0.0002136787,0.0002199574,0.00016450534],"domain_scores_gemma":[0.9994709,0.000096467054,0.00011129948,0.00018378285,0.00008985725,0.00004771983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005803464,0.00008999363,0.00008301572,0.0001296127,0.00010440023,0.00019003646,0.00018491803,0.00004064257,0.0001211909],"category_scores_gemma":[0.000072866176,0.00008545792,0.000031458272,0.0003200383,0.000027634127,0.0011218027,0.000063726635,0.00006218543,0.000052699976],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007036761,0.000015730237,0.00011621122,0.000012152343,0.0000049318846,0.00000959259,0.000084251464,0.00010162232,0.90171856,0.0014867944,0.0007820927,0.09566102],"study_design_scores_gemma":[0.00027133964,0.000027513499,0.00034451066,0.000026104659,0.0000052130563,0.000030276331,0.000028581706,0.10481115,0.8916565,0.002637374,0.000016490525,0.00014492033],"about_ca_topic_score_codex":0.0009097571,"about_ca_topic_score_gemma":0.000006197996,"teacher_disagreement_score":0.10470952,"about_ca_system_score_codex":0.000088334215,"about_ca_system_score_gemma":0.000041708226,"threshold_uncertainty_score":0.3484873},"labels":[],"label_agreement":null},{"id":"W2011890716","doi":"10.1016/j.cmpb.2013.07.026","title":"A Gauss–Newton approach to joint image registration and intensity correction","year":2013,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre; Western University; University of Ontario Institute of Technology","funders":"Canadian Institutes of Health Research; Canadian Cancer Society","keywords":"Hessian matrix; Regularization (linguistics); Jacobian matrix and determinant; Computer science; Image registration; Intensity (physics); Minification; Artificial intelligence; Transformation (genetics); Mathematics; Algorithm; Computer vision; Image (mathematics); Mathematical optimization; Applied mathematics; Optics","score_opus":0.06442470956937585,"score_gpt":0.35553506142253105,"score_spread":0.2911103518531552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011890716","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003642307,0.00010320596,0.99137294,0.0031495797,0.0005016251,0.0008161549,1.3438998e-7,0.00019717232,0.00021688611],"genre_scores_gemma":[0.006931518,0.00004355089,0.99122095,0.0014824133,0.00012324752,0.00011234904,0.0000097889115,0.0000068136883,0.00006938808],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984823,0.00023675877,0.0003674807,0.00049881457,0.00019428681,0.00022035831],"domain_scores_gemma":[0.9991658,0.00007970519,0.000095167015,0.00029903342,0.00013116,0.00022913128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015556545,0.00015353264,0.00027812002,0.00025534976,0.00005697855,0.00021952762,0.00019563579,0.000100891084,0.0000042057036],"category_scores_gemma":[0.000088038854,0.00011995232,0.000020432079,0.0005796685,0.00019424054,0.00037831723,0.0002796939,0.00023733602,0.0000027613985],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035044018,0.000073542964,0.00019691941,0.000039591207,0.00000475125,0.000002787183,0.00094178994,1.4739976e-7,0.010464368,0.00019469066,0.0047570895,0.98332083],"study_design_scores_gemma":[0.0020797097,0.0032988032,0.070102185,0.00066052633,0.000027520084,0.0006059552,0.0005300035,0.8623372,0.038832724,0.01359168,0.007018124,0.0009155845],"about_ca_topic_score_codex":0.00029504544,"about_ca_topic_score_gemma":0.0000015980175,"teacher_disagreement_score":0.98240525,"about_ca_system_score_codex":0.00003205828,"about_ca_system_score_gemma":0.000014016563,"threshold_uncertainty_score":0.48915136},"labels":[],"label_agreement":null},{"id":"W2012255096","doi":"10.1109/tmi.2014.2300694","title":"Prostate Segmentation: An Efficient Convex Optimization Approach With Axial Symmetry Using 3-D TRUS and MR Images","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Canadian Institutes of Health Research; Ontario Institute for Cancer Research","keywords":"Image segmentation; Regular polygon; Segmentation; Computer vision; Prostate; Artificial intelligence; Symmetry (geometry); Computer science; Mathematics; Medicine; Geometry","score_opus":0.010837606602551599,"score_gpt":0.26771511784311497,"score_spread":0.25687751124056335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012255096","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042300373,0.000023175915,0.9939608,0.00051105005,0.0002436403,0.00038660367,0.0000038022795,0.00043723852,0.00020366916],"genre_scores_gemma":[0.36640707,0.00001842106,0.6324245,0.001009021,0.000052176358,0.000044776316,0.00000637191,0.000022040034,0.000015637532],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973887,0.00027841903,0.00036212744,0.0006141481,0.001024944,0.00033162042],"domain_scores_gemma":[0.9988017,0.00014119368,0.00011977334,0.00036311927,0.00012240821,0.00045176793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007591746,0.00023085347,0.00023236334,0.00026522466,0.00034119937,0.00029089945,0.000360944,0.00007209348,0.00007328155],"category_scores_gemma":[0.00003341727,0.00019418301,0.000038147708,0.00046844836,0.00037760465,0.0007871833,0.000008022531,0.00037032072,0.0000028836885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005982482,0.00070166815,0.00008037767,0.00012076778,0.000046327194,0.00003959118,0.0016670093,0.23116821,0.0036629152,0.00023682116,0.00004614914,0.7621703],"study_design_scores_gemma":[0.0010113663,0.00011972509,0.000015810228,0.00007733533,0.000029267041,0.00014449467,0.00020709303,0.9602038,0.037923694,0.00003409088,0.0000045128313,0.00022878844],"about_ca_topic_score_codex":0.00004487549,"about_ca_topic_score_gemma":0.0000010020561,"teacher_disagreement_score":0.76194155,"about_ca_system_score_codex":0.00007582875,"about_ca_system_score_gemma":0.00011259291,"threshold_uncertainty_score":0.79185534},"labels":[],"label_agreement":null},{"id":"W2012694870","doi":"10.1002/ima.22065","title":"The clique potential of Markov random field in a random experiment for estimation of noise levels in 2D brain MRI","year":2013,"lang":"en","type":"article","venue":"International Journal of Imaging Systems and Technology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"NeuroRx Research (Canada); Concordia University","funders":"National Institute of Biomedical Imaging and Bioengineering","keywords":"Noise (video); Clique; Algorithm; Computer science; Markov random field; Random field; Parametric statistics; Pattern recognition (psychology); Markov chain; Shot noise; Artificial intelligence; Mathematics; Statistics; Image (mathematics); Machine learning; Image segmentation; Detector","score_opus":0.006257626222400392,"score_gpt":0.29206594277812575,"score_spread":0.28580831655572536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012694870","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054838702,0.00071313925,0.9319524,0.0117178485,0.0003868249,0.00036030033,0.000001408823,0.000011412643,0.000017928842],"genre_scores_gemma":[0.95819145,0.00006253251,0.041581806,0.00007296627,0.00002239926,0.00005113313,3.5472678e-7,0.0000032764933,0.000014056761],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987095,0.000076778466,0.00078042597,0.00009598247,0.00024139181,0.00009592811],"domain_scores_gemma":[0.9984513,0.0004929518,0.0005396553,0.00010914713,0.00038650716,0.000020445576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009942531,0.00006551485,0.00022400092,0.0004812496,0.000016572623,0.00005632146,0.0005210286,0.000050663228,0.0000029322655],"category_scores_gemma":[0.0005731143,0.000046897883,0.00004376882,0.00012588843,0.00008675225,0.00030058564,0.00008586722,0.00012238599,1.6075684e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005191763,0.0003121123,0.0063421037,0.0001418017,0.00019592688,0.00007961937,0.0013778291,0.0005772757,0.28801987,0.02606762,0.0032495905,0.6731171],"study_design_scores_gemma":[0.020014167,0.0004836569,0.0031069787,0.0015773094,0.000014108913,0.00070018595,0.0013737906,0.49395487,0.44651547,0.031753093,0.00025912977,0.00024724996],"about_ca_topic_score_codex":0.00021123941,"about_ca_topic_score_gemma":0.000005735125,"teacher_disagreement_score":0.9033528,"about_ca_system_score_codex":0.00004136666,"about_ca_system_score_gemma":0.00006245517,"threshold_uncertainty_score":0.191244},"labels":[],"label_agreement":null},{"id":"W2012819842","doi":"10.1109/icip.2011.6116262","title":"Tensor vector field based active contours","year":2011,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vector field; Tensor field; Kernel (algebra); Convolution (computer science); Computer science; Tensor (intrinsic definition); Artificial intelligence; Structure tensor; Computer vision; Sensitivity (control systems); Noise (video); Field (mathematics); Segmentation; Support vector machine; Algorithm; Pattern recognition (psychology); Mathematics; Image (mathematics); Geometry; Mathematical analysis; Artificial neural network; Engineering; Pure mathematics","score_opus":0.03688826921037465,"score_gpt":0.27306930562246906,"score_spread":0.23618103641209443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012819842","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003300618,0.000002764916,0.95733464,0.0007797571,0.000102510865,0.0001054112,4.3017107e-7,0.0003958051,0.040948603],"genre_scores_gemma":[0.2482051,0.0000010566615,0.7416577,0.00919591,0.000021692482,0.00002529132,6.2793254e-7,0.0000040045543,0.00088864565],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99942875,0.000032789158,0.0000957713,0.00016783545,0.00015158045,0.00012329817],"domain_scores_gemma":[0.9994459,0.00010642082,0.000033783665,0.0002685652,0.000056885438,0.000088410445],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008395844,0.00005901266,0.000068116264,0.000044412638,0.000026668251,0.000024686306,0.00041406235,0.000035531164,0.0018016262],"category_scores_gemma":[0.00011204441,0.000046266923,0.00002979847,0.000101116006,0.000025567113,0.00026710602,0.00005255078,0.00006797135,0.00008929818],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044892993,0.00034361097,0.0009817319,0.000017692359,0.00003405305,0.00008727112,0.002334036,1.7642603e-7,0.018255776,0.041950893,0.0995263,0.8364236],"study_design_scores_gemma":[0.00017644133,0.00015180442,0.0027714984,0.000006269589,0.000001938261,0.0000012666001,0.00003262092,0.0022402846,0.9933775,0.0008638667,0.00028820717,0.000088267945],"about_ca_topic_score_codex":0.00011560077,"about_ca_topic_score_gemma":0.000006106415,"teacher_disagreement_score":0.97512174,"about_ca_system_score_codex":0.000014319766,"about_ca_system_score_gemma":0.000034738252,"threshold_uncertainty_score":0.9991109},"labels":[],"label_agreement":null},{"id":"W2013424943","doi":"10.1007/s10278-014-9701-4","title":"Multi-Resolution Level Sets with Shape Priors: A Validation Report for 2D Segmentation of Prostate Gland in T2W MR Images","year":2014,"lang":"en","type":"article","venue":"Journal of Digital Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; King Abdulaziz City for Science and Technology","keywords":"Segmentation; Computer science; Prior probability; Level set (data structures); Context (archaeology); Convergence (economics); Artificial intelligence; Image segmentation; Level set method; Set (abstract data type); Pattern recognition (psychology); Boundary (topology); Medical imaging; Computer vision; Scale-space segmentation; Resolution (logic); Algorithm; Mathematics; Bayesian probability","score_opus":0.029499211293779547,"score_gpt":0.3152566962258015,"score_spread":0.28575748493202197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013424943","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08709655,0.000025298363,0.91196865,0.00046910573,0.00006823316,0.00028886346,0.0000073540896,0.000027383983,0.0000485382],"genre_scores_gemma":[0.5302041,0.000004175127,0.46965584,0.0000477438,0.000022889553,0.00000843064,0.000017549026,0.000008324553,0.000030896706],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99843496,0.000046828554,0.00072855176,0.00018211837,0.0004412668,0.00016630106],"domain_scores_gemma":[0.99823135,0.00011606255,0.00096037536,0.00015339536,0.0004653827,0.00007343942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000906306,0.00011209745,0.00020680962,0.00027381233,0.00003366179,0.00027549485,0.0002359686,0.000021043992,0.0000016348143],"category_scores_gemma":[0.0004594698,0.00008999145,0.000056692752,0.00021986272,0.00006084246,0.0035697895,0.000053272444,0.00010471728,5.790342e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020897028,0.00062662933,0.12771489,0.00031439518,0.00006955191,0.0002073276,0.0038230354,0.0007092784,0.1730222,0.000060872062,0.00088865013,0.6923542],"study_design_scores_gemma":[0.007471768,0.0010178196,0.05501673,0.0012481752,0.000059029626,0.0017173915,0.0005818305,0.32759443,0.60227513,0.0022805177,0.00018712853,0.000550059],"about_ca_topic_score_codex":0.0000078184175,"about_ca_topic_score_gemma":0.0000015297716,"teacher_disagreement_score":0.6918042,"about_ca_system_score_codex":0.00008289706,"about_ca_system_score_gemma":0.00009908132,"threshold_uncertainty_score":0.36697447},"labels":[],"label_agreement":null},{"id":"W2013525041","doi":"10.1016/j.imavis.2008.02.006","title":"Semiautomatic segmentation with compact shape prior","year":2008,"lang":"en","type":"article","venue":"Image and Vision Computing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Insight Design Labs (Canada); Western University","funders":"","keywords":"Segmentation; Computer vision; Artificial intelligence; Computer science; Pattern recognition (psychology); Mathematics","score_opus":0.016429121863253404,"score_gpt":0.3241962619706669,"score_spread":0.3077671401074135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013525041","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18696521,0.00003925934,0.8115757,0.00034353844,0.000039406856,0.00017984341,3.600025e-7,0.00041064096,0.00044603538],"genre_scores_gemma":[0.5261539,0.000015388117,0.47320092,0.00055478985,0.000029466344,0.0000013878694,0.0000028988927,0.0000073241986,0.00003394371],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987887,0.00007776207,0.00025536533,0.0003178654,0.0003579869,0.00020227744],"domain_scores_gemma":[0.99925303,0.0001694088,0.0001339865,0.00023178534,0.000085377134,0.00012642385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025652035,0.00013492552,0.00016442063,0.00010322356,0.00032339728,0.00017483953,0.00025859103,0.000031095176,0.000029725798],"category_scores_gemma":[0.000036675803,0.00010503556,0.000024518487,0.0002666293,0.00011221582,0.00077203516,0.00013426098,0.00012356185,0.00002333883],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000119885035,0.0001373702,0.0026484702,0.00010060921,0.00002279364,0.00022365859,0.004131081,0.000023311677,0.04806909,0.00020377511,0.0059228204,0.93850505],"study_design_scores_gemma":[0.0012720907,0.00048469706,0.031550415,0.00029753128,0.000010457607,0.00059120153,0.0001537145,0.8721055,0.09286102,0.00019479702,0.00009151458,0.0003870937],"about_ca_topic_score_codex":0.000012066752,"about_ca_topic_score_gemma":2.7865656e-7,"teacher_disagreement_score":0.9381179,"about_ca_system_score_codex":0.000026126954,"about_ca_system_score_gemma":0.000039727896,"threshold_uncertainty_score":0.42832255},"labels":[],"label_agreement":null},{"id":"W2014537729","doi":"10.1117/12.844367","title":"A probabilistic framework for ultrasound image decomposition","year":2010,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Computer science; Image segmentation; Segmentation; Pattern recognition (psychology); Computer vision; Noise (video); Medical imaging; Feature extraction; Statistical model; Image (mathematics); Probabilistic logic; A priori and a posteriori; Image formation; Ultrasound; Radiology","score_opus":0.009531633355314382,"score_gpt":0.2683354646049425,"score_spread":0.25880383124962814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014537729","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8549959,0.000015771953,0.14006402,0.002623435,0.00042070754,0.0010283387,0.000033988792,0.00023905006,0.0005787792],"genre_scores_gemma":[0.057886925,0.000018408327,0.9409356,0.0002320203,0.0003700687,0.00045784813,0.000009113105,0.000038767612,0.00005124985],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977153,1.5843401e-8,0.0006858916,0.00048715578,0.0007119009,0.00039975744],"domain_scores_gemma":[0.9965849,0.0006495251,0.00041979522,0.00011043919,0.002065721,0.00016961034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000894121,0.00028227392,0.00033945256,0.000103996295,0.00011628213,0.00031228756,0.0018222546,0.00023787015,0.000016512347],"category_scores_gemma":[0.0028495784,0.00024029109,0.00052999624,0.00034911186,0.00031316403,0.0010493882,0.00019768953,0.00047294292,0.0000018635432],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017595834,0.000084139894,0.000023220244,0.00022563971,0.00007298395,3.3197452e-8,0.000118000535,0.0000028674244,0.49802998,0.49894154,0.0018833994,0.00060059136],"study_design_scores_gemma":[0.0009086749,0.00045664134,0.00043359093,0.00031737162,0.00010551258,0.000038608785,0.00028479128,0.041145273,0.8543396,0.10042591,0.0010666123,0.00047743504],"about_ca_topic_score_codex":0.0000039863694,"about_ca_topic_score_gemma":1.2407209e-7,"teacher_disagreement_score":0.80087155,"about_ca_system_score_codex":0.000101062666,"about_ca_system_score_gemma":0.000048310732,"threshold_uncertainty_score":0.9798786},"labels":[],"label_agreement":null},{"id":"W2014748826","doi":"10.1109/tmi.2012.2195009","title":"Nonrigid 2D/3D Registration of Coronary Artery Models With Live Fluoroscopy for Guidance of Cardiac Interventions","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"École de technologie supérieure","keywords":"Fluoroscopy; Image registration; Computer vision; Affine transformation; Artificial intelligence; Computer science; Matching (statistics); Minification; Rigid transformation; Image (mathematics); Radiology; Medicine; Mathematics; Geometry","score_opus":0.030015949391895086,"score_gpt":0.31887715575017145,"score_spread":0.2888612063582764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014748826","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012683428,0.00027812365,0.9967323,0.00038467578,0.00047483447,0.0004296796,0.000030184954,0.00012873806,0.00027310703],"genre_scores_gemma":[0.63946867,0.00007361706,0.35997283,0.00020190686,0.00003913734,0.0001477444,0.000005571616,0.000013312356,0.00007721653],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99798745,0.00011557204,0.0006114929,0.00026159867,0.00074899994,0.0002748874],"domain_scores_gemma":[0.9985919,0.0002982418,0.00024414892,0.00041963678,0.00021469333,0.0002313866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080950017,0.0001497542,0.00028406177,0.00015757006,0.00008753546,0.000022586011,0.00041524472,0.00006612995,0.000080041376],"category_scores_gemma":[0.000039752333,0.000132577,0.00019453616,0.00023589504,0.0002994196,0.0011317026,0.000004993141,0.0002150434,0.0000039508604],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018626406,0.00233691,0.00030965402,0.0009781967,0.00032848044,0.000012426212,0.0039899223,0.0010400456,0.034413572,0.002913126,0.0032040786,0.95028734],"study_design_scores_gemma":[0.0014843603,0.00074178044,0.00039515493,0.0024403618,0.00022606211,0.00009171164,0.0005426102,0.28181633,0.7102752,0.0013278151,0.00013581285,0.0005228193],"about_ca_topic_score_codex":0.000037163078,"about_ca_topic_score_gemma":0.000005733036,"teacher_disagreement_score":0.9497645,"about_ca_system_score_codex":0.000054134078,"about_ca_system_score_gemma":0.000138798,"threshold_uncertainty_score":0.5406333},"labels":[],"label_agreement":null},{"id":"W2015071013","doi":"10.1016/j.ultrasmedbio.2004.08.020","title":"Surface extraction with a three-dimensional freehand ultrasound system","year":2004,"lang":"en","type":"article","venue":"Ultrasound in Medicine & Biology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Isosurface; Speckle pattern; Interpolation (computer graphics); Computer science; Ultrasound; Artificial intelligence; Computer vision; Acoustics; Visualization; Physics; Image (mathematics)","score_opus":0.016529553503226904,"score_gpt":0.29536907419893865,"score_spread":0.2788395206957118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015071013","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17678164,0.00035500975,0.81982285,0.00089789135,0.00043804094,0.00036017178,0.0000035101907,0.00035341192,0.0009874916],"genre_scores_gemma":[0.8478996,0.000036691217,0.15098757,0.00080210756,0.00015005734,0.000029802026,0.000042826116,0.000014362349,0.000036959078],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979133,0.0001507475,0.0005064209,0.00061203795,0.000389524,0.000427945],"domain_scores_gemma":[0.9978496,0.0011422527,0.00019768925,0.0005109787,0.00012699685,0.00017251806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084480696,0.00024968444,0.00040089307,0.00018787911,0.00010801519,0.00003224226,0.0005669059,0.00015661851,0.000096577554],"category_scores_gemma":[0.0005006339,0.0001720177,0.000032667333,0.0005912468,0.00053582946,0.00035294692,0.000047909856,0.0004212428,0.000041232364],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018999758,0.00058578234,0.054911114,0.00019533838,0.00013900547,0.00045293962,0.0022115188,0.0013892561,0.8475435,0.057375368,0.0035224701,0.031483717],"study_design_scores_gemma":[0.053680804,0.026225872,0.26157758,0.008527677,0.00041069466,0.026139736,0.0039561028,0.0060226065,0.44794425,0.144763,0.013760272,0.0069913995],"about_ca_topic_score_codex":0.0012725745,"about_ca_topic_score_gemma":0.0005371546,"teacher_disagreement_score":0.67111796,"about_ca_system_score_codex":0.0002718664,"about_ca_system_score_gemma":0.00016495066,"threshold_uncertainty_score":0.70146775},"labels":[],"label_agreement":null},{"id":"W2015418313","doi":"10.1109/tmi.2013.2237784","title":"3-D Carotid Multi-Region MRI Segmentation by Globally Optimal Evolution of Coupled Surfaces","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"Canadian Institutes of Health Research; Canada Research Chairs","keywords":"Algorithm; Image segmentation; Computer science; Computational complexity theory; Segmentation; Mathematics; Artificial intelligence","score_opus":0.010298017814674395,"score_gpt":0.2700402591628659,"score_spread":0.2597422413481915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015418313","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004993315,0.00009649748,0.9910123,0.002371966,0.0004962812,0.0005506941,0.0000061866963,0.0004195651,0.000053216856],"genre_scores_gemma":[0.7536045,0.00010832165,0.24520148,0.0007605651,0.000023608238,0.00013151561,0.0000071321942,0.000018492967,0.00014439515],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971013,0.00018257531,0.00058480183,0.00046484277,0.0013118923,0.00035458675],"domain_scores_gemma":[0.99865097,0.00017609073,0.0001897333,0.0003807696,0.00024712647,0.0003552964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044378886,0.0002173373,0.0002501829,0.00019149792,0.00016024454,0.00011965911,0.0007522699,0.00010896764,0.00037424007],"category_scores_gemma":[0.000048810343,0.00020457435,0.000108018685,0.000490535,0.00029027887,0.0012286591,0.000009406969,0.00034736074,0.00007683675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035972662,0.0022024715,0.0011558193,0.00020109228,0.00017905071,0.00004233196,0.0017021957,0.006883988,0.40574062,0.00020395713,0.026126647,0.55552584],"study_design_scores_gemma":[0.0010852569,0.000083327206,0.0003095114,0.00013050615,0.000021490197,0.000036107165,0.00027054804,0.76427245,0.2334618,0.00006574099,0.000022911554,0.00024032455],"about_ca_topic_score_codex":0.0009807148,"about_ca_topic_score_gemma":0.000013663389,"teacher_disagreement_score":0.7573885,"about_ca_system_score_codex":0.00024409532,"about_ca_system_score_gemma":0.0001714032,"threshold_uncertainty_score":0.83423},"labels":[],"label_agreement":null},{"id":"W2016247745","doi":"10.1117/12.770700","title":"Multi-phase image segmentation using level sets","year":2008,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Institute for Biodiagnostics","funders":"National Research Council Canada","keywords":"Region of interest; Artificial intelligence; Segmentation; Computer vision; Computer science; Image segmentation; Scale-space segmentation; Feature (linguistics); Region growing; Segmentation-based object categorization; Minimum spanning tree-based segmentation; Pattern recognition (psychology); Range segmentation; Image (mathematics)","score_opus":0.040627463912315816,"score_gpt":0.3036420007356918,"score_spread":0.263014536823376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016247745","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8882802,0.000038824142,0.10953219,0.00083302724,0.00020206996,0.0005865598,0.00003778336,0.00020088181,0.00028847376],"genre_scores_gemma":[0.03957334,0.00006572353,0.95975,0.00021033922,0.00014298415,0.00009987784,0.000009146374,0.000039475268,0.00010909215],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99734616,2.862092e-8,0.00079338707,0.0004808366,0.0009786438,0.0004009638],"domain_scores_gemma":[0.99741596,0.00011353838,0.00049026223,0.0000915109,0.0017116731,0.0001770357],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006307827,0.0003103116,0.00036401217,0.00015443223,0.00015813943,0.00014938855,0.0015169318,0.00015506851,0.000014415682],"category_scores_gemma":[0.0005407906,0.00027282207,0.00045854753,0.00046904097,0.0003266328,0.0017316547,0.00031281653,0.00028946498,0.0000026587866],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024969486,0.00026018356,0.0000993669,0.00020063155,0.00016213112,5.650468e-7,0.00051973807,0.000015609618,0.95379037,0.04057699,0.0026812758,0.0016681532],"study_design_scores_gemma":[0.0019964804,0.00025542284,0.00050772075,0.00017002136,0.000058515747,0.00006956057,0.00048778235,0.20393735,0.7914924,0.00050115393,0.00016919182,0.000354365],"about_ca_topic_score_codex":0.000015265228,"about_ca_topic_score_gemma":6.405931e-8,"teacher_disagreement_score":0.8502178,"about_ca_system_score_codex":0.00022339319,"about_ca_system_score_gemma":0.00006866737,"threshold_uncertainty_score":0.9999724},"labels":[],"label_agreement":null},{"id":"W2016302913","doi":"10.1118/1.1286722","title":"Prostate boundary segmentation from 2D ultrasound images","year":2000,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":177,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"London Health Sciences Centre; Western University; Robarts Clinical Trials","funders":"","keywords":"Initialization; Computer science; Artificial intelligence; Segmentation; Interpolation (computer graphics); Computer vision; Pixel; Image segmentation; Medical imaging; Active contour model; Boundary (topology); Image (mathematics); Mathematics","score_opus":0.009532163768703276,"score_gpt":0.27434349068851466,"score_spread":0.2648113269198114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016302913","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010409766,0.00010143816,0.98451245,0.0010946388,0.00020402495,0.00022105481,0.000014212094,0.00053616235,0.0029062561],"genre_scores_gemma":[0.5853277,0.0011651382,0.38307226,0.023868738,0.0017982827,0.00024471493,0.0005942763,0.000081505816,0.003847404],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977004,0.00010456988,0.0003106747,0.0004081184,0.0011951013,0.000281154],"domain_scores_gemma":[0.9988866,0.00025939604,0.00007044646,0.00042789846,0.00005155671,0.00030414516],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00027521624,0.00015859785,0.00017410323,0.000023950824,0.00013092649,0.00021613267,0.0007405395,0.00007763165,0.002995816],"category_scores_gemma":[0.00012750228,0.00014077903,0.000060428174,0.00028882723,0.00025648,0.00082211394,0.00008511402,0.00027785735,0.0005600185],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048270867,0.00012958152,0.00020806873,0.00000744987,0.000015642861,0.000034641333,0.00079247274,0.0000012040657,0.003625012,0.000054122636,0.02019589,0.97493106],"study_design_scores_gemma":[0.0010391523,0.00013338082,0.0020322956,0.00009880898,0.00001879547,0.000014772066,0.000044253644,0.00071422674,0.93821055,0.053931892,0.003320033,0.0004418402],"about_ca_topic_score_codex":0.00009607001,"about_ca_topic_score_gemma":0.0000023971347,"teacher_disagreement_score":0.9744893,"about_ca_system_score_codex":0.00005441166,"about_ca_system_score_gemma":0.00014872485,"threshold_uncertainty_score":0.99791557},"labels":[],"label_agreement":null},{"id":"W2016452392","doi":"10.1016/j.compmedimag.2005.10.007","title":"An automatic variational level set segmentation framework for computer aided dental X-rays analysis in clinical environments","year":2006,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Pattern recognition (psychology); Scale-space segmentation; Support vector machine; Segmentation-based object categorization; Image segmentation; Classifier (UML); Computer-aided; Principal component analysis; Level set method; Computer vision; Feature extraction","score_opus":0.02886535993748987,"score_gpt":0.3516194388672539,"score_spread":0.32275407892976404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016452392","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032544516,0.000050461556,0.9656052,0.00079984387,0.00040512992,0.0003803466,0.000018927845,0.00019249726,0.0000030724973],"genre_scores_gemma":[0.12812538,0.000046323705,0.86803627,0.0031204505,0.000278938,0.000059932336,0.00031390807,0.000014601993,0.0000041807266],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965875,0.0004181265,0.0010188756,0.00074272335,0.00087111123,0.00036162385],"domain_scores_gemma":[0.9980427,0.0008735208,0.0002615048,0.00042873988,0.000046764046,0.00034678887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018617042,0.0002398296,0.00046275544,0.000500779,0.00015153736,0.00026441846,0.00067584956,0.00018687456,0.000025026786],"category_scores_gemma":[0.00014133536,0.00023534872,0.00017900045,0.00076063373,0.00029428856,0.00046165855,0.00022342458,0.0003588971,0.0000025099482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051995827,0.0020632849,0.2250745,0.00014044832,0.0005007643,0.00014665819,0.00082201854,0.00043123125,0.00077051594,0.050097156,0.002549555,0.71735185],"study_design_scores_gemma":[0.0014025258,0.00007538271,0.18969727,0.00005578056,0.00006115796,0.000012954191,0.000013277267,0.79476464,0.000089781824,0.01356744,0.000047997502,0.00021183258],"about_ca_topic_score_codex":0.000053113567,"about_ca_topic_score_gemma":0.000013921755,"teacher_disagreement_score":0.7943334,"about_ca_system_score_codex":0.000045042587,"about_ca_system_score_gemma":0.000083568935,"threshold_uncertainty_score":0.9597242},"labels":[],"label_agreement":null},{"id":"W2017456026","doi":"10.1016/j.media.2010.10.002","title":"Semi-automatic segmentation for prostate interventions","year":2010,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Cancer Agency; University of British Columbia; University of British Columbia Hospital","funders":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Segmentation; Contouring; Artificial intelligence; Computer science; Initialization; Computer vision; Prostate; Prostate brachytherapy; Prostate gland; Brachytherapy; Image segmentation; Pattern recognition (psychology); Medicine; Radiology; Radiation therapy","score_opus":0.013718249757867933,"score_gpt":0.34870626617178313,"score_spread":0.3349880164139152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017456026","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033676366,0.000021251995,0.9930681,0.002274397,0.00018973152,0.00039426034,0.000009258934,0.00045446513,0.00022091386],"genre_scores_gemma":[0.101109,0.000022118069,0.89572424,0.0016103067,0.0001005984,0.0004680501,0.00015563179,0.0000151322765,0.0007949065],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977949,0.0000927851,0.0006001687,0.00041289514,0.0008169012,0.00028230334],"domain_scores_gemma":[0.99840385,0.00027697498,0.00019305735,0.0005579821,0.00022178864,0.00034637417],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013158843,0.0001419486,0.00029397896,0.00044066206,0.00013516119,0.00024391494,0.0008571431,0.00010043038,0.002067114],"category_scores_gemma":[0.0014892624,0.00012121115,0.00047688096,0.0013513971,0.00016453049,0.0006746991,0.000173838,0.00027410884,0.00007121656],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032495004,0.00042301544,0.0008497301,0.00021363226,0.0007969653,0.000041559953,0.00070952845,0.0000025253644,0.038038764,0.0011870196,0.014820696,0.9429133],"study_design_scores_gemma":[0.0012903023,0.00019165162,0.0023307174,0.00010240452,0.0012609953,0.00002061181,0.00015725484,0.85622835,0.13099173,0.0058785984,0.001025218,0.00052215223],"about_ca_topic_score_codex":0.000038999628,"about_ca_topic_score_gemma":0.000115860246,"teacher_disagreement_score":0.94239116,"about_ca_system_score_codex":0.000027796932,"about_ca_system_score_gemma":0.000084400104,"threshold_uncertainty_score":0.99884516},"labels":[],"label_agreement":null},{"id":"W2017681889","doi":"10.1016/j.compbiomed.2009.06.013","title":"Three-dimensional segmentation of tumors from CT image data using an adaptive fuzzy system","year":2009,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Calgary","keywords":"Artificial intelligence; Pixel; Segmentation; Computer science; Fuzzy logic; Region of interest; Pattern recognition (psychology); Computer vision; Image segmentation; Similarity (geometry); Defuzzification; Fuzzy set; Data mining; Image (mathematics); Fuzzy number","score_opus":0.058858850101654,"score_gpt":0.3569067483430295,"score_spread":0.2980478982413755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017681889","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09412447,0.00028798726,0.9047667,0.000287811,0.00024903435,0.0001649625,0.000010782692,0.000069877125,0.00003838739],"genre_scores_gemma":[0.4444197,0.000008411018,0.5548171,0.0005646123,0.000077273064,0.0000012226224,0.00010908501,0.0000022674635,3.393889e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881214,0.00014073758,0.00035374638,0.00042195048,0.00013143994,0.00014000892],"domain_scores_gemma":[0.9990888,0.00017141267,0.00016010892,0.00044553942,0.000046752335,0.00008735309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005026205,0.00011344677,0.00028160834,0.00014838715,0.000043785152,0.000007272227,0.00056324597,0.00004222429,0.000004928301],"category_scores_gemma":[0.000035600297,0.000089281544,0.000009839634,0.00017426789,0.00027368442,0.00035821632,0.00021289276,0.00011339088,5.931121e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014742483,0.0003573258,0.0050274837,0.0000718126,0.00007811512,0.00033699657,0.001640835,0.00006352668,0.27148986,0.025110008,0.0022832176,0.6933934],"study_design_scores_gemma":[0.0027268103,0.0019493446,0.025479674,0.0011203819,0.00003643513,0.00015373455,0.00037242565,0.9234632,0.017613124,0.026748357,0.000008648431,0.00032781268],"about_ca_topic_score_codex":0.0004057063,"about_ca_topic_score_gemma":0.000015401589,"teacher_disagreement_score":0.92339975,"about_ca_system_score_codex":0.000036605656,"about_ca_system_score_gemma":0.00003850273,"threshold_uncertainty_score":0.36407956},"labels":[],"label_agreement":null},{"id":"W2018191865","doi":"10.1118/1.2030977","title":"Sci-PM Thurs - 07: Registration of geometric cardiac models to magnetic resonance images","year":2005,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials","funders":"","keywords":"Image registration; Voxel; Segmentation; Artificial intelligence; Computer vision; Magnetic resonance imaging; Computer science; Affine transformation; Image quality; Image segmentation; Medical imaging; Visualization; Image processing; Image (mathematics); Medicine; Radiology; Mathematics","score_opus":0.0235992122070968,"score_gpt":0.28629169706015617,"score_spread":0.26269248485305935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018191865","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00092933164,0.0009948025,0.9914194,0.0023185082,0.00015573802,0.00023848223,0.0000069060534,0.00017752084,0.0037593618],"genre_scores_gemma":[0.70245653,0.0005376253,0.2914091,0.0035868532,0.0007905527,0.000090838344,0.000013000241,0.000026815102,0.0010886751],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972401,0.00008025389,0.00041142871,0.00037543825,0.0016160797,0.00027668322],"domain_scores_gemma":[0.99865,0.0001704359,0.0001095267,0.0005859064,0.00015813773,0.00032599948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006920493,0.00013571617,0.0002517516,0.000119745266,0.000050977524,0.00005098976,0.0009190514,0.00008067104,0.000065849796],"category_scores_gemma":[0.00037560606,0.00012505056,0.00007909862,0.0011736983,0.00018160125,0.00059537106,0.00022097045,0.0002015262,0.00008242807],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024305393,0.0000905692,0.0000234719,0.000016228563,0.0000029692949,0.0000034957152,0.0002290723,0.000042803582,0.00024721207,0.0014574128,0.027525922,0.97035843],"study_design_scores_gemma":[0.0013193784,0.0010516753,0.0028602444,0.0004598046,0.0000467692,0.000010208182,0.000064114196,0.07218685,0.8581075,0.04904089,0.013771827,0.0010807571],"about_ca_topic_score_codex":0.000042881114,"about_ca_topic_score_gemma":0.0000012507481,"teacher_disagreement_score":0.9692777,"about_ca_system_score_codex":0.000060856397,"about_ca_system_score_gemma":0.00016575799,"threshold_uncertainty_score":0.5099414},"labels":[],"label_agreement":null},{"id":"W2018757344","doi":"10.1109/isbi.2012.6235651","title":"Locally-adaptive similarity metric for deformable medical image registration","year":2012,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Cancer Institute","keywords":"Metric (unit); Weighting; Artificial intelligence; Image registration; Pattern recognition (psychology); Ranking (information retrieval); Similarity (geometry); Computer science; Set (abstract data type); Image (mathematics); Computer vision; Medical imaging","score_opus":0.031199967561013432,"score_gpt":0.32034909069084533,"score_spread":0.2891491231298319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018757344","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000056471137,0.000055436518,0.9857591,0.001245935,0.0001854016,0.0003485045,0.0000016332353,0.00038765406,0.0119599],"genre_scores_gemma":[0.08026327,0.000015994114,0.9163272,0.002544113,0.00012291718,0.00008499978,0.0000084784615,0.0000069232897,0.00062611385],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99839413,0.000060453676,0.00029394872,0.00020826297,0.0006844715,0.0003587219],"domain_scores_gemma":[0.9988496,0.00023306524,0.000092735245,0.00031611024,0.00016326652,0.00034522626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015673324,0.000104370076,0.00013020368,0.00011842309,0.00009750567,0.000085718435,0.0005742372,0.00010595966,0.0002651316],"category_scores_gemma":[0.0008333364,0.000083433864,0.000060062674,0.0004296767,0.000081556944,0.0018506284,0.0001448248,0.00013418542,0.00005117747],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031483538,0.00060308067,0.00036297238,0.00008320469,0.00004827945,0.000009576683,0.00056758913,0.0000024457106,0.0020971126,0.27114588,0.1751395,0.5499089],"study_design_scores_gemma":[0.0018026786,0.0006402067,0.0013588431,0.000049566377,0.000032777694,0.00007826193,0.00019245698,0.46708074,0.49705106,0.019497838,0.01146113,0.00075444754],"about_ca_topic_score_codex":0.00004776592,"about_ca_topic_score_gemma":0.000008489335,"teacher_disagreement_score":0.5491544,"about_ca_system_score_codex":0.00007563138,"about_ca_system_score_gemma":0.00012121231,"threshold_uncertainty_score":0.34023342},"labels":[],"label_agreement":null},{"id":"W2018949800","doi":"10.1016/s0933-3657(00)00095-6","title":"EvIdent™: a functional magnetic resonance image analysis system","year":2001,"lang":"en","type":"article","venue":"Artificial Intelligence in Medicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Institute for Biodiagnostics","funders":"","keywords":"Computer science; Cluster analysis; Artificial intelligence; Fuzzy logic; Fuzzy clustering; Functional magnetic resonance imaging; Data mining; Novelty; Set (abstract data type); Pattern recognition (psychology); Software; Machine learning","score_opus":0.05855219096777317,"score_gpt":0.34286218066644897,"score_spread":0.2843099896986758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018949800","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025747255,0.0010831263,0.99081385,0.002617551,0.00039731723,0.00025516073,8.5956816e-7,0.00030939074,0.0019480163],"genre_scores_gemma":[0.9028415,0.00039139733,0.09428276,0.0014492406,0.0004082292,0.0001262538,0.00001296774,0.000015952817,0.0004717181],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99710786,0.0001880251,0.00089740887,0.0005870472,0.0008382331,0.00038141408],"domain_scores_gemma":[0.99849015,0.00034891753,0.0001349275,0.00064449786,0.00020493215,0.00017656978],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001379667,0.00018534945,0.0003800733,0.0007772208,0.000092091985,0.00007989087,0.0008082966,0.000075500204,0.00093498925],"category_scores_gemma":[0.0005588401,0.00015912545,0.000085692816,0.0040644757,0.00031143855,0.0004946526,0.0001286969,0.00024751437,0.00026817774],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044794375,0.00014233222,0.003342518,0.00003172395,0.00003099944,0.0004995069,0.00122012,0.00013708965,0.005470251,0.04783,0.0019257091,0.939325],"study_design_scores_gemma":[0.00030049693,0.0009278137,0.027745489,0.0008622874,0.00027222678,0.00019022363,0.0043517416,0.88463557,0.042660493,0.034438513,0.0026558298,0.0009593097],"about_ca_topic_score_codex":0.0006425342,"about_ca_topic_score_gemma":0.000259757,"teacher_disagreement_score":0.93836564,"about_ca_system_score_codex":0.000165891,"about_ca_system_score_gemma":0.000052351406,"threshold_uncertainty_score":0.9999783},"labels":[],"label_agreement":null},{"id":"W2019306339","doi":"10.1109/tmi.2012.2216543","title":"The Relevance Voxel Machine (RVoxM): A Self-Tuning Bayesian Model for Informative Image-Based Prediction","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Center for Research Resources; National Institute of Neurological Disorders and Stroke; Solve ME/CFS Initiative; National Institute on Aging; Canadian Institutes of Health Research","keywords":"Voxel; Artificial intelligence; Computer science; Bayesian probability; Machine learning; Contrast (vision); Probabilistic logic; Pattern recognition (psychology); Relevance (law); Regression; Mathematics; Statistics","score_opus":0.011733826502055678,"score_gpt":0.28330647579392465,"score_spread":0.271572649291869,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019306339","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00002651759,0.00010007834,0.9923625,0.004873898,0.0007383229,0.0005839917,0.000023549463,0.0010006141,0.00029055128],"genre_scores_gemma":[0.3492981,0.00011968841,0.645319,0.004394185,0.00012527208,0.0005507318,0.000008628063,0.00003555322,0.0001488335],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972993,0.00014189957,0.00056715787,0.00030631767,0.0010472401,0.0006380718],"domain_scores_gemma":[0.9975941,0.0010432607,0.00016643567,0.00051358406,0.00017893872,0.00050366647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016472624,0.00023943609,0.00019278927,0.00017939933,0.0008218406,0.00019345127,0.00079925376,0.000090995185,0.00003689696],"category_scores_gemma":[0.00023100867,0.00018213745,0.00015036257,0.0003821904,0.00023291154,0.0019671232,0.0000088707075,0.0006406026,0.000019731568],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055942524,0.00042866557,0.000030375299,0.000081200815,0.00006270853,0.000004934058,0.003104967,0.0022075796,0.0011699735,0.00063874567,0.0049808105,0.9872341],"study_design_scores_gemma":[0.00076979084,0.000042279426,0.000009984617,0.000093304254,0.000025922529,0.00001809036,0.00007813504,0.97739536,0.02037284,0.00038300952,0.00063560414,0.00017565908],"about_ca_topic_score_codex":0.000012247566,"about_ca_topic_score_gemma":0.0000054476445,"teacher_disagreement_score":0.98705846,"about_ca_system_score_codex":0.00018648262,"about_ca_system_score_gemma":0.00024439226,"threshold_uncertainty_score":0.74273497},"labels":[],"label_agreement":null},{"id":"W2019683809","doi":"10.1006/nimg.2002.1221","title":"A Robust Method for Extraction and Automatic Segmentation of Brain Images","year":2002,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":159,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Baycrest Hospital; University of Toronto; Sunnybrook Health Science Centre; Health Sciences Centre","funders":"Canadian Institutes of Health Research","keywords":"Segmentation; Artificial intelligence; Reproducibility; Imaging phantom; Pattern recognition (psychology); Computer science; Histogram; Image segmentation; Repeatability; Partial volume; White matter; Mathematics; Nuclear medicine; Magnetic resonance imaging; Medicine; Image (mathematics); Radiology; Statistics","score_opus":0.054143506808435715,"score_gpt":0.3392751723480792,"score_spread":0.2851316655396435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019683809","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011674569,0.000046084522,0.9959373,0.0018659442,0.00007564716,0.00038338572,0.0000046325777,0.0002132568,0.0003062844],"genre_scores_gemma":[0.0065592965,0.000023672912,0.99218607,0.00082574366,0.000018359267,0.000054458305,0.0000023924151,0.000009270805,0.00032072014],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990509,0.00012917453,0.00024335616,0.00025903786,0.00019476945,0.00012274133],"domain_scores_gemma":[0.9990048,0.0005061485,0.00014885576,0.00022443617,0.00005967158,0.00005609391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036336575,0.00008627535,0.00012454257,0.00010998946,0.000052428724,0.0000763318,0.00018777234,0.00002997083,0.000068846275],"category_scores_gemma":[0.00031934193,0.00008413324,0.000035745812,0.000167662,0.000044118795,0.0006836926,0.000053499498,0.00006629824,0.000004422187],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001335846,0.000052963096,0.000019880188,0.00008292947,0.0000041811804,0.0000041290573,0.00028772914,0.0000059510485,0.43295094,0.00022108902,0.013717919,0.5526509],"study_design_scores_gemma":[0.0004995554,0.0001977167,0.0014139059,0.000020787877,0.000011582129,0.000039300325,0.000030516536,0.4712983,0.5253339,0.0008339447,0.00020852097,0.00011197791],"about_ca_topic_score_codex":0.000007784225,"about_ca_topic_score_gemma":5.0530775e-7,"teacher_disagreement_score":0.552539,"about_ca_system_score_codex":0.000013341726,"about_ca_system_score_gemma":0.0000064502206,"threshold_uncertainty_score":0.34308538},"labels":[],"label_agreement":null},{"id":"W2020274664","doi":"10.1109/iembs.2010.5627551","title":"Symmetric multi-scale image registration","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Image registration; Invertible matrix; Regularization (linguistics); Transformation (genetics); Artificial intelligence; Computer science; Computer vision; Similarity (geometry); Image (mathematics); Scale (ratio); Matrix similarity; Mathematics; Algorithm; Geography; Mathematical analysis","score_opus":0.0161492901324739,"score_gpt":0.30064179960769444,"score_spread":0.28449250947522053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020274664","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012629593,0.0000030090703,0.980802,0.00073091604,0.00022775398,0.00011440222,3.2698642e-7,0.00056536705,0.016293256],"genre_scores_gemma":[0.041253537,0.0000030958277,0.9562852,0.00067258714,0.000035388228,0.000013693236,0.0000020383552,0.0000036566182,0.0017308048],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992586,0.000021069025,0.00015579749,0.00021101219,0.00023010651,0.00012343029],"domain_scores_gemma":[0.9993128,0.000044494867,0.00005135082,0.00041572386,0.00007819048,0.00009739492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028458392,0.000061053135,0.00005824754,0.00011361501,0.00004747646,0.00015372371,0.00048989203,0.00004708145,0.00017473001],"category_scores_gemma":[0.0001655366,0.00005138965,0.000026415406,0.00040279835,0.00005368612,0.00065655704,0.00008219747,0.00015536924,0.00018652207],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.146934e-7,0.00010413416,0.00027016763,0.0000064825585,0.0000022181434,0.0000075593994,0.00009474207,4.9828948e-8,0.6751404,0.01614839,0.0144801745,0.29374516],"study_design_scores_gemma":[0.00027203502,0.000039436614,0.0052370024,0.0000029951675,0.000001960167,0.000017885895,0.000019288835,0.041193224,0.95043075,0.0014269322,0.00119739,0.00016108806],"about_ca_topic_score_codex":0.00007077989,"about_ca_topic_score_gemma":0.000092731796,"teacher_disagreement_score":0.29358408,"about_ca_system_score_codex":0.000010370349,"about_ca_system_score_gemma":0.000027842023,"threshold_uncertainty_score":0.2397426},"labels":[],"label_agreement":null},{"id":"W2020476766","doi":"10.1007/s11548-015-1180-7","title":"Open-source image registration for MRI–TRUS fusion-guided prostate interventions","year":2015,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; University of British Columbia","funders":"National Center for Research Resources; National Institute of Biomedical Imaging and Bioengineering; National Cancer Institute; Canadian Institutes of Health Research","keywords":"Image registration; Computer science; Artificial intelligence; Segmentation; Computer vision; Prostate biopsy; Image fusion; Medical imaging; Prostate cancer; Medical physics; Medicine; Image (mathematics)","score_opus":0.07809382574327538,"score_gpt":0.36661742589885105,"score_spread":0.28852360015557565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020476766","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00404909,0.00022452619,0.9873368,0.005773866,0.002257237,0.0002019377,0.0000049902987,0.00004842931,0.000103082835],"genre_scores_gemma":[0.12898172,0.00020205785,0.8671711,0.0024723867,0.00072311534,0.000033554774,0.000050117997,0.000016096516,0.0003498232],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980655,0.00027655586,0.00093298935,0.00023048626,0.00033767914,0.00015684249],"domain_scores_gemma":[0.99681425,0.0007121504,0.0008021035,0.0001758441,0.0013040638,0.00019158983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022937327,0.00012682713,0.00034012238,0.00036439535,0.000073129704,0.00040737772,0.0010858447,0.00008330361,0.000012238778],"category_scores_gemma":[0.00048748852,0.00010811793,0.0002066015,0.0001233778,0.00013237377,0.001163128,0.00033335236,0.00017391861,0.0000023770524],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034232548,0.0004254973,0.0040784087,0.000060836515,0.00051573414,0.0003599743,0.00092016574,0.00010722252,0.0012711335,0.004190028,0.46225676,0.5254719],"study_design_scores_gemma":[0.033234525,0.0073160003,0.1319977,0.0068759853,0.0005244334,0.09110271,0.00088321866,0.2625637,0.027909681,0.15826426,0.2751229,0.004204881],"about_ca_topic_score_codex":0.000012640557,"about_ca_topic_score_gemma":0.0000023842267,"teacher_disagreement_score":0.52126706,"about_ca_system_score_codex":0.00009046875,"about_ca_system_score_gemma":0.00025919158,"threshold_uncertainty_score":0.44089213},"labels":[],"label_agreement":null},{"id":"W2020513142","doi":"10.1109/lsp.2007.913625","title":"A New Model for Image Segmentation","year":2008,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Image segmentation; Segmentation; Computer science; Scale-space segmentation; Artificial intelligence; Segmentation-based object categorization; Image (mathematics); Computer vision; Set (abstract data type); Pattern recognition (psychology); Variable (mathematics); Mathematics","score_opus":0.03619138375877132,"score_gpt":0.2935405548139096,"score_spread":0.2573491710551383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020513142","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016083802,0.000025772502,0.99497473,0.0025619832,0.0000709328,0.00027894508,0.0000014325109,0.0004148821,0.0000629139],"genre_scores_gemma":[0.06710316,0.0000027946387,0.923253,0.009145204,0.00012530385,0.00005854288,0.0000046226296,0.000015594496,0.00029175525],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877656,0.00002275012,0.00024060857,0.00033812015,0.00036510476,0.00025683583],"domain_scores_gemma":[0.9994594,0.000044964298,0.00012403939,0.00015870335,0.00007966299,0.00013326971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015840327,0.0001332528,0.00012132121,0.00010521358,0.00021419155,0.00014767899,0.00046322707,0.000037571557,0.000011284321],"category_scores_gemma":[0.000014168399,0.00013005057,0.00005772819,0.00020718735,0.000071912895,0.0012500476,0.000029354915,0.00009826038,0.0000140951515],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007459884,0.000019456951,0.0000060524358,0.00004009576,0.000005192779,0.000014936413,0.0015003856,0.0006859695,0.7370626,0.000020995485,0.069765694,0.19087121],"study_design_scores_gemma":[0.0003847535,0.000029561195,0.000008420112,0.000027705495,0.000005214479,0.000024678433,0.000006996576,0.6164803,0.38204592,0.00079756574,0.000030639014,0.00015822818],"about_ca_topic_score_codex":0.000008572201,"about_ca_topic_score_gemma":3.30775e-7,"teacher_disagreement_score":0.61579436,"about_ca_system_score_codex":0.000060580813,"about_ca_system_score_gemma":0.00019320232,"threshold_uncertainty_score":0.53033084},"labels":[],"label_agreement":null},{"id":"W20210422","doi":"10.1007/978-3-642-33415-3_80","title":"Stochastic 3D Motion Compensation of Coronary Arteries from Monoplane Angiograms","year":2012,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer vision; Computer science; Artificial intelligence; Coronary arteries; Cardiac cycle; Probabilistic logic; Motion estimation; Feature (linguistics); Deformation (meteorology); Artery; Medicine; Cardiology; Geology","score_opus":0.01618943971228354,"score_gpt":0.2629971043616236,"score_spread":0.24680766464934006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W20210422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.081666276,0.00011186925,0.91704494,0.00012615419,0.00070912443,0.00019499542,0.0000029139262,0.0001395138,0.0000041826597],"genre_scores_gemma":[0.5485681,0.0000015389843,0.45115378,0.00019949007,0.000062213825,0.000005474108,0.0000063149605,0.0000029396679,1.0659387e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998256,0.0000834286,0.00032288508,0.00038959555,0.00058420765,0.00036385588],"domain_scores_gemma":[0.9987591,0.00034343693,0.00015780481,0.0005058911,0.000101596626,0.00013219424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005642423,0.00014637971,0.00020306084,0.00027870218,0.000085371,0.00010866203,0.0009317458,0.00006316063,0.000020810374],"category_scores_gemma":[0.00010920807,0.000132351,0.000036315978,0.0010167683,0.00041166716,0.0013061523,0.00032486932,0.00014313785,0.000011037692],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043712203,0.00010219323,0.007989874,0.0000080067275,0.0000035521869,0.000002812057,0.0025698654,0.006022118,0.011166249,0.00012732108,0.000004386657,0.9719992],"study_design_scores_gemma":[0.0002841408,0.00015218678,0.09341892,0.000080996906,0.0000053654476,0.000018062621,0.0000037985894,0.79505867,0.105461225,0.0052691326,0.000002783924,0.00024474648],"about_ca_topic_score_codex":0.000109664965,"about_ca_topic_score_gemma":0.000013471746,"teacher_disagreement_score":0.9717545,"about_ca_system_score_codex":0.00007698066,"about_ca_system_score_gemma":0.000056943147,"threshold_uncertainty_score":0.5397117},"labels":[],"label_agreement":null},{"id":"W2021736433","doi":"10.1088/0031-9155/50/12/010","title":"A feasibility study to investigate the use of thin-plate splines to account for prostate deformation","year":2005,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; CancerCare Manitoba","funders":"CancerCare Manitoba Foundation","keywords":"Prostate; Image warping; Magnetic resonance imaging; Prostate cancer; Computer science; Electromagnetic coil; Radiation treatment planning; Data set; Nuclear medicine; Radiation therapy; Artificial intelligence; Algorithm; Computer vision; Medicine; Radiology; Cancer; Physics","score_opus":0.3491279847697213,"score_gpt":0.44592553945183033,"score_spread":0.09679755468210904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021736433","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4910269,0.000009055353,0.50163025,0.006308932,0.000042610856,0.00095548015,0.00000281476,0.000019641611,0.000004304595],"genre_scores_gemma":[0.8735047,0.000016151556,0.11790222,0.008320899,0.00011571136,0.00011956148,0.0000065673225,0.0000032176572,0.000010968318],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926656,0.000077445875,0.00027460372,0.00018256542,0.00008263324,0.00011620998],"domain_scores_gemma":[0.9993188,0.00022131963,0.00007631472,0.00022418317,0.000105184816,0.000054202894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007494899,0.000071781295,0.00016300842,0.000054755372,0.0000339538,0.000012314155,0.00022216968,0.00001665991,0.0000010049457],"category_scores_gemma":[0.00036101768,0.000039341132,0.000010482181,0.00024970373,0.00009790671,0.00020635425,0.00013228065,0.0000640936,0.0000012131039],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001778382,0.00074402086,0.033775162,0.00012643768,0.000038679613,0.0000012358562,0.06676192,0.00075978943,0.045701634,0.012623034,0.006681878,0.83260834],"study_design_scores_gemma":[0.011254999,0.019328833,0.121137455,0.00076008827,0.00013570825,0.000018463756,0.0062217903,0.31449488,0.21335402,0.29949352,0.0123644285,0.0014358219],"about_ca_topic_score_codex":0.00013570978,"about_ca_topic_score_gemma":0.00008412451,"teacher_disagreement_score":0.8311725,"about_ca_system_score_codex":0.000018151046,"about_ca_system_score_gemma":0.000020459182,"threshold_uncertainty_score":0.16042848},"labels":[],"label_agreement":null},{"id":"W2022346954","doi":"10.1109/icip.2006.312675","title":"Active Contour Segmentation with a Parametric Shape Prior: Link with the Shape Gradient","year":2006,"lang":"en","type":"preprint","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Montana; Ryerson University","keywords":"Segmentation; Active contour model; Maxima and minima; Parametric statistics; A priori and a posteriori; Artificial intelligence; Scale-space segmentation; Energy minimization; Image segmentation; Computer science; Regularization (linguistics); Pattern recognition (psychology); Minification; Segmentation-based object categorization; Active shape model; Computer vision; Energy (signal processing); Representation (politics); Mathematics; Mathematical optimization; Physics; Mathematical analysis; Statistics","score_opus":0.019500031300917375,"score_gpt":0.2756739671783308,"score_spread":0.25617393587741344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022346954","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012012504,0.00008120122,0.9770868,0.0045432416,0.0001325278,0.0021405425,0.0000105817135,0.00076667726,0.0032259473],"genre_scores_gemma":[0.1820158,0.00006332418,0.81207657,0.0032460908,0.00019893807,0.0010597076,0.000107224696,0.000053466414,0.001178865],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966312,0.00022905205,0.00042891997,0.0010374981,0.0012440787,0.00042925737],"domain_scores_gemma":[0.99743366,0.00033760496,0.00059538067,0.001088305,0.0003710555,0.00017396429],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045251587,0.00047152006,0.00041198594,0.0003422957,0.00019658696,0.0006411884,0.0016674467,0.00020093757,0.00015989173],"category_scores_gemma":[0.000042571548,0.00025645632,0.000096766904,0.0008940927,0.00024787453,0.0004584502,0.00078987004,0.0008762987,0.000028573417],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000083284554,0.00026037073,0.00033749454,0.00010832932,0.00023190754,0.00007927292,0.0016397594,0.00031849818,0.00019463098,0.001420913,0.009738173,0.98558736],"study_design_scores_gemma":[0.0076462254,0.005266984,0.05338574,0.0015190644,0.0007518285,0.00030406745,0.0018023135,0.753046,0.15961435,0.008729172,0.002772296,0.005161965],"about_ca_topic_score_codex":0.00044794224,"about_ca_topic_score_gemma":0.00014119431,"teacher_disagreement_score":0.9804254,"about_ca_system_score_codex":0.0002988221,"about_ca_system_score_gemma":0.00036952813,"threshold_uncertainty_score":0.9999888},"labels":[],"label_agreement":null},{"id":"W2022935832","doi":"10.1016/j.neuroimage.2004.09.022","title":"Brain structural mapping using a novel hybrid implicit/explicit framework based on the level-set method","year":2004,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Center for Research Resources; National Institutes of Health; National Institute of Biomedical Imaging and Bioengineering; University of California, Los Angeles; U.S. Department of Energy","keywords":"Image warping; Computer science; Landmark; Artificial intelligence; Matching (statistics); Set (abstract data type); Similarity (geometry); Tensor (intrinsic definition); Feature (linguistics); Algorithm; Pattern recognition (psychology); Image (mathematics); Point (geometry); Orientation (vector space); Computer vision; Mathematics; Geometry","score_opus":0.10033376508084582,"score_gpt":0.3620297873475495,"score_spread":0.26169602226670363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022935832","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014977115,0.0000043447612,0.97571695,0.008151556,0.00021871776,0.000401248,0.000024732779,0.00033142208,0.0001739179],"genre_scores_gemma":[0.1756872,4.6979764e-7,0.7910533,0.03310766,0.000087009095,0.00001971129,0.0000046116465,0.000024549261,0.000015517096],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99778676,0.00022438231,0.0003323887,0.0006178786,0.0006206336,0.0004179556],"domain_scores_gemma":[0.99773306,0.0008562201,0.00016469123,0.0010389299,0.000069129754,0.00013797008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067283394,0.0002451796,0.00020144867,0.00015947993,0.00028228608,0.0003340024,0.001276759,0.000059987036,0.000053106534],"category_scores_gemma":[0.00097289897,0.00018681663,0.000108869026,0.0005645117,0.00007416132,0.00029777142,0.00029474695,0.0005428779,0.000024038813],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017512742,0.00009578794,0.00007039004,0.000039004695,0.000020401247,0.00013109115,0.0009908888,0.0034575802,0.9212373,0.037543636,0.0021252222,0.034271166],"study_design_scores_gemma":[0.0011697236,0.00026032625,0.0041403654,0.0002555363,0.000013001431,0.00021951461,0.00006728064,0.46186003,0.49342299,0.03729192,0.00060471904,0.0006946118],"about_ca_topic_score_codex":0.000064473956,"about_ca_topic_score_gemma":9.132973e-7,"teacher_disagreement_score":0.45840243,"about_ca_system_score_codex":0.00009688967,"about_ca_system_score_gemma":0.00012222082,"threshold_uncertainty_score":0.76181614},"labels":[],"label_agreement":null},{"id":"W2023392183","doi":"10.1118/1.3223631","title":"Demons deformable registration for CBCT‐guided procedures in the head and neck: Convergence and accuracy","year":2009,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":94,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Health Network; Princess Margaret Cancer Centre; Ontario Institute for Cancer Research; University of Toronto","funders":"National Cancer Institute; National Institutes of Health","keywords":"Image registration; Computer science; Robustness (evolution); Artificial intelligence; Computer vision; Cadaver; Cone beam computed tomography; Convergence (economics); Medical imaging; Algorithm; Medicine; Computed tomography; Image (mathematics); Radiology; Surgery","score_opus":0.032473567811261476,"score_gpt":0.3398532903902676,"score_spread":0.30737972257900614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023392183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027406037,0.00009110485,0.9629502,0.0088444045,0.000036966427,0.0003676638,8.2026884e-7,0.0000638066,0.00023894185],"genre_scores_gemma":[0.96743673,0.00015136418,0.024927828,0.007323452,0.00007946802,0.000048697802,0.0000047578305,0.0000029117375,0.000024806535],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99901766,0.00003635831,0.00020999237,0.00019422962,0.0003771815,0.00016456655],"domain_scores_gemma":[0.99938667,0.00023327711,0.00006565924,0.0001748662,0.00003916424,0.00010038051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048276866,0.00007879367,0.00010036884,0.000017898985,0.00008592524,0.000081619415,0.00034098348,0.00004857054,0.0000032274904],"category_scores_gemma":[0.00089816947,0.00005390836,0.000015978092,0.0001742059,0.00010610661,0.0004793151,0.000040567367,0.00012613814,9.548532e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015599306,0.00024549637,0.0013677907,0.0001339049,0.000006567902,0.000016191292,0.002884542,0.0000036733477,0.00084965775,0.033844456,0.030463018,0.9301691],"study_design_scores_gemma":[0.0044905473,0.0014728574,0.049456313,0.0006644119,0.000032018626,0.00019265503,0.00032169366,0.211227,0.09196747,0.6364467,0.0028669606,0.0008613442],"about_ca_topic_score_codex":0.000022253618,"about_ca_topic_score_gemma":0.000017357832,"teacher_disagreement_score":0.9400307,"about_ca_system_score_codex":0.000011516156,"about_ca_system_score_gemma":0.00012388856,"threshold_uncertainty_score":0.2198319},"labels":[],"label_agreement":null},{"id":"W2023471252","doi":"10.1109/fuzz-ieee.2013.6622480","title":"N-cuts parameter adjustment using evolving fuzzy inferencing","year":2013,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cut; Computer science; Artificial intelligence; Image segmentation; Segmentation; Generality; Fuzzy set; Fuzzy logic; Image (mathematics); Set (abstract data type); Partition (number theory); Eigenvalues and eigenvectors; Graph; Pattern recognition (psychology); Computer vision; Mathematics; Theoretical computer science","score_opus":0.03372129316038901,"score_gpt":0.2919997019007126,"score_spread":0.2582784087403236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023471252","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011681536,0.000043506487,0.9805682,0.00021402708,0.00017508457,0.0002537246,1.027557e-7,0.00043900413,0.006624855],"genre_scores_gemma":[0.21247922,0.0000051343327,0.78526527,0.0018405566,0.000036320613,0.000029614315,6.040071e-7,0.000005812848,0.00033746383],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887246,0.00005395548,0.00024185506,0.0002645422,0.00031226082,0.000254916],"domain_scores_gemma":[0.9991751,0.000114596165,0.00006646227,0.00038598158,0.000117317715,0.00014054647],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00015748291,0.00010809462,0.00011257694,0.000100239704,0.000063713414,0.00025519062,0.00045100535,0.000043831493,0.0009286393],"category_scores_gemma":[0.0001257999,0.00008714757,0.00003755346,0.00020136287,0.000030346737,0.0013256635,0.000281563,0.0000980938,0.0002466748],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.5407014e-7,0.00010386786,0.001385506,0.000034024022,0.000032382857,0.000010180941,0.0010832049,0.000028227489,0.10459989,0.0074007944,0.02637763,0.85894376],"study_design_scores_gemma":[0.00043734454,0.00013618177,0.010763669,0.00012037192,0.000013453263,0.000032491585,0.00018131279,0.6812208,0.2790079,0.027338533,0.00014017483,0.0006077374],"about_ca_topic_score_codex":0.00039465408,"about_ca_topic_score_gemma":0.000002768597,"teacher_disagreement_score":0.858336,"about_ca_system_score_codex":0.00008827076,"about_ca_system_score_gemma":0.000048307425,"threshold_uncertainty_score":0.9999846},"labels":[],"label_agreement":null},{"id":"W2023528786","doi":"10.1142/s0219467804001415","title":"AUTOMATIC IMAGE REGISTRATION USING VIRTUAL CIRCLES","year":2004,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Universidade do Porto; University of Toronto; Purdue University; National Aeronautics and Space Administration","keywords":"Computer vision; Computer science; Artificial intelligence; Similarity (geometry); Enhanced Data Rates for GSM Evolution; Set (abstract data type); Pixel; Image registration; RADIUS; Image (mathematics); Virtual image; Computer graphics (images)","score_opus":0.016932326916434447,"score_gpt":0.31308276158002607,"score_spread":0.29615043466359164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023528786","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.105076015,0.00006854314,0.892531,0.0018543265,0.0003094179,0.00004133525,0.0000018556874,0.00003645739,0.00008108765],"genre_scores_gemma":[0.4714468,0.00018758618,0.52737164,0.00083069183,0.00014784391,8.0842153e-7,0.0000016035424,0.0000060631032,0.000006954045],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986643,0.00003978744,0.00044309619,0.000116675285,0.0006400473,0.000096066884],"domain_scores_gemma":[0.99875164,0.0000594084,0.00040382065,0.00011447442,0.00057775475,0.00009289582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004501832,0.00008569511,0.000117660675,0.00030745816,0.000053196825,0.0003387691,0.00055173074,0.000040406205,0.000014618627],"category_scores_gemma":[0.000229714,0.0000774155,0.00007414946,0.00015641117,0.00014410584,0.0017606886,0.0000807863,0.00017443609,0.0000021939327],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004042648,0.00051006547,0.0005539393,0.00005046416,0.00035459228,0.0018734725,0.0027849597,0.00006172323,0.5087922,0.10173222,0.0019633733,0.38128257],"study_design_scores_gemma":[0.007210082,0.0014483079,0.016452437,0.0014072828,0.00013395472,0.009870935,0.00083191,0.06300421,0.5559537,0.3420559,0.0005492445,0.0010820901],"about_ca_topic_score_codex":0.000022684577,"about_ca_topic_score_gemma":0.0000025173256,"teacher_disagreement_score":0.38020048,"about_ca_system_score_codex":0.000054041404,"about_ca_system_score_gemma":0.00013003845,"threshold_uncertainty_score":0.32667583},"labels":[],"label_agreement":null},{"id":"W2023537367","doi":"10.1117/12.806089","title":"Ensemble registration: aligning many multi-sensor images simultaneously","year":2009,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pairwise comparison; Computer science; Artificial intelligence; Image registration; Cluster analysis; Computer vision; Pattern recognition (psychology); Image (mathematics)","score_opus":0.015262406688693336,"score_gpt":0.2609238214730677,"score_spread":0.24566141478437437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023537367","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8830196,0.000104595376,0.10367205,0.008060351,0.00025804536,0.0008892241,0.000017745802,0.0005091752,0.0034691636],"genre_scores_gemma":[0.12160829,0.000070601294,0.8768088,0.0005414265,0.00026057835,0.00007117777,0.0000051943093,0.00003045363,0.00060346094],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9971845,2.9173787e-8,0.00084537524,0.0005431469,0.0009844619,0.00044247578],"domain_scores_gemma":[0.9971761,0.00019971123,0.00051593536,0.00011630589,0.0018132743,0.00017863345],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00075665984,0.00033377006,0.000392701,0.00012310766,0.00011934942,0.0003163041,0.0017916313,0.00018372816,0.000008956256],"category_scores_gemma":[0.0010164795,0.00028899466,0.00047593817,0.00042415565,0.00019894226,0.0011721653,0.00017655667,0.00032800846,0.0000031629843],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020700149,0.00014533312,0.00003759284,0.00013701362,0.00010286346,7.2572533e-7,0.00023562493,0.000051718194,0.7370098,0.25256562,0.0063100555,0.0033829194],"study_design_scores_gemma":[0.0010178749,0.0005078133,0.00040422558,0.00029199594,0.00006698333,0.000059756356,0.00058269757,0.10078183,0.8919191,0.002621059,0.0012762885,0.0004703914],"about_ca_topic_score_codex":0.0000069590487,"about_ca_topic_score_gemma":6.351754e-8,"teacher_disagreement_score":0.7731368,"about_ca_system_score_codex":0.00014325108,"about_ca_system_score_gemma":0.00004345513,"threshold_uncertainty_score":0.9999562},"labels":[],"label_agreement":null},{"id":"W2023993038","doi":"10.1080/03772063.2002.11416270","title":"Texture Element Extraction via Cepstral Filtering in the Radon Domain","year":2002,"lang":"en","type":"article","venue":"IETE Journal of Research","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Preprocessor; Artificial intelligence; Weighting; Pattern recognition (psychology); Cepstrum; Radon transform; Computer science; Wavelet; Radon; Texture (cosmology); Filter (signal processing); Feature extraction; Computer vision; Mathematics; Acoustics; Image (mathematics); Physics","score_opus":0.09860321632490172,"score_gpt":0.42332007342203437,"score_spread":0.32471685709713266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023993038","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020092009,0.0002899008,0.9709405,0.007493142,0.00015313874,0.00018047943,2.8631584e-7,0.000016866557,0.0008337264],"genre_scores_gemma":[0.86738497,0.00032917838,0.1314887,0.0003137386,0.00027770654,0.000009864035,2.3276671e-7,0.0000060396205,0.00018954938],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99726003,0.0005720926,0.00034634216,0.000114677634,0.0014244615,0.00028241306],"domain_scores_gemma":[0.999119,0.0002796559,0.00011840667,0.00023369103,0.00016337755,0.00008588808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004713868,0.0000596635,0.00009372947,0.00032885803,0.000091110815,0.00018519125,0.0010052188,0.00004227242,0.00023718733],"category_scores_gemma":[0.00012926695,0.00003867013,0.000053738095,0.0005414814,0.00006631427,0.00060904777,0.000085078565,0.0008996881,0.000019644129],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025961122,0.00030485395,0.00045058565,0.000045786503,0.000018881767,0.0010158303,0.005705055,0.000031996893,0.1488947,0.00038810287,0.06189713,0.7812211],"study_design_scores_gemma":[0.008234995,0.008542442,0.04549813,0.0018283644,0.000024902798,0.009119706,0.0084555205,0.101716064,0.6428464,0.06111356,0.111333184,0.0012867369],"about_ca_topic_score_codex":0.0000144192,"about_ca_topic_score_gemma":0.000004286365,"teacher_disagreement_score":0.84729296,"about_ca_system_score_codex":0.000112133035,"about_ca_system_score_gemma":0.000026189746,"threshold_uncertainty_score":0.3908747},"labels":[],"label_agreement":null},{"id":"W2024351211","doi":"10.1016/s1077-3142(03)00099-7","title":"Object-level structured contour map extraction","year":2003,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Curvature; Artificial intelligence; Computer vision; Graph; Active contour model; Computer science; Mathematics; Contour line; Object (grammar); Process (computing); Pattern recognition (psychology); Algorithm; Topology (electrical circuits); Image (mathematics); Geometry; Image segmentation; Theoretical computer science; Combinatorics","score_opus":0.05317088986267293,"score_gpt":0.32008654828598265,"score_spread":0.2669156584233097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024351211","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00031796485,0.00008657981,0.9968739,0.00034454968,0.0006866913,0.00018955061,0.000001477348,0.00030290877,0.0011963327],"genre_scores_gemma":[0.28445235,0.00003367961,0.7144768,0.00080856466,0.00005873476,0.000003443293,0.000003190359,0.000011297427,0.00015188094],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998568,0.00014807886,0.0002678751,0.00043798605,0.000320178,0.00025786224],"domain_scores_gemma":[0.9992121,0.00013530685,0.000108983586,0.00030144816,0.000059411046,0.00018277788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038339975,0.00017900825,0.00018137581,0.00018295816,0.00024751457,0.0006097588,0.00026135822,0.00007456865,0.000096229836],"category_scores_gemma":[0.0000432309,0.000156249,0.000050666767,0.00020030283,0.0000954246,0.0011652961,0.00012892328,0.00018460849,0.000016656972],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038375718,0.00022072716,0.00022806173,0.00019401907,0.00008876376,0.0002830715,0.0028707872,0.000008278062,0.17468056,0.348001,0.10304615,0.3703402],"study_design_scores_gemma":[0.009157887,0.001894922,0.0050962376,0.00081383216,0.000070335846,0.0012863508,0.0026275576,0.25912893,0.34881932,0.35225606,0.015759729,0.0030888596],"about_ca_topic_score_codex":0.0000040795344,"about_ca_topic_score_gemma":0.0000019526874,"teacher_disagreement_score":0.36725134,"about_ca_system_score_codex":0.00015086019,"about_ca_system_score_gemma":0.000036973695,"threshold_uncertainty_score":0.63716495},"labels":[],"label_agreement":null},{"id":"W2024592371","doi":"10.1016/s0031-3203(99)00155-7","title":"Adaptive morphological operators, fast algorithms and their applications","year":2000,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Algorithm; Image processing; Degrees of freedom (physics and chemistry); Mathematical morphology; Basis (linear algebra); Artificial intelligence; Image (mathematics); Computer vision; Mathematics","score_opus":0.030293681591382522,"score_gpt":0.26519390421461037,"score_spread":0.23490022262322785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024592371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007844303,0.000044137505,0.9902341,0.00027698444,0.00002431168,0.00030316305,0.000030071567,0.0002652166,0.0009777235],"genre_scores_gemma":[0.69257355,0.00032275997,0.30059534,0.005097666,0.0002065369,0.00084652565,0.0001612422,0.000018149014,0.00017824903],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99921566,0.000075336786,0.00015297483,0.00030809382,0.00011174581,0.00013616773],"domain_scores_gemma":[0.99959594,0.000051559557,0.00003440453,0.00017064693,0.000052115654,0.00009533473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013014312,0.00010031757,0.00009245388,0.00004520662,0.000090323054,0.00009281297,0.00020505316,0.00005459542,0.0007189105],"category_scores_gemma":[0.0000060076854,0.00008001512,0.00002360487,0.00013991486,0.00006631091,0.00031813016,0.00005359823,0.000110610985,0.00030319847],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.704904e-7,0.0000378626,0.00003865396,0.000002345286,0.000003840533,0.000004198791,0.00014658488,1.7588403e-7,0.0005439195,0.000006391946,0.00020189218,0.9990132],"study_design_scores_gemma":[0.004607045,0.0022226495,0.018932996,0.00044122947,0.00007661944,0.0015493443,0.0014417273,0.3271327,0.5603709,0.069434,0.010281375,0.0035094016],"about_ca_topic_score_codex":0.000019603558,"about_ca_topic_score_gemma":0.0000015768658,"teacher_disagreement_score":0.9955038,"about_ca_system_score_codex":0.00001749531,"about_ca_system_score_gemma":0.000009276391,"threshold_uncertainty_score":0.7871565},"labels":[],"label_agreement":null},{"id":"W2025133043","doi":"10.5539/cis.v4n6p83","title":"Denoising, Segmentation and Characterization of Brain Tumor from Digital MR Images","year":2011,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Computer vision; MATLAB; Filter (signal processing); Noise (video); Noise reduction; Pattern recognition (psychology); Image segmentation; Scale-space segmentation; Identification (biology); Image processing; Digital image processing; Image (mathematics)","score_opus":0.01281341086701335,"score_gpt":0.2400617673568325,"score_spread":0.22724835648981914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025133043","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20369016,0.000004592969,0.79564875,0.00006723596,0.00008668394,0.00010240884,0.000008542445,0.000063798405,0.00032779697],"genre_scores_gemma":[0.747128,0.000019883164,0.25144115,0.0013525118,0.000018967386,0.0000055476075,0.000025518466,0.0000020404716,0.0000063726134],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99912643,0.0000144739815,0.0002940078,0.00015388346,0.00030426893,0.000106907704],"domain_scores_gemma":[0.99935985,0.000034934033,0.00019506075,0.00015882358,0.00016116448,0.000090150024],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0002772109,0.0000737483,0.00008559537,0.00021274108,0.00009994667,0.00045701937,0.0003206607,0.000016314338,0.000010841719],"category_scores_gemma":[0.000040730087,0.0000656778,0.000010465392,0.00034234894,0.00029387165,0.018406278,0.0002576644,0.000038629965,0.0000065544496],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000049032406,0.000025751826,0.0029349695,0.00002708544,0.000003916283,8.5491627e-7,0.010299484,4.4919386e-7,0.098519064,0.0032923545,0.00016315165,0.884728],"study_design_scores_gemma":[0.0004586904,0.00019140485,0.24813545,0.00006141337,0.0000035581395,0.00002200479,0.0001316782,0.05099265,0.69836,0.0011767871,0.00023635819,0.00023000302],"about_ca_topic_score_codex":0.000012168818,"about_ca_topic_score_gemma":7.4172206e-8,"teacher_disagreement_score":0.884498,"about_ca_system_score_codex":0.000013132414,"about_ca_system_score_gemma":0.000043261454,"threshold_uncertainty_score":0.99532276},"labels":[],"label_agreement":null},{"id":"W2025362214","doi":"10.1109/tmi.2013.2282932","title":"Interactive Hierarchical-Flow Segmentation of Scar Tissue From Late-Enhancement Cardiac MR Images","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Canadian Institutes of Health Research","keywords":"Segmentation; Computer science; Image segmentation; Potts model; Algorithm; Artificial intelligence; Flow (mathematics); Pattern recognition (psychology); Computer vision; Mathematics; Physics","score_opus":0.008203299413272401,"score_gpt":0.2824668191497131,"score_spread":0.2742635197364407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025362214","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005602703,0.00007557031,0.9892706,0.002860586,0.0010049071,0.00051631173,0.000025547435,0.00024250589,0.00040123105],"genre_scores_gemma":[0.6310065,0.00023028739,0.3662511,0.0018006576,0.0000951479,0.00032306527,0.000029783716,0.00002678442,0.00023670962],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99697095,0.00028975803,0.00061250955,0.0005413583,0.0012437757,0.0003416404],"domain_scores_gemma":[0.99821085,0.00054713985,0.0001691049,0.00049784844,0.00019180485,0.0003832601],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00036754372,0.00023547125,0.0003419613,0.00024370661,0.00013014935,0.0001367465,0.0006838236,0.00008669335,0.0037760423],"category_scores_gemma":[0.00006249553,0.00021563767,0.00013109556,0.00036607363,0.00028626045,0.0012420241,0.000012797363,0.000563266,0.00022079777],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000075374896,0.00021717591,0.000020163121,0.000017444516,0.00006014595,0.0000118787975,0.00083450245,0.00007469945,0.18339261,0.0000066459215,0.0024194482,0.81293774],"study_design_scores_gemma":[0.00045364382,0.00007600964,0.00014213425,0.00018898689,0.000027589258,0.000002916241,0.00015249568,0.06613035,0.93187165,0.00066363835,0.00008526608,0.0002053439],"about_ca_topic_score_codex":0.00055244926,"about_ca_topic_score_gemma":0.0000032207095,"teacher_disagreement_score":0.8127324,"about_ca_system_score_codex":0.00012699944,"about_ca_system_score_gemma":0.00011134439,"threshold_uncertainty_score":0.9971346},"labels":[],"label_agreement":null},{"id":"W2026105938","doi":"10.1016/j.neuroimage.2013.07.053","title":"Image registration of ex-vivo MRI to sparsely sectioned histology of hippocampal and neocortical temporal lobe specimens","year":2013,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Robarts Clinical Trials","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Magnetic resonance imaging; Histology; Ex vivo; Image registration; Hippocampal formation; Temporal lobe; Radiology; Surgical planning; Histopathology; Neuronavigation; Medicine; Computer science; Nuclear medicine; Epilepsy; Pathology; Artificial intelligence; In vivo; Biology; Image (mathematics)","score_opus":0.02854202608416044,"score_gpt":0.26938584804440113,"score_spread":0.24084382196024068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026105938","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17861715,0.00001194451,0.8157766,0.0019919474,0.0001672454,0.00047303434,0.0000038669805,0.00012446717,0.0028337499],"genre_scores_gemma":[0.5939643,0.00000932219,0.4049652,0.00066081836,0.000040978714,0.000024009973,0.0000028514237,0.000011317539,0.00032120047],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99846196,0.0001304506,0.0004988417,0.00038077275,0.0003281898,0.0001998122],"domain_scores_gemma":[0.9988704,0.00010996005,0.0002129775,0.0004598062,0.00017912882,0.00016775914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002082071,0.00012710594,0.00024440768,0.00014938676,0.000034621997,0.000051989427,0.00032978653,0.000060341263,0.00023350678],"category_scores_gemma":[0.00023242639,0.0001248573,0.00004296346,0.00027138303,0.00025472065,0.0005325199,0.00016266364,0.00013220435,0.000034362765],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016771377,0.00012881638,0.0011328366,0.000050276827,0.0000065273052,0.00002954612,0.00036207,0.0000011440168,0.9635853,0.001819101,0.024806757,0.008060821],"study_design_scores_gemma":[0.0011089384,0.0017964846,0.10030682,0.00005171299,0.00002707664,0.00017851827,0.000098924465,0.011223217,0.8766579,0.0067086373,0.0013851434,0.00045665295],"about_ca_topic_score_codex":0.00020622369,"about_ca_topic_score_gemma":0.000012896121,"teacher_disagreement_score":0.41534716,"about_ca_system_score_codex":0.00002521181,"about_ca_system_score_gemma":0.000050979248,"threshold_uncertainty_score":0.50915325},"labels":[],"label_agreement":null},{"id":"W2026595385","doi":"10.1016/j.media.2007.12.002","title":"Multimodal image registration using floating regressors in the joint intensity scatter plot","year":2008,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Institute of Biomedical Imaging and Bioengineering; Natural Sciences and Engineering Research Council of Canada","keywords":"Image registration; Computer science; Artificial intelligence; Histogram; Robustness (evolution); Curse of dimensionality; Computer vision; Pattern recognition (psychology); Image (mathematics)","score_opus":0.04663912200230379,"score_gpt":0.319221277277396,"score_spread":0.2725821552750922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026595385","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09346531,0.000025080863,0.90088177,0.0049676057,0.000052175696,0.00015939126,0.0000011280124,0.00014955203,0.0002980109],"genre_scores_gemma":[0.6033285,0.00002874295,0.39195624,0.0044984375,0.00009560678,0.000013674823,0.000018355337,0.000010026649,0.00005038209],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961294,0.0004973086,0.0007504596,0.0005527383,0.0016912551,0.000378858],"domain_scores_gemma":[0.9982291,0.00022036007,0.0002647408,0.00083994155,0.00023485618,0.00021098585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023571216,0.00019779448,0.0004267126,0.0004602578,0.0002534084,0.0001781653,0.0011183157,0.00011901721,0.00027748971],"category_scores_gemma":[0.0022342573,0.0001400818,0.0002513813,0.0022189436,0.0005613681,0.00090380374,0.00026543852,0.0005501527,0.000034564095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014495762,0.0049262093,0.09074339,0.00039608692,0.0030217322,0.029662246,0.07600166,0.00062100147,0.37078705,0.0010536261,0.0785287,0.34411335],"study_design_scores_gemma":[0.0005091833,0.000043034404,0.020009369,0.00007095972,0.00018311157,0.0001872057,0.00041249712,0.9423944,0.035598762,0.00023534603,0.000032385407,0.0003237494],"about_ca_topic_score_codex":0.0013282815,"about_ca_topic_score_gemma":0.000092926435,"teacher_disagreement_score":0.9417734,"about_ca_system_score_codex":0.00010152356,"about_ca_system_score_gemma":0.00012224852,"threshold_uncertainty_score":0.571237},"labels":[],"label_agreement":null},{"id":"W2027361807","doi":"10.1016/j.compmedimag.2013.06.007","title":"Synchronized 2D/3D optical mapping for interactive exploration and real-time visualization of multi-function neurological images","year":2013,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CancerCare Manitoba; University of Winnipeg; Centre for Imaging Technology Commercialization; Western University","funders":"","keywords":"Computer science; Visualization; Software; Volume rendering; Rendering (computer graphics); Interactivity; Artificial intelligence; Computer vision; Data visualization; Interactive visualization; Image processing; Computer graphics (images); Image (mathematics); Multimedia","score_opus":0.020906656744474648,"score_gpt":0.2984976817356936,"score_spread":0.2775910249912189,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027361807","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0074772686,0.00009235105,0.9890628,0.0022888482,0.000205519,0.00054083124,0.0000017629511,0.00030627692,0.000024365758],"genre_scores_gemma":[0.15347841,0.00093709794,0.843037,0.0021282951,0.00012837666,0.0001929277,0.000056194276,0.000024264638,0.000017407287],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817777,0.00024047891,0.00048277064,0.0004742968,0.00038738348,0.00023731138],"domain_scores_gemma":[0.99813944,0.00091347244,0.00018845791,0.00020154016,0.00029258843,0.00026452835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007049321,0.0001820934,0.0003253469,0.0002228594,0.00012404143,0.00019426971,0.00025151082,0.000111701665,0.00002219823],"category_scores_gemma":[0.0008247406,0.0001561702,0.00005518409,0.000270494,0.00043248665,0.0010390751,0.0002853107,0.00016903545,0.000002357161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007856633,0.00028165506,0.00046647832,0.00021258087,0.00005374614,0.000013330573,0.00086520496,0.0000023032574,0.09326645,0.004387311,0.001803767,0.89856863],"study_design_scores_gemma":[0.0016819523,0.00022109861,0.0035524208,0.00014347791,0.000019865687,0.00003967751,0.00004026452,0.98758966,0.002487133,0.0039654262,0.000072757,0.00018627699],"about_ca_topic_score_codex":0.00002337575,"about_ca_topic_score_gemma":2.848574e-7,"teacher_disagreement_score":0.98758733,"about_ca_system_score_codex":0.000011044088,"about_ca_system_score_gemma":0.000043489017,"threshold_uncertainty_score":0.63684356},"labels":[],"label_agreement":null},{"id":"W2027839406","doi":"10.1109/icpr.2014.106","title":"Efficient Interactive Brain Tumor Segmentation as Within-Brain kNN Classification","year":2014,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Support vector machine; Generalization; Pattern recognition (psychology); Machine learning; Context (archaeology); Feature vector; Set (abstract data type); Limiting; Feature (linguistics); Mathematics","score_opus":0.015350312645968365,"score_gpt":0.30782723743410745,"score_spread":0.2924769247881391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027839406","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015659414,0.0000023353768,0.9667777,0.008309644,0.00028055755,0.0003791171,6.7155474e-7,0.000595449,0.007995114],"genre_scores_gemma":[0.58198816,4.6760738e-7,0.3979866,0.017652644,0.00006913509,0.00010778988,0.000016106234,0.000014271446,0.0021648111],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813944,0.0002989189,0.000378603,0.0004691447,0.00051030377,0.00020359425],"domain_scores_gemma":[0.99848086,0.0005320386,0.00023552889,0.0004632787,0.00013681766,0.0001514563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009634236,0.00014733162,0.00013402279,0.00016652422,0.00010454291,0.00020919398,0.0005501913,0.000041828385,0.00021075076],"category_scores_gemma":[0.00083961966,0.00012741108,0.000046726498,0.00035545937,0.00007572432,0.0003671826,0.00013381946,0.00015865958,0.000482925],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002947978,0.00039156424,0.00013887043,0.00003806899,0.000032573033,0.000010627721,0.007478614,0.00023733756,0.40986574,0.21916878,0.046091583,0.31651676],"study_design_scores_gemma":[0.00046983655,0.00023681903,0.0009946098,0.0000352139,0.000004340049,0.000019405812,0.0005889498,0.57934594,0.4137013,0.003822149,0.0005428192,0.0002385901],"about_ca_topic_score_codex":0.0000551439,"about_ca_topic_score_gemma":0.0000064172727,"teacher_disagreement_score":0.5791086,"about_ca_system_score_codex":0.00013249101,"about_ca_system_score_gemma":0.00006391987,"threshold_uncertainty_score":0.62071854},"labels":[],"label_agreement":null},{"id":"W2028532540","doi":"10.2200/s00301ed1v01y201010bme038","title":"Analysis of Oriented Texture with Applications to the Detection of Architectural Distortion in Mammograms","year":2010,"lang":"en","type":"article","venue":"Synthesis lectures on biomedical engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Orientation (vector space); Artificial intelligence; Computer vision; Field (mathematics); Context (archaeology); Distortion (music); Pattern recognition (psychology); Geography; Mathematics; Geometry","score_opus":0.003744429129450684,"score_gpt":0.22750935887737717,"score_spread":0.22376492974792647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028532540","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04656983,0.000013024682,0.9526155,0.0004013585,0.000055084623,0.0002467352,0.0000052422106,0.000083206,0.000009995954],"genre_scores_gemma":[0.9637659,0.0000013867598,0.035959452,0.000059804926,0.00002534065,0.00017545949,0.0000044218523,0.000006492682,0.0000017441262],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99889135,0.000031578973,0.00026915796,0.00022383215,0.0004341102,0.0001499491],"domain_scores_gemma":[0.9990387,0.00032742272,0.00008394043,0.00040846335,0.000044510016,0.00009694142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027782342,0.00010642723,0.00020153365,0.00069949974,0.00002682843,0.0000132363875,0.0004249964,0.000072790186,0.000013534146],"category_scores_gemma":[0.00038848855,0.00006519107,0.000067964174,0.0027096262,0.00008350927,0.00004156848,0.000042420183,0.0002582413,8.4469707e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018523575,0.00009352627,0.00015918951,0.00002882474,0.000109508044,0.0000012997718,0.0003451692,0.0041286913,0.28679916,0.00035793483,0.0000044653843,0.7079537],"study_design_scores_gemma":[0.00019330956,0.0002828079,0.04700036,0.00009617548,0.00014969801,0.00000855775,0.000026904783,0.12828854,0.8220041,0.000047076974,0.0016402785,0.00026221588],"about_ca_topic_score_codex":0.00005991904,"about_ca_topic_score_gemma":0.00017566368,"teacher_disagreement_score":0.91719604,"about_ca_system_score_codex":0.000028691005,"about_ca_system_score_gemma":0.000018418443,"threshold_uncertainty_score":0.26584145},"labels":[],"label_agreement":null},{"id":"W2029056124","doi":"10.1016/j.inffus.2013.10.012","title":"A label field fusion model with a variation of information estimator for image segmentation","year":2014,"lang":"en","type":"article","venue":"Information Fusion","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Segmentation; Computer science; Cluster analysis; Artificial intelligence; Image segmentation; Scale-space segmentation; Segmentation-based object categorization; Pattern recognition (psychology); Constraint (computer-aided design); Data mining; Mathematics","score_opus":0.008685861637049035,"score_gpt":0.261351369039672,"score_spread":0.25266550740262295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029056124","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030454553,0.0000013968686,0.9937574,0.00073033903,0.000096956705,0.00082681625,0.000012789282,0.0002479817,0.0012808146],"genre_scores_gemma":[0.058820914,0.000008614978,0.939264,0.0014968048,0.000016994858,0.00015767144,0.00021235735,0.0000051826714,0.000017472657],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984878,0.0000388359,0.0006540654,0.000112833986,0.00054269057,0.00016379183],"domain_scores_gemma":[0.99824864,0.00015781772,0.0006126424,0.00031288003,0.000590046,0.000077978875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006215241,0.0001384865,0.00015552736,0.00031932667,0.00015054728,0.00018266904,0.00031602717,0.00009791123,0.000020289326],"category_scores_gemma":[0.00038224718,0.00011683084,0.000037353853,0.0003255061,0.000025837006,0.008678437,0.00009722442,0.00008154599,0.00003098604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002045177,0.00010297715,0.000035436555,0.0004906814,0.000016022334,1.3172304e-7,0.0076168,0.0028154117,0.04224149,0.034104515,0.0053758747,0.90699613],"study_design_scores_gemma":[0.0013458739,0.000398437,0.00016132933,0.00006815109,0.000010332871,0.0000035918513,0.000070589915,0.9100323,0.085223995,0.0021387918,0.00040450587,0.00014215765],"about_ca_topic_score_codex":0.0000280537,"about_ca_topic_score_gemma":0.0000017921454,"teacher_disagreement_score":0.90721685,"about_ca_system_score_codex":0.00005765745,"about_ca_system_score_gemma":0.00008814175,"threshold_uncertainty_score":0.6291657},"labels":[],"label_agreement":null},{"id":"W2029098063","doi":"10.1016/j.media.2007.12.003","title":"Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI","year":2008,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":360,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Segmentation; Active appearance model; Artificial intelligence; Computer science; Gauss; Pattern recognition (psychology); Statistical model; Computer vision; Image (mathematics)","score_opus":0.02068048989772678,"score_gpt":0.30107647428998674,"score_spread":0.28039598439225993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029098063","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03515038,0.00083786616,0.9634792,0.00022020708,0.000015170597,0.00014432479,0.000059224138,0.0000368748,0.000056710505],"genre_scores_gemma":[0.48296246,0.0007272988,0.5159617,0.00021885625,0.000014597946,0.000030405085,0.000034259254,0.0000069433972,0.000043457727],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976905,0.00013514284,0.0005887828,0.00045764842,0.0009007444,0.00022717804],"domain_scores_gemma":[0.99860436,0.00027413492,0.00018091581,0.0003991322,0.00023984151,0.00030161097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087440736,0.00013407345,0.00084487564,0.00060707034,0.000073165305,0.000023538882,0.00034758574,0.00008082969,0.000103140505],"category_scores_gemma":[0.0003619972,0.000113657705,0.00024931275,0.0021886204,0.0006326818,0.0001276276,0.00018727142,0.000087134984,4.4169346e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003174279,0.0040195044,0.05966026,0.0024734098,0.07786955,0.00040620347,0.017059365,0.06997238,0.061038118,0.049859155,0.019651208,0.63767344],"study_design_scores_gemma":[0.00018597698,0.00004955878,0.0029043846,0.000011556544,0.00196992,0.0000015049362,0.000020146894,0.98992246,0.0045453925,0.000268042,0.000014684889,0.00010634284],"about_ca_topic_score_codex":0.00018382544,"about_ca_topic_score_gemma":0.000008089553,"teacher_disagreement_score":0.9199501,"about_ca_system_score_codex":0.000013914936,"about_ca_system_score_gemma":0.00006548678,"threshold_uncertainty_score":0.46348265},"labels":[],"label_agreement":null},{"id":"W2029395414","doi":"10.1109/ipta.2010.5586811","title":"Co-parent selection for fast region merging in pyramidal image segmentation","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Segmentation; Pyramid (geometry); Image segmentation; Computer science; Minimum spanning tree-based segmentation; Artificial intelligence; Image (mathematics); Homogeneous; Partition (number theory); Pattern recognition (psychology); Node (physics); Segmentation-based object categorization; Set (abstract data type); Region growing; Scale-space segmentation; Computer vision; Mathematics; Combinatorics","score_opus":0.018970320716690386,"score_gpt":0.32754107820870637,"score_spread":0.308570757492016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029395414","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02170836,0.0000017574852,0.975654,0.00061026844,0.00021918872,0.00044148322,6.352236e-7,0.00033728807,0.0010270377],"genre_scores_gemma":[0.30127788,0.000005747647,0.6976947,0.00040505515,0.000072690294,0.00017395982,0.000015801752,0.000009433421,0.00034468446],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990486,0.000031584834,0.00023774973,0.00028148052,0.00021002704,0.00019054742],"domain_scores_gemma":[0.9995417,0.00006387745,0.00008101209,0.00016485289,0.00008167559,0.00006690202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030778153,0.000090313,0.000087615415,0.00017450827,0.00008329091,0.00012806933,0.00025614016,0.000054078457,0.00004278465],"category_scores_gemma":[0.00006898733,0.00008561362,0.00003277142,0.00024699056,0.000044821514,0.00078639033,0.000026590948,0.00014817275,0.000012078891],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008390666,0.00006860902,0.0018817397,0.000021674,0.0000038166877,0.0000023011,0.0005979913,0.0000058593364,0.79820645,0.0055346875,0.0047564483,0.18891205],"study_design_scores_gemma":[0.00047953182,0.00006999303,0.001539769,0.000009767685,0.0000021475655,0.000012322845,0.0001162486,0.06543783,0.93060607,0.0013189542,0.00027139363,0.00013596502],"about_ca_topic_score_codex":0.000095002426,"about_ca_topic_score_gemma":0.000121809986,"teacher_disagreement_score":0.27956954,"about_ca_system_score_codex":0.00006173962,"about_ca_system_score_gemma":0.000037579877,"threshold_uncertainty_score":0.34912223},"labels":[],"label_agreement":null},{"id":"W2030491910","doi":"10.1117/12.2082487","title":"Adaptive deformable image registration of inhomogeneous tissues","year":2015,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ontario Institute of Technology","funders":"","keywords":"Image registration; Computer science; Artificial intelligence; Computer vision; Elasticity (physics); Medical imaging; Image (mathematics)","score_opus":0.019209701530713674,"score_gpt":0.2552160084184536,"score_spread":0.23600630688773994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030491910","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96307194,0.00011630268,0.029019104,0.0014896131,0.00020336873,0.0005773802,0.00002156055,0.0001783876,0.0053223423],"genre_scores_gemma":[0.1408971,0.00005934502,0.858366,0.00010228407,0.0001713654,0.000101146376,0.000006183089,0.000029588875,0.00026696798],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99750745,2.7376045e-8,0.00079267717,0.00034221032,0.001057335,0.00030027807],"domain_scores_gemma":[0.9962015,0.000096058124,0.0006131054,0.0000914193,0.0028263722,0.00017152239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001001185,0.00023688548,0.0003596728,0.000112884525,0.000046829973,0.00012068669,0.0014688191,0.00014294946,0.000005932808],"category_scores_gemma":[0.00084630173,0.00019894473,0.00032327283,0.0004028555,0.00028635026,0.0013879368,0.00027703744,0.00021300674,0.000001923273],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006249834,0.00014196425,0.00006758648,0.00027168237,0.00019414762,2.815052e-7,0.0007002596,0.000059119335,0.51389915,0.4719963,0.010452128,0.002154886],"study_design_scores_gemma":[0.0008112547,0.0006692759,0.000095183044,0.00022653672,0.000057576206,0.00002852822,0.0011192389,0.07840915,0.91109216,0.006395088,0.0008202403,0.00027578959],"about_ca_topic_score_codex":0.00002844808,"about_ca_topic_score_gemma":1.8303514e-7,"teacher_disagreement_score":0.8293469,"about_ca_system_score_codex":0.0001624318,"about_ca_system_score_gemma":0.00008364644,"threshold_uncertainty_score":0.8112731},"labels":[],"label_agreement":null},{"id":"W2030811296","doi":"10.1109/iembs.2011.6091209","title":"Model-based 3D/2D deformable registration of MR images","year":2011,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Image registration; Artificial intelligence; Mutual information; Fiducial marker; Computer vision; Segmentation; Computer science; Similarity (geometry); Imaging phantom; Image segmentation; Feature extraction; Magnetic resonance imaging; Pattern recognition (psychology); Image (mathematics); Radiology; Medicine","score_opus":0.047054542275394406,"score_gpt":0.28020653414847996,"score_spread":0.23315199187308555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030811296","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000395612,0.0000071579266,0.9381953,0.000089560424,0.000027797123,0.00009361793,7.219435e-7,0.00022373506,0.060966533],"genre_scores_gemma":[0.27165377,0.0000028997254,0.7271702,0.00039371822,0.0000037552584,0.000009390064,0.0000016802552,0.0000026099565,0.0007619488],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992683,0.000021739288,0.00022526461,0.00014522893,0.00022855123,0.00011093606],"domain_scores_gemma":[0.99936914,0.00001691846,0.000102146245,0.00035671997,0.00009899147,0.000056091216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026339246,0.000060786657,0.00007834278,0.000064492444,0.00002623043,0.000022599072,0.00039772742,0.000031939548,0.00013646405],"category_scores_gemma":[0.00003324935,0.00004976033,0.000028136064,0.0001359779,0.00006183959,0.000622414,0.00004850258,0.000045310237,0.000014761003],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006207728,0.0011164933,0.0011389558,0.0003369407,0.000046070654,0.000030345927,0.0037556053,0.0014359592,0.40859938,0.15845498,0.08034607,0.34467712],"study_design_scores_gemma":[0.00009129137,0.000046995166,0.000061875086,0.0000068439963,0.0000016921988,7.8109264e-7,0.000006619065,0.33809644,0.65900654,0.0026235585,0.000009894776,0.000047442787],"about_ca_topic_score_codex":0.00010353857,"about_ca_topic_score_gemma":0.0000048410398,"teacher_disagreement_score":0.34462968,"about_ca_system_score_codex":0.00001461185,"about_ca_system_score_gemma":0.00007737856,"threshold_uncertainty_score":0.20291673},"labels":[],"label_agreement":null},{"id":"W2031083838","doi":"10.1016/j.neuroimage.2011.01.078","title":"Optimal weights for local multi-atlas fusion using supervised learning and dynamic information (SuperDyn): Validation on hippocampus segmentation","year":2011,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Medical Research Council; Fondation pour la Recherche sur Alzheimer; Michael Smith Health Research BC","keywords":"Segmentation; Artificial intelligence; Atlas (anatomy); Pattern recognition (psychology); Computer science; Fusion; Medicine","score_opus":0.033409732464476025,"score_gpt":0.2937446319449596,"score_spread":0.26033489948048355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031083838","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20223404,0.000008035095,0.7966992,0.00003926951,0.00015689543,0.00052387384,0.0000032405626,0.0002526052,0.00008281545],"genre_scores_gemma":[0.3350301,0.000026510965,0.6644819,0.00029996043,0.000014885003,0.00004070962,0.00006513589,0.000014584066,0.000026238235],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986127,0.00014967038,0.0003542675,0.00033351142,0.00031436616,0.0002354664],"domain_scores_gemma":[0.99929404,0.00010439194,0.0001575856,0.00020310811,0.0001291139,0.00011176079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030385275,0.00017865028,0.00014134591,0.00024485678,0.0002514283,0.00018271049,0.00023370047,0.00008085916,0.000030083314],"category_scores_gemma":[0.00011270073,0.00017309288,0.000047489048,0.00019183182,0.000075654985,0.0022958703,0.0001224643,0.00018266262,0.000021948292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102568636,0.00017669413,0.000492812,0.00013152251,0.000013374741,0.000012578285,0.0049467348,0.00046141932,0.12974943,0.00044393667,0.00009048969,0.86337847],"study_design_scores_gemma":[0.0010683653,0.00041091203,0.0011392974,0.00003773886,0.000013288066,0.000016602877,0.0001811278,0.86391056,0.13271856,0.00025499423,0.000054250922,0.00019431008],"about_ca_topic_score_codex":0.000027189066,"about_ca_topic_score_gemma":0.0000013013421,"teacher_disagreement_score":0.86344916,"about_ca_system_score_codex":0.00007866823,"about_ca_system_score_gemma":0.000036392972,"threshold_uncertainty_score":0.70585227},"labels":[],"label_agreement":null},{"id":"W2031113857","doi":"10.1016/j.media.2011.03.004","title":"Evaluation of visualization of the prostate gland in vibro-elastography images","year":2011,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Cancer Agency; University of British Columbia","funders":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; Medical Research and Materiel Command; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Prostate gland; Elastography; Visualization; Prostate; Artificial intelligence; Computer vision; Computer science; Medicine; Radiology; Ultrasound; Internal medicine","score_opus":0.02393714517619314,"score_gpt":0.3213250073869932,"score_spread":0.29738786221080005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031113857","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03498323,0.000113991206,0.963362,0.00012317747,0.000040621377,0.00023902513,0.0000027697874,0.00003524956,0.0010999637],"genre_scores_gemma":[0.9595424,0.00006643551,0.040192667,0.00012839645,0.000008061035,0.00003367513,0.000007547135,0.000005168662,0.000015648327],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9960604,0.00069209357,0.00059240544,0.00024746594,0.00226609,0.00014160309],"domain_scores_gemma":[0.99843407,0.00008298435,0.00031756598,0.0004875925,0.00059334346,0.000084419786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003887696,0.00009333652,0.00026672598,0.00055718515,0.000026115173,0.000016566646,0.0007325998,0.000064289175,0.0005785928],"category_scores_gemma":[0.0013583904,0.000063397245,0.00018060277,0.0037153119,0.00030887965,0.00036865775,0.00018028174,0.00010251786,0.000002102509],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003245704,0.0015935086,0.27778727,0.00017012293,0.001295045,0.000019804744,0.01032928,0.000053441727,0.04101192,0.0009812763,0.0012791788,0.6654467],"study_design_scores_gemma":[0.0010597603,0.00010317624,0.25667614,0.00011980481,0.0011420596,0.0000020830346,0.00013861721,0.17435366,0.5621234,0.004086711,0.000009416433,0.00018517554],"about_ca_topic_score_codex":0.00035203865,"about_ca_topic_score_gemma":0.00009689626,"teacher_disagreement_score":0.9245592,"about_ca_system_score_codex":0.000023062628,"about_ca_system_score_gemma":0.00014559393,"threshold_uncertainty_score":0.6335186},"labels":[],"label_agreement":null},{"id":"W2031633891","doi":"10.1007/1-84628-065-6_8","title":"Visualization and Segmentation Techniques in 3D Ultrasound Images","year":2005,"lang":"en","type":"book-chapter","venue":"Advances in pattern recognition","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials","funders":"","keywords":"3D ultrasound; Segmentation; Visualization; Computer science; Ultrasound; Computer vision; Artificial intelligence; Modality (human–computer interaction); Ultrasonography; Image segmentation; Medical physics; Radiology; Medicine","score_opus":0.015307977153903056,"score_gpt":0.3055773302835052,"score_spread":0.29026935312960217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031633891","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007623646,0.0014670895,0.9660061,0.000058596303,0.0001438674,0.00073335867,0.000030456189,0.00030452246,0.031179782],"genre_scores_gemma":[0.013690288,0.11731679,0.84721816,0.0048101977,0.00073283055,0.0009735484,0.002439095,0.00023213086,0.012586973],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99814975,0.00007180027,0.0006066873,0.00060737523,0.00035822392,0.00020617871],"domain_scores_gemma":[0.9990913,0.00019045924,0.00034407378,0.00022884479,0.000089208756,0.00005607508],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033765583,0.000291038,0.0002819505,0.00060507853,0.000037078447,0.000115543015,0.00024746824,0.00022769184,0.00017582595],"category_scores_gemma":[0.000055638026,0.00032346358,0.000033598248,0.00011188459,0.0001009839,0.0021271876,0.00009063878,0.0003023446,0.00003343565],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003382582,0.000027359298,0.00032382103,0.00008917955,0.0000032631497,0.000018004504,0.00017424846,6.7450026e-7,0.0006532815,0.00025461282,0.00006538599,0.9983868],"study_design_scores_gemma":[0.00392697,0.0011906645,0.0030632988,0.012479217,0.00011567806,0.00036318376,0.0002529351,0.0030337754,0.60744524,0.3426117,0.020298567,0.0052187787],"about_ca_topic_score_codex":0.000017604805,"about_ca_topic_score_gemma":0.00015884331,"teacher_disagreement_score":0.993168,"about_ca_system_score_codex":0.00019980804,"about_ca_system_score_gemma":0.000024440686,"threshold_uncertainty_score":0.99992174},"labels":[],"label_agreement":null},{"id":"W2031752028","doi":"10.1016/j.media.2014.12.007","title":"Elastic registration of prostate MR images based on estimation of deformation states","year":2015,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; University Health Network; Ontario Institute for Cancer Research; London Health Sciences Centre; McMaster University; Robarts Clinical Trials; Western University","funders":"Canadian Institutes of Health Research; Ontario Institute for Cancer Research","keywords":"Image registration; Artificial intelligence; Magnetic resonance imaging; Computer vision; Computer science; Metric (unit); Voxel; Position (finance); Scanner; Mathematics; Medicine; Image (mathematics); Radiology","score_opus":0.012774714612642086,"score_gpt":0.29812930048999836,"score_spread":0.2853545858773563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031752028","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007270987,0.000015981652,0.99079686,0.0011137427,0.00003481593,0.00015393349,0.0000092323935,0.00010679361,0.0004976297],"genre_scores_gemma":[0.7401074,0.000013059242,0.2593338,0.0002923473,0.000011634964,0.000021041553,0.00016630965,0.0000056939475,0.0000486751],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968595,0.00021178822,0.0007770696,0.00024560216,0.0017475103,0.00015854587],"domain_scores_gemma":[0.998044,0.00025040377,0.00051588815,0.0004693437,0.000481643,0.00023871378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017257193,0.00012476726,0.00032841304,0.0005455233,0.000029719076,0.000054936365,0.00045304096,0.0000705053,0.0001055513],"category_scores_gemma":[0.0026530293,0.00010120217,0.00012425898,0.001531122,0.00021951327,0.00075007905,0.000059536476,0.00012326227,0.000014386744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028639578,0.0026129647,0.0060239485,0.0010267505,0.001108505,0.000113637776,0.0058618947,0.122032456,0.012293947,0.0023587095,0.029815169,0.8164656],"study_design_scores_gemma":[0.00037061618,0.00020238888,0.0006390058,0.000052431467,0.00013440268,0.0000010049471,0.00005508665,0.8869649,0.11055725,0.000931621,0.0000070863994,0.000084198306],"about_ca_topic_score_codex":0.00013147497,"about_ca_topic_score_gemma":0.000008396715,"teacher_disagreement_score":0.8163814,"about_ca_system_score_codex":0.00005767161,"about_ca_system_score_gemma":0.00021729678,"threshold_uncertainty_score":0.41269046},"labels":[],"label_agreement":null},{"id":"W2031908348","doi":"10.1145/2616498.2616553","title":"Computational Anatomy Gateway","year":2014,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Computer science; Diffeomorphism; Metric (unit); Population; Computation; Convolution (computer science); Theoretical computer science; Algorithm; Artificial intelligence; Mathematics; Artificial neural network","score_opus":0.008531502417959233,"score_gpt":0.2841011307140003,"score_spread":0.27556962829604104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031908348","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018379987,0.000002900903,0.9739573,0.0013114726,0.00006205334,0.000044491633,1.09192314e-7,0.00046079242,0.023977099],"genre_scores_gemma":[0.18896315,5.5121404e-7,0.8070304,0.0035259542,0.000020252675,0.000005403162,0.000002165629,0.000002173461,0.0004499482],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999395,0.000040609804,0.0001057917,0.00014446229,0.00022214635,0.000091979484],"domain_scores_gemma":[0.9996003,0.00008483273,0.00002458423,0.00016834303,0.00004902967,0.000072889794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020138148,0.000043908065,0.00005016718,0.000048937058,0.00003584454,0.00006645731,0.00035287568,0.000017538694,0.0002241759],"category_scores_gemma":[0.00004288513,0.000036529353,0.000017637107,0.00013756046,0.000029197268,0.00024926662,0.00008019629,0.000040433162,0.00021342073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.0714306e-7,0.000034897846,0.00020085281,0.0000050452677,0.0000044261083,0.0000023162745,0.00009988653,0.000086911146,0.0004207175,0.5463764,0.037287872,0.41548023],"study_design_scores_gemma":[0.00042511255,0.00008707259,0.0025497924,0.000009596031,0.0000016881738,0.00001553643,0.000009746635,0.7708197,0.053516388,0.15682347,0.015507076,0.00023476842],"about_ca_topic_score_codex":0.000006418725,"about_ca_topic_score_gemma":4.266705e-7,"teacher_disagreement_score":0.7707328,"about_ca_system_score_codex":0.000010038791,"about_ca_system_score_gemma":0.000016170057,"threshold_uncertainty_score":0.27431628},"labels":[],"label_agreement":null},{"id":"W2032009698","doi":"10.1109/iembs.2010.5627807","title":"Edge-based partial volume averaging estimation for FLAIR MRI with white matter lesions","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sunnybrook Health Science Centre; Health Sciences Centre; Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Voxel; Fluid-attenuated inversion recovery; Partial volume; Enhanced Data Rates for GSM Evolution; White matter; Artificial intelligence; Pattern recognition (psychology); Nuclear medicine; Biomedical engineering; Magnetic resonance imaging; Computer science; Mathematics; Medicine; Radiology","score_opus":0.00993580135365389,"score_gpt":0.2657392351850609,"score_spread":0.25580343383140697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032009698","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015665224,6.739397e-7,0.989797,0.006761506,0.00018613065,0.0003619859,0.0000016148681,0.00043115308,0.0008934455],"genre_scores_gemma":[0.12458798,1.0344081e-7,0.8709174,0.002896118,0.00004885062,0.00012756015,0.0000123346545,0.000009461518,0.0014001828],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990962,0.000022427843,0.0001757394,0.00027449036,0.00023021169,0.00020088736],"domain_scores_gemma":[0.9992718,0.00006676885,0.00006536573,0.0003803969,0.000102433456,0.000113235226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022707906,0.00009989109,0.0000922298,0.00008090358,0.0001272066,0.00017944732,0.00035007214,0.00004597594,0.0007456085],"category_scores_gemma":[0.00003284541,0.00007683845,0.000033651915,0.00014275122,0.0000607781,0.0005199356,0.0000502007,0.00012440699,0.00014735997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006675726,0.00047280776,0.034315936,0.00019275455,0.000041331405,0.000022937642,0.0014652009,0.0022355034,0.034504313,0.010749445,0.5057146,0.4102184],"study_design_scores_gemma":[0.0004447665,0.00007989234,0.00290643,0.000019393194,0.0000066459756,0.00000537122,0.0000065025683,0.8809475,0.11332068,0.00053695426,0.0015610072,0.0001648971],"about_ca_topic_score_codex":0.000012157494,"about_ca_topic_score_gemma":0.00001240375,"teacher_disagreement_score":0.87871194,"about_ca_system_score_codex":0.000015435351,"about_ca_system_score_gemma":0.00007978875,"threshold_uncertainty_score":0.816389},"labels":[],"label_agreement":null},{"id":"W2032576393","doi":"10.1109/isspa.2012.6310557","title":"Simultaneous image de-noising and registration using graph cuts: Application to corrupted medical images","year":2012,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Graph; Artificial intelligence; Image restoration; Image (mathematics); Computer vision; Image quality; Cut; Image registration; Image processing; Image segmentation; Theoretical computer science","score_opus":0.01374165143009897,"score_gpt":0.3235686692881419,"score_spread":0.3098270178580429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032576393","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006377939,0.00005182346,0.99096674,0.0009913163,0.000063478255,0.00027283476,8.3560775e-7,0.00039869518,0.0008763108],"genre_scores_gemma":[0.37832522,0.0000131043025,0.61991686,0.0015980346,0.00006480501,0.000014194049,0.0000031921854,0.000006941829,0.00005766132],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986218,0.000082556646,0.00026305474,0.00026353064,0.00047580854,0.00029320407],"domain_scores_gemma":[0.9988701,0.00020715382,0.00008552377,0.0002995495,0.00010038859,0.00043730834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007635146,0.00011087728,0.000111376285,0.00011686064,0.00010890122,0.00015687424,0.000317218,0.00008136683,0.000045218527],"category_scores_gemma":[0.0006703543,0.00010236209,0.000020904856,0.00034734368,0.00009138718,0.0008378485,0.00014491606,0.00011296361,0.000016469445],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008536859,0.00013914588,0.001134167,0.000040517425,0.000010587011,0.000023947163,0.0010577065,0.00002604793,0.5355905,0.003926284,0.003280202,0.45476234],"study_design_scores_gemma":[0.0004904931,0.00012424284,0.0014439479,0.00009854071,0.000024747462,0.00045064685,0.00022647594,0.4203833,0.5730768,0.002366151,0.00073014345,0.00058452913],"about_ca_topic_score_codex":0.0001652013,"about_ca_topic_score_gemma":0.0000066084613,"teacher_disagreement_score":0.45417783,"about_ca_system_score_codex":0.000065215136,"about_ca_system_score_gemma":0.000059372895,"threshold_uncertainty_score":0.4174205},"labels":[],"label_agreement":null},{"id":"W2032864679","doi":"10.1016/j.patrec.2012.11.017","title":"Image thresholding based on semivariance","year":2012,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Thresholding; Semivariance; Artificial intelligence; Image (mathematics); Computer science; Computer vision; Pattern recognition (psychology); Binary image; Relation (database); Image processing; Mathematics; Statistics; Data mining; Spatial variability","score_opus":0.031228836495838328,"score_gpt":0.2771166004354553,"score_spread":0.24588776393961695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032864679","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076657925,0.0000067614897,0.9838019,0.0062337094,0.00043597928,0.00018523447,0.000007831152,0.0004411157,0.0012216555],"genre_scores_gemma":[0.5089804,0.0000041254816,0.37701675,0.11350036,0.00031980075,0.00010417356,0.000043185933,0.00002116556,0.000010024025],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986595,0.00011723595,0.00020925583,0.00027347286,0.00037981375,0.00036073965],"domain_scores_gemma":[0.99919415,0.00015319929,0.0001029933,0.00034895926,0.00003863976,0.0001620845],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004120855,0.00014219228,0.00010763905,0.00014789881,0.00008348781,0.00012961189,0.0003665941,0.000041883777,0.00044665276],"category_scores_gemma":[0.0000683323,0.00013942363,0.000059959064,0.00019738245,0.00004474486,0.0009687702,0.00005806052,0.00018116005,0.00085660466],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000066486036,0.0002108831,0.004605138,0.000055910237,0.000013717096,0.000033202312,0.00037995548,0.000005338031,0.10604754,0.000024423613,0.036257252,0.85236],"study_design_scores_gemma":[0.0018264147,0.00016234745,0.016655292,0.0005435952,0.000029829083,0.000041967083,0.000040746705,0.071128175,0.90610677,0.0005110906,0.0016442768,0.0013095086],"about_ca_topic_score_codex":0.0000092422715,"about_ca_topic_score_gemma":1.861795e-7,"teacher_disagreement_score":0.8510505,"about_ca_system_score_codex":0.0000631728,"about_ca_system_score_gemma":0.000009461582,"threshold_uncertainty_score":0.9999213},"labels":[],"label_agreement":null},{"id":"W2033078764","doi":"10.1049/iet-ipr.2012.0512","title":"Greedy framework for optical flow tracking of myocardium contours","year":2014,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Science and Technology Development Fund; Ministry of Scientific Research, Egypt; York University","keywords":"Optical flow; Computer science; Flow (mathematics); Tracking (education); Computer vision; Artificial intelligence; Greedy algorithm; Algorithm; Mathematics; Image (mathematics); Geometry","score_opus":0.021192121987703448,"score_gpt":0.32004018523345684,"score_spread":0.2988480632457534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033078764","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00038129665,0.00011058114,0.9972282,0.0007424741,0.00015965989,0.00022092476,0.0000018509977,0.0002932846,0.0008617512],"genre_scores_gemma":[0.2438195,0.0000030137694,0.7554084,0.0005742575,0.00013117304,0.00003229635,0.0000019880656,0.000013787051,0.000015588404],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985869,0.00004271038,0.00035591962,0.0003410367,0.00037300738,0.00030044187],"domain_scores_gemma":[0.998703,0.00033996048,0.00018468607,0.00032518027,0.0003258095,0.000121350844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075739174,0.00013774779,0.0002597542,0.00008167288,0.00010656406,0.00027200716,0.000630893,0.00009816067,0.000009954116],"category_scores_gemma":[0.0011732146,0.00012825959,0.00009004243,0.00023731025,0.00016643453,0.00088829326,0.00010697685,0.00018058752,0.0000051681895],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006887541,0.000043912518,0.000045705143,0.00018573405,0.000007224552,0.0000020313246,0.0004635174,0.0000053682443,0.017890053,0.0043781805,0.00034853464,0.9766229],"study_design_scores_gemma":[0.0006148833,0.0001991321,0.00039132117,0.00050822925,0.000029089491,0.000016143138,0.00008371069,0.19825836,0.7231723,0.07613992,0.00024926648,0.00033762882],"about_ca_topic_score_codex":0.0000021421417,"about_ca_topic_score_gemma":2.6780504e-7,"teacher_disagreement_score":0.9762852,"about_ca_system_score_codex":0.000023513567,"about_ca_system_score_gemma":0.00007449419,"threshold_uncertainty_score":0.5230274},"labels":[],"label_agreement":null},{"id":"W2033183588","doi":"10.1109/icip.2010.5653531","title":"Liver segmentation based on deformable registration and multi-layer segmentation","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Image registration; Computer vision; Image segmentation; Volume (thermodynamics); Layer (electronics); Intensity (physics); Image (mathematics); Pattern recognition (psychology); Materials science; Optics; Physics","score_opus":0.025987218390889806,"score_gpt":0.29712920844340895,"score_spread":0.27114199005251916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033183588","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010377219,0.0000041379813,0.9864687,0.00046408398,0.0001742251,0.00036692078,0.0000012028476,0.0003195384,0.0018239864],"genre_scores_gemma":[0.21380268,0.000008837623,0.78347635,0.002089832,0.000025006722,0.00005186605,0.000021716325,0.0000066375387,0.00051705254],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989114,0.000050632178,0.0002257229,0.000303811,0.00035521627,0.00015323966],"domain_scores_gemma":[0.99932945,0.00007141532,0.000110181085,0.0002984443,0.00008026117,0.0001102666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003743235,0.00011827182,0.00007864689,0.000125595,0.00012278411,0.00021483509,0.00019979855,0.00006998342,0.00017783338],"category_scores_gemma":[0.000058288988,0.000101864294,0.00002293806,0.00016631113,0.00005273629,0.0010969152,0.000040469,0.0001574693,0.000048619724],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014945094,0.00020649466,0.0008099972,0.000035618566,0.0000070602055,0.000009195586,0.0004957347,0.00006923061,0.5719371,0.0050216923,0.0030472337,0.41834572],"study_design_scores_gemma":[0.00055492134,0.00009983338,0.0021214993,0.0000074906343,0.0000036558379,0.00000513621,0.00003344974,0.5147646,0.4820993,0.0001319385,0.0000636754,0.00011446894],"about_ca_topic_score_codex":0.00006973477,"about_ca_topic_score_gemma":0.00006105984,"teacher_disagreement_score":0.5146954,"about_ca_system_score_codex":0.000033984055,"about_ca_system_score_gemma":0.000042330517,"threshold_uncertainty_score":0.41539052},"labels":[],"label_agreement":null},{"id":"W2033524596","doi":"10.1145/2330784.2330864","title":"Interactive differential evolution for prostate ultrasound image thresholding","year":2012,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakeridge Health","funders":"","keywords":"Thresholding; Artificial intelligence; Computer science; Grayscale; Computer vision; Image segmentation; Segmentation; Balanced histogram thresholding; Image (mathematics); Otsu's method; Pattern recognition (psychology); Image processing","score_opus":0.014743289894519701,"score_gpt":0.3050031910891575,"score_spread":0.29025990119463785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033524596","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007808196,0.00001628092,0.9895076,0.0001521598,0.0004050016,0.00041687442,0.0000030399233,0.0003437951,0.0013470318],"genre_scores_gemma":[0.6374366,0.0000024524338,0.3618671,0.00017326402,0.00009330869,0.000083480576,0.000006172581,0.0000055475234,0.00033203248],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991654,0.000033065488,0.00016119468,0.0001794012,0.00017965389,0.00028124332],"domain_scores_gemma":[0.9993503,0.00019258806,0.00006755359,0.00019634154,0.00008281574,0.00011040743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020936097,0.00008930374,0.00008531761,0.00006839867,0.00008210613,0.00013530233,0.0002775337,0.00002983935,0.00013040783],"category_scores_gemma":[0.00015562981,0.000072668954,0.00004790637,0.00010439033,0.000041423256,0.0018766734,0.000098600154,0.000075812255,0.000031933076],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035976034,0.00035604762,0.0024886308,0.000051167008,0.000043360367,0.0000015576759,0.0032875107,4.4594537e-7,0.8415415,0.06521645,0.026116306,0.060861003],"study_design_scores_gemma":[0.0005184046,0.00011416035,0.006347069,0.000024679839,0.000010737933,0.000019861951,0.0001872661,0.00672316,0.9773102,0.008220688,0.00027899526,0.00024478606],"about_ca_topic_score_codex":0.000013401636,"about_ca_topic_score_gemma":0.0000012530601,"teacher_disagreement_score":0.6296284,"about_ca_system_score_codex":0.00009001778,"about_ca_system_score_gemma":0.000017116476,"threshold_uncertainty_score":0.29633537},"labels":[],"label_agreement":null},{"id":"W2033578317","doi":"10.1007/s11548-011-0632-y","title":"Automatic recognition of major fissures in human lungs","year":2011,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Foothills Medical Centre; University of Calgary","funders":"","keywords":"Computer science; Artificial intelligence; Medicine; Pathology","score_opus":0.041191168322133175,"score_gpt":0.2924120137835366,"score_spread":0.25122084546140344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033578317","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41047522,0.00012426621,0.5881879,0.00030755447,0.0007332797,0.000039411516,0.0000011641952,0.00002240468,0.00010881959],"genre_scores_gemma":[0.8578311,0.000054439002,0.14156315,0.00043876172,0.00009825619,0.0000021490328,0.0000038110509,0.000003518394,0.000004802116],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99860096,0.00023789059,0.00072793616,0.00011361588,0.0002236232,0.00009599316],"domain_scores_gemma":[0.99864465,0.00044233695,0.00050449464,0.00008831257,0.000262499,0.00005773103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087622233,0.00007887818,0.0002860549,0.00059316814,0.00001825093,0.000021365839,0.00041206848,0.00007267214,0.00007733809],"category_scores_gemma":[0.00008823154,0.000067777044,0.000089845926,0.00011175929,0.00010896649,0.00037829383,0.00006819583,0.00015488273,0.000001004566],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063529646,0.00040257,0.054846756,0.000045261982,0.0003600867,0.00079905736,0.0017768467,0.000002441146,0.0043768277,0.001013541,0.0060985032,0.9302146],"study_design_scores_gemma":[0.001382822,0.00039026813,0.9375685,0.00071428425,0.000033069293,0.004497536,0.000042198113,0.008186776,0.029104756,0.01766986,0.0001086128,0.00030135535],"about_ca_topic_score_codex":0.000014905381,"about_ca_topic_score_gemma":0.0000018530422,"teacher_disagreement_score":0.9299132,"about_ca_system_score_codex":0.000025440342,"about_ca_system_score_gemma":0.000054760683,"threshold_uncertainty_score":0.27638677},"labels":[],"label_agreement":null},{"id":"W2034344854","doi":"10.1016/j.mri.2010.08.007","title":"Unsupervised MRI segmentation of brain tissues using a local linear model and level set","year":2010,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Pattern recognition (psychology); Level set (data structures); Level set method; White matter; Magnetic resonance imaging; Image segmentation; Computer vision","score_opus":0.0305411140393526,"score_gpt":0.3129307935829534,"score_spread":0.2823896795436008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034344854","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05032312,0.00079527416,0.94756603,0.0008498266,0.000070099624,0.00021253177,0.000008226572,0.00009134465,0.00008353718],"genre_scores_gemma":[0.16847438,0.00003821457,0.83057684,0.0006801291,0.000028881022,0.000012951925,0.0000039518222,0.000013440122,0.0001711918],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869,0.0000561557,0.00031549402,0.0003580708,0.00035639113,0.00022389289],"domain_scores_gemma":[0.9992623,0.0000770157,0.00009238376,0.00036407055,0.00010742631,0.000096802236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038713907,0.00013749533,0.00016116037,0.00011778781,0.00007277769,0.00008054025,0.00037502949,0.000040335704,0.00003178907],"category_scores_gemma":[0.00009102464,0.0001387673,0.000027066599,0.00022523935,0.0002721589,0.0004196351,0.00020532856,0.00017034715,0.000002513873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000044768094,0.000021548718,0.001167762,0.00002989319,0.0000010789809,0.000007636594,0.000972362,0.00015620605,0.24467172,0.00036873575,0.00048223668,0.7521163],"study_design_scores_gemma":[0.00039536293,0.000035050576,0.0015139101,0.000047526035,0.0000045561596,0.000025670915,0.000081546415,0.8941002,0.10245714,0.0009425441,0.00025758176,0.00013889387],"about_ca_topic_score_codex":0.00012799593,"about_ca_topic_score_gemma":0.000011938461,"teacher_disagreement_score":0.893944,"about_ca_system_score_codex":0.000017213591,"about_ca_system_score_gemma":0.000075799646,"threshold_uncertainty_score":0.5658766},"labels":[],"label_agreement":null},{"id":"W2034516566","doi":"10.1117/12.2082465","title":"Segmentation of the liver from abdominal MR images: a level-set approach","year":2015,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Level set (data structures); Segmentation; Image segmentation; Artificial intelligence; Computer vision; Level set method; Image (mathematics); Boundary (topology); Set (abstract data type); Pattern recognition (psychology); Mathematics","score_opus":0.030856107254088997,"score_gpt":0.2568697977852335,"score_spread":0.2260136905311445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034516566","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9430666,0.000109044464,0.053193085,0.0013011491,0.00025026198,0.0007078235,0.000076106924,0.000096809665,0.0011991492],"genre_scores_gemma":[0.1374287,0.000044657274,0.86175895,0.00021381222,0.00022290552,0.00014806402,0.000011540228,0.00003095823,0.0001404456],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9973207,7.1565296e-8,0.0007164673,0.00041254528,0.0012661979,0.0002840209],"domain_scores_gemma":[0.9972325,0.00013669577,0.0005787038,0.00012119463,0.0017816155,0.0001492896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091811636,0.0002596269,0.00033994665,0.00008520479,0.00006259624,0.00013758929,0.0022157999,0.0001451376,0.000007575636],"category_scores_gemma":[0.000762147,0.00018797605,0.00044993317,0.0004339705,0.0003380944,0.0008495472,0.00049657567,0.00027953892,0.0000014145481],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000079193414,0.000254013,0.00044959568,0.00034870382,0.00036523025,1.3410634e-7,0.0018975456,0.00007494306,0.8219472,0.14921065,0.021861834,0.0035109532],"study_design_scores_gemma":[0.0013854341,0.00026396423,0.0011793288,0.0002456463,0.00011401291,0.000015367328,0.002260051,0.069898896,0.9210113,0.00294372,0.0003666911,0.00031553168],"about_ca_topic_score_codex":0.000048478123,"about_ca_topic_score_gemma":1.3070522e-7,"teacher_disagreement_score":0.80856586,"about_ca_system_score_codex":0.00016892637,"about_ca_system_score_gemma":0.00008298835,"threshold_uncertainty_score":0.7665441},"labels":[],"label_agreement":null},{"id":"W2034904519","doi":"10.1109/fskd.2010.5569555","title":"Fuzzy inference system for endocardial edge detection","year":2010,"lang":"en","type":"article","venue":"2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Smoothness; Edge detection; Artificial intelligence; Enhanced Data Rates for GSM Evolution; Pattern recognition (psychology); Contrast (vision); Inference; Fuzzy logic; Adaptive neuro fuzzy inference system; Fuzzy set; Computer science; Detector; Set (abstract data type); Mathematics; Fuzzy control system; Computer vision; Image (mathematics); Image processing","score_opus":0.03133630431973737,"score_gpt":0.31403258893966707,"score_spread":0.2826962846199297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034904519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020680672,0.000100821126,0.88397145,0.00030390182,0.022483256,0.0012213585,0.00014425881,0.00046421262,0.070630044],"genre_scores_gemma":[0.9931856,0.000033226977,0.0030714958,0.00005567777,0.0007808628,0.000422568,0.000035254114,0.000018867426,0.0023964897],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99797857,0.00011056898,0.0005123462,0.00064181385,0.00044218666,0.00031453397],"domain_scores_gemma":[0.998275,0.00025022335,0.0002667834,0.00046503046,0.00054210547,0.00020089367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063897046,0.00027916534,0.00031529772,0.0002697046,0.00020017833,0.00096697453,0.00085481134,0.0001805672,0.000008682043],"category_scores_gemma":[0.00023287252,0.00024514497,0.0001239995,0.00014367049,0.000115390234,0.0012315699,0.00023438258,0.00036697334,0.00007454327],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005879283,0.00016884749,0.00018972374,0.00021588925,0.00009218734,0.000009034656,0.0004624026,0.0000041801454,0.044641368,0.8832275,0.0011861431,0.06974391],"study_design_scores_gemma":[0.014154206,0.0037898468,0.010377712,0.004618838,0.00027213016,0.0011095298,0.0056590843,0.35803953,0.4954458,0.062120616,0.037971523,0.0064411834],"about_ca_topic_score_codex":0.000108497494,"about_ca_topic_score_gemma":0.00008542254,"teacher_disagreement_score":0.9725049,"about_ca_system_score_codex":0.00010409291,"about_ca_system_score_gemma":0.00022310317,"threshold_uncertainty_score":0.9996721},"labels":[],"label_agreement":null},{"id":"W2035025397","doi":"10.1007/s11548-013-0826-6","title":"Fast rigid registration of pre-operative magnetic resonance images to intra-operative ultrasound for neurosurgery based on high confidence gradient orientations","year":2013,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital; McGill Genome Centre; McGill University Health Centre","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Image registration; Context (archaeology); Artificial intelligence; Orientation (vector space); Computer vision; Maximization; Magnetic resonance imaging; Image (mathematics); Medicine; Radiology; Mathematics","score_opus":0.013732604634525787,"score_gpt":0.29070075128691397,"score_spread":0.27696814665238817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035025397","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08107083,0.00010540763,0.9133315,0.0036998836,0.001396351,0.00031926643,0.000022986293,0.00002455747,0.000029183882],"genre_scores_gemma":[0.8181519,0.00008768175,0.17801866,0.0033585634,0.00022470557,0.000068466914,0.000020586518,0.000009116771,0.00006034599],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99778247,0.0003637099,0.00092859846,0.00030491065,0.00043877822,0.00018153204],"domain_scores_gemma":[0.99412894,0.003679128,0.0005381948,0.00019113974,0.0013127738,0.00014982143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007269877,0.0001721764,0.00039584664,0.0004716774,0.00008767466,0.00017600277,0.00047508493,0.000074157055,0.000039604587],"category_scores_gemma":[0.00064353336,0.00014416841,0.0001292078,0.00020336195,0.00023351949,0.00063424784,0.00004159461,0.00018594561,0.0000021235724],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013539131,0.0014209342,0.019336404,0.00009253506,0.0004465677,0.00033365676,0.004258264,0.006648079,0.05703997,0.022005307,0.1726932,0.7143712],"study_design_scores_gemma":[0.0025909792,0.0041004345,0.8274245,0.0009902695,0.000060409817,0.0017729886,0.00015000954,0.06098229,0.09418916,0.005413775,0.0014793958,0.00084578636],"about_ca_topic_score_codex":0.00002607218,"about_ca_topic_score_gemma":0.000002385548,"teacher_disagreement_score":0.8080881,"about_ca_system_score_codex":0.00006411069,"about_ca_system_score_gemma":0.00020404471,"threshold_uncertainty_score":0.5879017},"labels":[],"label_agreement":null},{"id":"W2035416707","doi":"10.1016/j.mri.2014.08.019","title":"4D MR phase and magnitude segmentations with GPU parallel computing","year":2014,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg; Research Manitoba; University of Manitoba","funders":"","keywords":"Graphics processing unit; Computer science; Segmentation; Algorithm; CUDA; General-purpose computing on graphics processing units; Contrast (vision); Image segmentation; Artificial intelligence; Gaussian; Graphics; Computer vision; Pattern recognition (psychology); Parallel computing; Computer graphics (images); Physics","score_opus":0.008905375579503562,"score_gpt":0.27809132675676673,"score_spread":0.2691859511772632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035416707","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01019271,0.0014257571,0.9849731,0.0015162383,0.00006131271,0.00027304215,0.000001788045,0.00035352306,0.0012025371],"genre_scores_gemma":[0.14359991,0.000046623707,0.85367614,0.0023248596,0.000056530975,0.00003479376,0.000005220686,0.0000174444,0.000238508],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983364,0.00011591506,0.00029257598,0.0005033631,0.00038830118,0.00036347876],"domain_scores_gemma":[0.9990421,0.00017212373,0.00010593857,0.0004267228,0.000090455826,0.00016268725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038018488,0.00018565278,0.00018216134,0.00011146611,0.00026163607,0.0004729908,0.00046260087,0.00002167541,0.000055484063],"category_scores_gemma":[0.00007910133,0.00016521805,0.000023696348,0.0002974748,0.0002776256,0.0005102262,0.00021057739,0.00016091845,0.0000142340605],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000060414854,0.000059345406,0.0028450398,0.000017781782,0.0000016353911,0.000023452765,0.0005028106,0.0000116892825,0.0015858059,0.0025112983,0.00081141794,0.9916237],"study_design_scores_gemma":[0.0061249333,0.0007080813,0.026801681,0.00033319637,0.000032650798,0.00026098365,0.0002789854,0.93724155,0.008444745,0.0030857702,0.01582519,0.00086223864],"about_ca_topic_score_codex":0.000046872934,"about_ca_topic_score_gemma":0.0000050719877,"teacher_disagreement_score":0.99076146,"about_ca_system_score_codex":0.000026640448,"about_ca_system_score_gemma":0.0000419317,"threshold_uncertainty_score":0.6737397},"labels":[],"label_agreement":null},{"id":"W2035801028","doi":"10.1117/12.430974","title":"&lt;title&gt;Unsupervised partial volume estimation using 3D and statistical priors&lt;/title&gt;","year":2001,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec","funders":"","keywords":"Prior probability; Markov random field; Segmentation; Artificial intelligence; Maximum a posteriori estimation; Autoregressive model; Computer science; A priori and a posteriori; Mixture model; Partial volume; Pattern recognition (psychology); Image segmentation; Gaussian; Scale-space segmentation; Computer vision; Mathematics; Bayesian probability; Statistics; Maximum likelihood","score_opus":0.014476091539474234,"score_gpt":0.25567389981686495,"score_spread":0.2411978082773907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035801028","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77070904,0.00011056283,0.21778594,0.0014753615,0.00034956314,0.00047381222,0.00002587845,0.00023890607,0.008830952],"genre_scores_gemma":[0.038487542,0.00007032773,0.96062946,0.00014445298,0.00021243287,0.000032738422,0.000007054816,0.000026245796,0.00038975698],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987554,2.9363502e-8,0.00033621906,0.00023969839,0.00046576298,0.00020291637],"domain_scores_gemma":[0.9991431,0.00006239075,0.0001307513,0.000046798876,0.0005194045,0.000097543394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031472338,0.0001431258,0.00017834676,0.00006292703,0.000049812366,0.000117520576,0.00046162214,0.00009683943,0.00010763713],"category_scores_gemma":[0.0003926739,0.00012549854,0.000109815344,0.00018135516,0.00014430971,0.00041135974,0.0001322862,0.0001378339,0.000011543877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019177753,0.00008860216,0.00011662706,0.00022837151,0.00012395463,5.572002e-7,0.00018442667,0.00003583061,0.22770193,0.729843,0.021450713,0.020206805],"study_design_scores_gemma":[0.0004899431,0.00014668179,0.00035249203,0.00015221558,0.00006627864,0.000033137945,0.00006062938,0.9579596,0.02624894,0.0025385958,0.011669811,0.00028168008],"about_ca_topic_score_codex":0.0000023355,"about_ca_topic_score_gemma":2.3272728e-8,"teacher_disagreement_score":0.95792377,"about_ca_system_score_codex":0.00007519263,"about_ca_system_score_gemma":0.000030140967,"threshold_uncertainty_score":0.5117682},"labels":[],"label_agreement":null},{"id":"W2036113022","doi":"10.1587/transinf.e93.d.3414","title":"Improved Demons Technique with Orthogonal Gradient Information for Medical Image Registration","year":2010,"lang":"en","type":"article","venue":"IEICE Transactions on Information and Systems","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Image registration; Artificial intelligence; Computer vision; Image (mathematics); Medical imaging","score_opus":0.006876777216274747,"score_gpt":0.2489098323704226,"score_spread":0.24203305515414786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036113022","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006025742,0.000002581758,0.995259,0.0008894907,0.00037678002,0.0014084454,0.00003358944,0.00039782058,0.0010297182],"genre_scores_gemma":[0.6421823,0.00003972981,0.35302705,0.0015093083,0.0000724316,0.0028669054,0.00016383256,0.000013935607,0.00012453516],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842054,0.000040726718,0.00065907463,0.00013660738,0.0005506145,0.0001924147],"domain_scores_gemma":[0.9987021,0.00012579875,0.00028716918,0.00030826402,0.0003553544,0.00022131656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091960176,0.00015499302,0.00014967426,0.0002979319,0.00028567523,0.0005154507,0.00028095153,0.00017812294,0.000030926854],"category_scores_gemma":[0.00008714698,0.0001244678,0.000045271452,0.0002646022,0.00009881666,0.0053490736,0.000005386181,0.00033857374,0.000017311924],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002656196,0.0002874879,0.00003034371,0.0011240887,0.00011079689,0.0000034528327,0.005191207,0.00018681092,0.012309912,0.08418027,0.0040900977,0.8922199],"study_design_scores_gemma":[0.004134942,0.0015405274,0.00038910456,0.00034946823,0.000053778807,0.00087554863,0.0015668068,0.8904696,0.05950003,0.0005061963,0.039612897,0.0010010863],"about_ca_topic_score_codex":0.000074583535,"about_ca_topic_score_gemma":0.000067913854,"teacher_disagreement_score":0.89121884,"about_ca_system_score_codex":0.000037322487,"about_ca_system_score_gemma":0.00018708326,"threshold_uncertainty_score":0.50756496},"labels":[],"label_agreement":null},{"id":"W2036250545","doi":"10.1007/s11548-011-0649-2","title":"3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set","year":2011,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Pattern recognition (psychology); Image segmentation; Feature (linguistics); Scale-space segmentation; Prior probability; Brain tumor; Level set method; Computer vision; Bayesian probability; Medicine; Pathology","score_opus":0.04452413227737394,"score_gpt":0.30190033786587106,"score_spread":0.2573762055884971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036250545","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10142803,0.00006443701,0.8945569,0.0017843918,0.0019974639,0.0000771388,0.000005417755,0.000041332387,0.0000449063],"genre_scores_gemma":[0.27093235,0.00003693981,0.71859634,0.009674778,0.00068624853,0.000004705717,0.000023854805,0.000013408191,0.00003134347],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9981034,0.00041903878,0.00059598085,0.00023546876,0.0004785305,0.00016761111],"domain_scores_gemma":[0.9977615,0.00090559,0.0006785694,0.0001380157,0.00039033507,0.00012597544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009662668,0.00015824154,0.00030837636,0.0005640061,0.00007419005,0.00008848933,0.0005207471,0.00010143329,0.00004682217],"category_scores_gemma":[0.0001586101,0.00013365688,0.00013048277,0.00015997922,0.000098481854,0.0006013637,0.000110787645,0.0002904879,0.0000027410163],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017660133,0.0015596787,0.094659396,0.000091496404,0.003440124,0.0061760605,0.012680294,0.0011555045,0.016756693,0.0073861424,0.13848324,0.71584535],"study_design_scores_gemma":[0.004948159,0.0013630419,0.69117945,0.0008994261,0.00013836432,0.035111386,0.00019407424,0.24689908,0.010013316,0.0055604316,0.0023467483,0.0013465341],"about_ca_topic_score_codex":0.000007640505,"about_ca_topic_score_gemma":6.58266e-7,"teacher_disagreement_score":0.7144988,"about_ca_system_score_codex":0.000105874766,"about_ca_system_score_gemma":0.00017181254,"threshold_uncertainty_score":0.5450369},"labels":[],"label_agreement":null},{"id":"W2036938784","doi":"10.1109/icpr.2010.824","title":"An Improved Fluid Vector Flow for Cavity Segmentation in Chest Radiographs","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Segmentation; Vector flow; Parametric statistics; Computer science; Active contour model; Artificial intelligence; Image segmentation; Radiography; Point distribution model; Active shape model; Computer vision; Edge detection; Pattern recognition (psychology); Mathematics; Radiology; Image processing; Medicine; Image (mathematics)","score_opus":0.010449562087909828,"score_gpt":0.2918916306397132,"score_spread":0.28144206855180337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036938784","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023244416,0.0000054542484,0.9750176,0.0003471536,0.00033893698,0.0005992243,0.000003939917,0.00034124427,0.000102047336],"genre_scores_gemma":[0.17090042,0.0000041449866,0.82821757,0.0005781527,0.00005189118,0.00017541843,0.000021224434,0.0000071711456,0.000044042416],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990443,0.000040032406,0.00022276529,0.00032717353,0.00016124346,0.00020447996],"domain_scores_gemma":[0.9992614,0.00007634574,0.000051084855,0.00040804982,0.00006933411,0.0001337929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004872915,0.000100014746,0.00010919911,0.00015903082,0.000052241667,0.00012758587,0.00049054716,0.00007321508,0.0000747606],"category_scores_gemma":[0.000075605494,0.00009030042,0.000044736586,0.0003199095,0.000047047182,0.00085387984,0.000038563845,0.0001299488,0.0000050362846],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004729002,0.00009239972,0.00045626363,0.000010274704,0.0000025401712,0.0000010083482,0.00026690194,0.0000010729107,0.8250664,0.0008456798,0.0005514128,0.17270133],"study_design_scores_gemma":[0.0005920673,0.00017046188,0.005982797,0.0000032911762,0.0000024501246,0.000002499637,0.000028695222,0.28037554,0.7115292,0.0010710356,0.00009420043,0.00014778004],"about_ca_topic_score_codex":0.00015638546,"about_ca_topic_score_gemma":0.00031694552,"teacher_disagreement_score":0.28037447,"about_ca_system_score_codex":0.000025778501,"about_ca_system_score_gemma":0.000046408077,"threshold_uncertainty_score":0.36823443},"labels":[],"label_agreement":null},{"id":"W2036986366","doi":"10.1016/j.ultrasmedbio.2008.12.018","title":"A Real-Time Intrasubject Elastic Registration Algorithm for Dynamic 2-D Ultrasound Images","year":2009,"lang":"en","type":"article","venue":"Ultrasound in Medicine & Biology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Image registration; Computer science; Similarity (geometry); Computer vision; Artificial intelligence; Feature (linguistics); Sequence (biology); Image (mathematics); Algorithm; Pattern recognition (psychology)","score_opus":0.009957509868305344,"score_gpt":0.31264860202607286,"score_spread":0.3026910921577675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036986366","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028006425,0.00017423739,0.99172384,0.0018720143,0.000338932,0.000690333,0.000015871492,0.00040679416,0.0019773433],"genre_scores_gemma":[0.13619746,0.0014723872,0.85824376,0.002457323,0.00040890605,0.00015307519,0.00045530676,0.000025501844,0.00058627164],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99748886,0.00023806667,0.0007490327,0.0007096423,0.00026520778,0.00054920744],"domain_scores_gemma":[0.99608105,0.0028038714,0.0002600486,0.00054836716,0.00014847373,0.00015817065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012717432,0.00028834093,0.00049721583,0.00034398583,0.000101364436,0.000053311487,0.0008017663,0.00019365395,0.00012431307],"category_scores_gemma":[0.00306149,0.00023650646,0.00006721161,0.0005529571,0.00042672496,0.0003582304,0.00002644677,0.0003058827,0.000025661993],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012555524,0.00009666115,0.000087523724,0.000011726494,0.000012368674,0.000010631767,0.0003598771,0.0000015892186,0.63371634,0.0016274356,0.006377991,0.3576853],"study_design_scores_gemma":[0.016533716,0.028001212,0.071173064,0.001254335,0.00028408456,0.0021443665,0.00087593845,0.059434406,0.18207859,0.62642163,0.00755165,0.0042470177],"about_ca_topic_score_codex":0.0001133994,"about_ca_topic_score_gemma":0.00001664278,"teacher_disagreement_score":0.6247942,"about_ca_system_score_codex":0.00017242644,"about_ca_system_score_gemma":0.00012049765,"threshold_uncertainty_score":0.96444535},"labels":[],"label_agreement":null},{"id":"W2037735229","doi":"10.1109/icosp.2014.7015165","title":"A variational Shearlet-based model for aortic stent detection","year":2014,"lang":"en","type":"preprint","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Nautical Research Society","funders":"Centre Lyonnais d'Acoustique, Université de Lyon; Université de Lyon; Agence Nationale de la Recherche","keywords":"Shearlet; Segmentation; Artificial intelligence; Piecewise; Computer science; Minification; Norm (philosophy); Regularization (linguistics); Computer vision; Image segmentation; Edge detection; Algorithm; Pattern recognition (psychology); Mathematics; Image (mathematics); Image processing; Mathematical optimization; Mathematical analysis","score_opus":0.03498600357140265,"score_gpt":0.30553038146106903,"score_spread":0.2705443778896664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037735229","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007041889,0.000004946071,0.99686295,0.00092200434,0.00039721772,0.0008336833,0.0000075541802,0.0006801374,0.00022107195],"genre_scores_gemma":[0.14480424,0.000001249227,0.85179406,0.0021769153,0.00008255628,0.0007154109,0.00003461495,0.000012807178,0.0003781206],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982977,0.00006642008,0.00038344655,0.0005651138,0.00048676305,0.00020054406],"domain_scores_gemma":[0.9986557,0.00016590327,0.00019606341,0.00061858003,0.00024542797,0.00011834047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005850786,0.00017734205,0.00019951497,0.00016718145,0.000071921524,0.00021195595,0.0006929075,0.00019600075,0.00003907996],"category_scores_gemma":[0.00016485588,0.00016445426,0.00014517982,0.000080579026,0.000027735112,0.00013650437,0.00035373642,0.00023425814,0.00001532165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046913818,0.0004871804,0.000023154424,0.0007777346,0.00012764311,0.0000020432767,0.00048914424,0.35475326,0.007485534,0.033878636,0.011239681,0.59068906],"study_design_scores_gemma":[0.00023861739,0.00004780671,0.0000382188,0.000029670602,0.000012223215,4.818139e-7,8.480867e-7,0.9588702,0.013665504,0.026893472,0.000027213271,0.00017579042],"about_ca_topic_score_codex":0.000029765437,"about_ca_topic_score_gemma":0.000010498719,"teacher_disagreement_score":0.6041169,"about_ca_system_score_codex":0.00014382783,"about_ca_system_score_gemma":0.00031036034,"threshold_uncertainty_score":0.67062503},"labels":[],"label_agreement":null},{"id":"W2037978672","doi":"10.1117/12.709515","title":"Large deformation registration of contrast-enhanced images with volume-preserving constraint","year":2007,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Image registration; Rigid transformation; Free-form deformation; Multigrid method; Constraint (computer-aided design); Contrast (vision); Transformation (genetics); Artificial intelligence; Divergence (linguistics); Vector field; Computer vision; Algorithm; Mathematics; Image (mathematics); Deformation (meteorology); Geometry; Physics; Mathematical analysis","score_opus":0.00874404931373965,"score_gpt":0.2410353561636462,"score_spread":0.23229130684990656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037978672","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7375863,0.0000250926,0.25816816,0.00089129276,0.00008175709,0.00048045212,0.000017993654,0.00011733489,0.002631605],"genre_scores_gemma":[0.5555876,0.000021743159,0.4440709,0.00008472998,0.00008388377,0.00004756533,0.00000661297,0.000017754917,0.00007918766],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99742484,2.9154503e-8,0.0009033351,0.00032055224,0.0009771864,0.00037405535],"domain_scores_gemma":[0.996473,0.00016268087,0.0007680844,0.00008900557,0.00238548,0.000121721896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016439842,0.00023214727,0.0003361839,0.00012863871,0.00007418146,0.00012565024,0.0011936384,0.00013743124,0.000012522184],"category_scores_gemma":[0.0007271217,0.00018612015,0.0002637413,0.00040167102,0.0002827647,0.001505808,0.00017667643,0.00024718506,6.7676973e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055744633,0.0001067331,0.0001778379,0.00036457775,0.00013732727,1.2030144e-7,0.00046990145,0.000022631455,0.7036788,0.29207486,0.0014130564,0.0014984302],"study_design_scores_gemma":[0.0010125082,0.00037691448,0.0011928906,0.00034778388,0.000047641763,0.000015394719,0.0016980659,0.02836918,0.965208,0.0013114837,0.00018902698,0.00023109779],"about_ca_topic_score_codex":0.000010704657,"about_ca_topic_score_gemma":4.8851535e-7,"teacher_disagreement_score":0.29076338,"about_ca_system_score_codex":0.0001259234,"about_ca_system_score_gemma":0.000051476836,"threshold_uncertainty_score":0.7589759},"labels":[],"label_agreement":null},{"id":"W2038253563","doi":"10.1117/12.844360","title":"Semi-automatic segmentation of major aorto-pulmonary collateral arteries (MAPCAs) for image guided procedures","year":2010,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; École de Technologie Supérieure","funders":"","keywords":"Segmentation; Image segmentation; Computer science; Artificial intelligence; Computer vision; Pattern recognition (psychology)","score_opus":0.009595003306389523,"score_gpt":0.25826023881166743,"score_spread":0.24866523550527792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038253563","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9801116,0.000023621427,0.015320518,0.0019602953,0.00031359526,0.0013558343,0.000059074973,0.00022432914,0.0006311143],"genre_scores_gemma":[0.21965154,0.000023941913,0.77922386,0.00016686207,0.0002146791,0.00052643416,0.000017216733,0.000045953624,0.00012950606],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9971915,3.905773e-8,0.0010802287,0.00046849155,0.00086598814,0.00039379252],"domain_scores_gemma":[0.9966006,0.00021516699,0.0007328719,0.00011345978,0.0021915333,0.00014632179],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00079879945,0.00033357783,0.0004700148,0.00016610158,0.000101879225,0.00020328125,0.001574742,0.00019598701,0.000021488333],"category_scores_gemma":[0.0008771893,0.00028352664,0.0005060804,0.00039165345,0.0003596229,0.0014202949,0.00025802365,0.00026640535,0.0000010767452],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032877862,0.00014071021,0.0001292531,0.0013070685,0.000169285,1.6269487e-7,0.00037755762,0.000005783607,0.8955302,0.09624257,0.0044902815,0.0015742104],"study_design_scores_gemma":[0.0008949815,0.00029159992,0.0004944594,0.00028680294,0.00008869506,0.00004443082,0.00053193944,0.07375203,0.9186509,0.004382692,0.00026626347,0.0003152057],"about_ca_topic_score_codex":0.0000077167415,"about_ca_topic_score_gemma":2.9819736e-7,"teacher_disagreement_score":0.7639034,"about_ca_system_score_codex":0.0000884176,"about_ca_system_score_gemma":0.0000886406,"threshold_uncertainty_score":0.9999617},"labels":[],"label_agreement":null},{"id":"W2038626130","doi":"10.1016/j.cviu.2005.05.001","title":"Segmentation of tissue boundary evolution from brain MR image sequences using multi-phase level sets","year":2005,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Institutes of Health","keywords":"Artificial intelligence; Computer science; Segmentation; Level set (data structures); Computer vision; Boundary (topology); Pattern recognition (psychology); Image segmentation; Level set method; Image registration; Consistency (knowledge bases); Image (mathematics); Mathematics","score_opus":0.09846050931042288,"score_gpt":0.390749975877398,"score_spread":0.29228946656697513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038626130","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019973893,0.00020087912,0.9782808,0.00075372134,0.00022051914,0.0003098062,0.000029032892,0.00019321719,0.000038176884],"genre_scores_gemma":[0.22497909,0.000023610779,0.77444005,0.00042756277,0.00007294716,0.0000025865256,0.000025924666,0.000011917083,0.000016283417],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980731,0.00018849364,0.0005115146,0.00052061275,0.00044273445,0.0002635717],"domain_scores_gemma":[0.9989628,0.00019706128,0.0002697178,0.00030423404,0.00010172036,0.00016446465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004679681,0.00022059296,0.0002669585,0.00028735743,0.0002888808,0.0004784343,0.0003691268,0.000082097875,0.000062909225],"category_scores_gemma":[0.000042212487,0.0002098029,0.000054994383,0.0003096221,0.0002985764,0.0026531443,0.0003076755,0.00015260419,0.000008795904],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017429933,0.00017346845,0.000040735995,0.000045879187,0.000022958962,0.00002492452,0.0018402009,0.000013777186,0.82851666,0.0010459583,0.0018348381,0.16642316],"study_design_scores_gemma":[0.0022729225,0.0003081857,0.00028228285,0.00027408608,0.000017415661,0.00003337265,0.00052159105,0.78515124,0.20601441,0.004715551,0.0000794154,0.00032953903],"about_ca_topic_score_codex":0.00009060203,"about_ca_topic_score_gemma":0.000010436698,"teacher_disagreement_score":0.7851375,"about_ca_system_score_codex":0.00042661082,"about_ca_system_score_gemma":0.000087032204,"threshold_uncertainty_score":0.8555514},"labels":[],"label_agreement":null},{"id":"W2038819887","doi":"10.1109/tmi.2014.2371823","title":"A Multi-Atlas-Based Segmentation Framework for Prostate Brachytherapy","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Brachytherapy; Initialization; Computer vision; Prostate brachytherapy; Atlas (anatomy); Image segmentation; Data set; Medical imaging; Pattern recognition (psychology); Radiation therapy; Medicine; Radiology","score_opus":0.017209753737356163,"score_gpt":0.32649247415339006,"score_spread":0.3092827204160339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038819887","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015749171,0.000020703597,0.9915035,0.0061501376,0.0007688442,0.0006440638,0.000006579216,0.0007277226,0.000020976182],"genre_scores_gemma":[0.11679482,0.000020818306,0.8711197,0.011402487,0.00007932999,0.00047291612,0.000006522384,0.000028908506,0.00007452254],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976873,0.00018272744,0.00041344503,0.0005166542,0.0008159474,0.00038397452],"domain_scores_gemma":[0.9980699,0.0008909979,0.00011896781,0.00044267124,0.00012225327,0.0003552389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008565115,0.00020710313,0.00020675707,0.0002153044,0.00026192216,0.00016586109,0.0005824826,0.000105627034,0.00017182698],"category_scores_gemma":[0.00018572615,0.000190285,0.00013455725,0.00035782417,0.0001543456,0.00046334165,0.0000026333428,0.00043845153,0.000042628515],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002265635,0.00029622356,0.000018681856,0.000043101554,0.00001514161,0.0000058016785,0.00034745887,0.00029945074,0.005832507,0.00027755144,0.00042347584,0.99241793],"study_design_scores_gemma":[0.0015490007,0.00013480532,0.000024564843,0.00016718879,0.000012509446,0.000007839271,0.000032561216,0.76551443,0.22995718,0.0018650616,0.00050710887,0.0002277605],"about_ca_topic_score_codex":0.000017393266,"about_ca_topic_score_gemma":0.0000056260724,"teacher_disagreement_score":0.9921902,"about_ca_system_score_codex":0.00008021652,"about_ca_system_score_gemma":0.0001315942,"threshold_uncertainty_score":0.77595973},"labels":[],"label_agreement":null},{"id":"W2039198210","doi":"10.1118/1.4829511","title":"Inter‐slice bidirectional registration‐based segmentation of the prostate gland in MR and CT image sequences","year":2013,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Ontario Tech University; University of Waterloo","funders":"FedDev Ontario","keywords":"Segmentation; Computer science; Image registration; Magnetic resonance imaging; Artificial intelligence; Prostate gland; Affine transformation; Image segmentation; Prostate; Similarity measure; Volume (thermodynamics); Computer vision; Sørensen–Dice coefficient; Similarity (geometry); Pattern recognition (psychology); Medicine; Image (mathematics); Radiology; Mathematics; Cancer","score_opus":0.014033479017401963,"score_gpt":0.2814319987721485,"score_spread":0.2673985197547465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039198210","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.079179406,0.000017280807,0.91567075,0.0041777873,0.00012576384,0.00035267047,0.0000024380363,0.000058772963,0.00041513797],"genre_scores_gemma":[0.96783483,0.000015568326,0.03071115,0.0012094794,0.000047414214,0.00007962393,0.0000075559356,0.0000047873987,0.00008956733],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987779,0.00010769917,0.0002543035,0.00018181701,0.0005705765,0.00010768006],"domain_scores_gemma":[0.99939686,0.00014556861,0.0001249161,0.00018185217,0.000075413656,0.00007539834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032002042,0.00007455035,0.000098503355,0.000032171007,0.00004143882,0.000060146907,0.00032079453,0.000023374818,0.00006781215],"category_scores_gemma":[0.00020636633,0.0000509445,0.000024683688,0.0003139791,0.00028946245,0.00048546493,0.00008672628,0.00014467485,0.000005780048],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011665582,0.0004874868,0.032956585,0.0002634076,0.000027446808,0.000020703092,0.0022329562,0.000014672078,0.06972813,0.0019564296,0.01237893,0.8799216],"study_design_scores_gemma":[0.0010137493,0.000116031326,0.02142292,0.000264734,0.0000068025597,0.000014000377,0.00009511274,0.034350973,0.91895497,0.02347313,0.00008760118,0.00020000091],"about_ca_topic_score_codex":0.0003534873,"about_ca_topic_score_gemma":0.000027750848,"teacher_disagreement_score":0.8886554,"about_ca_system_score_codex":0.000030662013,"about_ca_system_score_gemma":0.00013617154,"threshold_uncertainty_score":0.20774566},"labels":[],"label_agreement":null},{"id":"W2041300117","doi":"10.1117/12.912435","title":"Image segmentation using random-walks on the histogram","year":2012,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Histogram; Segmentation; Random walk; Computer science; Image segmentation; Artificial intelligence; Scalability; Pattern recognition (psychology); Image (mathematics); Market segmentation; Mean-shift; Computer vision; Mathematics; Statistics","score_opus":0.018613173966624076,"score_gpt":0.2641722117052297,"score_spread":0.24555903773860563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041300117","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95764107,0.00006988318,0.036153752,0.003013993,0.00038540352,0.0008222684,0.000010723581,0.00018210232,0.0017207781],"genre_scores_gemma":[0.13583158,0.000051874435,0.86260164,0.0006721706,0.00045463615,0.00023340544,0.000004823857,0.000046496825,0.000103366096],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99758595,5.508005e-8,0.000638139,0.00032154194,0.0010086658,0.00044565846],"domain_scores_gemma":[0.9979563,0.00032305907,0.00045386184,0.000100268146,0.0010115989,0.00015494632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001417823,0.00027973397,0.0003027467,0.00010881376,0.00014663611,0.0002103109,0.0015038905,0.00012857572,0.000023325158],"category_scores_gemma":[0.0007808414,0.00019477164,0.00047781246,0.00041001991,0.00026887798,0.0013555967,0.00024815326,0.00033111448,0.0000036816039],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035352372,0.00013687351,0.00010109459,0.00012708959,0.00014665865,4.6336567e-8,0.00045911275,0.000011372494,0.7117791,0.28073677,0.005015743,0.001450774],"study_design_scores_gemma":[0.0015715712,0.00029405003,0.00037650322,0.00029625423,0.00012894972,0.000024471732,0.001227053,0.096833326,0.89531326,0.0020253165,0.0014545888,0.0004546426],"about_ca_topic_score_codex":0.0000082146435,"about_ca_topic_score_gemma":2.8096268e-8,"teacher_disagreement_score":0.8264479,"about_ca_system_score_codex":0.00024948784,"about_ca_system_score_gemma":0.000030221345,"threshold_uncertainty_score":0.7942557},"labels":[],"label_agreement":null},{"id":"W2041529893","doi":"10.1109/icip.2006.312948","title":"Applying Ant Colony Optimization to Binary Thresholding","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Thresholding; Artificial intelligence; Ant colony optimization algorithms; Grayscale; Computer science; Pixel; Image (mathematics); Balanced histogram thresholding; Ant colony; Pattern recognition (psychology); Computer vision; Image processing","score_opus":0.012935965072840463,"score_gpt":0.2698036033910678,"score_spread":0.25686763831822734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041529893","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005834134,0.000012772731,0.98943144,0.000732459,0.000087533066,0.00037239195,2.4140877e-7,0.0005912093,0.00818853],"genre_scores_gemma":[0.03577125,0.00000271771,0.9609715,0.0024113818,0.00004305,0.00013707309,0.0000035506737,0.0000052350665,0.0006542373],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923056,0.000020382611,0.00016037576,0.00021351274,0.00022597412,0.0001492122],"domain_scores_gemma":[0.9996093,0.000035412108,0.00003279903,0.00021237892,0.00004463297,0.00006549017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018069538,0.000063877385,0.00006683854,0.000110132656,0.00007661866,0.00012329729,0.00033858404,0.000027055306,0.000107289736],"category_scores_gemma":[0.000023456425,0.000057505145,0.000017327666,0.00038159758,0.000013687276,0.00037596206,0.00016629187,0.00004310617,0.00003941059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011248487,0.00035075782,0.0014777716,0.000044010023,0.000013413473,0.00011802968,0.00044917746,0.17784841,0.2045722,0.08590613,0.30302367,0.22618517],"study_design_scores_gemma":[0.00023176282,0.00012008853,0.00044049602,0.000033046428,0.0000028322736,0.0000099299905,0.000030996125,0.72954607,0.26508653,0.0014906405,0.0027366914,0.00027089613],"about_ca_topic_score_codex":0.00007379243,"about_ca_topic_score_gemma":0.0000022646298,"teacher_disagreement_score":0.5516977,"about_ca_system_score_codex":0.000042184587,"about_ca_system_score_gemma":0.000018937231,"threshold_uncertainty_score":0.23449917},"labels":[],"label_agreement":null},{"id":"W2042139980","doi":"10.1117/12.813897","title":"Accurate optical flow field estimation using mechanical properties of soft tissues","year":2009,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Optical flow; Pixel; Hyperelastic material; Imaging phantom; Computer science; Deformation (meteorology); Displacement (psychology); Computer vision; Artificial intelligence; Compression (physics); Displacement field; Materials science; Optics; Image (mathematics); Finite element method; Physics","score_opus":0.021704856889687416,"score_gpt":0.26795406996306625,"score_spread":0.24624921307337883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042139980","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.838928,0.0000728525,0.15667358,0.0032141581,0.00015436273,0.00048543297,0.0000058156843,0.00016543613,0.00030036236],"genre_scores_gemma":[0.31828496,0.000025926443,0.6812923,0.00020241109,0.00011516219,0.00003209204,0.0000017975736,0.000017070093,0.000028317281],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974615,3.9790212e-8,0.000866157,0.00039213273,0.00093821215,0.00034195033],"domain_scores_gemma":[0.9977602,0.00014038403,0.00042138514,0.000094244126,0.0014480315,0.00013578139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069881906,0.0002701805,0.00042825044,0.00011646375,0.00007159794,0.00015862784,0.0014406232,0.0002012449,0.000009157895],"category_scores_gemma":[0.0014083189,0.00021686175,0.00038668694,0.0003685345,0.00016824518,0.0012229518,0.00023218404,0.0002906927,9.671884e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041742576,0.00012705833,0.0000071954532,0.0002487132,0.00009839189,1.3256458e-7,0.00021286085,0.00030198798,0.7422543,0.24801794,0.0007033535,0.007986289],"study_design_scores_gemma":[0.00027401285,0.00029282496,0.00002937577,0.0002844822,0.00003851611,0.000010048654,0.000118605654,0.39203352,0.60461086,0.0021223545,0.000038486953,0.00014690887],"about_ca_topic_score_codex":0.0000067581477,"about_ca_topic_score_gemma":3.9673505e-8,"teacher_disagreement_score":0.5246187,"about_ca_system_score_codex":0.000105768,"about_ca_system_score_gemma":0.000053164218,"threshold_uncertainty_score":0.88433653},"labels":[],"label_agreement":null},{"id":"W2042730632","doi":"10.1109/isspa.2012.6310667","title":"A novel topology based watershed segmentation","year":2012,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bishop's University","funders":"","keywords":"Maxima and minima; Watershed; Image segmentation; Segmentation; Computer science; Artificial intelligence; Morphological gradient; Scale-space segmentation; Mathematical morphology; Topology (electrical circuits); Pattern recognition (psychology); Computer vision; Image (mathematics); Mathematics; Image processing","score_opus":0.02944473625088369,"score_gpt":0.30464685028282024,"score_spread":0.27520211403193656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042730632","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007418639,0.0000074793243,0.9942711,0.0016487922,0.00019709203,0.00011694912,3.514384e-7,0.0003698409,0.0026465217],"genre_scores_gemma":[0.16083394,6.7387765e-7,0.833578,0.0050641494,0.000038282655,0.000026767793,0.0000063630864,0.0000033083727,0.00044849876],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993231,0.000036595182,0.00013193079,0.00012592513,0.00016792183,0.00021447933],"domain_scores_gemma":[0.99956274,0.000047456768,0.000034655746,0.00021349295,0.00002895934,0.00011271195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025729963,0.00006194778,0.00006248287,0.00006930535,0.000036286958,0.00003501604,0.00026390603,0.000034451397,0.00053158833],"category_scores_gemma":[0.000028255023,0.000049216003,0.00002271092,0.00012865839,0.000033999644,0.0006060359,0.000064651605,0.00004505426,0.00011947272],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005146798,0.000398231,0.0023384902,0.00001855869,0.000013563975,0.0000022491745,0.0012236425,0.000003771462,0.7869941,0.033873126,0.017638784,0.15749031],"study_design_scores_gemma":[0.00034968316,0.000040578274,0.0011613416,0.000002248664,0.000002525115,0.000007571323,0.00004083009,0.0066751973,0.9907398,0.00020159125,0.00067701546,0.00010158075],"about_ca_topic_score_codex":0.000027169412,"about_ca_topic_score_gemma":0.0000012352428,"teacher_disagreement_score":0.20374571,"about_ca_system_score_codex":0.000032282867,"about_ca_system_score_gemma":0.000017668415,"threshold_uncertainty_score":0.58205193},"labels":[],"label_agreement":null},{"id":"W2042894850","doi":"10.1109/tip.2007.904956","title":"Efficient Least Squares Multimodal Registration With a Globally Exhaustive Alignment Search","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"U.S. National Library of Medicine","keywords":"Image registration; Computer science; Artificial intelligence; Computer vision; Pixel; Medical imaging; Image (mathematics); Pattern recognition (psychology); Mathematics","score_opus":0.0159963923917801,"score_gpt":0.30111191611930754,"score_spread":0.28511552372752746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042894850","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005508624,0.000025313357,0.9920908,0.00033117688,0.000094056566,0.0004192782,0.000004238418,0.00050396664,0.0010225124],"genre_scores_gemma":[0.6182495,0.0000020336515,0.3813957,0.00019398268,0.000020455733,0.00002815331,0.0000010785886,0.000011713663,0.000097434626],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975875,0.000048440783,0.00035418983,0.00054133916,0.0010419346,0.00042658762],"domain_scores_gemma":[0.99896157,0.00006607504,0.0001233796,0.00032984783,0.00031623803,0.00020286636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007111719,0.00021449018,0.00015302618,0.00022961556,0.00039428764,0.00039786444,0.00045284117,0.00007084313,0.00003318454],"category_scores_gemma":[0.000007728081,0.00018496724,0.000052211602,0.00066889974,0.00021860668,0.0005326443,0.0000046789223,0.00029942053,0.000028175711],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022622652,0.0009467885,0.000018613688,0.00016454607,0.00003491702,0.00018189136,0.0038256065,0.013749726,0.0855824,0.000098922625,0.00009610213,0.89507425],"study_design_scores_gemma":[0.0007766929,0.00036457166,0.0002180347,0.00022284612,0.000020288384,0.00008097578,0.0007106232,0.1775216,0.8197315,0.000038219205,0.000011795157,0.00030289247],"about_ca_topic_score_codex":0.00007341564,"about_ca_topic_score_gemma":0.000020440519,"teacher_disagreement_score":0.8947714,"about_ca_system_score_codex":0.00024733928,"about_ca_system_score_gemma":0.00021024438,"threshold_uncertainty_score":0.75427455},"labels":[],"label_agreement":null},{"id":"W2045219179","doi":"10.1109/tmi.2007.898813","title":"Symmetric Data Attachment Terms for Large Deformation Image Registration","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Image registration; Invertible matrix; Inverse; Transformation (genetics); Matching (statistics); Computer science; Artificial intelligence; Computer vision; Inverse problem; Image (mathematics); Algorithm; Mathematics; Geometry; Mathematical analysis; Pure mathematics","score_opus":0.030649187646487615,"score_gpt":0.3551261709790074,"score_spread":0.3244769833325198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045219179","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006527386,0.000025697953,0.9943291,0.0029745696,0.0007771885,0.0005043128,0.000033493357,0.0005269209,0.0007634553],"genre_scores_gemma":[0.47472605,0.000059801907,0.51987696,0.0047290046,0.00019213486,0.00009683812,0.00012528908,0.000027951834,0.00016600045],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99712515,0.00006654649,0.00063066604,0.00052856054,0.0011704057,0.00047867748],"domain_scores_gemma":[0.9979931,0.00043370223,0.00016215093,0.0009375015,0.000120175355,0.0003533398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033268651,0.00017656357,0.00017056943,0.00042489567,0.00029917553,0.00019555131,0.0012768212,0.000089760004,0.00010672202],"category_scores_gemma":[0.00022035743,0.00016174647,0.00007685698,0.0006481278,0.000081879734,0.0020243276,0.000016129035,0.00035477732,0.00004650508],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001535148,0.00030408538,0.00001619968,0.00005239541,0.000017497166,0.0000276786,0.00018314761,0.0000045286847,0.0019171799,0.0010025336,0.0047863335,0.99167305],"study_design_scores_gemma":[0.0019692378,0.00012557948,0.00019073111,0.00015003872,0.00004600025,0.000111849324,0.00017807474,0.8273728,0.16388634,0.0010347338,0.004472268,0.00046234965],"about_ca_topic_score_codex":0.000021209213,"about_ca_topic_score_gemma":0.000020568878,"teacher_disagreement_score":0.9912107,"about_ca_system_score_codex":0.00015178473,"about_ca_system_score_gemma":0.00009509737,"threshold_uncertainty_score":0.65958303},"labels":[],"label_agreement":null},{"id":"W2045355836","doi":"10.1007/s12021-013-9190-5","title":"Deformable Templates Guided Discriminative Models for Robust 3D Brain MRI Segmentation","year":2013,"lang":"en","type":"article","venue":"Neuroinformatics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institute on Aging; Amorfix Life Sciences; Eli Lilly and Company; Bristol-Myers Squibb; National Institute of Biomedical Imaging and Bioengineering; Alzheimer's Drug Discovery Foundation; National Institutes of Health; National Science Foundation","keywords":"Discriminative model; Computer science; Artificial intelligence; Robustness (evolution); Segmentation; Generative model; Pattern recognition (psychology); Neuroimaging; Computer vision; Image segmentation; Template; Generative grammar; Neuroscience","score_opus":0.048266086904045886,"score_gpt":0.2909350582236118,"score_spread":0.24266897131956594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045355836","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007294501,0.0000055644277,0.99201775,0.0019084812,0.00016419082,0.0013279702,0.00000890935,0.00039337145,0.0034442982],"genre_scores_gemma":[0.0030207017,0.000020150035,0.9915988,0.004380863,0.000024959376,0.0003117256,0.00004934857,0.000015899011,0.00057755294],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839497,0.00003869156,0.00061936566,0.00018703664,0.0004099467,0.00034999242],"domain_scores_gemma":[0.99863,0.00028077807,0.0002681384,0.00041720035,0.00025822455,0.00014564816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002966084,0.00018368087,0.00018074446,0.00014934382,0.00018094487,0.0004064759,0.00065272534,0.000058350695,0.000041664684],"category_scores_gemma":[0.00015645905,0.00015553372,0.000063296015,0.00023920365,0.000058827514,0.0058345567,0.00019487788,0.000121684825,0.000094366034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009764907,0.00018239468,0.000033093496,0.0006444533,0.000051585244,0.0000042550632,0.018017408,0.040942576,0.004629714,0.01961673,0.6181381,0.29772994],"study_design_scores_gemma":[0.00042412244,0.00013604065,0.0000238381,0.000023416884,0.0000065346762,0.000015935073,0.0002967786,0.9644504,0.025396172,0.008799116,0.00025289741,0.00017475968],"about_ca_topic_score_codex":0.000027999678,"about_ca_topic_score_gemma":0.0000016284234,"teacher_disagreement_score":0.9235078,"about_ca_system_score_codex":0.00006604842,"about_ca_system_score_gemma":0.00005740385,"threshold_uncertainty_score":0.63424814},"labels":[],"label_agreement":null},{"id":"W2045364057","doi":"10.1109/icip.2006.312620","title":"A Partition Constrained Minimization Scheme for Efficient Multiphase Level Set Image Segmentation","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Initialization; Partition (number theory); Minification; Algorithm; Constraint (computer-aided design); Mathematical optimization; Image segmentation; Segmentation; Computer science; Mathematics; Artificial intelligence; Geometry; Combinatorics","score_opus":0.03768679578357536,"score_gpt":0.3190558646414887,"score_spread":0.28136906885791335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045364057","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034164796,0.0000059770377,0.99409753,0.00055023853,0.00010067345,0.0007993389,0.000035851484,0.00040972242,0.00058417453],"genre_scores_gemma":[0.07686821,0.0000011715508,0.9216862,0.00048957707,0.00005203706,0.00020926166,0.00030900928,0.000008603025,0.00037598395],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99881625,0.00004738257,0.00033528727,0.00031873942,0.00027899604,0.0002033409],"domain_scores_gemma":[0.99929893,0.00009280619,0.00012464524,0.00021324056,0.00020121924,0.000069149566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002689184,0.000117003314,0.00009996061,0.00011140037,0.00010779696,0.00015079159,0.00019779398,0.00004952552,0.000081073726],"category_scores_gemma":[0.00010340335,0.00011190657,0.000051538333,0.00022419129,0.000072754345,0.0003162492,0.000042456562,0.000038487287,0.000021357488],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003857402,0.0006144107,0.00013650404,0.000088562156,0.000019085512,0.000007853974,0.00045490364,0.0010092179,0.85626453,0.025212616,0.059795942,0.056357782],"study_design_scores_gemma":[0.0012220591,0.00007119085,0.00015329522,0.00001141255,0.00000503362,0.000004193464,0.00005547671,0.45154047,0.5459776,0.0006937942,0.00012832835,0.00013714722],"about_ca_topic_score_codex":0.000032103722,"about_ca_topic_score_gemma":0.00000727483,"teacher_disagreement_score":0.45053124,"about_ca_system_score_codex":0.000059409125,"about_ca_system_score_gemma":0.000054711916,"threshold_uncertainty_score":0.45634177},"labels":[],"label_agreement":null},{"id":"W2045975980","doi":"10.1016/j.neuroimage.2006.12.048","title":"Joint level-set shape modeling and appearance modeling for brain structure segmentation","year":2007,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Segmentation; Artificial intelligence; Computer science; Scale-space segmentation; Pattern recognition (psychology); Image segmentation; Segmentation-based object categorization; Computer vision; Joint (building); Set (abstract data type); Level set (data structures); Algorithm; Engineering","score_opus":0.08443471682022208,"score_gpt":0.3286245265290865,"score_spread":0.24418980970886445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045975980","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.078903675,0.00006524205,0.9195387,0.00067899335,0.00012407254,0.00042661632,0.000013495717,0.00020904736,0.00004016121],"genre_scores_gemma":[0.55299497,0.000009498489,0.4445057,0.0023658732,0.00006180808,0.000010855916,0.000010251672,0.000015196845,0.000025865083],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998542,0.000043979864,0.0003318376,0.00047919544,0.00030888568,0.00029407002],"domain_scores_gemma":[0.9993551,0.00005874947,0.000078262274,0.00028778307,0.000098966906,0.00012117557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005949156,0.00014974215,0.00014053684,0.00012261778,0.00013957264,0.00016928179,0.00029678235,0.000058679594,0.000006945228],"category_scores_gemma":[0.00017925269,0.0001502375,0.000039992163,0.00016460425,0.00003310003,0.00053347455,0.00013454742,0.00015904958,0.0000019856825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028604796,0.000026988107,0.000037661186,0.00012681862,0.000009396739,0.000020462958,0.0012054081,0.0067040627,0.68649924,0.0010403949,0.00088305667,0.30341792],"study_design_scores_gemma":[0.00038438992,0.000061608385,0.00008214169,0.000025506362,0.0000034192567,0.000017301185,0.00004249713,0.9433769,0.053184617,0.0026615479,0.00001770399,0.00014235982],"about_ca_topic_score_codex":0.000011414417,"about_ca_topic_score_gemma":0.0000060423345,"teacher_disagreement_score":0.93667287,"about_ca_system_score_codex":0.000026782596,"about_ca_system_score_gemma":0.000027415683,"threshold_uncertainty_score":0.61265075},"labels":[],"label_agreement":null},{"id":"W2046303004","doi":"10.1007/s11548-010-0535-3","title":"New prototype neuronavigation system based on preoperative imaging and intraoperative freehand ultrasound: system description and validation","year":2010,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"Canadian Institutes of Health Research","keywords":"Neuronavigation; Fiducial marker; Ultrasound; Computer science; 3D ultrasound; Artificial intelligence; Computer vision; Calibration; Image registration; Image-guided surgery; Medicine; Patient registration; Feature (linguistics); Navigation system; Radiology; Magnetic resonance imaging; Image (mathematics)","score_opus":0.011897950080524739,"score_gpt":0.25667623143696033,"score_spread":0.2447782813564356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046303004","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21155916,0.000054931745,0.7859315,0.0007893108,0.0014092065,0.0001728355,0.0000020924617,0.00005145635,0.000029449478],"genre_scores_gemma":[0.9145225,0.000019769805,0.08472049,0.00035977477,0.00034836083,0.000008986704,0.0000096509275,0.000006341946,0.000004069327],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984995,0.00035499493,0.0004911559,0.0002468391,0.0002967457,0.00011077833],"domain_scores_gemma":[0.9981678,0.0008192237,0.00036371825,0.000106921994,0.00040180847,0.00014051027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008022782,0.00014343932,0.00025966825,0.00032400727,0.00010327442,0.00034157716,0.00020568148,0.000081281025,0.0000041002554],"category_scores_gemma":[0.00011805166,0.00011681191,0.000044004177,0.00008547551,0.000112007,0.00080538564,0.00004449994,0.000322262,6.335198e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009010828,0.0002970449,0.18824242,0.00029866974,0.00055855775,0.00091871835,0.003341115,0.00044853025,0.086792246,0.021664819,0.0060281195,0.69050866],"study_design_scores_gemma":[0.0033128655,0.0012350731,0.4333068,0.0017650124,0.00009988583,0.022306934,0.00030145893,0.49422374,0.041808248,0.0006201674,0.00027114933,0.00074863003],"about_ca_topic_score_codex":0.000011996524,"about_ca_topic_score_gemma":0.0000014195703,"teacher_disagreement_score":0.7029634,"about_ca_system_score_codex":0.00006226308,"about_ca_system_score_gemma":0.00013384104,"threshold_uncertainty_score":0.47634515},"labels":[],"label_agreement":null},{"id":"W2047049749","doi":"10.1145/1179622.1179725","title":"Integrated 4D visualization of MRI and structure","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Visualization; Computer science; Data visualization; Artificial intelligence","score_opus":0.0062644970525829865,"score_gpt":0.2685844817386349,"score_spread":0.26231998468605194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047049749","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0077879224,0.000016480626,0.9911454,0.00008762164,0.000023598179,0.000054267,8.3642897e-7,0.00012550902,0.0007583884],"genre_scores_gemma":[0.44629186,0.0000034042362,0.5533254,0.00018622998,0.000008176759,0.0000010054598,0.000008135987,0.0000018877166,0.00017391043],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996157,0.000020385016,0.000113464914,0.00009442389,0.00011090569,0.00004508553],"domain_scores_gemma":[0.99977475,0.000015673342,0.00003768726,0.00009556108,0.000057987738,0.00001834132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000049331822,0.000036017664,0.00004986373,0.000047596233,0.00001620117,0.000039593662,0.00011183142,0.000024475548,0.00007705521],"category_scores_gemma":[0.000014567076,0.00002694762,0.000006224633,0.00017754949,0.000042997493,0.00017712191,0.000037916092,0.000021608457,5.0381624e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023536827,0.000051811516,0.0032335392,0.000036363443,0.0000066365046,0.0000028930165,0.0002623494,0.000013804151,0.2659913,0.586776,0.016252775,0.12737018],"study_design_scores_gemma":[0.0001156402,0.00003328414,0.0022227743,0.000008910922,0.0000014443791,0.0000031318423,0.000012785752,0.03573122,0.9475221,0.014088178,0.00020650182,0.000054031385],"about_ca_topic_score_codex":0.00011690046,"about_ca_topic_score_gemma":0.0000108750255,"teacher_disagreement_score":0.6815308,"about_ca_system_score_codex":0.000005792326,"about_ca_system_score_gemma":0.000014859984,"threshold_uncertainty_score":0.1098892},"labels":[],"label_agreement":null},{"id":"W2047709238","doi":"10.1109/memb.2009.935727","title":"Image-Based Motion Detection: Using the Concept of Weighted Directional Descriptors","year":2010,"lang":"en","type":"article","venue":"IEEE Engineering in Medicine and Biology Magazine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Robustness (evolution); Artificial intelligence; Computer science; Computer vision; Medical imaging; Feature (linguistics); Magnetic resonance imaging; Food and drug administration; Feature extraction; Medicine; Radiology; Risk analysis (engineering)","score_opus":0.024453501531503326,"score_gpt":0.28715363927872395,"score_spread":0.2627001377472206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047709238","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13629794,0.00006328419,0.8622452,0.00042750058,0.0007719806,0.00008983474,5.7445027e-7,0.00007826502,0.000025399384],"genre_scores_gemma":[0.8580892,0.000009894115,0.14147627,0.000193902,0.00020372216,0.000010689961,0.00000293329,0.0000053074177,0.000008076492],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993602,0.000041896776,0.00022759772,0.0001636522,0.000085961095,0.00012066561],"domain_scores_gemma":[0.9995031,0.00016921748,0.00006223982,0.00015462744,0.00006826617,0.000042554966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038771247,0.000091534894,0.00014724159,0.00017411725,0.000031825413,0.0000067168476,0.0001704911,0.00007632996,0.000030059391],"category_scores_gemma":[0.00017478099,0.000059738024,0.00001745821,0.00032108495,0.00026321542,0.00009728274,0.000022186856,0.00025534106,6.977085e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029688924,0.000013331137,0.00053127727,0.000011043242,0.0000053004446,0.0000020037767,0.00008902595,0.00005800794,0.9860133,0.00018765595,0.00012271125,0.012963408],"study_design_scores_gemma":[0.00085137616,0.00021409702,0.0055132257,0.000058325768,0.000010954563,0.000039446168,0.000011565343,0.4467818,0.5456711,0.00020609496,0.0005188934,0.0001231011],"about_ca_topic_score_codex":0.000041218176,"about_ca_topic_score_gemma":0.000008265623,"teacher_disagreement_score":0.72179127,"about_ca_system_score_codex":0.000013909901,"about_ca_system_score_gemma":0.00001732778,"threshold_uncertainty_score":0.24360459},"labels":[],"label_agreement":null},{"id":"W2048844180","doi":"10.1016/j.jneumeth.2014.12.005","title":"Registration of in-vivo to ex-vivo MRI of surgically resected specimens: A pipeline for histology to in-vivo registration","year":2014,"lang":"en","type":"article","venue":"Journal of Neuroscience Methods","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"London Health Sciences Centre; Robarts Clinical Trials; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute of Ukranian Studies, University of Alberta; Canadian Institutes of Health Research","keywords":"Image registration; Ex vivo; In vivo; Magnetic resonance imaging; Voxel; Histology; Histopathology; Medicine; Radiology; Artificial intelligence; Biomedical engineering; Computer science; Pathology; Nuclear medicine; Biology","score_opus":0.06405147653067506,"score_gpt":0.41159689904573005,"score_spread":0.347545422515055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048844180","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020697275,0.000013108848,0.9744894,0.0032852546,0.0005320277,0.00041906032,0.0000026432701,0.00001673661,0.000544504],"genre_scores_gemma":[0.059559565,0.0000229414,0.938979,0.0009821412,0.00006602645,0.000013014969,1.4779872e-7,0.000008184076,0.00036894553],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99588734,0.0010057458,0.0016484509,0.00041212834,0.0007543896,0.00029192033],"domain_scores_gemma":[0.9964323,0.0011125321,0.0011777411,0.00047523214,0.0005914016,0.00021078392],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0083108805,0.00014403503,0.00052528834,0.00088445557,0.000035007422,0.0000478473,0.0011507771,0.00008512656,0.000015520747],"category_scores_gemma":[0.01036975,0.0001285406,0.00009993972,0.0016185289,0.00017392638,0.0005588648,0.00011776236,0.00021779476,2.533191e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010734612,0.00012959586,0.00011383353,0.000030316112,8.4769295e-7,0.000014419598,0.00034216366,0.0003391699,0.9802293,0.0019441654,0.0031305323,0.013618307],"study_design_scores_gemma":[0.00071475934,0.00198737,0.002661376,0.00014816727,0.000006414818,0.000094407704,0.000033501114,0.02241991,0.96210915,0.0030296124,0.006642946,0.0001523652],"about_ca_topic_score_codex":0.000043578428,"about_ca_topic_score_gemma":0.000068978814,"teacher_disagreement_score":0.038862288,"about_ca_system_score_codex":0.00010736318,"about_ca_system_score_gemma":0.0002279003,"threshold_uncertainty_score":0.99796635},"labels":[],"label_agreement":null},{"id":"W2049737217","doi":"10.1117/12.431045","title":"&lt;title&gt;Fast noniterative registration of magnetic resonance images&lt;/title&gt;","year":2001,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Image registration; Preprocessor; Artificial intelligence; Voxel; Computer vision; Noise (video); Matching (statistics); Java; Pattern recognition (psychology); Image (mathematics); Mathematics","score_opus":0.010457773001754439,"score_gpt":0.24153959181276394,"score_spread":0.2310818188110095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049737217","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78424186,0.00081986794,0.026520241,0.0029652738,0.0005974299,0.0009812339,0.000055253415,0.00034026545,0.18347858],"genre_scores_gemma":[0.11051161,0.0005099099,0.88008374,0.00025000618,0.0004763223,0.00013742456,0.0000121519915,0.00005734403,0.00796152],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986334,2.9168508e-8,0.00041393322,0.0002368631,0.00053598953,0.00017977055],"domain_scores_gemma":[0.99870014,0.00006147043,0.00023592493,0.000065766864,0.0008714895,0.000065194086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003336657,0.0001466432,0.00018960991,0.00007362169,0.00003355,0.00007607101,0.0007380413,0.000093936724,0.00010226241],"category_scores_gemma":[0.00026313166,0.0001267216,0.0001923511,0.0002887938,0.00019354762,0.0004405312,0.000108555316,0.00014835829,0.000010169702],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016894714,0.00008691949,0.00007799121,0.00015721642,0.000054879645,3.5882525e-7,0.00019002729,0.0000042499905,0.35844257,0.583898,0.045061126,0.012009789],"study_design_scores_gemma":[0.001873025,0.0011765794,0.0033106243,0.0011756889,0.00014896701,0.000072474075,0.00040067197,0.107266,0.7682875,0.011507619,0.103732,0.0010488919],"about_ca_topic_score_codex":0.0000023810917,"about_ca_topic_score_gemma":6.3381954e-8,"teacher_disagreement_score":0.8535635,"about_ca_system_score_codex":0.000065981534,"about_ca_system_score_gemma":0.000030077183,"threshold_uncertainty_score":0.5167557},"labels":[],"label_agreement":null},{"id":"W2049858394","doi":"10.1109/icsai.2012.6223469","title":"Integrated segmentation of noisy image based on the spatial relationship","year":2012,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Image segmentation; Robustness (evolution); Artificial intelligence; Pixel; Markov random field; Computer science; Pattern recognition (psychology); Segmentation; Cut; Markov chain; Graph; Scale-space segmentation; Computer vision; Mathematics; Machine learning; Theoretical computer science","score_opus":0.03257680866907578,"score_gpt":0.2938853820926094,"score_spread":0.26130857342353364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049858394","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025279818,0.0000032262378,0.98894536,0.001412562,0.00016324787,0.00021730577,0.0000015723176,0.00015350577,0.0065752594],"genre_scores_gemma":[0.67975074,4.2071676e-7,0.31885728,0.0011309775,0.000020059095,0.000023380533,0.000008289469,0.0000035207677,0.00020531153],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990514,0.000182137,0.00020723078,0.000105608175,0.000327131,0.00012651009],"domain_scores_gemma":[0.998939,0.00050078897,0.000096783544,0.00032214136,0.000082247774,0.000059035596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062740495,0.000070735834,0.00006499799,0.0000815773,0.000057635327,0.000038069793,0.00031991547,0.00003185653,0.00047786435],"category_scores_gemma":[0.00035268738,0.00004297006,0.000032885047,0.00029603165,0.00006522022,0.00043117476,0.000038124937,0.00010731419,0.000073673764],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006268612,0.0013870529,0.05310382,0.000079636055,0.000042892632,0.0000040805835,0.0043783532,0.00006349938,0.28664988,0.24387902,0.09759319,0.31275585],"study_design_scores_gemma":[0.000247572,0.00009268728,0.02019815,0.000022877046,0.0000057187835,8.725372e-7,0.00010991871,0.074249074,0.90400696,0.0008479336,0.0001147263,0.00010353432],"about_ca_topic_score_codex":0.00008070947,"about_ca_topic_score_gemma":0.0000032646014,"teacher_disagreement_score":0.6772228,"about_ca_system_score_codex":0.000035119367,"about_ca_system_score_gemma":0.00003675644,"threshold_uncertainty_score":0.52322793},"labels":[],"label_agreement":null},{"id":"W2050089483","doi":"10.1049/iet-ipr.2013.0178","title":"Effective fuzzy clustering algorithm with Bayesian model and mean template for image segmentation","year":2014,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Computer science; Pattern recognition (psychology); Cluster analysis; Artificial intelligence; Image segmentation; Segmentation-based object categorization; Fuzzy logic; Fuzzy clustering; Segmentation; Bayesian probability; Scale-space segmentation; Image (mathematics); Mean-shift; Algorithm; Data mining","score_opus":0.009102309449400362,"score_gpt":0.2813493893574827,"score_spread":0.27224707990808233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050089483","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00050230254,0.00003675481,0.99760765,0.0002304095,0.000033635126,0.00070533523,0.000004181682,0.00039724621,0.00048250495],"genre_scores_gemma":[0.055310573,0.000004254737,0.94386524,0.00044768892,0.000053865806,0.0002305658,0.000008724196,0.00002870764,0.000050391838],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857825,0.00006768983,0.000242681,0.0005124728,0.0002934391,0.00030546292],"domain_scores_gemma":[0.9991504,0.00010765588,0.00017645043,0.00021990851,0.00020823069,0.00013735451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057953806,0.00020442322,0.00020386754,0.00011997087,0.00027363168,0.00062197185,0.00028310175,0.000051380066,0.0000017340965],"category_scores_gemma":[0.00005558289,0.00017587838,0.00002887839,0.00019968506,0.00013123121,0.0021738405,0.00013214652,0.00012598152,0.0000020312477],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013814582,0.000024049572,0.000010526186,0.00024831976,0.000009089622,0.0000043850678,0.0017451288,0.000059574915,0.058238383,0.000029864139,0.00011027926,0.9395066],"study_design_scores_gemma":[0.0007484043,0.0001566141,0.000034641416,0.00014204692,0.000016787699,0.000032990327,0.00009624931,0.86731637,0.12744021,0.0037898934,0.000006676302,0.00021913875],"about_ca_topic_score_codex":0.000009541959,"about_ca_topic_score_gemma":0.0000053840104,"teacher_disagreement_score":0.9392874,"about_ca_system_score_codex":0.00005320539,"about_ca_system_score_gemma":0.000048337966,"threshold_uncertainty_score":0.7172112},"labels":[],"label_agreement":null},{"id":"W2050125036","doi":"10.1117/12.2081388","title":"Optimization-based interactive segmentation interface for multi-region problems","year":2015,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials","funders":"","keywords":"Computer science; Interface (matter); Segmentation; Artificial intelligence; Computer vision; Parallel computing","score_opus":0.029233823334072342,"score_gpt":0.28248503199287134,"score_spread":0.253251208658799,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050125036","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.204855,0.000044026125,0.79038554,0.0025010349,0.0003253746,0.0013637436,0.000018775498,0.00022648928,0.00028000848],"genre_scores_gemma":[0.039681423,0.000017447395,0.9591044,0.0002508064,0.00014194786,0.00061091123,0.000016955943,0.00004403647,0.0001320759],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977126,4.6572527e-8,0.0007463341,0.00048266415,0.000723207,0.00033511533],"domain_scores_gemma":[0.9956365,0.00021266259,0.0005996843,0.000084235566,0.003278559,0.0001883582],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007711984,0.00029745634,0.00033938035,0.00015734507,0.000074621916,0.00023425935,0.0014019677,0.00015509185,0.0000042243164],"category_scores_gemma":[0.0011095793,0.0002556385,0.00039319147,0.00039537804,0.00017766209,0.0014356138,0.00019871064,0.00022644902,0.0000011024822],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041309348,0.0011477455,0.00043473372,0.0017266867,0.0009377757,3.0313132e-7,0.003707688,0.04556869,0.6461946,0.26742572,0.02586644,0.0065765213],"study_design_scores_gemma":[0.0015212203,0.0003783142,0.000019025929,0.0002262758,0.000044921504,0.0000051580732,0.00072589755,0.6810139,0.31505603,0.00049665984,0.00029299318,0.00021964434],"about_ca_topic_score_codex":0.000009463325,"about_ca_topic_score_gemma":1.432192e-7,"teacher_disagreement_score":0.6354452,"about_ca_system_score_codex":0.0003259315,"about_ca_system_score_gemma":0.000089510926,"threshold_uncertainty_score":0.99998957},"labels":[],"label_agreement":null},{"id":"W2050424943","doi":"10.1142/s0219467812500040","title":"CONTOUR INTERPOLATION USING LEVEL-SET ANALYSIS","year":2012,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Interpolation (computer graphics); Computer vision; Artificial intelligence; Curvature; Computer science; Visualization; Tracing; Contour line; Set (abstract data type); Boundary (topology); Trajectory; Motion (physics); Mathematics; Geometry; Geography; Cartography","score_opus":0.05400067830766171,"score_gpt":0.36270449708233565,"score_spread":0.30870381877467395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050424943","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041042544,0.00020742866,0.95780075,0.00044351805,0.00038276138,0.000026124912,0.0000050305234,0.00001246155,0.00007939757],"genre_scores_gemma":[0.7444266,0.00010505924,0.25463772,0.0006192286,0.00019227107,4.0810352e-7,0.000002880635,0.00000310708,0.000012703398],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989046,0.000064820175,0.000358846,0.000078327925,0.00048511298,0.000108292166],"domain_scores_gemma":[0.99880147,0.00007389852,0.00034543828,0.00009644708,0.00056130276,0.00012141459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006800384,0.00007269654,0.00013268272,0.0006402487,0.000033685574,0.00016422149,0.0004307002,0.000036088513,0.000029180477],"category_scores_gemma":[0.00015421037,0.000062101404,0.00012093654,0.00032706838,0.0000597665,0.0013726312,0.00010371703,0.00013541842,0.0000011141918],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019038624,0.000887931,0.48522434,0.000045176228,0.008003349,0.00034081677,0.01202171,0.00005231414,0.18215245,0.05619243,0.0127974665,0.24209164],"study_design_scores_gemma":[0.005637263,0.000782252,0.51110435,0.0004973397,0.001960331,0.003647621,0.0016711961,0.27475694,0.16422634,0.028174093,0.005757513,0.0017847999],"about_ca_topic_score_codex":0.00002418272,"about_ca_topic_score_gemma":0.0000034718123,"teacher_disagreement_score":0.7033841,"about_ca_system_score_codex":0.000024953637,"about_ca_system_score_gemma":0.000031113334,"threshold_uncertainty_score":0.25324216},"labels":[],"label_agreement":null},{"id":"W2050560110","doi":"10.1016/j.cviu.2014.03.011","title":"Hybrid structural and texture distinctiveness vector field convolution for region segmentation","year":2014,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Initialization; Optimal distinctiveness theory; Artificial intelligence; Computer science; Pattern recognition (psychology); Convolution (computer science); Computer vision; Segmentation; Texture (cosmology); Convergence (economics); Texture filtering; Feature (linguistics); Image texture; Image segmentation; Image (mathematics); Artificial neural network","score_opus":0.024201476946117684,"score_gpt":0.2931098703540574,"score_spread":0.2689083934079397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050560110","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044737076,0.0000382808,0.9938761,0.0008235654,0.0002888643,0.00030319646,0.0000016137384,0.00015143918,0.000043237476],"genre_scores_gemma":[0.7395737,0.000019893285,0.25958377,0.0006772358,0.00009905232,0.000009112601,0.000012152546,0.0000073895594,0.000017671324],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906504,0.0000816026,0.00017878067,0.00036479242,0.0001485517,0.00016121144],"domain_scores_gemma":[0.99927175,0.00032113402,0.00009586943,0.0001550462,0.0000551128,0.00010111516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021458491,0.00013318643,0.00014566659,0.00009264518,0.0002744524,0.0003602905,0.00014293671,0.00004214993,0.0000041950207],"category_scores_gemma":[0.000055489985,0.00011338014,0.000030635278,0.0000706553,0.00008546194,0.0007620379,0.00014440758,0.00008355448,6.0557807e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089529414,0.000030076346,0.00033101323,0.00027248228,0.000025977779,0.000011491776,0.0009275087,0.0000037985326,0.022223074,0.1088794,0.009482803,0.8577228],"study_design_scores_gemma":[0.0015857919,0.0008490235,0.0016637777,0.00017175333,0.000016017606,0.00009897851,0.00011480794,0.9187644,0.024137542,0.05195343,0.00030390883,0.00034056537],"about_ca_topic_score_codex":0.0000038460075,"about_ca_topic_score_gemma":6.655674e-7,"teacher_disagreement_score":0.9187606,"about_ca_system_score_codex":0.00008058271,"about_ca_system_score_gemma":0.000010063022,"threshold_uncertainty_score":0.46235082},"labels":[],"label_agreement":null},{"id":"W2050583110","doi":"10.1118/1.2126567","title":"A novel method for automatic detection of patient out‐of‐plane rotation by comparing a single portal image to a reference image","year":2005,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; McGill University; CancerCare Manitoba; Montreal General Hospital","funders":"","keywords":"Rotation (mathematics); Image plane; Distortion (music); Scaling; Translation (biology); Fourier transform; Magnification; Plane (geometry); Computer vision; Angle of rotation; Artificial intelligence; Perpendicular; Mathematics; Image processing; Image (mathematics); Computer science; Geometry; Mathematical analysis","score_opus":0.030183297697705894,"score_gpt":0.32734433664327756,"score_spread":0.29716103894557166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050583110","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008524132,0.000007452255,0.9903076,0.00029840932,0.000087539345,0.0004378847,0.000014716712,0.00013622406,0.00018603631],"genre_scores_gemma":[0.37395382,6.887652e-7,0.6256616,0.0002551418,0.000036200738,0.00006123128,0.000017234433,0.00000759599,0.0000064933038],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99815047,0.000054925575,0.0005492785,0.00027426615,0.00077549164,0.00019559401],"domain_scores_gemma":[0.9988071,0.00025213035,0.00029406237,0.00026829573,0.00019133203,0.0001871079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004414465,0.00012357048,0.00028716127,0.000059253798,0.000036647783,0.000028513872,0.0003831078,0.00006110108,0.000020876829],"category_scores_gemma":[0.0005336558,0.00011560982,0.00005969486,0.00024832238,0.00007426627,0.00035749297,0.00012283448,0.00012854222,0.0000072690077],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000058450805,0.00028813528,0.0000024295614,0.000065380606,0.0000084655485,5.794998e-7,0.00070938404,0.0000037571733,0.50354415,0.00008696919,0.0006094629,0.49467543],"study_design_scores_gemma":[0.00039790818,0.00032080675,0.00003150693,0.00009646606,0.000011495599,0.0000037800164,0.000034371486,0.10870997,0.88964325,0.000565986,0.000076167904,0.00010827593],"about_ca_topic_score_codex":0.000042722488,"about_ca_topic_score_gemma":0.000016947213,"teacher_disagreement_score":0.49456716,"about_ca_system_score_codex":0.00005351984,"about_ca_system_score_gemma":0.0000647316,"threshold_uncertainty_score":0.47144318},"labels":[],"label_agreement":null},{"id":"W2051684526","doi":"10.1118/1.4906129","title":"Rotationally resliced 3D prostate TRUS segmentation using convex optimization with shape priors","year":2015,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Prior probability; Regular polygon; Segmentation; Prostate; Artificial intelligence; Medicine; Computer vision; Computer science; Mathematics; Geometry; Bayesian probability; Internal medicine","score_opus":0.03328583903692379,"score_gpt":0.30189329226528694,"score_spread":0.26860745322836316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051684526","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071792016,0.000016029115,0.99087405,0.0006864548,0.00014741452,0.00044272258,0.0000022349284,0.00031331094,0.0003385735],"genre_scores_gemma":[0.05676769,0.000012219611,0.9400368,0.0027131005,0.00020319369,0.00006903931,0.00008711696,0.00002741892,0.00008341713],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99718237,0.00012418767,0.00033987427,0.00036319956,0.0017478239,0.00024251814],"domain_scores_gemma":[0.9986467,0.000091814116,0.00020743316,0.00027419775,0.00036951312,0.0004103538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005355826,0.00016026609,0.00018422716,0.00006025276,0.00008946777,0.00013030939,0.00044424596,0.00007701835,0.000082745704],"category_scores_gemma":[0.0002740403,0.00013323656,0.000027170478,0.0005462782,0.0001640622,0.0009712595,0.0001196449,0.00019614973,0.000019162837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017054631,0.00077544566,0.0022590717,0.0001495493,0.00013517932,0.00017808743,0.010309529,0.039567556,0.002542507,0.0029017504,0.0039473763,0.9370634],"study_design_scores_gemma":[0.0015109254,0.0002316231,0.00011817674,0.000092736926,0.000017503902,0.000017817545,0.000094761235,0.9766719,0.019642586,0.0012990984,0.000050706996,0.00025218402],"about_ca_topic_score_codex":0.00002488688,"about_ca_topic_score_gemma":0.0000016388497,"teacher_disagreement_score":0.93710434,"about_ca_system_score_codex":0.00014361936,"about_ca_system_score_gemma":0.0006796438,"threshold_uncertainty_score":0.5433229},"labels":[],"label_agreement":null},{"id":"W2051741004","doi":"10.1109/icip.2014.7025185","title":"A robust convergence index filter for breast cancer cell segmentation","year":2014,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Calgary","funders":"Fondation pour la Recherche Médicale; University of Alberta; Alberta Cancer Foundation","keywords":"Filter (signal processing); Computer science; Clutter; Convergence (economics); Kernel (algebra); Mathematics; Pixel; Artificial intelligence; Algorithm; Computer vision; Pattern recognition (psychology); Radar","score_opus":0.022689796554331603,"score_gpt":0.27467262388176206,"score_spread":0.25198282732743044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051741004","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030389676,0.000011368097,0.99653965,0.00084191706,0.0002497173,0.00030723028,0.000009199269,0.00025018485,0.0014868289],"genre_scores_gemma":[0.23112626,0.000031996096,0.76023185,0.0057013645,0.00011709309,0.00033577418,0.000007889756,0.000011951193,0.0024358034],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991062,0.000038201757,0.00018041805,0.00027630845,0.00021858938,0.0001802441],"domain_scores_gemma":[0.9993831,0.00007856944,0.00007535273,0.00024790716,0.00011744727,0.00009762544],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000222068,0.00009090666,0.000088518405,0.000033733704,0.0000595316,0.00008354041,0.00041331426,0.000039839135,0.0012391624],"category_scores_gemma":[0.000013620466,0.0000771541,0.00003652885,0.00011346732,0.000030643074,0.000625369,0.000079944024,0.000042572952,0.000027431928],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022897339,0.00018322874,0.0037145482,0.00016613433,0.000022403301,0.0000013140478,0.00080950034,0.0002618863,0.05955927,0.00719707,0.120055325,0.8080064],"study_design_scores_gemma":[0.0009803234,0.00010178616,0.004509992,0.000027179318,0.000008962037,0.000006689132,0.000073846364,0.49117523,0.5004617,0.0013900936,0.00094008114,0.00032412153],"about_ca_topic_score_codex":0.00020761964,"about_ca_topic_score_gemma":0.00004135184,"teacher_disagreement_score":0.8076823,"about_ca_system_score_codex":0.000055295794,"about_ca_system_score_gemma":0.00003700282,"threshold_uncertainty_score":0.99967384},"labels":[],"label_agreement":null},{"id":"W2051875912","doi":"10.1109/tmi.2014.2325596","title":"The Isometric Log-Ratio Transform for Probabilistic Multi-Label Anatomical Shape Representation","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Probabilistic logic; Artificial intelligence; Pattern recognition (psychology); Statistical model; Computer science; Mathematics; Segmentation; Simplex; Computer vision; Geometry","score_opus":0.031300494630485405,"score_gpt":0.3338142193489674,"score_spread":0.302513724718482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051875912","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017091265,0.00004479154,0.987817,0.009809045,0.00075208105,0.00081048935,0.0000048876095,0.00046715568,0.00012365224],"genre_scores_gemma":[0.5806879,0.00018894314,0.41200328,0.005308528,0.00021154636,0.0011048578,0.000010972568,0.000056593526,0.00042739473],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99717283,0.00021656383,0.0005921928,0.00056016404,0.0010052698,0.00045296617],"domain_scores_gemma":[0.9967516,0.0020697862,0.00010474545,0.00052440446,0.00018901353,0.0003604544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001452301,0.0002042371,0.0002242574,0.00027753928,0.0006010509,0.00026809858,0.0009830133,0.00010353685,0.00007896684],"category_scores_gemma":[0.0008037683,0.00015204567,0.00013770597,0.0008915072,0.00034526986,0.0005790357,0.0000054810466,0.0004763893,0.000034289067],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000148242325,0.00022582155,0.0000040366604,0.000022923818,0.000016346557,0.0000027779083,0.00015979582,0.000056040564,0.00069323275,0.0007993688,0.00069641205,0.99730843],"study_design_scores_gemma":[0.0013298547,0.00009139731,0.00002950934,0.00004134353,0.000023200475,0.000018667879,0.00004568017,0.96522254,0.02997422,0.002649212,0.00039432282,0.00018007516],"about_ca_topic_score_codex":0.000022652435,"about_ca_topic_score_gemma":0.000021968386,"teacher_disagreement_score":0.99712837,"about_ca_system_score_codex":0.00011860416,"about_ca_system_score_gemma":0.00014116989,"threshold_uncertainty_score":0.62002426},"labels":[],"label_agreement":null},{"id":"W2052029481","doi":"10.1186/1475-925x-6-10","title":"Spectral clustering for TRUS images","year":2007,"lang":"en","type":"article","venue":"BioMedical Engineering OnLine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cluster analysis; Computer science; Artificial intelligence; Biomedical engineering; Computer vision; Pattern recognition (psychology); Medicine","score_opus":0.01360539541961265,"score_gpt":0.29430923616320004,"score_spread":0.2807038407435874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052029481","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00097534276,0.00007353487,0.99646044,0.0007906766,0.0006018445,0.00018795699,0.000015077044,0.000857191,0.000037929844],"genre_scores_gemma":[0.02224225,0.000011814347,0.9766085,0.00038220992,0.00055080693,0.000014022449,0.00003976972,0.000018349561,0.00013227027],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861306,0.000006035753,0.0003410364,0.00027621997,0.0003397535,0.00042391996],"domain_scores_gemma":[0.9991441,0.00020433818,0.000043931257,0.00025897907,0.000042516345,0.0003061369],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005619048,0.00013858166,0.00016081541,0.00020577876,0.00003562367,0.00004750124,0.00054839015,0.000087787485,0.000025138159],"category_scores_gemma":[0.00035994046,0.00012718338,0.0000708544,0.00033298042,0.000053844866,0.00018490398,0.00013087053,0.00015066835,0.0000065691943],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020237805,0.00038496163,0.00001751332,0.00022000076,0.000046064783,0.00013608746,0.00021738991,0.00012269118,0.26939884,0.002386791,0.0071720015,0.7198774],"study_design_scores_gemma":[0.0018985464,0.0005748135,0.003031519,0.00017153061,0.000017553139,0.00011273809,0.000036070014,0.7189228,0.2336547,0.00045185463,0.040373314,0.00075457746],"about_ca_topic_score_codex":0.0000048558477,"about_ca_topic_score_gemma":0.0000011847518,"teacher_disagreement_score":0.7191228,"about_ca_system_score_codex":0.000064296015,"about_ca_system_score_gemma":0.000032448897,"threshold_uncertainty_score":0.51863873},"labels":[],"label_agreement":null},{"id":"W2052381478","doi":"10.1118/1.1414009","title":"Evaluation of three‐dimensional finite element‐based deformable registration of pre‐ and intraoperative prostate imaging","year":2001,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":234,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Center for Research Resources; National Cancer Institute; National Institute on Aging","keywords":"Image registration; Magnetic resonance imaging; Medicine; Prostate; Lithotomy position; Nuclear medicine; Supine position; Sørensen–Dice coefficient; Similarity (geometry); Prostate brachytherapy; Segmentation; Artificial intelligence; Computer vision; Radiology; Brachytherapy; Computer science; Image segmentation; Surgery; Image (mathematics); Radiation therapy","score_opus":0.03006516161355626,"score_gpt":0.32092437970652055,"score_spread":0.2908592180929643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052381478","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03294665,0.00006247821,0.9656333,0.000574214,0.000044530512,0.0003147357,0.0000027345793,0.000037667345,0.0003836953],"genre_scores_gemma":[0.9767226,0.0000125488195,0.022793178,0.00036974778,0.000031045176,0.000031912856,0.000022760101,0.000004589893,0.00001161499],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99733293,0.00012260729,0.00037326635,0.0001892501,0.0018550991,0.00012683457],"domain_scores_gemma":[0.9988451,0.00013521472,0.00021174389,0.00020133784,0.0005139194,0.000092654256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019446802,0.00008497416,0.00013948865,0.000040090024,0.000042860032,0.000019760786,0.00019135865,0.000030471714,0.000060866776],"category_scores_gemma":[0.0004533011,0.00007133924,0.000024824107,0.00023766508,0.00019964097,0.0004644189,0.000075109216,0.000107062006,0.0000011944868],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002560622,0.00026057812,0.001955639,0.00006288095,0.00002522935,0.000004563613,0.000577083,0.0010931753,0.0049199713,0.0013882009,0.00047016933,0.9892169],"study_design_scores_gemma":[0.0006839255,0.00009333256,0.0012068455,0.000095392206,0.000021188813,0.0000021501644,0.0000075967564,0.7968757,0.1903867,0.010549364,0.000011281963,0.00006652521],"about_ca_topic_score_codex":0.00005229314,"about_ca_topic_score_gemma":0.000010747294,"teacher_disagreement_score":0.9891504,"about_ca_system_score_codex":0.000037608315,"about_ca_system_score_gemma":0.00037507038,"threshold_uncertainty_score":0.29091296},"labels":[],"label_agreement":null},{"id":"W2052397198","doi":"10.1007/s11548-009-0392-0","title":"Seeded ND medical image segmentation by cellular automaton on GPU","year":2009,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Object Research Systems (Canada); Hôpital Notre-Dame","funders":"","keywords":"Segmentation; Computer science; Dijkstra's algorithm; Reproducibility; Image segmentation; Computation; Graph; Artificial intelligence; Shortest path problem; Algorithm; Mathematics; Theoretical computer science; Statistics","score_opus":0.010722278470363632,"score_gpt":0.2811003084867842,"score_spread":0.2703780300164206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052397198","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054737184,0.00020774575,0.934103,0.009278675,0.001443017,0.00005203389,0.0000019477884,0.00007699269,0.00009937563],"genre_scores_gemma":[0.76715696,0.00043796067,0.21081251,0.020548726,0.0009413008,0.0000040949612,0.00003924625,0.0000134927095,0.000045707802],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978029,0.0002850172,0.00071068224,0.00022087885,0.0008067078,0.00017377739],"domain_scores_gemma":[0.9981777,0.00075239065,0.00044293617,0.00013518633,0.00027729466,0.00021446371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011020635,0.00014748868,0.00032665714,0.00037725052,0.00005744229,0.00013353287,0.0006554958,0.00014379808,0.00006989183],"category_scores_gemma":[0.0001552873,0.0001225528,0.00013560608,0.00012020742,0.0001141812,0.00052767375,0.00005648841,0.00031709298,0.000006619645],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000110534726,0.00042595045,0.0010576162,0.0000081736025,0.00026050681,0.0016727069,0.0002355169,0.000009812831,0.038090378,0.0010443528,0.2302953,0.7267892],"study_design_scores_gemma":[0.011046963,0.004529372,0.17074645,0.0019162899,0.00015604413,0.031136157,0.000117733805,0.15053448,0.59691393,0.015701013,0.014594863,0.0026066792],"about_ca_topic_score_codex":0.0000015831663,"about_ca_topic_score_gemma":9.9924925e-8,"teacher_disagreement_score":0.7241825,"about_ca_system_score_codex":0.00007014655,"about_ca_system_score_gemma":0.00012548093,"threshold_uncertainty_score":0.49975577},"labels":[],"label_agreement":null},{"id":"W2052915721","doi":"10.1117/12.811029","title":"3D variational brain tumor segmentation on a clustered feature set","year":2009,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Pattern recognition (psychology); Voxel; Feature (linguistics); Image segmentation; Boundary (topology); Computer vision; Level set (data structures); Mathematics","score_opus":0.011544173084200235,"score_gpt":0.2555251412051694,"score_spread":0.24398096812096914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052915721","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9491466,0.000033048822,0.023257827,0.024138363,0.0003079863,0.00097408344,0.000049217768,0.0003243847,0.0017684493],"genre_scores_gemma":[0.044225637,0.000020941945,0.9512905,0.0034207269,0.00041543433,0.00015487713,0.000028376975,0.000035455807,0.00040807415],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972935,4.4899135e-8,0.00063192117,0.00050497,0.0012005259,0.0003690625],"domain_scores_gemma":[0.9977883,0.00023236718,0.00048337577,0.00009367788,0.0012458595,0.00015643938],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008145935,0.0003151306,0.00033787335,0.0001610913,0.00010503443,0.00023215875,0.0015056346,0.00016105719,0.000016625801],"category_scores_gemma":[0.00079784245,0.00026585555,0.00040194657,0.0004832319,0.00011679768,0.0010713842,0.00015438335,0.00037048315,0.0000033208999],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065902226,0.00017403366,0.000037815546,0.0001360136,0.00015188007,2.8912802e-7,0.00044410682,0.00009731093,0.5069595,0.45860177,0.029977571,0.0033537976],"study_design_scores_gemma":[0.0036061488,0.0020801453,0.0031425795,0.00072350027,0.00013612544,0.00007222193,0.0009341036,0.26376417,0.707631,0.013720106,0.0031674935,0.0010224221],"about_ca_topic_score_codex":0.0000034067118,"about_ca_topic_score_gemma":5.5406762e-8,"teacher_disagreement_score":0.92803264,"about_ca_system_score_codex":0.00022751074,"about_ca_system_score_gemma":0.00005473803,"threshold_uncertainty_score":0.9999794},"labels":[],"label_agreement":null},{"id":"W2053196843","doi":"10.1167/9.8.906","title":"Implementing curve detectors for contour integration","year":2010,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Orientation (vector space); Curvature; Artificial intelligence; Filter (signal processing); Computer vision; Smoothness; Perpendicular; Geometry; Bounded function; Computer science; Mathematics; Mathematical analysis","score_opus":0.01744465083755911,"score_gpt":0.3603772793752754,"score_spread":0.34293262853771633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053196843","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044379883,0.000011894956,0.9543954,0.0004555535,0.00056340935,0.0001010832,5.018056e-7,0.000025854652,0.00006643269],"genre_scores_gemma":[0.4751485,0.0000046607574,0.524518,0.00014961096,0.0001536061,0.0000020500568,5.237238e-7,0.0000033370961,0.00001972764],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99913496,0.000031288244,0.0003687692,0.00007815734,0.00026085787,0.0001259909],"domain_scores_gemma":[0.9989549,0.00014686519,0.00038540532,0.00011711976,0.00032118813,0.00007455261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014437983,0.000052766067,0.000098243705,0.00011814668,0.00007084847,0.00011600576,0.00034577184,0.000036333135,0.000035956593],"category_scores_gemma":[0.0005079991,0.00003828999,0.000075027274,0.000091655005,0.00001465494,0.00071284216,0.00005813988,0.00018888297,0.000001857812],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004870179,0.000018450883,0.00007971586,0.0000032639605,0.0000033040442,0.0000017550948,0.00013113854,1.5941e-7,0.50036186,0.0006790956,0.002561273,0.4961551],"study_design_scores_gemma":[0.0008315003,0.0008098795,0.0041800295,0.00007689622,0.000010081575,0.000057012407,0.000086303226,0.012472165,0.9694486,0.006058545,0.0058560306,0.000112934526],"about_ca_topic_score_codex":0.0000037022005,"about_ca_topic_score_gemma":0.000012939968,"teacher_disagreement_score":0.49604216,"about_ca_system_score_codex":0.000018652046,"about_ca_system_score_gemma":0.000041762607,"threshold_uncertainty_score":0.15614204},"labels":[],"label_agreement":null},{"id":"W2053305065","doi":"10.1145/1364901.1364954","title":"Multiresolution sphere packing tree","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Octree; Atomic packing factor; Packing problems; Sphere packing; Tree (set theory); Representation (politics); Tessellation (computer graphics); Computer science; Space partitioning; Geometry; Algorithm; Mathematics; Combinatorics; Physics","score_opus":0.03351385751240003,"score_gpt":0.2669010523672106,"score_spread":0.23338719485481058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053305065","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018390521,0.000023506404,0.9773915,0.00030108835,0.000072282,0.00006688227,8.926108e-8,0.0006404054,0.019665223],"genre_scores_gemma":[0.25171328,0.000018871771,0.7455205,0.00082567224,0.000030061648,0.00000734597,8.7180916e-7,0.000003135896,0.0018803125],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99936765,0.000027545875,0.000112372385,0.00015733056,0.00021135889,0.00012371727],"domain_scores_gemma":[0.99960935,0.00003086274,0.000028783314,0.0002325267,0.000033355114,0.00006514743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000848009,0.000051000876,0.000053612082,0.000029545487,0.00008113223,0.000024291981,0.00030407068,0.000026852984,0.00018347119],"category_scores_gemma":[0.000045127766,0.000042849017,0.000024687775,0.00015443665,0.00004211596,0.0003926202,0.00008679385,0.000053596697,0.00012911692],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012613466,0.00006319744,0.0013421908,0.0000052385517,0.000005004556,0.00006408067,0.0006419705,0.000002538733,0.014062318,0.007177196,0.050039623,0.9265954],"study_design_scores_gemma":[0.0010272148,0.00018276047,0.029906273,0.000041376956,0.0000039799193,0.00020735648,0.00009673486,0.14309852,0.81391835,0.003076519,0.0078896545,0.0005512346],"about_ca_topic_score_codex":0.000046459347,"about_ca_topic_score_gemma":0.000009554003,"teacher_disagreement_score":0.92604417,"about_ca_system_score_codex":0.00002435802,"about_ca_system_score_gemma":0.00002559631,"threshold_uncertainty_score":0.20088808},"labels":[],"label_agreement":null},{"id":"W2054940523","doi":"10.1016/j.media.2015.04.013","title":"Multiscale properties of weighted total variation flow with applications to denoising and registration","year":2015,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sunnybrook Health Science Centre; University of Toronto; Fields Institute for Research in Mathematical Sciences","funders":"Canadian Institutes of Health Research","keywords":"Total variation denoising; Noise reduction; Regularization (linguistics); Mathematics; Algorithm; Scale (ratio); Flow (mathematics); Noise (video); Image (mathematics); Pattern recognition (psychology); Artificial intelligence; Computer science; Geometry","score_opus":0.01850511152056049,"score_gpt":0.2748066895848789,"score_spread":0.2563015780643184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054940523","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012250629,0.00005387732,0.9855348,0.0016792486,0.0000105206245,0.00023184682,0.0000015709569,0.000104155806,0.00013334103],"genre_scores_gemma":[0.38019466,0.0000059934223,0.6193431,0.00023551841,0.000030989064,0.000073878844,0.000011891608,0.000004693988,0.00009927746],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982816,0.00009386145,0.00032369196,0.00030191403,0.0008726043,0.00012635515],"domain_scores_gemma":[0.99881685,0.00004046199,0.00011884505,0.00034615133,0.00032697173,0.0003507103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061352184,0.000091896545,0.0002143762,0.0002469316,0.000053552012,0.00009731388,0.00026966733,0.000054940934,0.000027970034],"category_scores_gemma":[0.0003236904,0.000067585905,0.000036909772,0.0013359634,0.00013417448,0.00045829956,0.000104090985,0.0000845951,0.0000071438144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010266008,0.0009509904,0.0036983865,0.0002055505,0.0013277796,0.00007578052,0.014000804,0.0003980994,0.15127032,0.0020719625,0.002911602,0.82298607],"study_design_scores_gemma":[0.00086115603,0.0002609552,0.004810895,0.00009614735,0.00051580457,0.00002988626,0.00027269602,0.84555185,0.1466544,0.00047417273,0.00012988986,0.00034212536],"about_ca_topic_score_codex":0.00020721811,"about_ca_topic_score_gemma":0.00006111296,"teacher_disagreement_score":0.84515375,"about_ca_system_score_codex":0.000034918812,"about_ca_system_score_gemma":0.00011165269,"threshold_uncertainty_score":0.27560732},"labels":[],"label_agreement":null},{"id":"W2055260435","doi":"10.1016/j.media.2012.05.005","title":"Mammography segmentation with maximum likelihood active contours","year":2012,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":79,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; Carleton University","funders":"","keywords":"Segmentation; Active contour model; Artificial intelligence; Level set (data structures); Pattern recognition (psychology); Computer science; Image segmentation; Mammography; Computer vision; Point distribution model; Divergence (linguistics); Digital mammography; Scale-space segmentation; Mathematics; Medicine","score_opus":0.006622734472672239,"score_gpt":0.2728543265296406,"score_spread":0.26623159205696834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055260435","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005034526,0.000115175295,0.99102014,0.0015350508,0.00008189882,0.00018899224,0.0000045377014,0.00035906117,0.0016606125],"genre_scores_gemma":[0.3535653,0.000098012366,0.6413029,0.0044327057,0.00021684877,0.00014766683,0.000098865625,0.000023855047,0.00011384321],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99670434,0.000232952,0.00037285656,0.00041880656,0.0016858192,0.000585199],"domain_scores_gemma":[0.9980249,0.00017061699,0.00019021942,0.00056606403,0.00018253743,0.0008656886],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008947229,0.00021491462,0.00037253276,0.00059682585,0.00011796636,0.00014786354,0.00077855156,0.00011096738,0.0015203911],"category_scores_gemma":[0.00019977287,0.00016087433,0.00023033361,0.0028550832,0.00025458608,0.0016486199,0.00016778744,0.0002845068,0.00009542705],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032705844,0.000757124,0.02981672,0.000028749868,0.002394105,0.00014512813,0.0022106362,0.0000010937998,0.0038392504,0.0003712812,0.006543277,0.9538599],"study_design_scores_gemma":[0.007511981,0.0012970082,0.16609147,0.00024484567,0.008345397,0.00020186583,0.004537989,0.033684123,0.7655545,0.0055575157,0.0032882772,0.0036850285],"about_ca_topic_score_codex":0.00012558958,"about_ca_topic_score_gemma":0.000042381096,"teacher_disagreement_score":0.9501749,"about_ca_system_score_codex":0.000068532056,"about_ca_system_score_gemma":0.000082472856,"threshold_uncertainty_score":0.99939233},"labels":[],"label_agreement":null},{"id":"W2055967874","doi":"10.1109/isbi.2013.6556673","title":"A FEM deformable mesh for active region segmentation","year":2013,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Segmentation; Scale-space segmentation; Computer science; Artificial intelligence; Image segmentation; Finite element method; Segmentation-based object categorization; Parametric statistics; Computer vision; Spline (mechanical); Regularization (linguistics); Free-form deformation; Minification; Algorithm; Pattern recognition (psychology); Mathematics; Deformation (meteorology)","score_opus":0.02263238516850791,"score_gpt":0.2835574408553586,"score_spread":0.2609250556868507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055967874","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00096951454,0.0000035826693,0.99194044,0.0012424423,0.00008357243,0.0007726346,3.7767833e-7,0.0003596965,0.0046277693],"genre_scores_gemma":[0.04718502,0.000010153332,0.9457357,0.0027779676,0.0000325991,0.00068463886,0.000009858516,0.0000069326684,0.0035571256],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993173,0.000022149985,0.00014702328,0.00018612214,0.0001627046,0.00016469565],"domain_scores_gemma":[0.9994609,0.00006338344,0.00006669131,0.00020376453,0.00012761414,0.00007767736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009397664,0.00006934515,0.000070960035,0.000061020128,0.00007122074,0.00012962602,0.00028647066,0.00003347697,0.00012404114],"category_scores_gemma":[0.00003523174,0.000055387267,0.000034555884,0.00013980732,0.000021049878,0.0016739293,0.00006487183,0.000039788712,0.00010051819],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000462296,0.000045706944,0.000032684464,0.00001930291,0.000012673358,9.097643e-7,0.0007277072,0.0000017218379,0.016927129,0.013572512,0.093338355,0.8753167],"study_design_scores_gemma":[0.0005193568,0.00016689817,0.00033001063,0.000012153266,0.000004073261,0.000009425933,0.00028817772,0.060995035,0.91891783,0.01789049,0.00070618215,0.00016035399],"about_ca_topic_score_codex":0.00007458781,"about_ca_topic_score_gemma":0.0000022622337,"teacher_disagreement_score":0.9019907,"about_ca_system_score_codex":0.0000588798,"about_ca_system_score_gemma":0.000027063781,"threshold_uncertainty_score":0.22586273},"labels":[],"label_agreement":null},{"id":"W2056236664","doi":"10.1109/tmi.2013.2294630","title":"Nonrigid Registration of Ultrasound and MRI Using Contextual Conditioned Mutual Information","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Mutual information; Image registration; Artificial intelligence; Computer science; Similarity (geometry); Stochastic gradient descent; Metric (unit); Similarity measure; Gradient descent; Pattern recognition (psychology); Markov random field; Computer vision; Image (mathematics); Artificial neural network; Image segmentation","score_opus":0.011060932041281316,"score_gpt":0.27835176430086883,"score_spread":0.2672908322595875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056236664","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048856186,0.000008559921,0.99340075,0.0006456946,0.00027756926,0.0001592767,0.0000066424213,0.00017217443,0.00044369182],"genre_scores_gemma":[0.9243442,0.00002869796,0.07395649,0.0015905056,0.00003384278,0.000012951392,0.0000082129145,0.00000615231,0.000018980994],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99821264,0.00013605024,0.0005042875,0.00019837145,0.0007752745,0.0001733885],"domain_scores_gemma":[0.9987881,0.0004438495,0.00018236825,0.00024518452,0.0001356879,0.00020480125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075202144,0.00012387271,0.00017147204,0.00020439632,0.00015186318,0.00010711241,0.00025862068,0.00007348122,0.00012289127],"category_scores_gemma":[0.0001930004,0.00011980623,0.00004592894,0.00022474419,0.00032116595,0.0016182178,0.0000034007148,0.0002496053,0.000012591976],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040354917,0.00027040284,0.00009891901,0.00014548565,0.000055167566,0.0000104283035,0.0025847661,0.00046348758,0.040683683,0.005931255,0.002351985,0.9473641],"study_design_scores_gemma":[0.0016931943,0.00016439785,0.00020746625,0.00025434568,0.000038131937,0.0002498806,0.00036652168,0.7515552,0.2431108,0.0013827375,0.00064273115,0.00033460805],"about_ca_topic_score_codex":0.00007132933,"about_ca_topic_score_gemma":0.0000055395476,"teacher_disagreement_score":0.9470295,"about_ca_system_score_codex":0.000042028387,"about_ca_system_score_gemma":0.00009787822,"threshold_uncertainty_score":0.48855564},"labels":[],"label_agreement":null},{"id":"W2057172862","doi":"10.2196/mhealth.2550","title":"The Architecture of an Automatic eHealth Platform With Mobile Client for Cerebrovascular Disease Detection","year":2013,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; People’s Liberation Army Navy General Hospital; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Image segmentation; Segmentation; Computer vision; Rendering (computer graphics)","score_opus":0.01849229371031273,"score_gpt":0.33027570150418467,"score_spread":0.3117834077938719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057172862","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39094692,0.00025710542,0.6046903,0.0009342154,0.00008289662,0.002871295,0.000009156006,0.00019527979,0.000012797047],"genre_scores_gemma":[0.87460613,0.00023676934,0.12119119,0.0016079318,0.00007855528,0.0022181226,0.00001550801,0.000020364205,0.000025423496],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982574,0.00011476432,0.00044761208,0.0003455898,0.0003788305,0.00045580912],"domain_scores_gemma":[0.9980556,0.00020071915,0.0002803752,0.0005622575,0.00012422011,0.00077682594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006971172,0.00014541204,0.00020982939,0.00009215882,0.00045631416,0.0000910573,0.00033860063,0.000049247927,0.0000060602906],"category_scores_gemma":[0.000036923146,0.00008697797,0.000041320684,0.00022741036,0.00012819121,0.00035938213,0.000048740407,0.0001748011,0.000002301864],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003458341,0.00013211953,0.00015962287,0.0012258724,0.0000071670647,3.6228474e-7,0.00084779086,0.0000138622145,0.00003548504,0.0006047301,0.0001901733,0.9967482],"study_design_scores_gemma":[0.0092508765,0.021588156,0.3093328,0.00070939836,0.00016510177,0.00012527948,0.0021567794,0.6011474,0.0074577034,0.04088564,0.005891366,0.0012894998],"about_ca_topic_score_codex":0.0001859562,"about_ca_topic_score_gemma":0.00006937851,"teacher_disagreement_score":0.9954587,"about_ca_system_score_codex":0.00007639062,"about_ca_system_score_gemma":0.00038025665,"threshold_uncertainty_score":0.35468584},"labels":[],"label_agreement":null},{"id":"W2057508308","doi":"10.1016/j.patrec.2007.05.002","title":"Globally adaptive region information for automatic color–texture image segmentation","year":2007,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Artificial intelligence; Image texture; Region growing; Image segmentation; Computer vision; Scale-space segmentation; Segmentation-based object categorization; Segmentation; Computer science; Pattern recognition (psychology); Range segmentation; Minimum spanning tree-based segmentation; Partition (number theory); Image (mathematics); Color image; Homogeneous; Texture (cosmology); Boundary (topology); Mathematics; Image processing","score_opus":0.020512840807588636,"score_gpt":0.2712800684977635,"score_spread":0.25076722769017484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057508308","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009438657,0.0000044988756,0.9848094,0.00336902,0.00035679937,0.001141601,0.00002583255,0.00058372994,0.00027048276],"genre_scores_gemma":[0.12023533,0.000009820073,0.82675385,0.051865675,0.0001885693,0.00035624095,0.00055640686,0.000020843794,0.000013264364],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983405,0.000076085336,0.0005263856,0.00026751906,0.00045786615,0.00033164743],"domain_scores_gemma":[0.9988572,0.00020248283,0.0003683984,0.00022961169,0.00021826243,0.00012403076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064025226,0.00018253653,0.00015030082,0.0002473018,0.00013670775,0.00026696053,0.00038165593,0.00008813363,0.00005355815],"category_scores_gemma":[0.000092744725,0.00018585523,0.00008901626,0.0002525472,0.00006139158,0.0028403015,0.00006403696,0.00012809323,0.00023075445],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016959133,0.00003185702,0.000070823495,0.000063635045,0.000018939101,0.000010972266,0.00072244165,0.0000016460992,0.008926787,0.000028534043,0.017032836,0.97307456],"study_design_scores_gemma":[0.0105342725,0.0017738336,0.021744493,0.0009687276,0.00022326095,0.00041591047,0.002371756,0.2142793,0.7335842,0.0078768665,0.0031349023,0.0030924347],"about_ca_topic_score_codex":0.000021754231,"about_ca_topic_score_gemma":0.000007161893,"teacher_disagreement_score":0.96998215,"about_ca_system_score_codex":0.00020856947,"about_ca_system_score_gemma":0.000027339358,"threshold_uncertainty_score":0.7578956},"labels":[],"label_agreement":null},{"id":"W2057777946","doi":"10.1109/embc.2013.6610698","title":"Fully automated segmentation of corpus callosum in midsagittal brain MRIs","year":2013,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Segmentation; Artificial intelligence; Computer science; Corpus callosum; Initialization; Cluster analysis; Pattern recognition (psychology); Image segmentation; Computer vision; Anatomy; Medicine","score_opus":0.008908406627703248,"score_gpt":0.27152378305114194,"score_spread":0.2626153764234387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057777946","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04383619,0.000014938195,0.950414,0.0018076709,0.00009855314,0.00041105665,9.890616e-7,0.0008243962,0.0025921944],"genre_scores_gemma":[0.4450505,0.0000065000595,0.5522232,0.0019047704,0.000009366987,0.000080773374,0.000010460525,0.000007740191,0.0007066776],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99885243,0.000087191256,0.0003698143,0.00021228225,0.00030762976,0.00017065938],"domain_scores_gemma":[0.9993665,0.00010928688,0.000103973805,0.00024393213,0.00009804699,0.00007825966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022483873,0.000087629174,0.00012996985,0.00016730517,0.000016497548,0.00006206747,0.00039653198,0.00005154304,0.0007412162],"category_scores_gemma":[0.00009517672,0.00007725606,0.000028167466,0.00040530047,0.000058734462,0.0007261454,0.000112646805,0.00007125544,0.00009902865],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003074545,0.00023882175,0.00552385,0.000051641262,0.0000148158015,0.00001832277,0.0010594728,0.00001677341,0.36353117,0.0028111853,0.0780995,0.54863137],"study_design_scores_gemma":[0.0007748289,0.00017156915,0.05267124,0.000023075721,0.0000020531086,0.000008437495,0.00012120266,0.15556507,0.78861624,0.0017812357,0.000060300772,0.00020478023],"about_ca_topic_score_codex":0.000811526,"about_ca_topic_score_gemma":0.00002752988,"teacher_disagreement_score":0.5484266,"about_ca_system_score_codex":0.000052852556,"about_ca_system_score_gemma":0.000044686527,"threshold_uncertainty_score":0.81157976},"labels":[],"label_agreement":null},{"id":"W2058973101","doi":"10.1118/1.2777005","title":"Fast prostate segmentation in 3D TRUS images based on continuity constraint using an autoregressive model","year":2007,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Robarts Clinical Trials","funders":"Canadian Institutes of Health Research","keywords":"Segmentation; Clockwise; Coronal plane; 3D ultrasound; Computer science; Image segmentation; Autoregressive model; Artificial intelligence; Computer vision; Standard deviation; Mathematics; Algorithm; Ultrasound; Rotation (mathematics); Medicine; Physics; Acoustics; Anatomy; Statistics","score_opus":0.025955893182037133,"score_gpt":0.3299681540906255,"score_spread":0.3040122609085884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058973101","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022657506,0.0000042592737,0.97593224,0.00019104435,0.000113847935,0.0003513312,0.000009063142,0.00021745636,0.00052327284],"genre_scores_gemma":[0.7536979,0.0000018476288,0.2441256,0.002032222,0.000077790566,0.000015948825,0.000021965197,0.000012493419,0.000014247568],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99751365,0.000121289704,0.0004117496,0.00042531546,0.0011560357,0.00037194378],"domain_scores_gemma":[0.9988721,0.00018044474,0.00017023522,0.00033852138,0.000101930644,0.00033678938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011348822,0.00017864077,0.00021883701,0.000102075945,0.00007227507,0.0000840708,0.00047170158,0.00009968594,0.000029925583],"category_scores_gemma":[0.00021268855,0.00015952547,0.00004351313,0.00027556214,0.00032156258,0.0005509437,0.00008697786,0.00036507112,0.0000058394876],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028728775,0.0006561666,0.0008018095,0.000031248295,0.0000058053747,0.0001820218,0.0015231913,0.0029260847,0.007871522,0.00061009167,0.00012660696,0.9852367],"study_design_scores_gemma":[0.0008534604,0.00010667919,0.0005354598,0.000116328214,0.0000038627277,0.0000028065942,0.000048755286,0.77842385,0.2171617,0.0025804122,0.0000013254738,0.00016534803],"about_ca_topic_score_codex":0.00004571074,"about_ca_topic_score_gemma":0.000011908679,"teacher_disagreement_score":0.98507136,"about_ca_system_score_codex":0.00018383299,"about_ca_system_score_gemma":0.0003269619,"threshold_uncertainty_score":0.650526},"labels":[],"label_agreement":null},{"id":"W2059419492","doi":"10.1118/1.4736164","title":"WE-E-213CD-08: A Novel Level Set Active Contour Algorithm Using the Jensen-Renyi Divergence for Tumor Segmentation in PET","year":2012,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Algorithm; Active contour model; Level set (data structures); Medical imaging; Artificial intelligence; Divergence (linguistics); Segmentation; Image segmentation; Pattern recognition (psychology); Computer science; Mathematics; Set (abstract data type); Computer vision","score_opus":0.0930241727833892,"score_gpt":0.35834702477608826,"score_spread":0.26532285199269906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059419492","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006565236,0.00004481561,0.9911385,0.0010006912,0.00050446927,0.00057789107,0.000055919732,0.000085264495,0.000027177466],"genre_scores_gemma":[0.2854775,0.000036955254,0.7086875,0.0043067113,0.0010094529,0.00027178111,0.000052230473,0.000031942734,0.00012596349],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977839,0.00011225993,0.00035158655,0.0003106532,0.0009773306,0.00046428278],"domain_scores_gemma":[0.9987139,0.0004165731,0.00018544824,0.0003190688,0.000106316606,0.00025870028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083469856,0.00018177391,0.00022068578,0.000048162856,0.00014939581,0.00005726577,0.000682871,0.00005272044,0.000043692617],"category_scores_gemma":[0.00037804933,0.00013765965,0.00007982079,0.0003846387,0.00018617422,0.00067105424,0.00029057657,0.0002846352,0.000013286618],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026066631,0.00076258875,0.00071921485,0.000076016535,0.000061073944,0.000022813034,0.010862097,0.000020907619,0.014170268,0.0017687957,0.0053270897,0.96618307],"study_design_scores_gemma":[0.0049079936,0.00029856132,0.0047666407,0.0004543147,0.00008032413,0.00014059077,0.0027741329,0.4774143,0.49606523,0.010949435,0.001020186,0.0011282713],"about_ca_topic_score_codex":0.00011217788,"about_ca_topic_score_gemma":0.000009575603,"teacher_disagreement_score":0.9650548,"about_ca_system_score_codex":0.00014317654,"about_ca_system_score_gemma":0.0001954956,"threshold_uncertainty_score":0.56135976},"labels":[],"label_agreement":null},{"id":"W2059731980","doi":"10.1109/isda.2010.5687030","title":"Image thresholding using neural network","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Thresholding; Artificial intelligence; Computer science; Artificial neural network; Pattern recognition (psychology); Image segmentation; Image (mathematics); Segmentation; Generalization; Balanced histogram thresholding; Computer vision; Process (computing); Sample (material); Set (abstract data type); Image processing; Mathematics","score_opus":0.025101279951910353,"score_gpt":0.3136338897451749,"score_spread":0.28853260979326456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059731980","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024396172,0.0000057113425,0.9696691,0.00034281428,0.00053199567,0.000076163466,8.941242e-8,0.0004998006,0.004478172],"genre_scores_gemma":[0.090985745,8.436998e-7,0.9070853,0.0016582076,0.00017500752,0.0000027627002,3.7798594e-7,0.0000050234894,0.00008675526],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992512,0.000019948142,0.00013499953,0.00018992985,0.00018891861,0.00021500564],"domain_scores_gemma":[0.99944466,0.00004442734,0.00003888779,0.00033640856,0.00003977021,0.00009583618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026424282,0.00006786928,0.0000682531,0.000034168064,0.000091639085,0.00019322244,0.000529441,0.000035483048,0.00030400316],"category_scores_gemma":[0.000042415053,0.00005683821,0.00002903096,0.00021300878,0.00005364878,0.00068856863,0.00022345419,0.0001950572,0.000025648447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014967385,0.000037759906,0.0013596321,0.000009248104,0.0000075308953,0.000052277104,0.00017232765,0.000080250866,0.78462917,0.03523713,0.024252128,0.15416104],"study_design_scores_gemma":[0.00010711385,0.00001630985,0.0004568914,0.000005880479,0.0000021858502,0.000036745103,0.0000063437305,0.89610654,0.09790812,0.004853024,0.00035094065,0.00014991625],"about_ca_topic_score_codex":0.000024742927,"about_ca_topic_score_gemma":0.00000479389,"teacher_disagreement_score":0.89602625,"about_ca_system_score_codex":0.0000076671395,"about_ca_system_score_gemma":0.000019619454,"threshold_uncertainty_score":0.33286214},"labels":[],"label_agreement":null},{"id":"W2060278501","doi":"10.1117/12.596148","title":"3D live-wire-based semi-automatic segmentation of medical images","year":2005,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Computer vision; Image segmentation; Automation; Visualization; Set (abstract data type); Medical imaging; Market segmentation; Path (computing); Pattern recognition (psychology); Engineering","score_opus":0.01060581284375983,"score_gpt":0.2578794271643712,"score_spread":0.24727361432061135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060278501","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9493522,0.000103261904,0.04146695,0.0072637973,0.0001714443,0.0006366515,0.000021445794,0.00026825356,0.00071598165],"genre_scores_gemma":[0.14707947,0.0000944146,0.8517477,0.0005111596,0.00026286655,0.00017591435,0.000008975341,0.000039222177,0.000080277285],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99602526,5.8248165e-8,0.0010700282,0.0004317553,0.0021019797,0.00037089468],"domain_scores_gemma":[0.997219,0.00031181527,0.00064578064,0.00010575895,0.0015075253,0.00021007542],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013660574,0.0003026702,0.00045050072,0.00017158718,0.000067891735,0.00014078403,0.002234555,0.00022830264,0.00008530442],"category_scores_gemma":[0.001076724,0.0002548193,0.00047733678,0.00045158106,0.00034819503,0.0011082243,0.00030476844,0.0003330521,0.0000041289063],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032781667,0.0004681793,0.00032970213,0.0012428203,0.00035722842,3.4268336e-7,0.00067749247,0.000147269,0.7965637,0.16550685,0.010551894,0.024121752],"study_design_scores_gemma":[0.0010044927,0.00024672988,0.00031832134,0.00048633423,0.00006472701,0.000011145598,0.00036873488,0.31984004,0.6766079,0.00046286068,0.000334024,0.0002546777],"about_ca_topic_score_codex":0.000013242658,"about_ca_topic_score_gemma":1.5337903e-7,"teacher_disagreement_score":0.81028074,"about_ca_system_score_codex":0.00018574344,"about_ca_system_score_gemma":0.000109720924,"threshold_uncertainty_score":0.9999904},"labels":[],"label_agreement":null},{"id":"W2060776942","doi":"10.1007/s00138-013-0497-x","title":"3D segmentation of abdominal CT imagery with graphical models, conditional random fields and learning","year":2013,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Conditional random field; CRFS; Graphical model; Artificial intelligence; Computer science; Segmentation; Cut; Inference; Markov random field; Discriminative model; Machine learning; Image segmentation; Pattern recognition (psychology); Structured prediction; Scale-space segmentation; Belief propagation; Algorithm","score_opus":0.006238367072077861,"score_gpt":0.2636565299567762,"score_spread":0.2574181628846983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060776942","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011756449,0.00008737743,0.9865216,0.00079385546,0.0000046760906,0.00038149036,0.0000036611943,0.000073236246,0.00037767625],"genre_scores_gemma":[0.7721574,0.00009548847,0.2270482,0.0003315291,0.000012030108,0.00024484962,0.000039656934,0.00000456598,0.00006632736],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927634,0.00005927226,0.00018644483,0.00020930321,0.0001889886,0.00007963339],"domain_scores_gemma":[0.9994617,0.00016755683,0.00008691476,0.00012795646,0.00006981743,0.00008604332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016864855,0.000081193626,0.00011359324,0.00009351682,0.00015039157,0.0000781035,0.000108243556,0.000021840595,0.0000701629],"category_scores_gemma":[0.0000124103435,0.000060172853,0.00001753831,0.00016538992,0.00013450917,0.00039705873,0.000069220594,0.00014457834,0.0000035014289],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025879564,0.00018812918,0.0012847479,0.00006618914,0.000025894851,0.000002230719,0.0002989723,0.00025319937,0.017604927,0.024949087,0.0011281597,0.9541726],"study_design_scores_gemma":[0.0026980003,0.00033089984,0.007869466,0.000049617804,0.00002838019,0.00009284717,0.00013733075,0.9383739,0.010474724,0.03918813,0.00047036362,0.00028630148],"about_ca_topic_score_codex":0.00007135392,"about_ca_topic_score_gemma":0.0000021789037,"teacher_disagreement_score":0.9538863,"about_ca_system_score_codex":0.000004470669,"about_ca_system_score_gemma":0.0000128004685,"threshold_uncertainty_score":0.24537778},"labels":[],"label_agreement":null},{"id":"W2061553978","doi":"10.1109/isbi.2010.5490091","title":"Semi-supervised prostate cancer segmentation with multispectral MRI","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University Health Network; Mount Sinai Hospital","funders":"","keywords":"Multispectral image; Prostate cancer; Magnetic resonance imaging; Segmentation; Computer science; Artificial intelligence; Computer vision; Prostate; Ultrasound; Image segmentation; Cancer detection; Cancer; Radiology; Medicine; Pattern recognition (psychology)","score_opus":0.007790053952985117,"score_gpt":0.27582696277457397,"score_spread":0.26803690882158887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061553978","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037846938,0.000008795987,0.9578083,0.0016377423,0.00019040627,0.00042073557,0.0000022074305,0.00060778135,0.0014771075],"genre_scores_gemma":[0.12143518,0.000026824082,0.875363,0.0014722425,0.000051285668,0.00017631506,0.000006650764,0.000010810294,0.001457694],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99896127,0.00002525143,0.00015777862,0.00030390278,0.00033604066,0.00021575188],"domain_scores_gemma":[0.99938107,0.000029069,0.00005501455,0.0003107905,0.000091768394,0.00013227933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013900518,0.00011144813,0.00008944442,0.000063327534,0.00007036558,0.00014594743,0.00038064318,0.00003920308,0.00058762537],"category_scores_gemma":[0.000011187839,0.00007986596,0.0000201177,0.0002434255,0.0000680091,0.00075416744,0.00006296612,0.00018703182,0.00003460061],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015619782,0.00010629317,0.007199264,0.000024577897,0.000023542403,0.000024610825,0.0022099898,0.000017957234,0.68130445,0.0030770223,0.0064930627,0.29950362],"study_design_scores_gemma":[0.00071394583,0.00010004228,0.002768467,0.000014400909,0.000005141909,0.00001304331,0.00007078839,0.025525164,0.96998876,0.0002942809,0.00030341872,0.00020252455],"about_ca_topic_score_codex":0.00023668139,"about_ca_topic_score_gemma":0.00030411332,"teacher_disagreement_score":0.29930112,"about_ca_system_score_codex":0.000027137532,"about_ca_system_score_gemma":0.00008234978,"threshold_uncertainty_score":0.64340854},"labels":[],"label_agreement":null},{"id":"W2062205667","doi":"10.1186/1471-2342-8-8","title":"Application of reinforcement learning for segmentation of transrectal ultrasound images","year":2008,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Reinforcement learning; Modality (human–computer interaction); Image segmentation; Computer vision; Ultrasound; Pattern recognition (psychology); Radiology; Medicine","score_opus":0.01575274532697471,"score_gpt":0.298222903476396,"score_spread":0.28247015814942134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062205667","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035398186,0.00008771428,0.9953449,0.0001255282,0.00006867535,0.00041843025,0.0000010787752,0.00012957177,0.00028429666],"genre_scores_gemma":[0.6478542,0.00007108981,0.3517426,0.0001280812,0.000034632914,0.0000902199,0.000024646333,0.000007993062,0.000046530957],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981289,0.000085908076,0.0005657999,0.00024814453,0.00078673905,0.00018449714],"domain_scores_gemma":[0.998478,0.00074308267,0.00027191988,0.00021371343,0.00016592347,0.00012735756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006914399,0.00010431768,0.00020297781,0.000119679,0.000082210245,0.000012009584,0.00043784225,0.000041312072,0.000053804684],"category_scores_gemma":[0.0008472445,0.000098961034,0.00008934438,0.00023145955,0.0002375857,0.0003842843,0.000050338025,0.000113090355,0.0000024794579],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042454478,0.00021228421,0.021722566,0.000655073,0.000036345817,0.0000061303745,0.0026327448,0.0006998937,0.6206416,0.0018160595,0.0016658913,0.34986895],"study_design_scores_gemma":[0.00096013496,0.00008354625,0.0022420725,0.000089050285,0.000013249408,0.000046056855,0.00015658457,0.14244404,0.8534472,0.00031640285,0.000062341416,0.00013933086],"about_ca_topic_score_codex":0.000044079898,"about_ca_topic_score_gemma":0.0000015278986,"teacher_disagreement_score":0.6443144,"about_ca_system_score_codex":0.00003262834,"about_ca_system_score_gemma":0.00017918072,"threshold_uncertainty_score":0.40355137},"labels":[],"label_agreement":null},{"id":"W2062322640","doi":"10.1016/j.compmedimag.2012.04.005","title":"An edge-region force guided active shape approach for automatic lung field detection in chest radiographs","year":2012,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"University of Alberta","keywords":"Segmentation; Radiography; Computer science; Artificial intelligence; Active shape model; Computer vision; Lung; Field (mathematics); Edge detection; Sensitivity (control systems); Radiology; Image segmentation; Computer-aided diagnosis; Pattern recognition (psychology); Image processing; Medicine; Image (mathematics); Mathematics; Engineering","score_opus":0.020988304581501274,"score_gpt":0.30864239393535703,"score_spread":0.2876540893538558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062322640","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014454716,0.00034075955,0.9829914,0.0007406468,0.00039481506,0.00055728375,8.316796e-7,0.00047348265,0.000046075816],"genre_scores_gemma":[0.68742526,0.00013071069,0.30910584,0.0029124587,0.00021382127,0.00017105804,0.000019138783,0.000018135499,0.0000036021602],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979815,0.00020207287,0.00043094959,0.00045357595,0.00044153252,0.00049036427],"domain_scores_gemma":[0.998474,0.00040815186,0.00013679787,0.00037910495,0.000079683865,0.00052228215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010140793,0.00021832953,0.0003131304,0.00044218852,0.00016032114,0.00014439087,0.0005913903,0.00015940961,0.0000050730914],"category_scores_gemma":[0.00027965955,0.00020083784,0.00009841163,0.0006628054,0.00019694056,0.0010514669,0.0001539675,0.00035702027,3.0841184e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014523352,0.00021529636,0.0020679734,0.00016666253,0.000019204484,0.000008069984,0.00089830183,7.791135e-7,0.0011592314,0.00079284084,0.00077489275,0.99388224],"study_design_scores_gemma":[0.0011960266,0.00008111218,0.0043101097,0.00010531138,0.000016553164,0.00011653918,0.000049784376,0.987243,0.005285296,0.001249737,0.00010032712,0.00024619338],"about_ca_topic_score_codex":0.000026231972,"about_ca_topic_score_gemma":0.0000015065933,"teacher_disagreement_score":0.993636,"about_ca_system_score_codex":0.000034900557,"about_ca_system_score_gemma":0.000052340423,"threshold_uncertainty_score":0.818993},"labels":[],"label_agreement":null},{"id":"W2062421008","doi":"10.1117/12.506118","title":"Wavelet variance components in image space for spatio-temporal neuroimaging data","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Wavelet; Computer science; Thresholding; Estimator; Artificial intelligence; Pattern recognition (psychology); Domain (mathematical analysis); Wavelet transform; Parametric statistics; Image (mathematics); Variance (accounting); Mathematics; Statistics","score_opus":0.02810774351081561,"score_gpt":0.26954359774994385,"score_spread":0.24143585423912825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062421008","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86878693,0.000058446138,0.12325256,0.004942056,0.0003673561,0.0012101615,0.00008037391,0.00018818318,0.0011139397],"genre_scores_gemma":[0.06461728,0.00003993413,0.93462825,0.00026527204,0.00012274407,0.00015774148,0.00003347945,0.000043031287,0.000092254624],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99730176,5.8357507e-8,0.0007944559,0.00067087676,0.00076834054,0.00046453992],"domain_scores_gemma":[0.99787515,0.0002594061,0.00044759645,0.00018614886,0.0010924978,0.0001392044],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014652684,0.00029877192,0.00040217364,0.0001452969,0.00007789557,0.00024855218,0.0027207548,0.000118619224,0.000008269785],"category_scores_gemma":[0.0017247322,0.00027473937,0.00026235436,0.0004786814,0.00020171537,0.0018866442,0.00045826455,0.0003167318,0.0000012765548],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004018466,0.00020666704,0.00056683255,0.00046624773,0.00011473188,4.1985518e-7,0.0002141295,0.00001913679,0.40425026,0.58427954,0.008924684,0.0009171567],"study_design_scores_gemma":[0.0033162257,0.0003724047,0.0016098872,0.00061410357,0.00008883243,0.00004052559,0.00054593646,0.53365815,0.43753308,0.0111076785,0.010261652,0.00085152406],"about_ca_topic_score_codex":0.000019103316,"about_ca_topic_score_gemma":3.1060412e-7,"teacher_disagreement_score":0.8113757,"about_ca_system_score_codex":0.00014116625,"about_ca_system_score_gemma":0.000061072475,"threshold_uncertainty_score":0.9999705},"labels":[],"label_agreement":null},{"id":"W2063237438","doi":"10.1117/12.465551","title":"&lt;title&gt;Numerical environment for simulating 3D angiographic imaging of the coronary arteries&lt;/title&gt;","year":2002,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Canadian Institutes of Health Research","keywords":"Coronary arteries; Computer science; Projection (relational algebra); Cardiac cycle; 3D reconstruction; Computer vision; Artificial intelligence; Artery; Biomedical engineering; Algorithm; Medicine; Cardiology","score_opus":0.011084166992221202,"score_gpt":0.22197049010632314,"score_spread":0.21088632311410194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063237438","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8370391,0.000835787,0.12434534,0.008035094,0.0011558827,0.0022348526,0.000095165975,0.00047227336,0.025786515],"genre_scores_gemma":[0.33509642,0.00011257325,0.6634522,0.00028956908,0.00024802002,0.00012315539,0.0000039138304,0.000046639827,0.00062751444],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986839,3.4882117e-8,0.0003829276,0.00022922043,0.0004951099,0.00020877234],"domain_scores_gemma":[0.9992334,0.00010985305,0.00022883099,0.000073036594,0.0002945718,0.00006031229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002672801,0.00014932243,0.00018732372,0.00006043207,0.000057020072,0.00005003424,0.00081918726,0.000067826266,0.000095398165],"category_scores_gemma":[0.00019987524,0.0001140685,0.00037082998,0.00020538496,0.00018324972,0.000266974,0.0001894833,0.0001299864,0.000004896356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021220176,0.0002558799,0.0007660437,0.0004841909,0.00034758856,2.65204e-7,0.00044829014,0.00012085545,0.42150837,0.48711744,0.052645985,0.036283866],"study_design_scores_gemma":[0.0012006012,0.0003682125,0.0017493409,0.000463623,0.00016663459,0.00003065876,0.00021438109,0.8222851,0.1112094,0.0043268297,0.05733284,0.000652334],"about_ca_topic_score_codex":7.877686e-7,"about_ca_topic_score_gemma":8.047221e-9,"teacher_disagreement_score":0.8221643,"about_ca_system_score_codex":0.000060717713,"about_ca_system_score_gemma":0.000010169544,"threshold_uncertainty_score":0.46515784},"labels":[],"label_agreement":null},{"id":"W2063296514","doi":"10.1109/icip.2010.5652559","title":"Fully automatic brain tumor segmentation using a normalized Gaussian Bayesian Classifier and 3D Fluid Vector Flow","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Segmentation; Computer science; Bayesian probability; Gaussian; Mixture model; Image segmentation; Support vector machine; Physics","score_opus":0.013298463840443918,"score_gpt":0.28125145589548306,"score_spread":0.26795299205503914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063296514","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059295826,0.00000526613,0.9366783,0.0019319464,0.00024660784,0.00039022247,0.0000019772976,0.0005871771,0.00086267706],"genre_scores_gemma":[0.08756523,0.0000010875068,0.908935,0.0031171418,0.00005543899,0.00003799298,0.000005235365,0.000015066667,0.00026779645],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840343,0.00013066999,0.00037338183,0.00039110004,0.00040498874,0.00029640648],"domain_scores_gemma":[0.9989866,0.00015844188,0.00012303749,0.0004137138,0.000063765896,0.00025443904],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00053581735,0.00018259462,0.00019095078,0.00017406222,0.00015334564,0.0003587845,0.00037862343,0.0000735828,0.0012764046],"category_scores_gemma":[0.0002024465,0.00015598132,0.000040427985,0.0003156583,0.00010926695,0.0009827105,0.00015723023,0.0001608571,0.00003035028],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046472383,0.000066796645,0.00013946727,0.0000493049,0.000016083915,0.000032166627,0.0007435377,0.0000011166652,0.80950886,0.0029810728,0.0031593647,0.18329757],"study_design_scores_gemma":[0.00052484067,0.00007080108,0.00086984533,0.000030968975,0.00000850735,0.00007379013,0.00006725939,0.85399467,0.14343244,0.0005825015,0.00011170305,0.0002326704],"about_ca_topic_score_codex":0.00006227856,"about_ca_topic_score_gemma":0.000040493163,"teacher_disagreement_score":0.85399354,"about_ca_system_score_codex":0.000042332227,"about_ca_system_score_gemma":0.00011046064,"threshold_uncertainty_score":0.9996366},"labels":[],"label_agreement":null},{"id":"W2063351862","doi":"10.1115/sbc2013-14704","title":"Direct Structured Finite Element Mesh Generation From Three-Dimensional Medical Images","year":2013,"lang":"en","type":"article","venue":"Volume 1A: Abdominal Aortic Aneurysms; Active and Reactive Soft Matter; Atherosclerosis; BioFluid Mechanics; Education; Biotransport Phenomena; Bone, Joint and Spine Mechanics; Brain Injury; Cardiac Mechanics; Cardiovascular Devices, Fluids and Imaging; Cartilage and Disc Mechanics; Cell and Tissue Engineering; Cerebral Aneurysms; Computational Biofluid Dynamics; Device Design, Human Dynamics, and Rehabilitation; Drug Delivery and Disease Treatment; Engineered Cellular Environments","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Finite element method; Mesh generation; Construct (python library); Computer science; Medical imaging; Computer vision; Algorithm; Artificial intelligence; Structural engineering; Engineering; Programming language","score_opus":0.004649094970792246,"score_gpt":0.1828852157015715,"score_spread":0.17823612073077927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063351862","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19784164,0.033009,0.76498926,0.0008428192,0.00032848027,0.0022348473,0.00059058337,0.00016008138,0.0000032612263],"genre_scores_gemma":[0.9616453,0.015252639,0.01988519,0.000540268,0.00021119007,0.00048307684,0.0016982219,0.00016674396,0.000117393894],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944317,0.000415282,0.0012327362,0.002247099,0.0008499605,0.00082327664],"domain_scores_gemma":[0.9969997,0.00032452645,0.0003644202,0.00066086225,0.0002179263,0.0014326031],"candidate_categories":["metaepi_narrow"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.000780643,0.00131172,0.0014635137,0.0004924547,0.00082837977,0.0005132836,0.0002731801,0.00031509608,0.00006242677],"category_scores_gemma":[0.00006205157,0.0012313763,0.00037105658,0.00032596974,0.0002162411,0.0011660549,0.00034036578,0.00042022593,0.0000075238304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046599985,0.002811613,0.0017935652,0.003987606,0.0038550277,0.00021627771,0.0044373465,0.001951909,0.77567196,0.033868548,0.00042060995,0.17051953],"study_design_scores_gemma":[0.0029503175,0.0010531272,0.003231738,0.00044988404,0.0020118882,0.000100241174,0.00097591593,0.96036774,0.018622726,0.007589186,0.0002560163,0.0023912368],"about_ca_topic_score_codex":0.00043662023,"about_ca_topic_score_gemma":0.00002352192,"teacher_disagreement_score":0.9584158,"about_ca_system_score_codex":0.0002957607,"about_ca_system_score_gemma":0.00020421491,"threshold_uncertainty_score":0.9999634},"labels":[],"label_agreement":null},{"id":"W2063525506","doi":"10.1109/embc.2012.6346638","title":"Application of scale-space descriptors for the reliable detection of keypoints for image registration in optical mapping studies in whole heart preparations","year":2012,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Image registration; Process (computing); Scale (ratio); Pattern recognition (psychology); Contrast (vision); Scale space; Set (abstract data type); Image (mathematics); Image processing; Geography; Cartography","score_opus":0.05994764206989855,"score_gpt":0.36054903225919793,"score_spread":0.3006013901892994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063525506","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01021844,0.00008526029,0.98728484,0.0009137038,0.000079492595,0.0012965989,0.0000014189675,0.000038430633,0.000081818514],"genre_scores_gemma":[0.55051374,0.000007803047,0.44875848,0.000037949205,0.00001625063,0.00058633246,0.0000019413394,0.0000030896313,0.00007443296],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999104,0.000034092536,0.00040522672,0.0001568656,0.0001515188,0.00014827463],"domain_scores_gemma":[0.9990314,0.00037322473,0.00013913983,0.00024938225,0.00017953805,0.000027281178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011774699,0.00006378505,0.000139774,0.000113750524,0.00004866337,0.00001635291,0.00015030713,0.000042094733,7.897641e-7],"category_scores_gemma":[0.00039637633,0.000050472132,0.000038869082,0.00033983786,0.00010530402,0.0006666619,0.00004184208,0.000048984166,9.0327467e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037857568,0.0002880491,0.0022822125,0.00026788397,0.00001592452,3.9174e-8,0.0042219223,0.0001719813,0.96477705,0.009755769,0.003245138,0.014936143],"study_design_scores_gemma":[0.0003357077,0.00009812082,0.005290124,0.000050692728,0.000006514132,0.0000015240886,0.0011126666,0.097050734,0.89333916,0.0022212723,0.00042239527,0.00007107523],"about_ca_topic_score_codex":0.00007615659,"about_ca_topic_score_gemma":0.00007826268,"teacher_disagreement_score":0.5402953,"about_ca_system_score_codex":0.000071352806,"about_ca_system_score_gemma":0.000025859086,"threshold_uncertainty_score":0.20581938},"labels":[],"label_agreement":null},{"id":"W2063932100","doi":"10.1117/12.844476","title":"Design of a predictive targeting error simulator for MRI-guided prostate biopsy","year":2010,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"National Cancer Institute","keywords":"Contouring; Computer science; Prostate biopsy; Segmentation; Computer vision; Rendering (computer graphics); Workflow; Simulation; Artificial intelligence; Prostate cancer; Computer graphics (images); Medicine","score_opus":0.01576482775359769,"score_gpt":0.2643082477467352,"score_spread":0.24854341999313753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063932100","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7782279,0.000024763034,0.21816805,0.0013411891,0.00031137455,0.0015240588,0.000046404217,0.00019482367,0.00016139825],"genre_scores_gemma":[0.12755738,0.00001777077,0.87161267,0.000094327836,0.00020797185,0.00039097999,0.0000064959927,0.00004483329,0.00006755907],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972163,3.728989e-8,0.0009827464,0.000509729,0.0008558591,0.00043533478],"domain_scores_gemma":[0.9954718,0.0004059225,0.0007273472,0.00009937486,0.0031262098,0.00016939543],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014720396,0.0003144386,0.00045289885,0.00014417939,0.00009238001,0.00012730274,0.0017558942,0.00021168962,0.0000086936125],"category_scores_gemma":[0.0019442778,0.00026164268,0.00047496185,0.00041629295,0.00032954718,0.0009935884,0.0002902477,0.0003654149,6.2846743e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007845883,0.0001275229,0.0001102236,0.0004368448,0.00021802742,7.747204e-8,0.0005055413,0.00033116012,0.8585514,0.13568254,0.0035258778,0.00043232014],"study_design_scores_gemma":[0.0008562493,0.0003861253,0.00010127722,0.00015347348,0.000058118407,0.000008461289,0.00025718487,0.37787604,0.6170483,0.002788563,0.0002373172,0.00022886775],"about_ca_topic_score_codex":0.0000064359883,"about_ca_topic_score_gemma":2.7672145e-8,"teacher_disagreement_score":0.65344465,"about_ca_system_score_codex":0.00008644548,"about_ca_system_score_gemma":0.00008515566,"threshold_uncertainty_score":0.99998355},"labels":[],"label_agreement":null},{"id":"W2063987818","doi":"10.1109/tip.2015.2427514","title":"Enhancement of Textural Differences Based on Morphological Component Analysis","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Artificial intelligence; Image texture; Preprocessor; Texture filtering; Texture (cosmology); Pattern recognition (psychology); Texture compression; Computer science; Segmentation; Image segmentation; Computer vision; Principal component analysis; Feature (linguistics); Component (thermodynamics); Projective texture mapping; Image (mathematics)","score_opus":0.04384041046378219,"score_gpt":0.3101940206130372,"score_spread":0.266353610149255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063987818","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008539825,0.000019174413,0.99007773,0.00046813523,0.00010318324,0.00014598272,0.000003985852,0.00019198249,0.000449974],"genre_scores_gemma":[0.74471223,0.0000028008133,0.254878,0.00030972686,0.000007138212,0.000036283138,0.0000019496779,0.000004300368,0.000047567493],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99824965,0.00011958108,0.00036022012,0.0003753424,0.0006928144,0.00020236646],"domain_scores_gemma":[0.9990578,0.00009813623,0.00016271092,0.00032313872,0.00019574145,0.00016243791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026741094,0.00016207785,0.00027531912,0.00037299749,0.00010811292,0.0001248362,0.0004713237,0.00005321699,0.00009540045],"category_scores_gemma":[0.000016453836,0.00012575963,0.00012256678,0.0009184061,0.00016462125,0.00037413364,0.000003647586,0.00019517864,0.000016755546],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016723736,0.0027919381,0.00016674772,0.00012909956,0.00018472825,0.00008272291,0.0014728183,0.005155558,0.15244754,0.00003035696,0.0003042765,0.83706695],"study_design_scores_gemma":[0.00036907938,0.00033109423,0.0002552882,0.00005236179,0.00007271204,0.0000031330414,0.000059156377,0.3944187,0.604184,0.00010363601,0.0000032090882,0.00014764049],"about_ca_topic_score_codex":0.000019973353,"about_ca_topic_score_gemma":0.00000251943,"teacher_disagreement_score":0.8369193,"about_ca_system_score_codex":0.00008162635,"about_ca_system_score_gemma":0.00009417559,"threshold_uncertainty_score":0.5128329},"labels":[],"label_agreement":null},{"id":"W2064081873","doi":"10.1167/13.9.1386","title":"Underlying sources for decoding of oriented gratings in fMRI","year":2013,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Voxel; Orientation (vector space); Decoding methods; Visual cortex; Spatial frequency; Artificial intelligence; Computer science; Image resolution; Pattern recognition (psychology); Computer vision; Optics; Mathematics; Psychology; Physics; Neuroscience; Telecommunications; Geometry","score_opus":0.03105089739600484,"score_gpt":0.3516475638355123,"score_spread":0.32059666643950746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064081873","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24994911,0.000032717988,0.74943984,0.00037510644,0.00007936541,0.00008911289,7.908985e-8,0.000008943102,0.000025733922],"genre_scores_gemma":[0.53854895,0.000011784964,0.46130416,0.00010518266,0.000014344485,0.0000020333928,1.2222247e-7,0.0000024302788,0.000010998054],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99908274,0.000037684957,0.00045925257,0.000070666356,0.0002577076,0.0000919614],"domain_scores_gemma":[0.9990216,0.00020505425,0.00041997273,0.000082871746,0.0002172812,0.000053231233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061543746,0.000045980076,0.00013319266,0.00019877951,0.000027525692,0.000050339775,0.000274038,0.000027895041,0.000017894747],"category_scores_gemma":[0.00030226688,0.000034549103,0.00005083585,0.00018800769,0.000019660321,0.0007577508,0.000049964932,0.00008937841,0.0000011016178],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014221714,0.00013623264,0.0035628974,0.000066034285,0.000010635649,0.0000060108923,0.002295864,0.000036067002,0.53448015,0.0011660623,0.0034872133,0.45473862],"study_design_scores_gemma":[0.0031729667,0.0027436058,0.0558485,0.0019648368,0.000016138645,0.00007831666,0.0016020981,0.107826695,0.8005652,0.02546493,0.00039424124,0.0003225031],"about_ca_topic_score_codex":0.00001249299,"about_ca_topic_score_gemma":0.0000011405591,"teacher_disagreement_score":0.45441613,"about_ca_system_score_codex":0.000029820136,"about_ca_system_score_gemma":0.000030135347,"threshold_uncertainty_score":0.14088714},"labels":[],"label_agreement":null},{"id":"W2064283421","doi":"10.1109/crv.2013.20","title":"Worst-Case Local Boundary Precision in Global Measures of Segmentation Reproducibility","year":2013,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Reproducibility; Segmentation; Boundary (topology); Sørensen–Dice coefficient; Artificial intelligence; Similarity (geometry); Computer science; Pattern recognition (psychology); Joint (building); Measure (data warehouse); Correlation coefficient; Coefficient of variation; Mathematics; Image segmentation; Computer vision; Statistics; Data mining; Image (mathematics); Mathematical analysis; Engineering","score_opus":0.03191276010900259,"score_gpt":0.31434973121432286,"score_spread":0.2824369711053203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064283421","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06373296,0.00005107279,0.9339024,0.00035721017,0.00009925703,0.00049304235,0.0000010412442,0.00014877394,0.0012142878],"genre_scores_gemma":[0.64028794,0.000004110493,0.35939297,0.00022606071,0.000008560683,0.00003512164,0.0000017008467,0.0000021646565,0.000041351243],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980966,0.00016548646,0.00048057135,0.00064780185,0.00045435937,0.00015519175],"domain_scores_gemma":[0.99852943,0.000079604186,0.000101288606,0.0010253264,0.00017528282,0.00008908017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015354882,0.00009128315,0.00014310333,0.00007003658,0.000033408447,0.00008208159,0.00035921053,0.000052533127,0.0001859322],"category_scores_gemma":[0.0004631173,0.00007587,0.000035753037,0.00049043493,0.00013494138,0.00095384516,0.00019816359,0.000079929196,0.000035651155],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000057440607,0.0001383217,0.0033506597,0.000016193686,0.0000034875104,0.00003082863,0.0002544809,0.000011258286,0.0037170849,0.00061736704,0.0020336695,0.9898209],"study_design_scores_gemma":[0.001262023,0.00040063204,0.051627215,0.00011105877,0.000008194413,0.00035208266,0.00078795804,0.03530468,0.8142894,0.095317245,0.00009356872,0.0004459663],"about_ca_topic_score_codex":0.0015824878,"about_ca_topic_score_gemma":0.00016669562,"teacher_disagreement_score":0.98937494,"about_ca_system_score_codex":0.0001489244,"about_ca_system_score_gemma":0.00008070853,"threshold_uncertainty_score":0.30938888},"labels":[],"label_agreement":null},{"id":"W2064718659","doi":"10.1109/embc.2012.6347189","title":"3D curve constrained deformable registration using a neuro-fuzzy transformation model","year":2012,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University; SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"","keywords":"Maxima and minima; Image registration; Artificial intelligence; Computer vision; Computer science; Point (geometry); Fuzzy logic; Energy (signal processing); Image (mathematics); Mathematics; Geometry","score_opus":0.04868882440095781,"score_gpt":0.304927561522867,"score_spread":0.2562387371219092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064718659","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002588132,0.0000109478315,0.97449523,0.00029729534,0.00009649459,0.00023209665,0.0000013753172,0.0003999552,0.021878494],"genre_scores_gemma":[0.4256868,0.000004965374,0.5732983,0.0008786783,0.000022598648,0.000009513227,0.0000063361485,0.0000042401225,0.00008857591],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893457,0.000048377216,0.00030323412,0.0001343296,0.00031032215,0.00026915182],"domain_scores_gemma":[0.99942935,0.00003287953,0.000097103264,0.00023371076,0.000068849244,0.00013813605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048171147,0.000099288576,0.00009163441,0.00008620958,0.000098177086,0.00011031562,0.00024381687,0.000052428946,0.000037894784],"category_scores_gemma":[0.000045967656,0.00008781852,0.00003506261,0.00021071575,0.000047943573,0.0038689466,0.000031286796,0.00009334457,0.000018724963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024088995,0.00045735555,0.00038803497,0.00017414773,0.000036356156,0.000005681629,0.011449623,0.010069403,0.19426091,0.4847949,0.0059695444,0.29236993],"study_design_scores_gemma":[0.00018295298,0.000020523275,0.00002222574,0.000007913582,0.0000045611514,0.000028429939,0.000035237365,0.9469765,0.050924875,0.0016311913,0.0000583223,0.000107297456],"about_ca_topic_score_codex":0.000043050582,"about_ca_topic_score_gemma":0.0000024974752,"teacher_disagreement_score":0.93690705,"about_ca_system_score_codex":0.000052042546,"about_ca_system_score_gemma":0.00007528371,"threshold_uncertainty_score":0.35811353},"labels":[],"label_agreement":null},{"id":"W2064793782","doi":"10.1016/j.imavis.2006.07.002","title":"Multiscale contour corner detection based on local natural scale and wavelet transform","year":2006,"lang":"en","type":"article","venue":"Image and Vision Computing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Wavelet transform; Scale (ratio); Maxima and minima; Wavelet; Artificial intelligence; Pattern recognition (psychology); Mathematics; Continuous wavelet transform; Natural (archaeology); Image (mathematics); Computer science; Algorithm; Computer vision; Discrete wavelet transform; Mathematical analysis; Geology","score_opus":0.0050359870955423445,"score_gpt":0.2727660583818327,"score_spread":0.26773007128629034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064793782","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0369549,0.00005805412,0.9616152,0.00040048052,0.00012828894,0.00017321284,0.000001177437,0.00023341549,0.0004352487],"genre_scores_gemma":[0.85338175,0.0000031307975,0.14587034,0.00062386453,0.00006518265,0.0000023378411,0.0000036534977,0.000007214762,0.000042500666],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886614,0.00006464909,0.00023904482,0.0003663473,0.00025540259,0.00020841352],"domain_scores_gemma":[0.99945045,0.00017842912,0.00006449146,0.00015579787,0.000064295426,0.00008654244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030828363,0.00013703643,0.00014653185,0.000100037396,0.00021362312,0.00022690654,0.00013878639,0.00005663916,0.0000064167307],"category_scores_gemma":[0.000019762872,0.00011540537,0.000035325847,0.00014357721,0.00013100619,0.0003630455,0.00006988575,0.00018635951,0.0000046914315],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010971366,0.00004244028,0.00006293975,0.000018487437,0.0000013010537,0.000012928889,0.00007230334,0.000017087672,0.021956475,0.00003122948,0.0003918051,0.977382],"study_design_scores_gemma":[0.00073422445,0.00015321237,0.0052759685,0.00006686757,0.000003087475,0.000018012073,0.000021242107,0.84736747,0.14591777,0.00018895893,0.00011784182,0.00013536493],"about_ca_topic_score_codex":0.00013158808,"about_ca_topic_score_gemma":0.000027934188,"teacher_disagreement_score":0.97724664,"about_ca_system_score_codex":0.000029045023,"about_ca_system_score_gemma":0.0000112560665,"threshold_uncertainty_score":0.47060943},"labels":[],"label_agreement":null},{"id":"W2064907489","doi":"10.1109/mwscas.2010.5548757","title":"Image registration using feature points, Zernike moments and an M-estimator","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Zernike polynomials; Artificial intelligence; Affine transformation; Feature (linguistics); Pattern recognition (psychology); Outlier; Image registration; Wavelet; Computer vision; Computer science; Feature extraction; Mathematics; Noise (video); Image (mathematics)","score_opus":0.0174037924557265,"score_gpt":0.32716359168806797,"score_spread":0.3097597992323415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064907489","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030685272,0.0000042214288,0.9664032,0.0010980163,0.00013817141,0.00013065497,0.0000010393371,0.00029924582,0.001240218],"genre_scores_gemma":[0.025448237,0.000002192418,0.97348607,0.00062523026,0.000046612207,0.0000042264296,0.000004755952,0.0000056524864,0.00037701125],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999237,0.000029958437,0.00011757168,0.00026151814,0.00022741672,0.00012653357],"domain_scores_gemma":[0.9993565,0.000018223795,0.00006512094,0.00034755986,0.00006145982,0.0001511182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024333307,0.00008370369,0.00007201202,0.000053707903,0.00009081501,0.0002932033,0.0002813708,0.00006310852,0.00007475987],"category_scores_gemma":[0.00007593705,0.000071267794,0.000012367284,0.00010373592,0.000072332696,0.0013655883,0.00009143188,0.0001609873,0.000008498106],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002120069,0.00006070011,0.00051722565,0.000015091965,0.0000042967554,0.000018040115,0.00018612664,1.3423002e-7,0.94482285,0.009072684,0.008240956,0.037059806],"study_design_scores_gemma":[0.00075193506,0.00019224446,0.00742642,0.000034624085,0.000013475721,0.00021197597,0.00009038024,0.35115913,0.62685776,0.011542488,0.001179796,0.00053976185],"about_ca_topic_score_codex":0.00004199536,"about_ca_topic_score_gemma":0.000011822081,"teacher_disagreement_score":0.351159,"about_ca_system_score_codex":0.000011718615,"about_ca_system_score_gemma":0.000032982818,"threshold_uncertainty_score":0.2906216},"labels":[],"label_agreement":null},{"id":"W2065221023","doi":"10.1117/12.2006465","title":"Interactive 3D segmentation of the prostate in magnetic resonance images using shape and local appearance similarity analysis","year":2013,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Similarity (geometry); Magnetic resonance imaging; Segmentation; Artificial intelligence; Computer science; Image segmentation; Computer vision; Pattern recognition (psychology); Nuclear magnetic resonance; Physics; Image (mathematics); Medicine; Radiology","score_opus":0.008784525807109924,"score_gpt":0.24268693155645435,"score_spread":0.23390240574934443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065221023","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98620826,0.0002158496,0.011506807,0.0011112557,0.00006278803,0.0007202899,0.000013657238,0.000039120823,0.00012199209],"genre_scores_gemma":[0.6245963,0.000078080266,0.37498137,0.00014280612,0.000031424916,0.000119070966,0.0000012975694,0.000016029528,0.00003361393],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981091,9.864184e-8,0.00064492226,0.00036647584,0.00062723673,0.00025216688],"domain_scores_gemma":[0.9982687,0.000101310856,0.00041039012,0.00007738361,0.0010732346,0.00006895158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053088105,0.00020218128,0.0003346675,0.00014599143,0.000053795808,0.00012685632,0.0009557416,0.00009157711,0.000010607767],"category_scores_gemma":[0.0003333202,0.0001523675,0.00026239431,0.00085246895,0.00039734264,0.0011648238,0.0003406451,0.0002655868,2.8715948e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004765518,0.00017825907,0.0068325624,0.0005014184,0.0002742373,1.32399e-7,0.0012818122,0.0002916104,0.9405032,0.027048381,0.0003365873,0.022704164],"study_design_scores_gemma":[0.00065651373,0.0001585546,0.032692526,0.00038553288,0.00011880095,0.00000567879,0.0008391604,0.57720536,0.3859758,0.0017211942,0.000023196215,0.00021769572],"about_ca_topic_score_codex":0.0000823443,"about_ca_topic_score_gemma":6.1550975e-7,"teacher_disagreement_score":0.5769137,"about_ca_system_score_codex":0.00013667962,"about_ca_system_score_gemma":0.000029608027,"threshold_uncertainty_score":0.62133664},"labels":[],"label_agreement":null},{"id":"W2065814531","doi":"10.1118/1.2031005","title":"Po‐Poster ‐ 26: Investigation of normalized mutual information for co‐registration of CT — MR images of permanent prostate implants","year":2005,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Cancer Foundation","funders":"","keywords":"Imaging phantom; Image registration; Prostate; Mutual information; Dosimetry; Image fusion; Medicine; Computer vision; Data set; Medical imaging; Computer science; Nuclear medicine; Artificial intelligence; Visualization; Image quality; Fiducial marker; Image (mathematics)","score_opus":0.017272118393818273,"score_gpt":0.29573682461907247,"score_spread":0.2784647062252542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065814531","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08517721,0.000010493121,0.91322666,0.0006813687,0.000058459686,0.00047313495,0.000047544934,0.00006555914,0.00025954895],"genre_scores_gemma":[0.97491264,0.000018241948,0.024393229,0.0004030943,0.00006033523,0.00003431132,0.0001553969,0.000006003906,0.000016772263],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980774,0.000054899316,0.000748636,0.00011608391,0.00086715975,0.00013583338],"domain_scores_gemma":[0.99872285,0.00013720129,0.0005728289,0.00021862461,0.00024178876,0.00010669487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005716108,0.00009810422,0.00022146458,0.000063363455,0.000026130412,0.00001714434,0.00032907957,0.000045232806,0.000017319258],"category_scores_gemma":[0.00021318485,0.00008552605,0.000061955245,0.00015994473,0.00023008439,0.0012269919,0.000059132013,0.000085956286,0.000005571227],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008293225,0.00022338504,0.0013711936,0.0009796076,0.000044182743,0.000001533152,0.0054226546,0.000023255112,0.039371498,0.0014106967,0.012470654,0.9385984],"study_design_scores_gemma":[0.0008065052,0.00018166227,0.00080857717,0.00009900846,0.000009872954,0.0000046618466,0.000024861605,0.002641451,0.993874,0.0013742811,0.000097079086,0.00007800165],"about_ca_topic_score_codex":0.000031365314,"about_ca_topic_score_gemma":0.0000022256188,"teacher_disagreement_score":0.9545025,"about_ca_system_score_codex":0.0000258954,"about_ca_system_score_gemma":0.00017349077,"threshold_uncertainty_score":0.34876513},"labels":[],"label_agreement":null},{"id":"W2066805929","doi":"10.1109/tmi.2013.2264467","title":"Uncertainty Driven Probabilistic Voxel Selection for Image Registration","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Voxel; Probabilistic logic; Computer science; Artificial intelligence; Robustness (evolution); Bayesian probability; Image registration; Selection (genetic algorithm); Sampling (signal processing); Transformation (genetics); Computer vision; Pattern recognition (psychology); Image (mathematics)","score_opus":0.013456291386160913,"score_gpt":0.28963898009042605,"score_spread":0.27618268870426516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066805929","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003988675,0.000006712232,0.9900459,0.007056034,0.00048575745,0.0009485155,0.000003738081,0.00073200255,0.0003224709],"genre_scores_gemma":[0.5148331,0.000025265042,0.47923416,0.0035875996,0.00017775407,0.0014757736,0.000012179876,0.00003592859,0.0006182203],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977625,0.00011776707,0.00045161747,0.00051343086,0.00078295264,0.0003717555],"domain_scores_gemma":[0.9985154,0.00037415794,0.000115883246,0.0003396853,0.00031430094,0.00034052247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049216417,0.00018902021,0.0001830752,0.0001795515,0.00027923207,0.0002671301,0.0005482294,0.00009315874,0.00053055433],"category_scores_gemma":[0.00023466644,0.00017344566,0.00011523064,0.00038904118,0.00020497739,0.0011257512,0.000003775612,0.0003808237,0.000108616696],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012812907,0.00038234034,0.000013320371,0.000098951554,0.000030836465,0.000008382298,0.00032825972,0.00096237333,0.02569809,0.00084491615,0.017498614,0.9541211],"study_design_scores_gemma":[0.0006031157,0.00010106178,0.00003264345,0.000089087625,0.000018127812,0.000049614413,0.000049244114,0.9510285,0.043548398,0.003938975,0.00031013187,0.00023106896],"about_ca_topic_score_codex":0.00020536383,"about_ca_topic_score_gemma":0.00003524504,"teacher_disagreement_score":0.95389,"about_ca_system_score_codex":0.0001752329,"about_ca_system_score_gemma":0.00020572895,"threshold_uncertainty_score":0.7072909},"labels":[],"label_agreement":null},{"id":"W2066993558","doi":"10.1118/1.2123350","title":"2D‐3D registration of coronary angiograms for cardiac procedure planning and guidance","year":2005,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":83,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"London Health Sciences Centre; Robarts Clinical Trials; Western University","funders":"Canadian Institutes of Health Research","keywords":"Offset (computer science); Image registration; Artificial intelligence; Computer vision; Patient registration; Cardiac cycle; Medicine; Computer science; Nuclear medicine; Cardiology","score_opus":0.021343320052363366,"score_gpt":0.309286989899083,"score_spread":0.2879436698467196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066993558","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023792293,0.00048233982,0.9953686,0.0010217162,0.0000712572,0.0002318542,0.0000035906746,0.00010981803,0.00033161527],"genre_scores_gemma":[0.6880669,0.000035521156,0.30993798,0.0012684575,0.00045390235,0.000083734885,0.000024032615,0.000010985771,0.00011847682],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99891376,0.00002374217,0.00022959175,0.00020667879,0.00048745517,0.00013877948],"domain_scores_gemma":[0.9993878,0.00012289449,0.00010919286,0.0001854395,0.000071609385,0.00012306323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035755103,0.00007863843,0.00014677114,0.000017971157,0.000043028605,0.000021883703,0.00026810955,0.00006404765,0.0000048370002],"category_scores_gemma":[0.00021514832,0.00007069085,0.000046061297,0.00013486708,0.00013428759,0.00028346563,0.000068006586,0.00008863617,0.000001202827],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006679347,0.000078208446,0.0015723777,0.000110269124,0.000016959457,0.0000027068843,0.0005669534,0.0000068068266,0.0009836543,0.002048993,0.017591972,0.9770144],"study_design_scores_gemma":[0.003374779,0.0015727356,0.02612634,0.0016213414,0.000117557945,0.00004574037,0.00019267532,0.1692116,0.7354887,0.04196518,0.018947393,0.0013359403],"about_ca_topic_score_codex":0.0000045771953,"about_ca_topic_score_gemma":5.8125653e-7,"teacher_disagreement_score":0.9756785,"about_ca_system_score_codex":0.000015932035,"about_ca_system_score_gemma":0.00008502521,"threshold_uncertainty_score":0.2882689},"labels":[],"label_agreement":null},{"id":"W2067177760","doi":"10.1117/12.480852","title":"MRI tissue segmentation incorporating a bias field modulated smoothness prior","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Voxel; Partial volume; Artificial intelligence; Computer science; Segmentation; Robustness (evolution); Smoothness; Probabilistic logic; Pattern recognition (psychology); Gaussian; Prior probability; Computer vision; Image segmentation; Bayesian probability; Mathematics; Physics","score_opus":0.01632667698321838,"score_gpt":0.2599358364264257,"score_spread":0.24360915944320732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067177760","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92908484,0.000047846064,0.06576859,0.002137169,0.0002855227,0.00076663273,0.000008084566,0.00024201075,0.001659284],"genre_scores_gemma":[0.15386134,0.00003714474,0.8451888,0.00035743514,0.00012952185,0.0001929838,0.0000059300282,0.000038480677,0.00018836591],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99742925,5.6373892e-8,0.0008305299,0.00047556713,0.00091004325,0.00035452322],"domain_scores_gemma":[0.9974581,0.00024786714,0.000584686,0.00009642905,0.0014633042,0.0001496026],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001034825,0.00029565956,0.00035720784,0.00013917906,0.00011587195,0.00024239476,0.0012946492,0.00019120649,0.000019884372],"category_scores_gemma":[0.0013370652,0.0002555456,0.0003037269,0.00060627656,0.00014345782,0.001187144,0.00019950715,0.00030378503,0.0000023071696],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014848634,0.00009664999,0.00020355308,0.00024098856,0.00012896235,2.0537294e-7,0.00028608923,0.000053948144,0.63562965,0.35768583,0.0023258636,0.0033334072],"study_design_scores_gemma":[0.0007138139,0.0003062218,0.00016045815,0.00020791576,0.000044596247,0.000016233218,0.0004923525,0.03393099,0.9591619,0.0040863967,0.00058305287,0.00029608226],"about_ca_topic_score_codex":0.00001301318,"about_ca_topic_score_gemma":1.3611374e-7,"teacher_disagreement_score":0.7794202,"about_ca_system_score_codex":0.00015283801,"about_ca_system_score_gemma":0.00005949877,"threshold_uncertainty_score":0.9999897},"labels":[],"label_agreement":null},{"id":"W2067248598","doi":"10.1109/34.908970","title":"Encoding visual information using anisotropic transformations","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Anisotropy; Artificial intelligence; Computer science; Entropy (arrow of time); Anisotropic diffusion; Information theory; Scale space; Encoding (memory); Computer vision; Pattern recognition (psychology); Statistical physics; Mathematics; Image processing; Image (mathematics); Physics; Optics","score_opus":0.024562285134394877,"score_gpt":0.30881098098332577,"score_spread":0.28424869584893087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067248598","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035036518,0.000018200177,0.9957392,0.0002109853,0.00010083453,0.00013357625,0.00001042739,0.00016777785,0.00011532955],"genre_scores_gemma":[0.9791055,0.0004030717,0.019792808,0.000636988,0.000009836146,0.000015864844,0.0000072179528,0.000004414268,0.00002434245],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987595,0.00006259162,0.00045352237,0.00021828621,0.00031485507,0.00019121489],"domain_scores_gemma":[0.99938506,0.000064822016,0.00010467586,0.00023917215,0.00008047673,0.00012577439],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018227755,0.00015492826,0.00019281353,0.00072057155,0.0002603136,0.00022517858,0.00027813896,0.000053553915,0.00023094002],"category_scores_gemma":[0.00000454844,0.00014175149,0.00014073424,0.001266694,0.000049110597,0.0014759401,0.0000035347484,0.00018787217,0.000029492925],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003372556,0.000067803296,0.00027872983,0.000010262349,0.000118670825,0.000003394743,0.000666142,0.006579863,0.00080084644,0.000058841644,0.0000032494063,0.9914088],"study_design_scores_gemma":[0.00009293174,0.00009264682,0.00030708904,0.000019292236,0.00019229078,0.000021542715,0.0001220202,0.7785471,0.22019686,0.00014213615,0.000061474246,0.00020463385],"about_ca_topic_score_codex":0.00049174967,"about_ca_topic_score_gemma":0.00015481457,"teacher_disagreement_score":0.9912042,"about_ca_system_score_codex":0.000051244428,"about_ca_system_score_gemma":0.00002102453,"threshold_uncertainty_score":0.5780458},"labels":[],"label_agreement":null},{"id":"W2067250060","doi":"10.1007/s00216-003-2169-6","title":"Three-dimensional ultrasound imaging and its use in quantifying organ and pathology volumes","year":2003,"lang":"en","type":"review","venue":"Analytical and Bioanalytical Chemistry","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"3D ultrasound; Segmentation; Ultrasound; Computer science; Modality (human–computer interaction); Artificial intelligence; Computer vision; Ultrasonography; Radiology; Medicine; Biomedical engineering","score_opus":0.06143315693441574,"score_gpt":0.33281550727929526,"score_spread":0.2713823503448795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067250060","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00062762946,0.9842859,0.014331768,0.0002435407,0.000037672206,0.00025082097,0.000018534158,0.00010123534,0.0001028881],"genre_scores_gemma":[0.0013517352,0.98623735,0.011563818,0.00044993093,0.00006460873,0.000017139731,0.00003421876,0.000033309578,0.0002478899],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99717134,0.000099006014,0.0007605453,0.0011252646,0.00035086178,0.0004929723],"domain_scores_gemma":[0.99789655,0.0010361492,0.0001497671,0.00035593362,0.00008523061,0.00047635095],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00051943376,0.00047483054,0.0012405791,0.0001492966,0.00009233536,0.00037592818,0.0003254695,0.0003736911,0.000078620345],"category_scores_gemma":[0.00093026675,0.00037141333,0.00013277578,0.00052122906,0.00055390724,0.00033749294,0.0004526362,0.00071808486,0.000007175655],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000075091834,0.00025022632,0.0023945312,0.009541448,0.000228929,0.001535858,0.00002737764,8.778816e-8,0.00026547365,0.004964767,0.00074311503,0.98004067],"study_design_scores_gemma":[0.007398285,0.0006137883,0.0029229182,0.04727188,0.010067359,0.040492553,0.00021822396,0.38713068,0.0043548243,0.020613156,0.4613413,0.01757504],"about_ca_topic_score_codex":0.000024459372,"about_ca_topic_score_gemma":0.000008744309,"teacher_disagreement_score":0.96246564,"about_ca_system_score_codex":0.000054089167,"about_ca_system_score_gemma":0.0001254018,"threshold_uncertainty_score":0.99987376},"labels":[],"label_agreement":null},{"id":"W2067624083","doi":"10.1007/s11548-012-0747-9","title":"Non-iterative partial view 3D ultrasound to CT registration in ultrasound-guided computer-assisted orthopedic surgery","year":2012,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Vancouver Coastal Health","funders":"","keywords":"Image registration; Computer science; Imaging phantom; Computer vision; Artificial intelligence; Projection (relational algebra); 3D ultrasound; Segmentation; Patient registration; Image-guided surgery; Orthopedic surgery; Ultrasound; Image (mathematics); Medicine; Nuclear medicine; Radiology; Algorithm; Surgery","score_opus":0.03307873929200904,"score_gpt":0.3128803235411991,"score_spread":0.27980158424919005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067624083","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24375345,0.00030952273,0.7503991,0.0012269969,0.0040621427,0.00014905138,0.0000041289572,0.000051745355,0.00004383992],"genre_scores_gemma":[0.8697403,0.00032969555,0.12431166,0.0038762807,0.0016456273,0.000017845237,0.00004269181,0.00001629801,0.00001962356],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99600583,0.00077215733,0.0016925099,0.00038185355,0.0006869144,0.0004607346],"domain_scores_gemma":[0.99364966,0.004267677,0.0008845797,0.00027192326,0.0005304215,0.00039572525],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0031823828,0.00030054685,0.0007994311,0.0010063169,0.00009661209,0.0002680535,0.00060528866,0.0001317392,0.00004366042],"category_scores_gemma":[0.00045554608,0.0002662292,0.0002670477,0.0004878228,0.00017224316,0.001438518,0.00013323061,0.00044723324,0.000010567531],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020539064,0.0009927677,0.40910074,0.000064609,0.0007167649,0.0016447509,0.0021121586,0.0002272208,0.0032596122,0.00059440173,0.100193575,0.480888],"study_design_scores_gemma":[0.0011464892,0.00027350575,0.9436763,0.0008551294,0.00004899985,0.03917423,0.000034619738,0.0055998145,0.003145965,0.00020161827,0.0050686304,0.0007746828],"about_ca_topic_score_codex":0.00002341193,"about_ca_topic_score_gemma":0.000006713427,"teacher_disagreement_score":0.6260875,"about_ca_system_score_codex":0.0001875121,"about_ca_system_score_gemma":0.0002906563,"threshold_uncertainty_score":0.999979},"labels":[],"label_agreement":null},{"id":"W2067678991","doi":"10.1109/iembs.2010.5627187","title":"Real time MRI prostate segmentation based on wavelet multiscale products flow tracking","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Manitoba; CancerCare Manitoba","funders":"","keywords":"Prostate cancer; Computer science; Wavelet; Segmentation; Prostate; Artificial intelligence; Magnetic resonance imaging; Data set; Image segmentation; Computer vision; Process (computing); Wavelet transform; Tracking (education); Noise (video); Pattern recognition (psychology); Radiology; Medicine; Cancer; Image (mathematics)","score_opus":0.010027902050596657,"score_gpt":0.2660971620080737,"score_spread":0.25606925995747704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067678991","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0077786627,5.903915e-7,0.9820527,0.0034314012,0.0003103763,0.0007146021,0.0000044022395,0.00096430245,0.0047429823],"genre_scores_gemma":[0.027838973,0.0000034415077,0.9689072,0.0011915507,0.000077153134,0.0000671478,0.00004069745,0.00001539634,0.0018584899],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99841094,0.00007550878,0.00026144838,0.0004891409,0.00050759356,0.00025539758],"domain_scores_gemma":[0.9989925,0.00009590989,0.00009388016,0.00054526783,0.00014552174,0.00012690481],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004971011,0.00014740738,0.00012093121,0.00012137125,0.00010091251,0.00019593518,0.00042400075,0.000063415755,0.00032566063],"category_scores_gemma":[0.00013605731,0.00012334768,0.000032206106,0.00029822625,0.000060860053,0.000656271,0.000064523985,0.00022738527,0.00026416965],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008712142,0.00014256434,0.00006847598,0.00001584995,0.0000032896294,0.000012589517,0.0003351839,0.000038858292,0.56717503,0.00018116737,0.008625367,0.4233929],"study_design_scores_gemma":[0.00035078265,0.00009734752,0.0006593974,0.000012536117,0.0000020329892,0.000002903322,0.000004400302,0.32575586,0.67265517,0.00013180512,0.00019545642,0.00013233205],"about_ca_topic_score_codex":0.000028731416,"about_ca_topic_score_gemma":0.000006814179,"teacher_disagreement_score":0.42326057,"about_ca_system_score_codex":0.00003527172,"about_ca_system_score_gemma":0.00008092869,"threshold_uncertainty_score":0.5029972},"labels":[],"label_agreement":null},{"id":"W2067788234","doi":"10.1109/tpami.2014.2307856","title":"A MultiScale Particle Filter Framework for Contour Detection","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Particle filter; Computer science; Artificial intelligence; Computer vision; Segmentation; Active contour model; Pattern recognition (psychology); Image segmentation; Filter (signal processing); Detector","score_opus":0.02312533191502177,"score_gpt":0.30286996460298604,"score_spread":0.2797446326879643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067788234","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011549111,0.000016036785,0.9979649,0.0003657314,0.00013944322,0.00018354562,0.000011776461,0.00015355975,0.000010079564],"genre_scores_gemma":[0.9129569,0.000035776207,0.08577727,0.001006267,0.000022821143,0.0001008265,0.0000014393937,0.0000073637666,0.00009134267],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988408,0.00008964614,0.00028988684,0.00039276888,0.00019171964,0.00019515048],"domain_scores_gemma":[0.9989553,0.0003871306,0.00008200496,0.00037118225,0.00006857613,0.00013576927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032252388,0.00013632084,0.00020641014,0.00017357929,0.00016363246,0.00012499516,0.00026244737,0.000064623186,0.000109418965],"category_scores_gemma":[0.000031636206,0.000117824886,0.00017289253,0.0004654837,0.000052383017,0.00020452072,0.00000362008,0.00016454712,0.000017537976],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007346048,0.00007960208,0.00010422401,0.000009644855,0.000104793406,5.556918e-7,0.00019435784,0.00066837226,0.0020871107,0.00013514973,0.000005333679,0.9966035],"study_design_scores_gemma":[0.0000620426,0.00012284068,0.00024017671,0.000009988866,0.00009794353,0.0000015458255,0.000010855849,0.41108996,0.58703655,0.001197118,0.000030597377,0.00010038057],"about_ca_topic_score_codex":0.00027079385,"about_ca_topic_score_gemma":0.00046564237,"teacher_disagreement_score":0.9965031,"about_ca_system_score_codex":0.000019329382,"about_ca_system_score_gemma":0.0000049163164,"threshold_uncertainty_score":0.48047593},"labels":[],"label_agreement":null},{"id":"W2067910980","doi":"10.1155/2012/431095","title":"Label Fusion Strategy Selection","year":2012,"lang":"en","type":"article","venue":"International Journal of Biomedical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Institut Universitaire en Santé Mentale de Québec","funders":"McGill University","keywords":"Computer science; Segmentation; Fusion; Pattern recognition (psychology); Selection (genetic algorithm); Data mining; Artificial intelligence; Voting","score_opus":0.01998383639041494,"score_gpt":0.342524628045837,"score_spread":0.32254079165542204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067910980","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055100312,0.00022681302,0.9881818,0.0034675975,0.0020167553,0.000034633602,9.409983e-7,0.00006608525,0.00049533544],"genre_scores_gemma":[0.7785018,0.00006119495,0.21876141,0.0014694463,0.0011401421,0.0000016773176,0.0000032965916,0.0000071743248,0.000053855914],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99799395,0.000074132506,0.00047792413,0.000099855504,0.0011408185,0.00021330192],"domain_scores_gemma":[0.9987855,0.00008741015,0.00031924402,0.00008070411,0.0004474801,0.0002796421],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088349835,0.00008619566,0.00011383651,0.00036857757,0.000039070725,0.00013989398,0.00086036633,0.00003694312,0.00019690901],"category_scores_gemma":[0.00023227763,0.00006991663,0.00006244848,0.00024176642,0.00008621183,0.0017595424,0.00015532339,0.00024851877,0.000029077864],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012012179,0.00029382893,0.002061473,0.0000035384599,0.00004214403,0.000055395012,0.00022961352,6.7757685e-7,0.09422583,0.0015099484,0.012005893,0.8895596],"study_design_scores_gemma":[0.010149963,0.0014515403,0.040446218,0.0011439031,0.00012767632,0.01708434,0.0008684371,0.14186725,0.64356285,0.020886848,0.12069415,0.0017168266],"about_ca_topic_score_codex":0.000009409565,"about_ca_topic_score_gemma":1.3663204e-7,"teacher_disagreement_score":0.88784283,"about_ca_system_score_codex":0.00013015485,"about_ca_system_score_gemma":0.00009674325,"threshold_uncertainty_score":0.28511176},"labels":[],"label_agreement":null},{"id":"W2068392268","doi":"10.1016/j.compbiomed.2004.06.011","title":"Cardiac video analysis using Hodge–Helmholtz field decomposition","year":2004,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Alberta","funders":"","keywords":"Smoothing; Cardiac Ventricle; Maxima and minima; Artificial intelligence; Computer science; Algorithm; Computer vision; Mathematics; Mathematical analysis; Ventricle","score_opus":0.01843129451475892,"score_gpt":0.37387972631882277,"score_spread":0.3554484318040638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068392268","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048264515,0.000477041,0.9486065,0.0019048561,0.00043936205,0.00010116792,5.425473e-7,0.00008057242,0.0001254295],"genre_scores_gemma":[0.5680464,0.00021534026,0.42746013,0.004130936,0.00011917871,0.0000062028707,0.000015349522,0.0000029057671,0.0000035479004],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990742,0.000107332824,0.00024653337,0.00031545185,0.00008387081,0.0001726143],"domain_scores_gemma":[0.9993661,0.00021135583,0.00006696977,0.00023260352,0.00003065593,0.00009229026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039943657,0.00010200902,0.0002977685,0.00038152217,0.000061888924,0.000012304036,0.00025564598,0.000098928336,0.000010318499],"category_scores_gemma":[0.000058808408,0.0000831288,0.000041202078,0.00061520666,0.00017906041,0.00012668101,0.00013048245,0.00014029582,0.0000013015474],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008087033,0.00024049493,0.08397764,0.00011341577,0.0009923517,0.0002588526,0.0076168757,0.0012712041,0.1203368,0.091979854,0.0032038153,0.6899278],"study_design_scores_gemma":[0.015836056,0.006553836,0.18550564,0.002084646,0.0015052729,0.0003378845,0.00074004865,0.2606637,0.19099873,0.3306127,0.002149727,0.0030117799],"about_ca_topic_score_codex":0.00021456776,"about_ca_topic_score_gemma":0.000008454437,"teacher_disagreement_score":0.68691605,"about_ca_system_score_codex":0.00006557644,"about_ca_system_score_gemma":0.000030041518,"threshold_uncertainty_score":0.3389894},"labels":[],"label_agreement":null},{"id":"W2068647418","doi":"10.1109/coginf.2007.4341910","title":"A Novel Segmentation and Navigation Method for Polyps Detection using Mathematical Morphology and Active Contour Models","year":2007,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Sinai Hospital; Toronto Metropolitan University","funders":"","keywords":"Contouring; Mathematical morphology; Artificial intelligence; Segmentation; Computer science; Image segmentation; Active contour model; Computer vision; Maxima and minima; Image texture; Curvature; Pattern recognition (psychology); Texture (cosmology); Level set (data structures); Set (abstract data type); Image processing; Image (mathematics); Mathematics; Computer graphics (images)","score_opus":0.05281923023837278,"score_gpt":0.3746117511782784,"score_spread":0.3217925209399056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068647418","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02973493,0.000009240829,0.9694723,0.00009865651,0.000038414237,0.00046981426,0.0000019148765,0.00009489307,0.00007982234],"genre_scores_gemma":[0.13906147,0.0000018249287,0.86057776,0.00029172323,0.000017958953,0.000022237946,0.0000023777445,0.000005467561,0.000019151534],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99921244,0.000035485144,0.00021613987,0.00025134842,0.000137646,0.00014696083],"domain_scores_gemma":[0.9993248,0.00031623882,0.00009500668,0.00009553825,0.00008552548,0.00008289526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007401584,0.0000846576,0.000116051,0.0000954792,0.00008783742,0.000059390517,0.000071074224,0.00007345318,0.0000044200983],"category_scores_gemma":[0.000057849902,0.00007687303,0.000017195292,0.00010569412,0.00004794315,0.0007007416,0.00005612969,0.0000611412,3.5558412e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015668118,0.000023045712,0.000002419382,0.000019295756,0.000007855613,8.324928e-7,0.00060457573,0.000009273004,0.8069998,0.008277528,0.0000029590067,0.18403676],"study_design_scores_gemma":[0.00035313723,0.000058621954,0.00006835114,0.000009070058,0.000008244249,0.00009362811,0.00019046759,0.45217723,0.5245998,0.02238304,4.9248115e-7,0.00005792642],"about_ca_topic_score_codex":0.000071583665,"about_ca_topic_score_gemma":0.0000068603485,"teacher_disagreement_score":0.45216796,"about_ca_system_score_codex":0.000053931417,"about_ca_system_score_gemma":0.00001406043,"threshold_uncertainty_score":0.31347913},"labels":[],"label_agreement":null},{"id":"W2069031798","doi":"10.1109/isspa.2012.6310542","title":"Multi scale classification approach for coronary artery detection from X-ray angiography","year":2012,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Hessian matrix; Robustness (evolution); False positive paradox; Angiography; Artificial intelligence; Computer science; Feature extraction; Coronary arteries; Radiology; Support vector machine; Pattern recognition (psychology); Computer vision; Artery; Medicine; Mathematics; Internal medicine","score_opus":0.04363235645961141,"score_gpt":0.28267009442318236,"score_spread":0.23903773796357095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069031798","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024858115,0.00009596344,0.9957527,0.000079972364,0.00022360019,0.00045594297,0.000004726762,0.00053452706,0.00036676097],"genre_scores_gemma":[0.4100463,0.0000030667284,0.5893344,0.00031529582,0.000066427136,0.00015735462,0.000029045823,0.000005103606,0.00004297694],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990811,0.000059081354,0.00019177758,0.00026801173,0.00018839694,0.00021164912],"domain_scores_gemma":[0.99931943,0.0000822367,0.00007250123,0.0003503558,0.000043143566,0.00013231678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023958822,0.00009445262,0.00008968056,0.00012563434,0.00008581589,0.000064138854,0.00031528942,0.00007498305,0.000025262789],"category_scores_gemma":[0.00001922502,0.00008302542,0.00011465055,0.00019822692,0.000046627323,0.0009301173,0.00005054326,0.000062848034,0.000014669875],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010810874,0.0004693988,0.0057630804,0.000013082669,0.00003901421,2.1128878e-7,0.00085283763,0.0000018531496,0.5016692,0.0001861976,0.0028199265,0.48817438],"study_design_scores_gemma":[0.0007986745,0.000112522655,0.19200565,0.0000080291575,0.00003085631,0.000006603651,0.00029664015,0.40395182,0.4013124,0.0005933058,0.00053227314,0.00035122328],"about_ca_topic_score_codex":0.000025715919,"about_ca_topic_score_gemma":0.0000019758209,"teacher_disagreement_score":0.48782316,"about_ca_system_score_codex":0.000020455072,"about_ca_system_score_gemma":0.000008443864,"threshold_uncertainty_score":0.33856785},"labels":[],"label_agreement":null},{"id":"W2069130385","doi":"10.1016/j.mcm.2004.02.035","title":"Progressive transmission of images: Adaptive best strategies","year":2005,"lang":"en","type":"article","venue":"Mathematical and Computer Modelling","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Slicing; Computer science; Disjoint sets; A priori and a posteriori; Sequence (biology); Algorithm; Set (abstract data type); Node (physics); Image (mathematics); Remainder; Transmission (telecommunications); Process (computing); Artificial intelligence; Mathematics; Computer graphics (images); Combinatorics","score_opus":0.03124701582775341,"score_gpt":0.27666481458240727,"score_spread":0.24541779875465386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069130385","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019089967,0.00024841473,0.9966967,0.00019716227,0.000012129284,0.00016017904,6.205034e-7,0.000116698924,0.0006590857],"genre_scores_gemma":[0.1496445,0.000021727092,0.85017866,0.00006617849,0.000040870895,0.000011179308,4.9613027e-7,0.000005175504,0.000031214055],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990169,0.00003569385,0.00030641578,0.00023379721,0.0002523857,0.00015480648],"domain_scores_gemma":[0.99947184,0.00010101736,0.00007873087,0.0001734554,0.00007149578,0.00010347739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015162225,0.000118133285,0.00019941371,0.00005449764,0.00004958739,0.00010574834,0.00028416808,0.000045294466,0.000025317175],"category_scores_gemma":[0.0000019878064,0.000088803885,0.000043667733,0.00009324722,0.00010347334,0.0005412836,0.0001035064,0.00010045679,0.000009905843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008492942,0.00035267972,0.000001051312,0.00018150048,0.000023254468,0.000016065482,0.0034738507,0.007094088,0.00068245427,0.23465057,0.00010821215,0.7534078],"study_design_scores_gemma":[0.00013076022,0.00012562194,8.3183113e-7,0.0001978194,0.000006339335,0.000018343353,0.0000468012,0.91994005,0.016296994,0.063119166,0.00002243719,0.00009481807],"about_ca_topic_score_codex":0.0000017785726,"about_ca_topic_score_gemma":2.5390724e-8,"teacher_disagreement_score":0.91284597,"about_ca_system_score_codex":0.000007993362,"about_ca_system_score_gemma":0.00002475687,"threshold_uncertainty_score":0.3621317},"labels":[],"label_agreement":null},{"id":"W2069397935","doi":"10.1118/1.1543153","title":"Automatic quantitative low contrast analysis of digital chest phantom radiographs","year":2003,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Imaging phantom; Densitometer; Contrast (vision); Computer science; Radiography; Computer vision; Artificial intelligence; Noise (video); Pixel; Digital radiography; Computation; Medical imaging; Nuclear medicine; Algorithm; Radiology; Image (mathematics); Medicine; Optics; Physics","score_opus":0.017946303969109127,"score_gpt":0.3004875998162913,"score_spread":0.2825412958471822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069397935","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011105615,0.000060505074,0.9871525,0.00013087073,0.000095029776,0.00012720205,0.000010188137,0.00019335195,0.0011247044],"genre_scores_gemma":[0.95991325,0.000019881887,0.039633185,0.00035502252,0.000017995271,0.000015158025,0.000018982479,0.000008064552,0.000018445464],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977875,0.00010802845,0.0004378717,0.0002947267,0.0011296836,0.00024221487],"domain_scores_gemma":[0.9984869,0.0005176604,0.00019092261,0.0003997906,0.00012262713,0.00028213084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004479173,0.00014414123,0.00043831632,0.00019881786,0.000042152813,0.000077574754,0.0006380854,0.00006962319,0.00022279209],"category_scores_gemma":[0.0009060323,0.00012191037,0.00022887732,0.0025030086,0.00037491389,0.0006395291,0.00006420365,0.00016384263,0.00001933604],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000068429895,0.0019032701,0.006978453,0.00019513413,0.0024249281,0.00008908919,0.0030568854,0.000022528573,0.0015741719,0.10061055,0.0025758385,0.8805623],"study_design_scores_gemma":[0.003160314,0.0007898371,0.009380163,0.00039980272,0.0012224967,0.000016690614,0.00037768405,0.49442035,0.41488686,0.0737179,0.00029882282,0.0013290594],"about_ca_topic_score_codex":0.00000835526,"about_ca_topic_score_gemma":0.0000015829816,"teacher_disagreement_score":0.94880766,"about_ca_system_score_codex":0.000024687042,"about_ca_system_score_gemma":0.00013483725,"threshold_uncertainty_score":0.4971361},"labels":[],"label_agreement":null},{"id":"W2069444915","doi":"10.1016/j.patcog.2004.04.015","title":"Unsupervised image segmentation using a simple MRF model with a new implementation scheme","year":2004,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Segmentation; Image segmentation; Computer science; Pattern recognition (psychology); Markov random field; Scale-space segmentation; Weighting; Segmentation-based object categorization; Computer vision","score_opus":0.05403315852528015,"score_gpt":0.32822104637894495,"score_spread":0.27418788785366477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069444915","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21805704,0.000005058121,0.7807952,0.00029389662,0.000032450993,0.00046480584,0.000015264577,0.00028928232,0.00004700134],"genre_scores_gemma":[0.19409458,0.000009825487,0.803962,0.0014796926,0.000060645587,0.00006681831,0.00029635493,0.000022830867,0.0000072616886],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984312,0.000046738278,0.00033770548,0.00042843696,0.00046981263,0.00028612238],"domain_scores_gemma":[0.9992091,0.00002044037,0.0001841666,0.00025913556,0.00016189959,0.00016527534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018530458,0.00018687565,0.0001399113,0.00017552833,0.00012460457,0.00023237494,0.00024825032,0.0000524619,0.00018263304],"category_scores_gemma":[0.0000138832975,0.00018010235,0.000045242934,0.00033860863,0.000033165536,0.001816134,0.000073761614,0.00011743568,0.00007233351],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017959257,0.00012309385,0.00091623707,0.00005658907,0.00003515767,0.000018132281,0.0019739007,0.00030868125,0.28323463,0.00003094679,0.00025599217,0.71302867],"study_design_scores_gemma":[0.004300486,0.00028363065,0.00065777183,0.00013930076,0.000046190144,0.000055680222,0.0005650858,0.14985085,0.8257972,0.017763324,0.000003534962,0.00053695653],"about_ca_topic_score_codex":0.00055866694,"about_ca_topic_score_gemma":0.00009619911,"teacher_disagreement_score":0.7124917,"about_ca_system_score_codex":0.00021863228,"about_ca_system_score_gemma":0.00020975387,"threshold_uncertainty_score":0.73443604},"labels":[],"label_agreement":null},{"id":"W2070557604","doi":"10.1117/12.593948","title":"Computing the thickness of the ventricular heart wall from 3D MRI images","year":2005,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Ventricle; Voxel; Segmentation; Image segmentation; Artificial intelligence; Medicine; Cardiology; Computer science","score_opus":0.008972983975757002,"score_gpt":0.23804427693214156,"score_spread":0.22907129295638456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070557604","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9638117,0.0002008499,0.016532432,0.017816786,0.00025824143,0.00063924823,0.000020623258,0.0001367837,0.000583331],"genre_scores_gemma":[0.29042432,0.00005940126,0.7078727,0.0009984995,0.00044472053,0.000066043795,0.0000033034141,0.000036434925,0.000094559284],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99736047,8.922548e-8,0.0007631018,0.00038716264,0.0011458967,0.0003433108],"domain_scores_gemma":[0.9975496,0.00035292163,0.0005281424,0.00014895662,0.0013284319,0.00009198037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010491135,0.00025912203,0.00035239544,0.000060574406,0.00014418755,0.00017304569,0.0028939568,0.00013857448,0.000013211766],"category_scores_gemma":[0.0006225142,0.00015912463,0.0005784342,0.00047504043,0.0003827047,0.00067979784,0.00063558755,0.00042257013,0.0000018764408],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002614487,0.00024634087,0.0008515067,0.00029155877,0.00043844676,1.7993122e-7,0.0011432328,0.00044814474,0.717381,0.25747338,0.017495237,0.004204857],"study_design_scores_gemma":[0.0007593301,0.00012725117,0.0029439256,0.00045897774,0.000112600654,0.000025066274,0.0005445344,0.15383133,0.83459014,0.0022695903,0.004013838,0.00032340043],"about_ca_topic_score_codex":0.00003319226,"about_ca_topic_score_gemma":1.5547269e-7,"teacher_disagreement_score":0.69134027,"about_ca_system_score_codex":0.00012372149,"about_ca_system_score_gemma":0.00004750353,"threshold_uncertainty_score":0.6488914},"labels":[],"label_agreement":null},{"id":"W2070797097","doi":"10.1155/2010/248393","title":"Wavelet‐Based Image Registration and Segmentation Framework for the QuantitativeEvaluation of Hydrocephalus","year":2010,"lang":"en","type":"article","venue":"International Journal of Biomedical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; University of British Columbia; Saint Mary's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Hydrocephalus; Segmentation; Artificial intelligence; Wavelet; Image registration; Wavelet transform; Volume (thermodynamics); Computer vision; Pattern recognition (psychology); Image (mathematics); Data mining; Radiology; Medicine","score_opus":0.020864085572435978,"score_gpt":0.3757447977806999,"score_spread":0.35488071220826395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070797097","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005022557,0.00007190654,0.97874737,0.014884571,0.0010588544,0.00016512029,0.000006055111,0.000018899882,0.000024679734],"genre_scores_gemma":[0.3542802,0.000021464264,0.6449727,0.00052577595,0.0001780555,0.000007575968,0.000006271281,0.000004586949,0.0000033757449],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998118,0.00006371277,0.0005970023,0.00013779734,0.0009820245,0.00010150791],"domain_scores_gemma":[0.996918,0.0011840978,0.00074288726,0.00013617778,0.00093485956,0.00008392552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016683127,0.00008545523,0.00012473232,0.00023547771,0.000061811515,0.00015448868,0.00066743995,0.000044354823,0.000037102065],"category_scores_gemma":[0.0021461423,0.000060852908,0.00007673109,0.00015789,0.00033813913,0.0007397257,0.00005694561,0.00025216804,8.594813e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045132274,0.000114899136,0.00015429778,0.000021478723,0.00006242776,0.000011684228,0.00060624786,0.0000020077714,0.46094975,0.014750752,0.0009117993,0.5223695],"study_design_scores_gemma":[0.0024847153,0.00034212903,0.0043001836,0.0002910507,0.000078910205,0.0002445539,0.00053998147,0.38225737,0.5293734,0.07897915,0.0008788228,0.00022974648],"about_ca_topic_score_codex":0.000009949268,"about_ca_topic_score_gemma":0.000001229398,"teacher_disagreement_score":0.5221398,"about_ca_system_score_codex":0.00003459544,"about_ca_system_score_gemma":0.0001586031,"threshold_uncertainty_score":0.2569288},"labels":[],"label_agreement":null},{"id":"W2071289785","doi":"10.2316/journal.206.2014.4.206-4086","title":"A SPARSE BASED RAIN REMOVAL ALGORITHM FOR IMAGE SEQUENCES","year":2014,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Image (mathematics); Computer science; Artificial intelligence; Algorithm; Pattern recognition (psychology); Computer vision","score_opus":0.015402499606297962,"score_gpt":0.30038902350213015,"score_spread":0.2849865238958322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071289785","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041728216,0.000019378745,0.9950043,0.0038745732,0.00049481017,0.000071007795,0.000002522874,0.00003830307,0.000077853525],"genre_scores_gemma":[0.023934692,0.000013985424,0.97521377,0.0006196831,0.00018057674,0.0000024361873,0.0000047041135,0.0000039634774,0.000026219192],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903923,0.000051886258,0.0003302927,0.0000937454,0.00040991246,0.000074950476],"domain_scores_gemma":[0.99881357,0.00018304911,0.00034322884,0.000069653455,0.00052871526,0.00006181101],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075156236,0.00006348456,0.00010208519,0.00016703177,0.000036215453,0.00021760508,0.0003817402,0.000030237856,0.000007123612],"category_scores_gemma":[0.00026757363,0.00005370061,0.000052870644,0.000060606155,0.000040428957,0.00057347224,0.00003492634,0.000062353,0.0000014216868],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046009595,0.00004388153,0.000016432552,0.000008833968,0.000025511372,0.000022235636,0.00008922954,0.0008307023,0.005506948,0.008032698,0.00086875533,0.9845502],"study_design_scores_gemma":[0.0005480799,0.00012440656,0.00018144604,0.000060277256,0.0000066869334,0.00014887295,0.000009249782,0.9764096,0.012021785,0.009858957,0.00056776445,0.000062877414],"about_ca_topic_score_codex":0.0000025960999,"about_ca_topic_score_gemma":3.6994763e-7,"teacher_disagreement_score":0.9844873,"about_ca_system_score_codex":0.000037878464,"about_ca_system_score_gemma":0.00006064716,"threshold_uncertainty_score":0.21898472},"labels":[],"label_agreement":null},{"id":"W2071389696","doi":"10.1016/j.advwatres.2009.08.005","title":"Statistical fusion of two-scale images of porous media","year":2009,"lang":"en","type":"article","venue":"Advances in Water Resources","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Porous medium; Scale (ratio); Computer science; Ground truth; Resolution (logic); Artificial intelligence; Sampling (signal processing); Matching (statistics); Image resolution; Data mining; Pattern recognition (psychology); Computer vision; Porosity; Geology; Mathematics; Statistics; Cartography; Geography","score_opus":0.007025446060351246,"score_gpt":0.2905324954543441,"score_spread":0.28350704939399285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071389696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15090589,0.00081179943,0.84549993,0.00026129364,0.000085000975,0.00013858627,0.000006874076,0.00009054704,0.0022000966],"genre_scores_gemma":[0.7063654,0.00016307176,0.2933013,0.00011846007,0.000017842207,0.000003668192,0.0000049438595,0.0000033728793,0.000021916594],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998679,0.000088451314,0.0004147075,0.00022750185,0.00039249018,0.00019786763],"domain_scores_gemma":[0.9993589,0.0001512838,0.00010098188,0.00027908076,0.000051858857,0.00005791406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029762374,0.00009495077,0.00022041924,0.0001354727,0.00001880117,0.000015885225,0.000540589,0.00003057051,0.00005249853],"category_scores_gemma":[0.000067190005,0.00006555397,0.000024226472,0.00015867753,0.00019216126,0.00048018422,0.00011937715,0.00008961593,0.0000042939128],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033890454,0.0002348598,0.0030720222,0.000068345544,0.0000035022422,0.000045535813,0.0074273692,0.00009187265,0.14430298,0.002015316,0.00015353053,0.84255075],"study_design_scores_gemma":[0.00038355662,0.00019976217,0.0034357773,0.000076489116,0.0000030687902,0.000005790349,0.00008171767,0.0003470394,0.9683163,0.02636209,0.0006789249,0.000109463836],"about_ca_topic_score_codex":0.000035497476,"about_ca_topic_score_gemma":0.000010421474,"teacher_disagreement_score":0.8424413,"about_ca_system_score_codex":0.000012505452,"about_ca_system_score_gemma":0.0000076825,"threshold_uncertainty_score":0.26732132},"labels":[],"label_agreement":null},{"id":"W2071969132","doi":"10.1109/iranianmvip.2011.6121577","title":"Brain Tissue Segmentation by FCM and Dempster-Shafer Theory","year":2011,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McGill University","keywords":"Artificial intelligence; Dempster–Shafer theory; Segmentation; Fuzzy logic; Pattern recognition (psychology); Similarity (geometry); Computer science; Image segmentation; Possibility theory; Salient; Noise (video); Computer vision; Fuzzy set; Image (mathematics)","score_opus":0.019915115535035735,"score_gpt":0.29088087229793247,"score_spread":0.27096575676289675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071969132","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020383215,0.000061601764,0.987013,0.0005443105,0.000055958113,0.00016076674,0.0000010257232,0.0002909101,0.009834135],"genre_scores_gemma":[0.057563458,0.0000255738,0.9226375,0.010451684,0.000020448615,0.000042802574,0.000007580046,0.000010839134,0.009240084],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992282,0.00010701347,0.00014786188,0.00022657764,0.00016280009,0.00012757005],"domain_scores_gemma":[0.9995209,0.000092191316,0.000041957202,0.000220726,0.000025673353,0.00009856352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038169706,0.00007929978,0.0000707945,0.00004694076,0.000045067845,0.000060838596,0.00028987127,0.000038485792,0.0007498175],"category_scores_gemma":[0.000043816777,0.00006542911,0.000010567658,0.000096626114,0.00006257912,0.0005703751,0.0001336248,0.000053721014,0.000065900385],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005088382,0.0000602817,0.00026055824,0.000011804576,0.000010797526,0.000006951083,0.0028277894,1.4744559e-8,0.0890794,0.028298091,0.078013204,0.801426],"study_design_scores_gemma":[0.00025646866,0.00012946388,0.00069112936,0.000008066334,0.0000036335794,0.000011268864,0.00012379013,0.0003777562,0.97908175,0.01790913,0.0012562939,0.00015127903],"about_ca_topic_score_codex":0.000032280033,"about_ca_topic_score_gemma":0.0000020845048,"teacher_disagreement_score":0.8900023,"about_ca_system_score_codex":0.0000121851135,"about_ca_system_score_gemma":0.00001052281,"threshold_uncertainty_score":0.8209976},"labels":[],"label_agreement":null},{"id":"W2073211476","doi":"10.1109/iembs.2011.6091213","title":"GPU implementation of a deformable 3D image registration algorithm","year":2011,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; CUDA; Image registration; Graphics processing unit; Speedup; Interpolation (computer graphics); Computer vision; Imaging phantom; Image resolution; Graphics; Algorithm; Displacement (psychology); Medical imaging; Image processing; Artificial intelligence; Image (mathematics); Computer graphics (images); Parallel computing","score_opus":0.026578121992628233,"score_gpt":0.30655161057983893,"score_spread":0.2799734885872107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073211476","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041907086,0.000004992111,0.9846625,0.00006586268,0.000052647276,0.00016549362,0.0000016252072,0.00018303367,0.0144448],"genre_scores_gemma":[0.01645708,0.000009645942,0.98304343,0.00020916632,0.00001228698,0.000020648535,0.000006778084,0.000003397766,0.000237545],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99916536,0.000030335763,0.00028997668,0.00014962828,0.00024065217,0.00012406586],"domain_scores_gemma":[0.9994139,0.00001190545,0.00014363682,0.00026694973,0.00011156775,0.000052027604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029124238,0.000062068,0.00007623321,0.00007401424,0.00003105602,0.000030366165,0.00030619343,0.000024461277,0.00066584774],"category_scores_gemma":[0.000010883639,0.000053816824,0.000026270474,0.0001765313,0.000041377207,0.0011419373,0.000070840964,0.000039480503,0.000026024029],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014192443,0.000051046052,0.00012584153,0.000014464615,0.000008258377,0.0000039760253,0.0014785038,5.98015e-8,0.014513778,0.00885798,0.0034409985,0.9715037],"study_design_scores_gemma":[0.00024813184,0.00014340626,0.0013792872,0.0000057695834,0.0000040470945,0.00000765768,0.00028959976,0.009776218,0.98487645,0.0030823813,0.000100292695,0.00008678364],"about_ca_topic_score_codex":0.000612578,"about_ca_topic_score_gemma":0.000022162842,"teacher_disagreement_score":0.9714169,"about_ca_system_score_codex":0.000022032353,"about_ca_system_score_gemma":0.0000514574,"threshold_uncertainty_score":0.7290566},"labels":[],"label_agreement":null},{"id":"W2074562992","doi":"10.1117/12.473100","title":"Automatic prostate boundary detection in ultrasound images using multiresolution deformable models and fuzzy logic","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Initialization; Artificial intelligence; Computer vision; Fuzzy logic; Image segmentation; Edge detection; Segmentation; Process (computing); Pattern recognition (psychology); Image processing; Image (mathematics)","score_opus":0.015469731983675431,"score_gpt":0.2494085703033587,"score_spread":0.23393883831968326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074562992","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9587498,0.00014052627,0.039430983,0.00024396247,0.000118629774,0.0006169185,0.000007152375,0.00014655059,0.0005454804],"genre_scores_gemma":[0.39114338,0.00010152447,0.6085077,0.00007326248,0.00003433608,0.00009247048,0.0000015422158,0.000021439057,0.00002436203],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804056,6.308699e-8,0.000640932,0.00037825984,0.0005727125,0.0003674686],"domain_scores_gemma":[0.99867254,0.00012348872,0.00032917672,0.000060671915,0.0007095055,0.00010459778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001002732,0.00024074085,0.00029148525,0.00016520299,0.000111884656,0.00024220913,0.00058327,0.00013631102,0.0000029476114],"category_scores_gemma":[0.0006440797,0.00020763469,0.00018719977,0.00043467386,0.00023329364,0.0019976175,0.00013244436,0.00026619967,3.9421752e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015724176,0.00009466061,0.00015509572,0.00042024313,0.00007284183,1.7608536e-7,0.00043028168,0.0003592915,0.8054122,0.19035836,0.000111675385,0.00256947],"study_design_scores_gemma":[0.0007612551,0.00016683647,0.0003699666,0.00023530758,0.000033706296,0.000055340708,0.00046209115,0.5381433,0.4373207,0.022154488,0.000041898104,0.00025512493],"about_ca_topic_score_codex":0.00002295956,"about_ca_topic_score_gemma":2.7923997e-7,"teacher_disagreement_score":0.5690767,"about_ca_system_score_codex":0.00025421905,"about_ca_system_score_gemma":0.000047239235,"threshold_uncertainty_score":0.84670967},"labels":[],"label_agreement":null},{"id":"W2075391096","doi":"10.1117/12.2043192","title":"Smoothness parameter tuning for generalized hierarchical continuous max-flow segmentation","year":2014,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials","funders":"","keywords":"Segmentation; Smoothing; Computer science; Estimator; Mathematical optimization; Regularization (linguistics); Image segmentation; Computation; Smoothness; Algorithm; Iterative method; Simplex; Artificial intelligence; Mathematics; Computer vision; Statistics","score_opus":0.01333354579661769,"score_gpt":0.2522396340066367,"score_spread":0.238906088210019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075391096","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7043633,0.000022398975,0.29149824,0.0023916825,0.00028298402,0.00080880366,0.000019387982,0.00018257154,0.00043061466],"genre_scores_gemma":[0.03589792,0.000028465261,0.9624746,0.0005484905,0.00034040457,0.00049976306,0.000022721202,0.00004629189,0.00014133207],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973529,6.710988e-8,0.0008242077,0.0005355084,0.00083175726,0.0004555863],"domain_scores_gemma":[0.99731123,0.0004832659,0.00046540008,0.00010094353,0.0014610816,0.00017808977],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011922076,0.00031670072,0.0004706414,0.00013427254,0.000116752475,0.00027740255,0.0015654217,0.00018886157,0.000013014755],"category_scores_gemma":[0.0013674775,0.00026742235,0.0005691865,0.0003156747,0.0002371104,0.0008959773,0.0002510002,0.00027657766,0.0000010691437],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059397316,0.00010392879,0.00010543006,0.00037233127,0.0001958581,6.0750544e-8,0.00029690092,0.00005197975,0.5757482,0.40687186,0.003949278,0.012244795],"study_design_scores_gemma":[0.0020710211,0.0005213587,0.00020605372,0.00023232678,0.00011360955,0.000013366034,0.00025452406,0.5316448,0.4501894,0.012394503,0.0019155373,0.00044343402],"about_ca_topic_score_codex":0.000006005875,"about_ca_topic_score_gemma":5.648965e-8,"teacher_disagreement_score":0.67097634,"about_ca_system_score_codex":0.00012773629,"about_ca_system_score_gemma":0.000033455373,"threshold_uncertainty_score":0.9999778},"labels":[],"label_agreement":null},{"id":"W2075790599","doi":"10.1016/j.brachy.2007.02.031","title":"3D prostate segmentation in TRUS images using image warping and ellipsoid fitting","year":2007,"lang":"en","type":"article","venue":"Brachytherapy","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Cancer Agency; University of British Columbia","funders":"","keywords":"Segmentation; Medicine; Artificial intelligence; Image warping; Computer vision; Volume (thermodynamics); Image segmentation; Prostate; Repeatability; Scale-space segmentation; Computer science; Mathematics; Statistics; Cancer","score_opus":0.014948240267021379,"score_gpt":0.31278748067709383,"score_spread":0.2978392404100725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075790599","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2157869,0.0002736724,0.78309035,0.00016716383,0.00007610987,0.00029889887,7.7784125e-7,0.00016893963,0.00013720458],"genre_scores_gemma":[0.07022647,0.00018036316,0.92852813,0.00088234,0.00006009457,0.0000133086505,0.0000034887616,0.000017605338,0.0000881766],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986017,0.000083495026,0.00038121425,0.0003524523,0.0002578597,0.00032331087],"domain_scores_gemma":[0.9993717,0.0001332936,0.00014962709,0.00020828084,0.000050578372,0.00008652822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011158573,0.00013878965,0.00014768579,0.00022114944,0.00010259763,0.00018404816,0.00020345666,0.000048492257,0.000016284916],"category_scores_gemma":[0.00004698276,0.00013776803,0.00002408682,0.00038546947,0.000075193835,0.0011469438,0.00006914848,0.0001507504,0.000004382602],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009094621,0.00003393566,0.0030502828,0.000022452567,0.0000046760174,0.00006669097,0.0018035392,0.0000075306384,0.34083435,0.00007127616,0.000034875866,0.65406126],"study_design_scores_gemma":[0.0015278248,0.00014773705,0.015932914,0.00014269499,0.0000042764705,0.00011050256,0.0002894511,0.031763725,0.9478289,0.001692471,0.00014829676,0.00041124696],"about_ca_topic_score_codex":0.00007399879,"about_ca_topic_score_gemma":0.000006407616,"teacher_disagreement_score":0.65365005,"about_ca_system_score_codex":0.000068798334,"about_ca_system_score_gemma":0.000032645385,"threshold_uncertainty_score":0.5618017},"labels":[],"label_agreement":null},{"id":"W2076049802","doi":"10.1117/12.527199","title":"An iterative linear algorithm for the analysis of oriented patterns","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Algorithm; Measure (data warehouse); Phase portrait; Iterative method; Weighting; Noise (video); Mathematics; Nonlinear system; Computer science; Non-linear least squares; Mathematical optimization; Estimation theory; Artificial intelligence; Image (mathematics); Statistics","score_opus":0.011748210786407183,"score_gpt":0.26912465624358584,"score_spread":0.25737644545717864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076049802","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56780124,0.000026652451,0.4300722,0.0012150354,0.000117465,0.00054097484,0.00008883818,0.000083476385,0.000054123095],"genre_scores_gemma":[0.08558891,0.000055255838,0.913565,0.00023774267,0.00019605957,0.00026380294,0.000022198317,0.000029056388,0.00004201649],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99769235,3.0466435e-8,0.00075117,0.00042119023,0.0008263735,0.00030887255],"domain_scores_gemma":[0.99651796,0.00026310558,0.00050197594,0.000121045385,0.0024734011,0.00012251471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082237564,0.0002518686,0.0004340665,0.00019365334,0.00010015115,0.00012025692,0.0018219157,0.00012717083,0.000009907158],"category_scores_gemma":[0.0004118502,0.00017729052,0.00073440105,0.00090509496,0.00023115498,0.0008948293,0.0001862327,0.00021943971,3.031982e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004804861,0.00044975747,0.00029490524,0.0002873675,0.0029431954,1.5307543e-7,0.0019757946,0.0009778413,0.37552217,0.6037433,0.0006147071,0.013142717],"study_design_scores_gemma":[0.00096035487,0.0004986412,0.0009168526,0.00014315103,0.00049205305,0.0000035736907,0.001048795,0.5394653,0.45486632,0.0011107352,0.00024629143,0.0002479533],"about_ca_topic_score_codex":0.00002549635,"about_ca_topic_score_gemma":3.4678263e-7,"teacher_disagreement_score":0.6026326,"about_ca_system_score_codex":0.00012903722,"about_ca_system_score_gemma":0.00004631196,"threshold_uncertainty_score":0.7229698},"labels":[],"label_agreement":null},{"id":"W2076133825","doi":"10.1002/hbm.20780","title":"Comparison of piece‐wise linear, linear, and nonlinear atlas‐to‐patient warping techniques: Analysis of the labeling of subcortical nuclei for functional neurosurgical applications","year":2009,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Image warping; Artificial intelligence; Computer science; Atlas (anatomy); Brain atlas; Nonlinear system; Pattern recognition (psychology); Linear model; Computer vision; Anatomy; Medicine; Machine learning; Physics","score_opus":0.04931572513456597,"score_gpt":0.3499202438290925,"score_spread":0.30060451869452653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076133825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08816816,0.000028601975,0.910178,0.0007912789,0.000021655582,0.00066486374,0.000010409782,0.0000869533,0.000050097853],"genre_scores_gemma":[0.55064875,0.0000031625261,0.44861197,0.0006081666,0.000036253212,0.000055206172,0.00001480124,0.00000778628,0.000013921139],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99801564,0.00008919755,0.000911882,0.0003557096,0.00044017454,0.00018739056],"domain_scores_gemma":[0.9983036,0.00041332166,0.0004092827,0.00047303946,0.00030203682,0.00009874634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049808685,0.00013428634,0.00044082062,0.0003688591,0.00016900261,0.000021511058,0.0004516558,0.00007432457,0.000008897553],"category_scores_gemma":[0.0002891259,0.00011416424,0.00018327769,0.0012073512,0.00016324896,0.00010658954,0.00019996952,0.00016107033,3.4303454e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026594616,0.0006607693,0.0048407167,0.00016127453,0.00015327787,6.681731e-7,0.0016146043,0.0014444991,0.93581367,0.013655482,0.0005478252,0.04108061],"study_design_scores_gemma":[0.0007424041,0.0009391376,0.05142785,0.00037554733,0.00030291517,0.000004312065,0.0002603923,0.6305349,0.3104503,0.0016233466,0.0028742661,0.00046465072],"about_ca_topic_score_codex":0.000006387641,"about_ca_topic_score_gemma":0.0000025251557,"teacher_disagreement_score":0.6290904,"about_ca_system_score_codex":0.00002434207,"about_ca_system_score_gemma":0.000037632664,"threshold_uncertainty_score":0.46554825},"labels":[],"label_agreement":null},{"id":"W2076793642","doi":"10.1117/12.652132","title":"Deformable registration using scale space keypoints","year":2006,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Image registration; Artificial intelligence; Affine transformation; Computer vision; Pixel; Computer science; Segmentation; Feature (linguistics); Feature vector; Displacement (psychology); Pattern recognition (psychology); Scale (ratio); Scale space; Image (mathematics); Mathematics; Image processing; Geography; Geometry","score_opus":0.012113294161363537,"score_gpt":0.24187456099290613,"score_spread":0.2297612668315426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076793642","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9528773,0.00004645635,0.04052564,0.0018495788,0.0002019209,0.00047496692,0.000010919756,0.00021120545,0.0038020483],"genre_scores_gemma":[0.10663447,0.00002720463,0.8923865,0.00013509113,0.00031474582,0.000072311595,0.000006537253,0.000034742967,0.0003884001],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99751866,1.7901401e-8,0.00074078265,0.00041067012,0.0009384376,0.00039144896],"domain_scores_gemma":[0.997784,0.00008278819,0.00052099716,0.0000918107,0.0014087734,0.000111627625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007811189,0.00026181556,0.00031187278,0.000119583965,0.000112556656,0.00025334928,0.0013393044,0.00016001886,0.0000088582665],"category_scores_gemma":[0.0002787561,0.00022636924,0.00039747302,0.00046864303,0.00021664274,0.001571953,0.00023816366,0.00024807954,0.0000019369322],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014714348,0.0000865401,0.00020495622,0.00018678034,0.000060339662,9.816201e-8,0.00008682242,0.00012361676,0.6268921,0.36695036,0.0049813036,0.0004124168],"study_design_scores_gemma":[0.00068146916,0.00016622107,0.00051807344,0.00024207358,0.00005561324,0.000031412506,0.00028456238,0.19490452,0.795392,0.0064348713,0.0009629619,0.00032623624],"about_ca_topic_score_codex":0.00005712381,"about_ca_topic_score_gemma":3.5944845e-7,"teacher_disagreement_score":0.8518609,"about_ca_system_score_codex":0.00022116478,"about_ca_system_score_gemma":0.00005350705,"threshold_uncertainty_score":0.92310697},"labels":[],"label_agreement":null},{"id":"W2077115170","doi":"10.1016/j.neuroimage.2014.04.054","title":"Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates","year":2014,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":371,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Baycrest Hospital; McGill University; Douglas Mental Health University Institute; Hospital for Sick Children; University of Toronto; Centre for Addiction and Mental Health","funders":"National Institute of Mental Health; National Institute on Aging; Centre for Addiction and Mental Health Foundation; Canadian Institutes of Health Research; Ontario Mental Health Foundation; Anaesthetics Research Society; W. Garfield Weston Foundation; Government of Ontario; Canada Foundation for Innovation; Ontario Research Foundation; University of Toronto; National Alliance for Research on Schizophrenia and Depression","keywords":"Segmentation; Computer science; Atlas (anatomy); Artificial intelligence; Brain atlas; Pattern recognition (psychology); Neuroimaging; Image segmentation; Computer vision; Medicine","score_opus":0.027078178028535294,"score_gpt":0.2799718842006162,"score_spread":0.2528937061720809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077115170","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38567594,0.000010984321,0.6137118,0.0002033936,0.000101372025,0.00016172869,0.0000015223015,0.000106129075,0.000027124204],"genre_scores_gemma":[0.6197984,0.000002993661,0.37958544,0.00055600883,0.000013657253,0.0000056256094,0.0000016152087,0.0000073053257,0.00002891763],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988262,0.00023576581,0.0002775564,0.0002568686,0.00025394236,0.00014963358],"domain_scores_gemma":[0.9991163,0.00021436077,0.00014404951,0.0003782611,0.00007962251,0.00006737989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026538328,0.00010824093,0.00013187551,0.000055319095,0.00010782679,0.0000989373,0.00039089264,0.000051671508,0.000008149563],"category_scores_gemma":[0.0003935723,0.00008020526,0.000034414545,0.00022486805,0.00012539097,0.000322928,0.00021932542,0.00012400655,0.0000042474594],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014322765,0.000041456078,0.0029067444,0.000028699165,0.0000040684035,0.0000020617636,0.0002378388,0.00004122924,0.955939,0.000050393355,0.00021433212,0.04053276],"study_design_scores_gemma":[0.00037519154,0.000044624052,0.012119114,0.000021900349,0.0000065083245,0.000011554932,0.000010967902,0.60804045,0.37894586,0.00032530073,0.000021208396,0.00007735708],"about_ca_topic_score_codex":0.000036552978,"about_ca_topic_score_gemma":0.000006007234,"teacher_disagreement_score":0.6079992,"about_ca_system_score_codex":0.00001243245,"about_ca_system_score_gemma":0.000026704749,"threshold_uncertainty_score":0.32706755},"labels":[],"label_agreement":null},{"id":"W2077201829","doi":"10.5555/1921479.1921499","title":"A work-efficient GPU algorithm for level set segmentation","year":2010,"lang":"en","type":"article","venue":"High Performance Graphics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Algorithm; Computational complexity theory; Granularity; Field (mathematics); Domain (mathematical analysis); Set (abstract data type); Segmentation; Image segmentation; Reduction (mathematics); Logarithm; Artificial intelligence; Mathematics","score_opus":0.03171319170204532,"score_gpt":0.28716297543160463,"score_spread":0.2554497837295593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077201829","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10428062,0.000011006434,0.8937885,0.00026214996,0.00083671993,0.00047625686,0.0000198571,0.0002862407,0.00003864858],"genre_scores_gemma":[0.21092024,0.000044081036,0.7876523,0.00080825266,0.00012854922,0.00024310037,0.000054761764,0.000015110746,0.0001335734],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851453,0.000024250945,0.0002966455,0.00036789157,0.0004629599,0.00033372958],"domain_scores_gemma":[0.99897623,0.0000760376,0.00013013941,0.00048163516,0.00020322864,0.00013275394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058337295,0.00016384709,0.00013699626,0.000225505,0.00024386343,0.00013574421,0.0006397148,0.00011587758,0.000025080386],"category_scores_gemma":[0.00003399332,0.0001530669,0.000062917665,0.0006892461,0.00012581915,0.0003502527,0.000116885916,0.00029044625,0.00002247089],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009113432,0.00013559678,0.00081153377,0.000050242365,0.000018688916,0.00000242441,0.0006505503,0.000046915204,0.006614889,0.0073399916,0.005316146,0.9790039],"study_design_scores_gemma":[0.0017608096,0.00044866998,0.020256547,0.000076217686,0.000025042877,0.000029201463,0.000052198484,0.6621204,0.3097568,0.0013885538,0.0033213445,0.000764235],"about_ca_topic_score_codex":0.0000093984345,"about_ca_topic_score_gemma":0.0000035800622,"teacher_disagreement_score":0.97823966,"about_ca_system_score_codex":0.00002158362,"about_ca_system_score_gemma":0.0000681686,"threshold_uncertainty_score":0.62418866},"labels":[],"label_agreement":null},{"id":"W2077593871","doi":"10.1118/1.3471020","title":"Deformable image registration of heterogeneous human lung incorporating the bronchial tree","year":2010,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Princess Margaret Cancer Centre","funders":"National Institutes of Health; Terry Fox Foundation; National Cancer Institute; Cancer Care Ontario","keywords":"Hyperelastic material; Image registration; Elasticity (physics); Finite element method; Human lung; Lung; Voxel; Tree (set theory); Mathematics; Medicine; Biomedical engineering; Radiology; Materials science; Image (mathematics); Physics; Computer science; Mathematical analysis; Artificial intelligence; Composite material","score_opus":0.012128194515597834,"score_gpt":0.2880815464810549,"score_spread":0.27595335196545706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077593871","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027556593,0.000006629044,0.96830976,0.00068338,0.00022131867,0.00015751718,0.0000010967942,0.00013471511,0.0029290114],"genre_scores_gemma":[0.9616401,0.0000017364633,0.037355307,0.00055739755,0.0003512364,0.000018867586,0.000010750901,0.000008255914,0.00005632773],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99828774,0.000061625564,0.00037501103,0.00019303389,0.0009096911,0.00017291568],"domain_scores_gemma":[0.9989165,0.00010281262,0.00024924788,0.0005080663,0.00009059338,0.00013278675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070930715,0.00010118827,0.00013763315,0.000019924079,0.00015639087,0.000071686794,0.00094441354,0.000077697136,0.000054022235],"category_scores_gemma":[0.00025919714,0.00006979146,0.00006265348,0.00020362229,0.00037915786,0.00040107942,0.0002079192,0.0004188816,0.0000076224583],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000063893517,0.00034935912,0.0005435178,0.00011006877,0.000035685072,0.000041823034,0.0010344984,0.000009754388,0.2533073,0.041362025,0.010405413,0.69279414],"study_design_scores_gemma":[0.0003716087,0.00010468169,0.00026124425,0.000034896704,0.000010342359,0.000023757835,0.000010211336,0.039934363,0.92487156,0.034129016,0.00009469231,0.00015364605],"about_ca_topic_score_codex":0.00007246699,"about_ca_topic_score_gemma":0.000058229838,"teacher_disagreement_score":0.9340835,"about_ca_system_score_codex":0.000016538104,"about_ca_system_score_gemma":0.00012964079,"threshold_uncertainty_score":0.2846013},"labels":[],"label_agreement":null},{"id":"W2077842764","doi":"10.1016/j.cmpb.2003.10.003","title":"Software for interactive segmentation of the carotid artery from 3D black blood magnetic resonance images","year":2003,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Segmentation; Computer vision; Magnetic resonance imaging; Computer science; Artificial intelligence; Image segmentation; Software; Algorithm; Medicine; Radiology","score_opus":0.0304596888875784,"score_gpt":0.34330469714344697,"score_spread":0.3128450082558686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077842764","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044131936,0.0012524131,0.99262893,0.00028818854,0.00043939924,0.0008906701,0.0000071283075,0.00006394854,0.000016113578],"genre_scores_gemma":[0.004198967,0.00009044341,0.99496216,0.00053013803,0.0000652768,0.00010757418,0.000010755169,0.000009015688,0.000025674035],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99827325,0.000516771,0.00041597852,0.00039320946,0.00020556265,0.00019520528],"domain_scores_gemma":[0.99857104,0.0006847341,0.00016942849,0.00037646678,0.00012975694,0.000068578076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009982189,0.00015152221,0.00027021434,0.000109232664,0.00004480492,0.000052358035,0.00040087898,0.000078727295,0.00000913412],"category_scores_gemma":[0.00019844278,0.00010418643,0.000045366345,0.00053616497,0.00034420626,0.00018579404,0.0001525427,0.0001743397,3.0554244e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006491498,0.0001093532,0.0036800988,0.00005069497,0.000012895742,0.0000022160727,0.0015578755,4.0193754e-7,0.007509927,0.00009229804,0.00020597837,0.98677176],"study_design_scores_gemma":[0.010177244,0.0051402445,0.07196103,0.0023916503,0.00021685756,0.000096685144,0.0007581751,0.027115291,0.83364785,0.03945948,0.007994958,0.0010405624],"about_ca_topic_score_codex":0.00003318431,"about_ca_topic_score_gemma":0.000002461212,"teacher_disagreement_score":0.9857312,"about_ca_system_score_codex":0.000019556628,"about_ca_system_score_gemma":0.000035627807,"threshold_uncertainty_score":0.42485994},"labels":[],"label_agreement":null},{"id":"W2078883193","doi":"10.1117/12.527132","title":"Image model: new perspective for image processing and computer vision","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bishop's University; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anisotropic diffusion; Mathematics; Image processing; Pixel; Dimension (graph theory); Computer vision; Image (mathematics); Computer science; Artificial intelligence; Algorithm; Pure mathematics","score_opus":0.01117027205567641,"score_gpt":0.269555896165452,"score_spread":0.2583856241097756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078883193","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4310085,0.000078902274,0.56130517,0.005983433,0.0000956648,0.00076691003,0.000014537268,0.00021547341,0.0005314487],"genre_scores_gemma":[0.019019349,0.00004578948,0.9801473,0.00029556968,0.00027233866,0.00010671294,0.000003085928,0.000041480096,0.00006836237],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978469,1.3942393e-8,0.00056368095,0.0005579374,0.0006601248,0.00037131956],"domain_scores_gemma":[0.9971282,0.00009487508,0.0003419415,0.00006887601,0.0021674098,0.00019872659],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00054696936,0.00029685782,0.0003548586,0.000120676275,0.000119579636,0.0003934196,0.0011573914,0.00014358778,0.0000022637093],"category_scores_gemma":[0.000364884,0.00025042484,0.00035958303,0.0002922256,0.00028523806,0.0019392581,0.00033901617,0.00024850562,6.4847967e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003802556,0.00009698322,0.000004179011,0.00034791566,0.00008899833,1.3250157e-7,0.0007708536,0.00007600011,0.45820117,0.53425765,0.0031483243,0.0029697812],"study_design_scores_gemma":[0.0020733515,0.00066607393,0.00008442689,0.00049164676,0.00008056616,0.000033582997,0.00078661065,0.6522189,0.29746652,0.045479577,0.0001779044,0.00044089754],"about_ca_topic_score_codex":0.000012561688,"about_ca_topic_score_gemma":9.329333e-8,"teacher_disagreement_score":0.6521428,"about_ca_system_score_codex":0.00024139175,"about_ca_system_score_gemma":0.00010360143,"threshold_uncertainty_score":0.9999948},"labels":[],"label_agreement":null},{"id":"W2080195010","doi":"10.1016/j.imavis.2011.07.007","title":"Non-local adaptive structure tensors","year":2011,"lang":"en","type":"article","venue":"Image and Vision Computing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Structure tensor; Local structure; Regularization (linguistics); Smoothing; Tensor product; Mathematics; Anisotropic diffusion; Tensor (intrinsic definition); Computer science; Algorithm; Artificial intelligence; Computer vision; Geometry; Pure mathematics; Image (mathematics); Physics","score_opus":0.01871451958228381,"score_gpt":0.3041115412344427,"score_spread":0.28539702165215886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080195010","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017706066,0.00003318905,0.98049426,0.000056411278,0.000104663595,0.00011065378,9.2270915e-7,0.00021847365,0.0012753854],"genre_scores_gemma":[0.54312986,0.0000031752809,0.45640013,0.00041207203,0.000030798736,5.3645414e-7,6.7353733e-7,0.0000047547555,0.000017965718],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899095,0.000044089833,0.00020696988,0.0003417167,0.00021066958,0.00020558551],"domain_scores_gemma":[0.9993899,0.000059691854,0.00008502516,0.0002438193,0.0000935231,0.00012802573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019614826,0.0001218354,0.00013622173,0.000084039515,0.0001424601,0.000099533674,0.0003649022,0.000053214782,0.00005670749],"category_scores_gemma":[0.00002804469,0.00010012464,0.000030466954,0.00018027554,0.00012011616,0.00048129488,0.00040151872,0.00017004811,0.000017510194],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006598501,0.00003202363,0.00016028383,0.0000128373595,0.000008216776,0.00006082728,0.0028435187,0.0000023362193,0.008145979,0.00071187626,0.0021482406,0.98586726],"study_design_scores_gemma":[0.0007266012,0.00056630844,0.01764887,0.00017612141,0.000010964836,0.000104346786,0.0006103741,0.7431342,0.23073019,0.0056736907,0.00013589743,0.00048244942],"about_ca_topic_score_codex":0.000035895147,"about_ca_topic_score_gemma":0.0000010814825,"teacher_disagreement_score":0.9853848,"about_ca_system_score_codex":0.000013219519,"about_ca_system_score_gemma":0.000018693641,"threshold_uncertainty_score":0.40829644},"labels":[],"label_agreement":null},{"id":"W2080428195","doi":"10.4236/eng.2013.510b047","title":"Cell Segmentation and Tracking in Microfluidic Platform","year":2013,"lang":"en","type":"article","venue":"Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Winnipeg Regional Health Authority; University of Manitoba; University of Winnipeg","funders":"","keywords":"Segmentation; Microfluidics; Scalability; Ellipse; Tracking (education); Artificial intelligence; Computer vision; Image segmentation; Computer science; Process (computing); Trajectory; Active contour model; Scheme (mathematics); Scale-space segmentation; Face (sociological concept); Pattern recognition (psychology); Nanotechnology; Mathematics; Materials science; Geometry; Physics","score_opus":0.008743494185622716,"score_gpt":0.22499208185319514,"score_spread":0.21624858766757243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080428195","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13603997,0.000317558,0.86319685,0.000052004427,0.000052654133,0.000113755705,7.9401985e-8,0.00013501795,0.00009213541],"genre_scores_gemma":[0.7263277,0.0000687699,0.2734096,0.0001071929,0.00001436631,0.000033235272,0.0000010908044,0.0000060838074,0.000031949177],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99957347,0.000004139487,0.0001139863,0.00011246909,0.00008174236,0.000114202136],"domain_scores_gemma":[0.99981445,0.00003235336,0.000016179332,0.00008282011,0.000009946202,0.000044250894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008845624,0.000054625358,0.000051614097,0.000097763,0.000012372682,0.000084152816,0.000114332666,0.000022359061,0.000020130221],"category_scores_gemma":[0.000014070042,0.000055922566,0.0000075117914,0.00012273544,0.0000054698626,0.00070598035,0.00004376415,0.00007136352,0.00001563962],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.7305227e-7,0.000012747358,0.0005598853,0.000040875315,0.0000015342586,0.00000558034,0.0010439401,0.00005986348,0.8803361,0.0002166172,0.00079649995,0.116926156],"study_design_scores_gemma":[0.00021348233,0.000016413373,0.007527409,0.000030948075,6.941494e-7,0.0000053642684,0.000047713245,0.056919403,0.9349214,0.00013573478,0.00006952507,0.000111924914],"about_ca_topic_score_codex":0.0000266084,"about_ca_topic_score_gemma":1.995877e-7,"teacher_disagreement_score":0.59028774,"about_ca_system_score_codex":0.000028166225,"about_ca_system_score_gemma":0.000004987385,"threshold_uncertainty_score":0.22804561},"labels":[],"label_agreement":null},{"id":"W2080439263","doi":"10.4103/2153-3539.92038","title":"Atlas-guided correction of brain histology distortion","year":2012,"lang":"en","type":"article","venue":"Journal of Pathology Informatics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Chinese University of Hong Kong","keywords":"Atlas (anatomy); Computer science; Brain atlas; Artificial intelligence; Image registration; Computer vision; Distortion (music); Pattern recognition (psychology); Brain tissue; Biomedical engineering; Anatomy; Image (mathematics); Medicine","score_opus":0.02021797302478694,"score_gpt":0.3040480408947066,"score_spread":0.28383006786991966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080439263","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06527445,0.000058336478,0.93152684,0.00027237076,0.00198022,0.000051059324,4.609173e-7,0.000023494733,0.0008127606],"genre_scores_gemma":[0.5262468,0.000044372435,0.4721636,0.0012657394,0.00013242167,0.0000025953852,0.0000026288164,0.0000050460494,0.00013684182],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99847305,0.00011331849,0.0009882774,0.000028597029,0.00024193144,0.00015483414],"domain_scores_gemma":[0.9980076,0.00015269716,0.0013413257,0.0001694017,0.00023518149,0.00009376896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012319662,0.00006874854,0.00021962894,0.0002379021,0.000030583044,0.0000086086775,0.00032536447,0.000098960176,0.000022407434],"category_scores_gemma":[0.00066155853,0.000056713503,0.0000696823,0.00015641261,0.00010454129,0.0011478302,0.00006475747,0.00020904095,0.000012430446],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052352512,0.000675297,0.025559584,0.00024743256,0.00010056401,0.000110910754,0.05077564,0.0001704193,0.037090737,0.0061775083,0.30135146,0.5776881],"study_design_scores_gemma":[0.005497901,0.005201901,0.08497974,0.00043813704,0.00025565777,0.0466983,0.0041600494,0.043914266,0.76369274,0.005896275,0.038031295,0.0012337499],"about_ca_topic_score_codex":0.000001380431,"about_ca_topic_score_gemma":3.9673367e-7,"teacher_disagreement_score":0.726602,"about_ca_system_score_codex":0.0000902453,"about_ca_system_score_gemma":0.00006199507,"threshold_uncertainty_score":0.23127095},"labels":[],"label_agreement":null},{"id":"W2080622120","doi":"10.1007/s11263-010-0406-y","title":"Global Minimization for Continuous Multiphase Partitioning Problems Using a Dual Approach","year":2010,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":175,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Norges Forskningsråd; Ministry of Education, India; National Science Foundation","keywords":"Smoothing; Mathematical optimization; Entropy maximization; Mathematics; Maximization; Minification; Potts model; Relaxation (psychology); Dual (grammatical number); Thresholding; Computer science; Algorithm; Principle of maximum entropy; Artificial intelligence; Image (mathematics)","score_opus":0.021057715064080645,"score_gpt":0.34306727337826315,"score_spread":0.3220095583141825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080622120","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02563744,0.0000151917575,0.97135603,0.00036095153,0.0023145603,0.00020449678,0.000006545165,0.000057575046,0.000047182588],"genre_scores_gemma":[0.26315838,0.000003012407,0.73591197,0.00028839963,0.000612767,0.000004251567,0.0000098623495,0.0000056526474,0.000005708605],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983274,0.000055612505,0.00060399045,0.00019960808,0.0006667775,0.00014661375],"domain_scores_gemma":[0.99790007,0.00010305272,0.0005636535,0.00014366372,0.001169878,0.000119672666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053726614,0.000115323826,0.00017329502,0.0001781789,0.0000690457,0.00039545837,0.0007389853,0.00007132177,0.000011215816],"category_scores_gemma":[0.00011679993,0.00010156331,0.00012292832,0.00014759565,0.000050988077,0.0011115245,0.00015959218,0.00015440062,0.0000013803098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019643341,0.0013953287,0.0014298358,0.000047747108,0.00024095894,0.00013480842,0.0010410659,0.019919952,0.058428746,0.011833385,0.0090441415,0.8962876],"study_design_scores_gemma":[0.0016844806,0.00032867282,0.000757211,0.00010735265,0.000012147169,0.0008640773,0.000009829396,0.987606,0.0048577,0.002571958,0.0010591414,0.00014145204],"about_ca_topic_score_codex":0.000005847595,"about_ca_topic_score_gemma":0.0000011461059,"teacher_disagreement_score":0.967686,"about_ca_system_score_codex":0.000075223885,"about_ca_system_score_gemma":0.00010224877,"threshold_uncertainty_score":0.41416317},"labels":[],"label_agreement":null},{"id":"W2080783594","doi":"10.1109/spcom.2010.5560547","title":"Improved image registration technique based on Demons and symmetric orthogonal gradient information","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer vision; Artificial intelligence; Image registration; Computer science; Image (mathematics)","score_opus":0.006243507585205517,"score_gpt":0.24460268021963183,"score_spread":0.23835917263442633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080783594","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015722103,0.0000010383903,0.98852724,0.00097075675,0.00009994093,0.00046132167,0.0000022132438,0.00044078968,0.007924514],"genre_scores_gemma":[0.2639329,0.0000027136107,0.7344641,0.0014016012,0.000015565674,0.00010542099,0.000015560283,0.0000035175078,0.000058587368],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999079,0.000033908014,0.0002647413,0.00018852555,0.00028668932,0.0001471173],"domain_scores_gemma":[0.99917203,0.000100308236,0.00012218255,0.00036069466,0.00011725845,0.00012750704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005546952,0.00010475476,0.00008062563,0.00034633238,0.00008843259,0.00023071977,0.0002746495,0.00008190375,0.000048787988],"category_scores_gemma":[0.0003242134,0.00008771173,0.0000272242,0.00044932257,0.00007179126,0.00129594,0.000059138758,0.0002377259,0.00001454395],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013256472,0.00013994784,0.00019373307,0.000050788753,0.000005169528,0.000004849455,0.00010561804,0.0000013667634,0.3434654,0.09771541,0.0047466555,0.5535578],"study_design_scores_gemma":[0.0004777107,0.00036762518,0.0056648613,0.000018652796,0.000005040161,0.000026044756,0.000012244597,0.38154888,0.60795796,0.002903737,0.0007483688,0.00026885452],"about_ca_topic_score_codex":0.00003230461,"about_ca_topic_score_gemma":0.000014071288,"teacher_disagreement_score":0.55328894,"about_ca_system_score_codex":0.000022579457,"about_ca_system_score_gemma":0.00006912612,"threshold_uncertainty_score":0.35767803},"labels":[],"label_agreement":null},{"id":"W2081345262","doi":"10.1007/s11548-014-1098-5","title":"Brain-shift compensation by non-rigid registration of intra-operative ultrasound images with preoperative MR images based on residual complexity","year":2014,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Imaging phantom; Image registration; Computer science; Artificial intelligence; Residual; Computer vision; Feature (linguistics); Wavelet; Data set; Fiducial marker; Similarity measure; Image-guided surgery; Pattern recognition (psychology); Medicine; Image (mathematics); Radiology; Algorithm","score_opus":0.016357443860118545,"score_gpt":0.28436016498584016,"score_spread":0.2680027211257216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081345262","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0663185,0.000028877312,0.92652744,0.00635455,0.00039553325,0.00012201665,0.000017426202,0.000030283465,0.00020539627],"genre_scores_gemma":[0.8774284,0.000023511238,0.119895525,0.0023544484,0.00020984458,0.0000050016797,0.000057734913,0.000008025473,0.000017486724],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99733734,0.0008030853,0.0008028443,0.00029293183,0.00060352625,0.00016026829],"domain_scores_gemma":[0.99457616,0.0034978196,0.0008660257,0.0001980948,0.000752164,0.00010972471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014206443,0.00020246228,0.00047295634,0.00034820958,0.00009582758,0.00016469845,0.0005002145,0.00009799268,0.000022106511],"category_scores_gemma":[0.00032688957,0.0001542346,0.000091355134,0.00013640207,0.00055104954,0.00066676957,0.00004278205,0.00031106794,0.0000010718836],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0040666857,0.0030675482,0.116423726,0.00022361154,0.0027273824,0.00065384054,0.0063472195,0.0063584945,0.12750196,0.017971355,0.542493,0.1721652],"study_design_scores_gemma":[0.0047129714,0.003992596,0.6645767,0.00095675664,0.0000758826,0.0020124358,0.00011066284,0.051416624,0.26768973,0.0030000475,0.0005582579,0.0008973246],"about_ca_topic_score_codex":0.000016453438,"about_ca_topic_score_gemma":0.0000039161123,"teacher_disagreement_score":0.8111099,"about_ca_system_score_codex":0.000063509484,"about_ca_system_score_gemma":0.00018652264,"threshold_uncertainty_score":0.6289504},"labels":[],"label_agreement":null},{"id":"W2081609930","doi":"10.1109/icip.2014.7025186","title":"VFCCV snake: A novel active contour model combining edge and regional information","year":2014,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Active contour model; Vector flow; Artificial intelligence; Computer vision; Computer science; Parametric statistics; Edge detection; Image segmentation; Boundary (topology); Convolution (computer science); Image gradient; Segmentation; Enhanced Data Rates for GSM Evolution; Pattern recognition (psychology); Image (mathematics); Mathematics; Image processing; Image texture","score_opus":0.025108653687959726,"score_gpt":0.2686524431909801,"score_spread":0.24354378950302036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081609930","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013286221,0.0000021405413,0.98764235,0.0011399252,0.000035536574,0.00010933127,8.5106547e-7,0.00022246088,0.009518787],"genre_scores_gemma":[0.26815537,0.000006601688,0.72379494,0.0077465307,0.000019019737,0.000022373066,0.000007908654,0.0000030826784,0.00024421164],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993509,0.000019244168,0.0001642778,0.00012233145,0.00022909,0.000114126924],"domain_scores_gemma":[0.9994884,0.00008311569,0.000078704375,0.00016261567,0.00009505581,0.00009212227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023469723,0.00007075283,0.00008443002,0.000069993745,0.00006474144,0.000120670935,0.00022888351,0.000039895556,0.000011898523],"category_scores_gemma":[0.00007345424,0.00006017769,0.000015695254,0.00008493874,0.000050307546,0.0018109794,0.00009457212,0.00008298774,0.000013722056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011891749,0.00006512236,0.000031039384,0.000022805782,0.000013989684,3.6822135e-7,0.004493142,0.0000791598,0.006162054,0.50833195,0.021246303,0.45954219],"study_design_scores_gemma":[0.00056970795,0.00005188017,0.00029050928,0.000013865583,0.0000018828596,0.000007818434,0.000060814848,0.98181516,0.0096267965,0.0063848007,0.001070665,0.000106124695],"about_ca_topic_score_codex":0.000018325949,"about_ca_topic_score_gemma":0.0000019751442,"teacher_disagreement_score":0.981736,"about_ca_system_score_codex":0.000020234098,"about_ca_system_score_gemma":0.00003208299,"threshold_uncertainty_score":0.2453975},"labels":[],"label_agreement":null},{"id":"W2082214883","doi":"10.1088/0031-9155/49/21/007","title":"Prostate segmentation algorithm using dyadic wavelet transform and discrete dynamic contour","year":2004,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; University of Waterloo","funders":"Canada Research Chairs","keywords":"Artificial intelligence; Computer science; Segmentation; Discrete wavelet transform; Computer vision; Wavelet; Pixel; Interpolation (computer graphics); Smoothing; Active contour model; Algorithm; Pattern recognition (psychology); Wavelet transform; Mathematics; Image segmentation; Image (mathematics)","score_opus":0.08783069299977876,"score_gpt":0.3933152395601233,"score_spread":0.30548454656034457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082214883","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024606355,0.00025662596,0.9726858,0.0020391573,0.00007650297,0.00023556454,0.0000036005865,0.000041251642,0.00005513671],"genre_scores_gemma":[0.49013007,0.001194139,0.5066268,0.0018459788,0.0001068606,0.00002777053,0.00004750107,0.0000090443355,0.000011841224],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999268,0.00004285229,0.00020174828,0.00024284852,0.00007826065,0.0001662974],"domain_scores_gemma":[0.99971265,0.000046863504,0.000058900754,0.0000944096,0.000024930869,0.00006227363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022436911,0.000101468424,0.00018940192,0.00006698471,0.000046466557,0.000013459361,0.00010397854,0.000042818036,0.0000024435844],"category_scores_gemma":[0.00001543298,0.00007471358,0.000010307652,0.00016182596,0.0002703299,0.00022127075,0.000041478823,0.00011744877,4.370625e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037349662,0.000019674422,0.00011406778,0.000025442323,0.000008695705,0.000008425559,0.0017701588,0.0000033570882,0.042965367,0.0035592744,0.00000856694,0.95151323],"study_design_scores_gemma":[0.008945946,0.0019671929,0.0018209651,0.00051931076,0.00005626058,0.00013696443,0.0015439813,0.23723382,0.06987546,0.6770937,0.0001336925,0.00067273097],"about_ca_topic_score_codex":0.00019569191,"about_ca_topic_score_gemma":0.000015416817,"teacher_disagreement_score":0.95084053,"about_ca_system_score_codex":0.000042472773,"about_ca_system_score_gemma":0.000028117382,"threshold_uncertainty_score":0.3046731},"labels":[],"label_agreement":null},{"id":"W2082304695","doi":"10.1016/j.media.2013.05.002","title":"Left ventricle segmentation in MRI via convex relaxed distribution matching","year":2013,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CARE Canada; Robarts Clinical Trials; Western University","funders":"","keywords":"Segmentation; Algorithm; Computer science; Regular polygon; Mathematics; Matching (statistics); Artificial intelligence; Pattern recognition (psychology); Mathematical optimization; Geometry","score_opus":0.00498603816576751,"score_gpt":0.26662508064793256,"score_spread":0.261639042482165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082304695","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022915184,0.000057751073,0.9733745,0.002962775,0.000061892344,0.00024536197,0.0000028008758,0.00023114,0.00014858415],"genre_scores_gemma":[0.85058945,0.0000889713,0.1469896,0.0017540187,0.000057677924,0.000091635105,0.0002776366,0.000012140717,0.00013886142],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99713784,0.00027515722,0.0006180762,0.00046087414,0.0011507601,0.00035729687],"domain_scores_gemma":[0.9987253,0.00019445075,0.0001629622,0.00044376086,0.00013528405,0.00033826564],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00092978554,0.0001550923,0.0003297645,0.00034214067,0.00009568726,0.00022268179,0.00070602214,0.0001200377,0.0035169998],"category_scores_gemma":[0.00038032344,0.00014021175,0.00015524517,0.0018364836,0.00013184406,0.0012906196,0.00021848583,0.00030765214,0.0003961198],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011337469,0.0010182612,0.03536415,0.00008573523,0.00077651616,0.00048513766,0.003703581,0.00010139823,0.07468843,0.0010171155,0.01972951,0.8630188],"study_design_scores_gemma":[0.0013674053,0.00009074061,0.063054405,0.00007043987,0.00029252123,0.000028487679,0.0004631495,0.8102377,0.11305661,0.01049564,0.00021170659,0.0006312103],"about_ca_topic_score_codex":0.00094659586,"about_ca_topic_score_gemma":0.00005617064,"teacher_disagreement_score":0.8623876,"about_ca_system_score_codex":0.00016883315,"about_ca_system_score_gemma":0.00005542373,"threshold_uncertainty_score":0.9973939},"labels":[],"label_agreement":null},{"id":"W2082544654","doi":"10.1016/j.media.2007.06.001","title":"A protocol for evaluation of similarity measures for non-rigid registration","year":2007,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; McGill University; Vanderbilt University","keywords":"Similarity (geometry); Artificial intelligence; Similarity measure; Mutual information; Image registration; Range (aeronautics); Protocol (science); Maxima and minima; Pattern recognition (psychology); Measure (data warehouse); Computer science; Computer vision; Mathematics; Position (finance); Task (project management); Image (mathematics); Data mining; Medicine","score_opus":0.07331654037583747,"score_gpt":0.4519022011277982,"score_spread":0.37858566075196076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082544654","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009780087,0.0000021246433,0.9514142,0.00076326466,0.000026216425,0.047155455,0.000005077924,0.00008013798,0.0004556947],"genre_scores_gemma":[0.02722165,9.79977e-7,0.82124585,0.00071108574,0.0001467262,0.15051183,0.000060759805,0.000011996597,0.00008913062],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961935,0.0001346517,0.00075621187,0.0003975922,0.002263279,0.00025471838],"domain_scores_gemma":[0.9968358,0.0004970025,0.0003701956,0.00049983547,0.0015834688,0.00021366937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013222912,0.00012627932,0.00032795154,0.0003445325,0.00008870536,0.000067795816,0.0006273019,0.00013524335,0.00016484165],"category_scores_gemma":[0.0058367117,0.00010566655,0.0003076312,0.0010527648,0.00014469441,0.0004089883,0.00005667921,0.00010138673,0.0000015525322],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015584502,0.00073470233,0.0006369773,0.00035427636,0.00075891346,0.0000065309728,0.0005324952,0.000021318527,0.014791921,0.0011858381,0.021731952,0.9590892],"study_design_scores_gemma":[0.0031486205,0.00026019558,0.0016800549,0.000050927018,0.00081289373,0.0000014816051,0.00004267973,0.6824836,0.30340436,0.006639437,0.0012649547,0.00021079562],"about_ca_topic_score_codex":0.000048545524,"about_ca_topic_score_gemma":0.00014095039,"teacher_disagreement_score":0.95887846,"about_ca_system_score_codex":0.000083195264,"about_ca_system_score_gemma":0.000328034,"threshold_uncertainty_score":0.6987511},"labels":[],"label_agreement":null},{"id":"W2083534882","doi":"10.1088/0031-9155/51/7/014","title":"Prostate segmentation by feature enhancement using domain knowledge and adaptive region based operations","year":2006,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Initialization; Computer science; Artificial intelligence; Segmentation; Computer vision; Pixel; Brachytherapy; Feature (linguistics); Image segmentation; Pattern recognition (psychology); Medicine; Radiology; Radiation therapy","score_opus":0.10607930113774292,"score_gpt":0.38377788149084935,"score_spread":0.27769858035310646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083534882","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022010496,0.00061211875,0.97506124,0.0018472023,0.00005232422,0.00024521071,0.0000018496763,0.000025765818,0.0001437862],"genre_scores_gemma":[0.61723506,0.00026490292,0.3801801,0.0018034448,0.00020148739,0.000076763405,0.0001340612,0.0000078580515,0.000096345815],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99934405,0.00010944992,0.00014368517,0.00023220191,0.000054934215,0.000115693045],"domain_scores_gemma":[0.99973327,0.00006138538,0.000045340366,0.00008920339,0.00003876258,0.00003201316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001736266,0.00008719019,0.00012849022,0.000053531774,0.000065368054,0.000013443831,0.000072795236,0.000041341384,0.0000023885364],"category_scores_gemma":[0.000008787309,0.00006610889,0.0000068148597,0.00015842536,0.00017771039,0.00013823951,0.000043080323,0.00008875949,4.012461e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021316337,0.00021608543,0.0016572565,0.000047956913,0.000012068844,0.000006639315,0.002046362,0.00003312136,0.7968455,0.027361196,0.010302994,0.1614495],"study_design_scores_gemma":[0.0073442175,0.0026222216,0.001351935,0.0005770317,0.000050924664,0.000029522786,0.0010543982,0.35386333,0.5140174,0.11548226,0.0027469592,0.0008597929],"about_ca_topic_score_codex":0.00013428797,"about_ca_topic_score_gemma":0.000020352853,"teacher_disagreement_score":0.59522456,"about_ca_system_score_codex":0.000034392433,"about_ca_system_score_gemma":0.000026152524,"threshold_uncertainty_score":0.26958424},"labels":[],"label_agreement":null},{"id":"W2083624400","doi":"10.1109/cjece.2007.4413127","title":"Morphometric analysis of trabecular bone thickness using different algorithms","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Trabecular bone; Voxel; Algorithm; Software; Thresholding; Computer science; Pixel; Object (grammar); Artificial intelligence; Image (mathematics); Osteoporosis","score_opus":0.010833252383009128,"score_gpt":0.2263448345464551,"score_spread":0.21551158216344596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083624400","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16012052,0.0009221122,0.8387655,0.00002343525,0.00012142334,0.000031659703,6.379624e-7,0.000011926984,0.0000027959943],"genre_scores_gemma":[0.7702446,0.000018915815,0.22958344,0.0000772781,0.000067793706,2.373129e-7,5.191392e-7,0.0000053248577,0.0000018713704],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885476,0.000021411852,0.00045369533,0.00012794221,0.0002580439,0.00028414465],"domain_scores_gemma":[0.9988716,0.00016850552,0.00014533037,0.00011940232,0.00013075808,0.00056440604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004328186,0.000109594366,0.00035744312,0.0023578377,0.00003553001,0.00006368246,0.0003138145,0.000056823665,0.000006081101],"category_scores_gemma":[0.00006339144,0.00009447948,0.00012644635,0.0026831876,0.000024924142,0.00016360536,0.000024456982,0.00021767127,8.241148e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011013691,0.00014179588,0.013926905,0.000094890616,0.0018858465,0.001853001,0.0010106171,0.025444893,0.026740108,0.0046707815,0.00017684845,0.9240433],"study_design_scores_gemma":[0.00019109379,0.00014998436,0.037923258,0.000033279437,0.00014084362,0.00021240705,0.0000025434526,0.9511261,0.009991979,0.000044533914,0.000044545435,0.00013945541],"about_ca_topic_score_codex":0.00025531783,"about_ca_topic_score_gemma":0.000042537267,"teacher_disagreement_score":0.9256812,"about_ca_system_score_codex":0.00010602331,"about_ca_system_score_gemma":0.00009627352,"threshold_uncertainty_score":0.38527614},"labels":[],"label_agreement":null},{"id":"W2083886415","doi":"10.1016/j.media.2013.12.003","title":"Self-similarity weighted mutual information: A new nonrigid image registration metric","year":2013,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":104,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Image registration; Mutual information; Artificial intelligence; Similarity measure; Mathematics; Affine transformation; Stochastic gradient descent; Similarity (geometry); Pattern recognition (psychology); Metric (unit); Gradient descent; Computer vision; Computer science; Image (mathematics); Geometry","score_opus":0.0070905747271410155,"score_gpt":0.26632723646321915,"score_spread":0.2592366617360781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083886415","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00039690133,0.000046629513,0.9827133,0.008532376,0.00009653382,0.00034644373,0.000003320832,0.00084005453,0.0070244567],"genre_scores_gemma":[0.012181365,0.00012590837,0.98069906,0.005777212,0.0001921629,0.00008410974,0.00017514304,0.000011066313,0.00075397035],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954831,0.0002512208,0.0010001013,0.000478726,0.0023299071,0.00045692245],"domain_scores_gemma":[0.9968281,0.00026492984,0.00036696417,0.00098913,0.0005761798,0.00097473845],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0011352681,0.0002671621,0.00048505256,0.0010970286,0.000164319,0.0009784923,0.0015281554,0.00022636155,0.0071093854],"category_scores_gemma":[0.001605783,0.00022557213,0.00031982714,0.0058832187,0.0001746444,0.0061086393,0.00034014424,0.0004587572,0.0016485748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008361393,0.00052131846,0.00087451475,0.00011271922,0.0015414185,0.00016875028,0.0019689219,0.0000015951331,0.0009399342,0.0022064864,0.50512296,0.48653302],"study_design_scores_gemma":[0.0017661552,0.0002245908,0.0070471726,0.00003695146,0.0010854868,0.00005258229,0.00018951722,0.9446153,0.021889288,0.004352232,0.017702077,0.0010386654],"about_ca_topic_score_codex":0.0009043822,"about_ca_topic_score_gemma":0.00004097361,"teacher_disagreement_score":0.9446137,"about_ca_system_score_codex":0.00012962837,"about_ca_system_score_gemma":0.0004130459,"threshold_uncertainty_score":0.99912876},"labels":[],"label_agreement":null},{"id":"W2084180431","doi":"10.1109/3dtv.2013.6676652","title":"3D-PIC: Power Iteration Clustering for segmenting three-dimensional models","year":2013,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Robustness (evolution); Cluster analysis; Market segmentation; Segmentation; Spectral clustering; Artificial intelligence; Image segmentation; CAD; Noise (video); Computer vision; Pattern recognition (psychology); Image (mathematics)","score_opus":0.02549452100318968,"score_gpt":0.2701781740652234,"score_spread":0.24468365306203368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084180431","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015274537,0.000013278009,0.99455595,0.00066232705,0.00015028083,0.00054718414,6.152091e-7,0.00037915405,0.0021637834],"genre_scores_gemma":[0.0728493,4.949474e-7,0.9235582,0.0025921497,0.000033649994,0.00017060088,0.000006236657,0.000008426612,0.0007809503],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989715,0.000018781684,0.00025125113,0.00027695633,0.0002674707,0.0002140186],"domain_scores_gemma":[0.99936074,0.00008395531,0.00006639483,0.00024680173,0.0001598812,0.00008222168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022882654,0.00009754607,0.00009215429,0.00006991367,0.00010927427,0.00028538014,0.00029938135,0.000043307977,0.00033924196],"category_scores_gemma":[0.000028592898,0.00008208311,0.00003985476,0.00009610871,0.000018664927,0.0017856562,0.00020455924,0.0000569907,0.000058033947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009802714,0.00021625012,0.00014604609,0.00009002902,0.000057868205,0.0000052084956,0.0010331045,0.0057721445,0.13031115,0.03208463,0.06964051,0.76063323],"study_design_scores_gemma":[0.00019158053,0.000043505825,0.00005956023,0.00001731367,0.0000015121328,0.0000035870899,0.0000063167863,0.96983397,0.020459238,0.009227084,0.000048058308,0.000108256914],"about_ca_topic_score_codex":0.000041004514,"about_ca_topic_score_gemma":0.000008344396,"teacher_disagreement_score":0.96406186,"about_ca_system_score_codex":0.000029435947,"about_ca_system_score_gemma":0.000022179327,"threshold_uncertainty_score":0.37144616},"labels":[],"label_agreement":null},{"id":"W2084405042","doi":"10.1371/journal.pone.0033616","title":"Colorization and Automated Segmentation of Human T2 MR Brain Images for Characterization of Soft Tissues","year":2012,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University Health Network","funders":"","keywords":"Artificial intelligence; Segmentation; Computer science; Voxel; Computer vision; Pattern recognition (psychology); Centroid; Magnetic resonance imaging; Image segmentation; Grayscale; White matter; Medicine; Image (mathematics); Radiology","score_opus":0.03860754577123772,"score_gpt":0.3024643561483341,"score_spread":0.2638568103770964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084405042","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36654234,0.000018009681,0.63257676,0.00019492392,0.00001882062,0.00041291362,0.000013267677,0.00020547553,0.000017497763],"genre_scores_gemma":[0.7673643,0.000015040065,0.23205887,0.000120745266,0.000040766874,0.000075905235,0.00018472546,0.000009556067,0.00013008263],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992228,0.000054605545,0.0002710886,0.00011569941,0.00023046425,0.00010535126],"domain_scores_gemma":[0.99932915,0.00008297545,0.00025079504,0.00012386238,0.00016656706,0.000046678262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027203784,0.00006491664,0.00015006452,0.00009704688,0.00004150631,0.000022261716,0.00011816949,0.00004026499,0.000017053168],"category_scores_gemma":[0.00011853031,0.00006703214,0.00001315378,0.00015183217,0.00005292849,0.0007654405,0.000045210294,0.000023208335,0.0000010796057],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000036354775,0.00031484914,0.0019319026,0.00020026787,0.000021324407,4.4150823e-8,0.0007980069,1.04615914e-7,0.9919893,0.0005930948,0.0001295731,0.004017861],"study_design_scores_gemma":[0.00025827723,0.00017041355,0.010450023,0.00007684586,0.000021375663,2.385995e-7,0.000019235345,0.006949581,0.98186874,0.00012063596,0.0000035741677,0.00006104764],"about_ca_topic_score_codex":0.0000050061717,"about_ca_topic_score_gemma":2.79245e-7,"teacher_disagreement_score":0.40082195,"about_ca_system_score_codex":0.000015858846,"about_ca_system_score_gemma":0.000011322921,"threshold_uncertainty_score":0.27334914},"labels":[],"label_agreement":null},{"id":"W2084979303","doi":"10.1109/iembs.2010.5626494","title":"Targeting error simulator for image-guided prostate needle placement","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"National Institute of Biomedical Imaging and Bioengineering; National Cancer Institute","keywords":"Computer science; Ground truth; Image warping; Prostate biopsy; Segmentation; Computer vision; Image registration; Artificial intelligence; Image segmentation; Prostate; Image (mathematics); Medicine","score_opus":0.022220525771464276,"score_gpt":0.3295995374427827,"score_spread":0.3073790116713184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084979303","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004629483,0.0000039778206,0.9913285,0.0012350804,0.0003787628,0.00083404296,0.0000031272607,0.0006359852,0.0009510565],"genre_scores_gemma":[0.05010208,8.9476487e-7,0.94591796,0.0016890937,0.00007978574,0.00015728165,0.000010065524,0.000012848528,0.0020299875],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987686,0.000021520307,0.00031074128,0.000325182,0.00027579945,0.0002981039],"domain_scores_gemma":[0.99907964,0.00013284694,0.00009374441,0.00035741762,0.00018395396,0.00015241701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058568263,0.000119650904,0.00011844709,0.00006887298,0.00014321897,0.0002579496,0.0005379891,0.000047458452,0.00029962388],"category_scores_gemma":[0.00036630212,0.00009876918,0.000049032365,0.00014511716,0.000068177535,0.0005608875,0.00018730346,0.00013633388,0.000057318673],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001088371,0.00013003974,0.00009905344,0.00006485347,0.0000165149,0.0000070875644,0.0010859204,0.0000848785,0.7953742,0.006216985,0.1727036,0.024205983],"study_design_scores_gemma":[0.00072680117,0.000105541076,0.000020603524,0.000007322595,0.0000040544137,0.0000038155717,0.00008670036,0.32823545,0.6612248,0.0015429838,0.007819405,0.00022252157],"about_ca_topic_score_codex":0.000015658927,"about_ca_topic_score_gemma":0.0000021066317,"teacher_disagreement_score":0.32815057,"about_ca_system_score_codex":0.000020429394,"about_ca_system_score_gemma":0.00007926028,"threshold_uncertainty_score":0.40276903},"labels":[],"label_agreement":null},{"id":"W2086467596","doi":"10.1086/381786","title":"A Fast Algorithm for Cosmic‐Ray Removal from Single Images","year":2004,"lang":"en","type":"article","venue":"Publications of the Astronomical Society of the Pacific","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":142,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Histogram; Algorithm; Pixel; Cosmic ray; COSMIC cancer database; Noise (video); Image processing; Image (mathematics); Identification (biology); Artificial intelligence; Computer vision; Physics; Astrophysics","score_opus":0.01591086368167957,"score_gpt":0.24602390052992393,"score_spread":0.23011303684824436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086467596","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010446481,0.000035992925,0.9667147,0.02186121,0.00014506595,0.0004711779,0.00014283582,0.00007977873,0.000102794584],"genre_scores_gemma":[0.1307001,0.0000023842072,0.86879826,0.000092561415,0.000049151644,0.000064396016,0.00001355814,0.000009171015,0.0002704137],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988298,0.00005643407,0.0003791513,0.00027393023,0.00025619482,0.00020453618],"domain_scores_gemma":[0.99811405,0.00018290758,0.00036644336,0.0010985786,0.00016758127,0.00007046847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030794035,0.00011268307,0.00017636556,0.000022959193,0.00018931691,0.00008266419,0.0022585716,0.000060220187,0.000012949894],"category_scores_gemma":[0.00011966536,0.00007592659,0.00045395608,0.0003219268,0.00049163494,0.0003117858,0.00051419315,0.0001398417,0.0000038309386],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013509103,0.0021915655,0.0043848623,0.000055429115,0.00049025874,1.5225138e-8,0.0036521435,0.0017295737,0.31032196,0.009334654,0.11283082,0.55499524],"study_design_scores_gemma":[0.0017082416,0.00011210727,0.040713117,0.00010285132,0.00007841673,0.0000027317517,0.0010241779,0.040490434,0.8801993,0.030357096,0.004847619,0.00036388775],"about_ca_topic_score_codex":0.00006311751,"about_ca_topic_score_gemma":8.98658e-7,"teacher_disagreement_score":0.5698774,"about_ca_system_score_codex":0.00014815404,"about_ca_system_score_gemma":0.00013942162,"threshold_uncertainty_score":0.41970256},"labels":[],"label_agreement":null},{"id":"W2086509186","doi":"10.1155/2014/820205","title":"Nonlocal Intracranial Cavity Extraction","year":2014,"lang":"en","type":"article","venue":"International Journal of Biomedical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"National Institute on Aging; Engineering and Physical Sciences Research Council; Canadian Institutes of Health Research; Ministerio de Ciencia e Innovación; Ministério da Ciência, Tecnologia e Inovação; National Center for Research Resources; National Institute of Mental Health; Aarhus Universitet","keywords":"Normalization (sociology); Computer science; Reproducibility; Pattern recognition (psychology); Estimation; Artificial intelligence; Data mining; Statistics; Mathematics","score_opus":0.006876211560690303,"score_gpt":0.3126654113891304,"score_spread":0.3057891998284401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086509186","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021964714,0.000028033666,0.9842762,0.009864961,0.0030762833,0.00003399051,0.0000011695265,0.000071142196,0.0004517842],"genre_scores_gemma":[0.6550782,0.000024108349,0.34103334,0.0024002541,0.0014262007,0.0000015477534,0.0000033442427,0.0000076078677,0.00002539501],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99757814,0.000120972574,0.0006043785,0.0001656146,0.0013596168,0.00017129042],"domain_scores_gemma":[0.9984697,0.0002214477,0.00039456435,0.00014629641,0.0005207645,0.00024719827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012226464,0.000100986246,0.00015838396,0.00035038576,0.000042165288,0.00020818428,0.0012853604,0.000046406123,0.00012107998],"category_scores_gemma":[0.00075706095,0.000084665364,0.00010726716,0.00017089568,0.00016859648,0.0011559546,0.00016409135,0.00035445567,0.000029271601],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013418023,0.00013213203,0.00023772423,0.0000028850213,0.000027341533,0.00023784248,0.00008985609,0.0000016698464,0.024790112,0.0009073503,0.004064518,0.9694952],"study_design_scores_gemma":[0.00582791,0.00065368804,0.009679737,0.0005886448,0.000064091546,0.013150323,0.00017755677,0.69836664,0.13184804,0.024864994,0.1138762,0.0009021692],"about_ca_topic_score_codex":0.000017937007,"about_ca_topic_score_gemma":5.849374e-7,"teacher_disagreement_score":0.968593,"about_ca_system_score_codex":0.00012522677,"about_ca_system_score_gemma":0.00010020503,"threshold_uncertainty_score":0.34525532},"labels":[],"label_agreement":null},{"id":"W2086545710","doi":"10.1109/tmi.2014.2319231","title":"Registration of Whole-Mount Histology and Volumetric Imaging of the Prostate Using Particle Filtering","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Image registration; Artificial intelligence; Computer vision; Magnetic resonance imaging; Medical imaging; Biomedical engineering; Pattern recognition (psychology); Medicine; Radiology","score_opus":0.014295604758395723,"score_gpt":0.2788224012820867,"score_spread":0.264526796523691,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086545710","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032464694,0.000056915,0.96491563,0.0020327487,0.00027953179,0.00013092047,0.0000016408674,0.000067901165,0.000050010796],"genre_scores_gemma":[0.95513266,0.0000118442495,0.04435313,0.00044819104,0.000014011172,0.0000086954215,2.4303515e-7,0.000007657062,0.000023549192],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983854,0.00017651812,0.00044892673,0.0002422856,0.0005600372,0.00018679396],"domain_scores_gemma":[0.9990522,0.0002068637,0.0001979132,0.00034344415,0.00008675208,0.00011278183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007615673,0.000098379875,0.00016994445,0.00013665274,0.0001194431,0.000032997366,0.00036102353,0.000031882,0.000021851738],"category_scores_gemma":[0.00017302974,0.000079465404,0.00005338144,0.00045908385,0.00041061814,0.00034933348,0.000012117949,0.00021877067,0.0000010474201],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008748614,0.00012454372,0.000706889,0.000095712945,0.000012290781,0.000005917432,0.0006553206,0.00041745367,0.17316523,0.00049218343,0.000076824195,0.8242389],"study_design_scores_gemma":[0.0002793899,0.000026091571,0.0005339738,0.0001324743,0.000016196553,0.00005032022,0.000051509833,0.70652854,0.2918729,0.0003768806,0.00005274597,0.00007897219],"about_ca_topic_score_codex":0.00014774404,"about_ca_topic_score_gemma":0.000006885941,"teacher_disagreement_score":0.922668,"about_ca_system_score_codex":0.000060075396,"about_ca_system_score_gemma":0.00007236909,"threshold_uncertainty_score":0.32405052},"labels":[],"label_agreement":null},{"id":"W2086600784","doi":"10.1109/tmi.2014.2354352","title":"Automatic Deformable MR-Ultrasound Registration for Image-Guided Neurosurgery","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Image registration; Artificial intelligence; Computer vision; Computer science; Outlier; Image (mathematics)","score_opus":0.01845746497014685,"score_gpt":0.2978656237888659,"score_spread":0.27940815881871905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086600784","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009238179,0.000009763243,0.9921536,0.0038481557,0.0010566713,0.00038510977,0.0000040735003,0.0010254594,0.0005933767],"genre_scores_gemma":[0.50454104,0.00004921899,0.48379147,0.01045805,0.00020585525,0.00038947508,0.000012709998,0.00005176763,0.000500423],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972359,0.00018041853,0.00064772536,0.00049161055,0.0009914824,0.00045286506],"domain_scores_gemma":[0.9975257,0.0011723485,0.00016569666,0.000588733,0.00015148422,0.0003960295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001553044,0.00021783129,0.00025547206,0.00023554126,0.00033738388,0.00029851665,0.0006832455,0.0000886168,0.00026882076],"category_scores_gemma":[0.0006216239,0.00020240425,0.00016339839,0.00038309014,0.00020981574,0.0011760856,0.0000043756713,0.0003686767,0.00006598339],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007914552,0.00025556696,0.000014403286,0.00014835304,0.000022987042,0.000023228336,0.00022449909,0.00014589482,0.018913582,0.0005610273,0.023472868,0.95620966],"study_design_scores_gemma":[0.0006666689,0.0000639903,0.00003057804,0.00011922519,0.000021985827,0.00019573762,0.000022846636,0.8537777,0.14213456,0.0016284104,0.0010801614,0.0002581217],"about_ca_topic_score_codex":0.000030423642,"about_ca_topic_score_gemma":0.000004664072,"teacher_disagreement_score":0.9559516,"about_ca_system_score_codex":0.00008314279,"about_ca_system_score_gemma":0.00016334438,"threshold_uncertainty_score":0.82538056},"labels":[],"label_agreement":null},{"id":"W2087328731","doi":"10.1093/cercor/bhh165","title":"A Three-dimensional MRI Atlas of the Mouse Brain with Estimates of the Average and Variability","year":2004,"lang":"en","type":"article","venue":"Cerebral Cortex","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":333,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Toronto; Montreal Neurological Institute and Hospital; Hospital for Sick Children","funders":"","keywords":"Atlas (anatomy); Neuroscience; Brain mapping; Cartography; Biology; Geography; Anatomy","score_opus":0.008078449961300738,"score_gpt":0.23155051971348883,"score_spread":0.22347206975218809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087328731","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40277514,0.000010861308,0.5949588,0.0017908058,0.000040845698,0.00027745106,0.0000052015894,0.000044774482,0.00009614699],"genre_scores_gemma":[0.892574,4.9460283e-7,0.10687355,0.00049972656,0.0000063970515,0.0000062607915,8.1036956e-7,0.000004704529,0.000034070494],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99906826,0.000056950725,0.0001993869,0.00021296483,0.00034886255,0.00011358593],"domain_scores_gemma":[0.99904436,0.00013934652,0.00014531844,0.00054536096,0.00007862941,0.000046959456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002805956,0.00009032907,0.00013157574,0.000017650864,0.000071595445,0.000020476919,0.0005455773,0.000035836823,0.000028213019],"category_scores_gemma":[0.00015558267,0.000044765846,0.000041900315,0.0002211725,0.0004878551,0.00015764106,0.00035588743,0.00011805311,9.1568495e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018499365,0.0015721105,0.26936188,0.0006909635,0.0002946671,0.000022779197,0.0053322706,0.0022287935,0.43771735,0.22452544,0.008967289,0.049101457],"study_design_scores_gemma":[0.0010261573,0.00022969232,0.27977416,0.00013890042,0.000017086077,0.000039099912,0.000011015871,0.014490064,0.62377656,0.080302395,0.00001540312,0.00017946395],"about_ca_topic_score_codex":0.00016452327,"about_ca_topic_score_gemma":0.00006308885,"teacher_disagreement_score":0.48979884,"about_ca_system_score_codex":0.000023758874,"about_ca_system_score_gemma":0.00012412951,"threshold_uncertainty_score":0.18254982},"labels":[],"label_agreement":null},{"id":"W2087350728","doi":"10.1118/1.2030995","title":"Po‐Poster ‐ 16: Correcting geometric distortion of EPID images","year":2005,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Regional Cancer Foundation; Carleton University","funders":"","keywords":"Distortion (music); Geometric transformation; Computer vision; Artificial intelligence; Centroid; Image plane; Affine transformation; Mathematics; Computer science; Image (mathematics); Geometry","score_opus":0.017237203233964892,"score_gpt":0.2939803587673186,"score_spread":0.2767431555333537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087350728","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029474704,0.000095890944,0.993559,0.0009263432,0.0002962862,0.0001012914,0.0000014744029,0.00019631434,0.001875924],"genre_scores_gemma":[0.98666185,0.000020094194,0.011568574,0.0011800863,0.0003477567,0.00001027856,0.000007007937,0.000008470051,0.00019587546],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981157,0.0000700942,0.000379321,0.00024849008,0.00097300526,0.00021334366],"domain_scores_gemma":[0.9989347,0.00026023254,0.00017646543,0.0003292927,0.00010331812,0.00019598183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000542298,0.000105759624,0.00019557422,0.00009449834,0.00004938216,0.000028563263,0.0005919592,0.0000681437,0.00020149596],"category_scores_gemma":[0.0007704033,0.000092000315,0.000074582,0.000702296,0.00013334073,0.00048040957,0.00020726187,0.00022669579,0.00006443718],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012161453,0.00010988453,0.00043442546,0.000018661472,0.0000054431644,0.0000028937066,0.000120801364,0.0000010788686,0.000040522358,0.000004600546,0.005976894,0.99328357],"study_design_scores_gemma":[0.00019612066,0.000058756752,0.0006543089,0.00004665342,0.000005268694,0.000007041701,0.0000074666623,0.000047424975,0.998005,0.0008005811,0.0000727217,0.000098688775],"about_ca_topic_score_codex":0.000026585349,"about_ca_topic_score_gemma":0.0000015618728,"teacher_disagreement_score":0.99796444,"about_ca_system_score_codex":0.00009240797,"about_ca_system_score_gemma":0.00006998995,"threshold_uncertainty_score":0.37516642},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low"}],"label_agreement":"split"},{"id":"W2087503665","doi":"10.1117/12.878285","title":"Intensity-based hierarchical clustering in CT-scans: application to interactive segmentation in cardiology","year":2011,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Cluster analysis; Piecewise; Segmentation; Computer science; Histogram; Artificial intelligence; Image segmentation; Hierarchical clustering; Pattern recognition (psychology); Scale-space segmentation; Computation; Constant (computer programming); Computer vision; Image (mathematics); Algorithm; Mathematics","score_opus":0.015350002935168078,"score_gpt":0.2564832969390999,"score_spread":0.2411332940039318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087503665","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86518055,0.000009402232,0.13158798,0.0012306473,0.00014614456,0.0008374997,0.000007693232,0.00009556681,0.0009044924],"genre_scores_gemma":[0.49553704,0.000010731784,0.5035834,0.0003587225,0.00006602157,0.00040870512,0.0000051182237,0.00002035742,0.000009953151],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979474,7.686612e-8,0.0007429764,0.00048695636,0.0004825268,0.00034008536],"domain_scores_gemma":[0.9986077,0.000145299,0.0002624161,0.000084244086,0.0007788068,0.00012151078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081630197,0.00023188948,0.00039147836,0.0003207804,0.000032299497,0.00006172826,0.0012242445,0.00010221226,0.000004356784],"category_scores_gemma":[0.00048189826,0.00021393607,0.00021673649,0.0006595553,0.00013966256,0.0007151103,0.00029580342,0.0003724335,0.000001731346],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027017354,0.0002993475,0.004754635,0.00036563573,0.00015513408,0.0000015723928,0.0022740306,0.0007273195,0.8260139,0.15596409,0.0007485149,0.0084256055],"study_design_scores_gemma":[0.0020318334,0.0006704047,0.014287867,0.0006227702,0.00004325833,0.00003476325,0.0020360346,0.35193625,0.62254107,0.00499393,0.00019742308,0.0006044211],"about_ca_topic_score_codex":0.00007430602,"about_ca_topic_score_gemma":0.000002540674,"teacher_disagreement_score":0.37199536,"about_ca_system_score_codex":0.00033433342,"about_ca_system_score_gemma":0.000043458662,"threshold_uncertainty_score":0.87240595},"labels":[],"label_agreement":null},{"id":"W2087671293","doi":"10.1109/cvpr.2010.5540069","title":"Finding image distributions on active curves","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; CARE Canada; Western University","funders":"","keywords":"Image segmentation; Active contour model; Boundary (topology); Feature (linguistics); Geodesic; Image (mathematics); Similarity (geometry); Artificial intelligence; Mathematics; Segmentation; Function (biology); Computer science; Pattern recognition (psychology); Mathematical analysis","score_opus":0.01592714325004186,"score_gpt":0.3223789752676998,"score_spread":0.30645183201765797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087671293","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012149339,0.0000020452298,0.9739414,0.0030862095,0.00017268573,0.00010419706,0.000006364666,0.0004070405,0.021065133],"genre_scores_gemma":[0.16908228,0.000021673963,0.82570344,0.0037236963,0.000066100285,0.000051746298,0.000024292682,0.0000063558255,0.0013204049],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99939597,0.0000215308,0.00009261226,0.00017420568,0.00018870302,0.00012696834],"domain_scores_gemma":[0.9994235,0.00010897228,0.000033119606,0.00030042176,0.00004961835,0.00008436869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013734741,0.000058282043,0.000055063963,0.00004214157,0.00008223477,0.00006896437,0.0004154314,0.000027398295,0.0005651405],"category_scores_gemma":[0.0002626618,0.00004702031,0.000027200582,0.00017391735,0.000059468257,0.00044824235,0.00010000747,0.00020330268,0.00017938284],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000022932536,0.00019953967,0.000071058916,0.000021428908,0.000011467265,0.000018021457,0.0001794314,5.666732e-8,0.31586102,0.23309545,0.16497837,0.28556186],"study_design_scores_gemma":[0.00008115021,0.000037419337,0.0019162573,0.000025295773,0.0000016051688,0.000004550835,0.000008551287,0.00056622876,0.9928461,0.0033094627,0.0011092987,0.00009408399],"about_ca_topic_score_codex":0.00001040124,"about_ca_topic_score_gemma":0.0000043340315,"teacher_disagreement_score":0.6769851,"about_ca_system_score_codex":0.000015454836,"about_ca_system_score_gemma":0.000025822836,"threshold_uncertainty_score":0.61878926},"labels":[],"label_agreement":null},{"id":"W2087829477","doi":"10.1016/j.media.2009.10.007","title":"An automatic geometrical and statistical method to detect acoustic shadows in intraoperative ultrasound brain images","year":2009,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Artificial intelligence; Computer science; Robustness (evolution); Computer vision; Segmentation; Ultrasound; Acoustic shadow; 3D ultrasound; Statistical model; Acoustic impedance; Pattern recognition (psychology); Acoustics; Ultrasonic sensor","score_opus":0.00835886815253391,"score_gpt":0.3524337114892521,"score_spread":0.34407484333671823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087829477","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043294057,0.000056999077,0.99036026,0.004519586,0.000021189053,0.0002928167,0.0000134003085,0.0002976575,0.00010868733],"genre_scores_gemma":[0.24216555,0.000020597603,0.7506686,0.0070153372,0.000037959577,0.00003489571,0.00002102886,0.000009022951,0.000027048394],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99520993,0.0010751385,0.00078950176,0.00093873584,0.0014166778,0.00057001575],"domain_scores_gemma":[0.9951492,0.0027910129,0.00009510334,0.000630321,0.00014326787,0.0011911034],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0032000742,0.00028958954,0.00074618426,0.0015912692,0.00010542495,0.00047748288,0.0010389169,0.0001638601,0.0011322041],"category_scores_gemma":[0.0095477,0.00024229503,0.000101832185,0.0054141567,0.00021882735,0.0007967598,0.00015828041,0.00051036844,0.00003690704],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007824106,0.00026384555,0.0003427594,0.000017071305,0.00013549463,0.0006226535,0.00082660385,0.000063343505,0.029757403,0.0002511103,0.0027241353,0.96498775],"study_design_scores_gemma":[0.00094590744,0.0011234344,0.07839705,0.0000594493,0.000423027,0.000107296684,0.00022731686,0.8853024,0.027101757,0.005491506,0.000038313014,0.000782504],"about_ca_topic_score_codex":0.000171955,"about_ca_topic_score_gemma":0.000087055545,"teacher_disagreement_score":0.96420527,"about_ca_system_score_codex":0.00011916678,"about_ca_system_score_gemma":0.0001277888,"threshold_uncertainty_score":0.9997809},"labels":[],"label_agreement":null},{"id":"W2088270170","doi":"10.1109/icsipa.2011.6144087","title":"A contrario edge detection with edgelets","year":2011,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Pixel; Computer science; Edge detection; Enhanced Data Rates for GSM Evolution; Feature (linguistics); Artificial intelligence; Set (abstract data type); Monte Carlo method; Image (mathematics); Pattern recognition (psychology); Computer vision; Image processing; Algorithm; Mathematics; Statistics","score_opus":0.02502295462617996,"score_gpt":0.23000875059444956,"score_spread":0.2049857959682696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088270170","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012609776,0.000006562194,0.9618516,0.000037320144,0.00006961637,0.0001140421,6.472134e-8,0.0005274852,0.036132358],"genre_scores_gemma":[0.521569,0.0000017504481,0.4770199,0.00078589044,0.000015606038,0.000022869584,1.962549e-7,0.0000035306969,0.00058123126],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994635,0.000023010818,0.00009027383,0.0001647894,0.00014155492,0.00011686533],"domain_scores_gemma":[0.99958885,0.000015777914,0.000031848776,0.00023329064,0.000052577056,0.00007768592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010501865,0.00005710001,0.00005756279,0.000048199763,0.000036507474,0.000037960945,0.0002783558,0.000025976111,0.00019961792],"category_scores_gemma":[0.000014907189,0.00003988135,0.000013630306,0.00014881691,0.000041060146,0.00040400276,0.000044234766,0.000061771854,0.000076336335],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001277147,0.0000984982,0.00029809904,0.000005787677,0.0000152448665,0.000036490674,0.0012663978,4.6149896e-8,0.019604381,0.010179486,0.0018889912,0.9665938],"study_design_scores_gemma":[0.0002347012,0.000225132,0.0028687392,0.000006625001,0.0000025892252,0.000028537952,0.000014932898,0.001041427,0.99365866,0.0014223736,0.00039568904,0.00010056715],"about_ca_topic_score_codex":0.000083022336,"about_ca_topic_score_gemma":0.000041313873,"teacher_disagreement_score":0.9740543,"about_ca_system_score_codex":0.000015803409,"about_ca_system_score_gemma":0.000026039264,"threshold_uncertainty_score":0.21856762},"labels":[],"label_agreement":null},{"id":"W2088538025","doi":"10.1118/1.4734946","title":"SU‐E‐J‐110: A Novel Level Set Active Contour Algorithm for Multimodality Joint Segmentation/Registration Using the Jensen‐Rényi Divergence","year":2012,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Multimodality; Medical imaging; Joint (building); Divergence (linguistics); Algorithm; Segmentation; Image registration; Artificial intelligence; Active contour model; Image segmentation; Level set (data structures); Computer science; Mathematics; Pattern recognition (psychology); Computer vision; Image (mathematics)","score_opus":0.15343149022190744,"score_gpt":0.3706114053745483,"score_spread":0.21717991515264085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088538025","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020089436,0.000022169397,0.9953613,0.0010331697,0.0005871397,0.0007177692,0.00009063615,0.00013451152,0.000044312157],"genre_scores_gemma":[0.29458436,0.0000110450665,0.7012764,0.0027208435,0.0010363003,0.0001762492,0.00008582182,0.000021025531,0.00008791929],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974965,0.00013207473,0.00041361927,0.000347716,0.0011809478,0.00042916444],"domain_scores_gemma":[0.9983749,0.000332066,0.00028209243,0.00043874103,0.00024663043,0.00032560533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011381097,0.00019794337,0.00021688083,0.000027754159,0.00030551717,0.00010465621,0.0006278535,0.00011134015,0.00004238949],"category_scores_gemma":[0.0005327264,0.00014926447,0.000114251765,0.00026822995,0.00027120466,0.0007923935,0.00023930673,0.00026460824,0.000009640284],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000106292,0.0005776937,0.00025724887,0.00004356486,0.000083381216,0.000002680739,0.0041697295,0.000019036697,0.017143358,0.0028011717,0.004218992,0.9706725],"study_design_scores_gemma":[0.0019050045,0.00013968692,0.0039851004,0.00009544621,0.0000688696,0.000027371903,0.0006462927,0.45999256,0.5269036,0.0053406656,0.00032176074,0.00057357707],"about_ca_topic_score_codex":0.00022942653,"about_ca_topic_score_gemma":0.000006654615,"teacher_disagreement_score":0.9700989,"about_ca_system_score_codex":0.00015163048,"about_ca_system_score_gemma":0.00024338962,"threshold_uncertainty_score":0.6086828},"labels":[],"label_agreement":null},{"id":"W2088693473","doi":"10.1109/isbi.2011.5872815","title":"Spatial intensity prior correction for tissue segmentation in the developing human brain","year":2011,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Mental Health","keywords":"Computer science; Segmentation; Intensity (physics); Artificial intelligence; Image segmentation; Human brain; Brain tissue; Computer vision; Pattern recognition (psychology); Physics; Optics; Neuroscience; Biology","score_opus":0.05731196862119606,"score_gpt":0.33442737639393455,"score_spread":0.27711540777273846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088693473","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054932134,0.0000014903644,0.99168116,0.0008848896,0.00031810167,0.00057444314,1.9353293e-7,0.00014715992,0.0008993271],"genre_scores_gemma":[0.31727675,0.0000010742981,0.6776694,0.0045288517,0.000044072654,0.00013284168,0.0000070195542,0.0000048348134,0.00033516344],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99926984,0.00007087092,0.00019774084,0.00018403983,0.00015592252,0.00012159736],"domain_scores_gemma":[0.99957144,0.00009849095,0.00006513949,0.00016803968,0.00007500979,0.000021867518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006135082,0.000065109045,0.00007195806,0.0000774387,0.00010467246,0.000056045687,0.00035284137,0.00003342287,0.000041411357],"category_scores_gemma":[0.00012137443,0.000047776357,0.000016836611,0.00016725041,0.000028443248,0.000335368,0.00005924706,0.00006905976,0.000008591139],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014238056,0.00009209687,0.0009797063,0.00002056896,0.0000062516838,0.0000060177836,0.014658887,3.187734e-7,0.03488522,0.010463107,0.019136325,0.9197373],"study_design_scores_gemma":[0.00046947727,0.0002693281,0.046854127,0.000030529067,0.0000036036085,0.000015962949,0.0007341585,0.0052562645,0.93859744,0.0071896203,0.0003936214,0.00018585891],"about_ca_topic_score_codex":0.0006927417,"about_ca_topic_score_gemma":0.0004543265,"teacher_disagreement_score":0.9195514,"about_ca_system_score_codex":0.000057433703,"about_ca_system_score_gemma":0.00002852426,"threshold_uncertainty_score":0.19482633},"labels":[],"label_agreement":null},{"id":"W2089254731","doi":"10.1007/s11760-012-0394-1","title":"Entropy-based image registration method using the curvelet transform","year":2012,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Curvelet; Artificial intelligence; Affine transformation; Image registration; Wavelet transform; Pattern recognition (psychology); Computer science; Transformation (genetics); Entropy (arrow of time); Computer vision; Complex wavelet transform; Wavelet; Mathematics; Image (mathematics); Discrete wavelet transform","score_opus":0.03211949389381047,"score_gpt":0.3479080397373956,"score_spread":0.3157885458435851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089254731","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00050295616,0.0004885,0.99675554,0.0013584404,0.000045541354,0.00022964612,0.0000016891127,0.00018452329,0.00043317323],"genre_scores_gemma":[0.16862504,0.000008544042,0.82982665,0.0013354175,0.0001342416,0.000019852045,0.0000041898766,0.000012517908,0.000033531996],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99844384,0.00022636172,0.00030968906,0.00027161633,0.000381974,0.00036653943],"domain_scores_gemma":[0.99916565,0.00016623945,0.00016911734,0.00021509132,0.00013631604,0.00014760805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001724937,0.00016571433,0.00015207536,0.00008011724,0.0003786965,0.0006309918,0.00034688716,0.00005568971,0.00004199114],"category_scores_gemma":[0.00008917103,0.000117039126,0.00005246425,0.00031423077,0.00016967868,0.0030251483,0.000051445033,0.00020591913,0.0000039809115],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009027062,0.00005436869,0.00005156541,0.00011285286,0.000006593171,0.0000064143464,0.0011780297,0.000002208051,0.5861826,0.00046705594,0.00035418637,0.4115751],"study_design_scores_gemma":[0.0002964,0.000040174396,0.0000771045,0.000088728615,0.000028960521,0.000045141463,0.00014950572,0.1354968,0.8612912,0.0018993134,0.0003821626,0.0002044986],"about_ca_topic_score_codex":0.000034982408,"about_ca_topic_score_gemma":0.0000010544244,"teacher_disagreement_score":0.4113706,"about_ca_system_score_codex":0.00003943913,"about_ca_system_score_gemma":0.00012828654,"threshold_uncertainty_score":0.60846686},"labels":[],"label_agreement":null},{"id":"W2089564233","doi":"10.1016/j.compbiomed.2010.11.006","title":"Tumor segmentation from computed tomography image data using a probabilistic pixel selection approach","year":2010,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Calgary","keywords":"Artificial intelligence; Segmentation; Pixel; Computer science; Pattern recognition (psychology); Probabilistic logic; Image segmentation; Region growing; Computer vision; Scale-space segmentation; Segmentation-based object categorization; Noise (video); Image (mathematics)","score_opus":0.03663525062595781,"score_gpt":0.34488727650664003,"score_spread":0.30825202588068223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089564233","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07306677,0.00009774046,0.92527044,0.00027980818,0.0006981604,0.00034973072,0.00000555715,0.00017582794,0.000055967663],"genre_scores_gemma":[0.11933207,0.000011261518,0.87919456,0.0009803686,0.00021450617,0.000013226102,0.0002459245,0.000006320719,0.0000017503057],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998463,0.00017707211,0.00035078748,0.0006636729,0.00012753377,0.00021795435],"domain_scores_gemma":[0.9989396,0.00025228763,0.00013930713,0.00048977725,0.00006353927,0.00011549385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006219523,0.00016086522,0.000268001,0.0002812406,0.000095618656,0.000035000805,0.00074991066,0.0000996024,0.000013615123],"category_scores_gemma":[0.00013365681,0.00013199137,0.000014724713,0.0005059673,0.0005027313,0.00036836037,0.00041721886,0.00035674314,0.0000010119884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004348022,0.00038682256,0.021339642,0.00010144978,0.000079822064,0.000025864132,0.0017984697,0.00001704841,0.7487926,0.0079671405,0.0027392728,0.21670842],"study_design_scores_gemma":[0.0017362218,0.00025567532,0.01486865,0.000112005604,0.000028825261,0.0001125176,0.00007019588,0.9662459,0.0038192905,0.012323999,0.000143447,0.0002832851],"about_ca_topic_score_codex":0.00025035854,"about_ca_topic_score_gemma":0.000033009725,"teacher_disagreement_score":0.96622884,"about_ca_system_score_codex":0.000020901189,"about_ca_system_score_gemma":0.000044190012,"threshold_uncertainty_score":0.5382452},"labels":[],"label_agreement":null},{"id":"W2090061382","doi":"10.1118/1.1584043","title":"Testing and optimization of a semiautomatic prostate boundary segmentation algorithm using virtual operators","year":2003,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"London Health Sciences Centre; Robarts Clinical Trials; Western University","funders":"","keywords":"Algorithm; Medical imaging; Computer science; Segmentation; Boundary (topology); Computer vision; Image segmentation; Artificial intelligence; Medical physics; Medicine; Mathematics","score_opus":0.018212264117580163,"score_gpt":0.28565739436604676,"score_spread":0.2674451302484666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090061382","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010605158,0.00003267522,0.9888077,0.00003507268,0.00010284259,0.00021436275,0.0000016888001,0.00012995933,0.00007057722],"genre_scores_gemma":[0.04509786,0.000010546036,0.9544775,0.0003459086,0.000032720756,0.000012725746,0.0000048939423,0.000010521181,0.0000073454125],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985169,0.00012891841,0.0003240778,0.00021906401,0.0006567466,0.00015428287],"domain_scores_gemma":[0.99924755,0.0001830113,0.00014056754,0.00015814615,0.00011805758,0.00015264835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048655734,0.00010607432,0.00015835698,0.00004400494,0.00009432585,0.00007398717,0.00017539169,0.00005412058,0.000021432195],"category_scores_gemma":[0.00071486604,0.00009746586,0.000018464916,0.00046092548,0.00017524578,0.0005314124,0.00008412699,0.00012427897,0.0000012139147],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.751233e-7,0.00012431732,0.00045219253,0.00007597079,0.000016955782,0.000013057482,0.0009764188,0.0016797268,0.004669768,0.00045078836,0.000079083315,0.99146074],"study_design_scores_gemma":[0.0004004895,0.00010854912,0.000035833807,0.00013196116,0.000010074638,0.000019418912,0.000053631273,0.84477776,0.15244631,0.001888691,0.0000035801684,0.0001236704],"about_ca_topic_score_codex":0.000013144753,"about_ca_topic_score_gemma":1.0587333e-7,"teacher_disagreement_score":0.99133706,"about_ca_system_score_codex":0.000043636846,"about_ca_system_score_gemma":0.00026140767,"threshold_uncertainty_score":0.39745426},"labels":[],"label_agreement":null},{"id":"W2090475106","doi":"10.1088/0031-9155/57/15/4905","title":"Tracking the motion trajectories of junction structures in 4D CT images of the lung","year":2012,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Cancer Institute; National Research Council Canada","keywords":"Artificial intelligence; Computer vision; Computer science; Trajectory; Tracking (education); Maxima and minima; Voxel; Metric (unit); Image registration; Motion estimation; Pattern recognition (psychology); Mathematics; Image (mathematics); Physics","score_opus":0.12330265242634757,"score_gpt":0.391696619214017,"score_spread":0.2683939667876694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090475106","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4169685,0.0006974764,0.58070827,0.0009990733,0.0003670243,0.00014286733,7.1637515e-7,0.000011129317,0.00010494283],"genre_scores_gemma":[0.9977356,0.00006782011,0.0019011227,0.00018898588,0.00009861875,0.0000040814557,0.0000011563765,0.0000012713749,0.0000013358841],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99948144,0.00013081402,0.0001652866,0.0000717249,0.000069577094,0.000081176404],"domain_scores_gemma":[0.99959224,0.00015830596,0.00009477143,0.00012026107,0.000024198354,0.000010231187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040636508,0.000045081684,0.000114110735,0.000033558852,0.000021440766,0.000002505648,0.0001640751,0.000015807776,0.0000034192503],"category_scores_gemma":[0.00011775993,0.000021170563,0.000012413717,0.00021753082,0.00034195586,0.000118051044,0.000048381436,0.00010159516,2.355887e-8],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007748251,0.00007453174,0.27861974,0.0000938235,0.000012972466,3.312837e-7,0.007556801,0.000013682948,0.15783249,0.073165014,0.00039852335,0.48222435],"study_design_scores_gemma":[0.00050904363,0.000113538794,0.47855186,0.000111444606,0.000014269034,0.0000067178044,0.00077318185,0.001454016,0.4367894,0.08155989,0.000037456786,0.000079196114],"about_ca_topic_score_codex":0.00019001427,"about_ca_topic_score_gemma":0.000008444806,"teacher_disagreement_score":0.5807671,"about_ca_system_score_codex":0.0000083955,"about_ca_system_score_gemma":0.000009332956,"threshold_uncertainty_score":0.12599507},"labels":[],"label_agreement":null},{"id":"W2090829803","doi":"10.1016/j.ics.2004.03.050","title":"Robust 3D organ segmentation using a fast hybrid algorithm","year":2004,"lang":"en","type":"article","venue":"International Congress Series","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials","funders":"","keywords":"Robustness (evolution); Computer science; Segmentation; Algorithm; Dilation (metric space); Fast marching method; Artificial intelligence; Computer vision; Image segmentation; Pattern recognition (psychology); Mathematics","score_opus":0.027827011501633372,"score_gpt":0.28897426267182713,"score_spread":0.2611472511701938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090829803","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045071356,0.000032861753,0.99145836,0.0010421235,0.0019349968,0.00016244488,0.000023027522,0.00034186727,0.0004971694],"genre_scores_gemma":[0.0314245,0.000030884803,0.9669138,0.00073359127,0.00017571593,0.000029397828,0.000053219723,0.00001602016,0.00062284997],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985546,0.000037255548,0.00029996497,0.00032744554,0.00059512927,0.00018561217],"domain_scores_gemma":[0.999181,0.000027750437,0.00015725255,0.00023062773,0.00031223346,0.00009114459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014284874,0.0001480742,0.00012140107,0.00016503462,0.00011906248,0.00039027748,0.0007498893,0.000035487592,0.0001983925],"category_scores_gemma":[0.00006901575,0.0001488734,0.000042785046,0.0001710386,0.00012870439,0.0021354633,0.00024118654,0.000117027026,0.00004418369],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007415614,0.00063113717,0.0013443967,0.00007623071,0.0004561271,0.0010990695,0.0043758885,0.008639958,0.1045458,0.039788134,0.00555137,0.8334177],"study_design_scores_gemma":[0.0013695759,0.00014519217,0.00028383484,0.0001601735,0.000015739459,0.0005950783,0.00031915942,0.05338745,0.93321264,0.008332426,0.0016742554,0.00050450966],"about_ca_topic_score_codex":0.00009176413,"about_ca_topic_score_gemma":0.000008921205,"teacher_disagreement_score":0.8329132,"about_ca_system_score_codex":0.00022641271,"about_ca_system_score_gemma":0.00011381725,"threshold_uncertainty_score":0.60708815},"labels":[],"label_agreement":null},{"id":"W2090961668","doi":"10.1109/tip.2013.2251644","title":"A Continuous Method for Reducing Interpolation Artifacts in Mutual Information-Based Rigid Image Registration","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mutual information; Image registration; Interpolation (computer graphics); Entropy (arrow of time); Probability density function; Joint entropy; Mathematics; Artificial intelligence; Smoothness; Image scaling; Algorithm; Computer vision; Computer science; Pointwise mutual information; Mathematical optimization; Pattern recognition (psychology); Image processing; Image (mathematics); Principle of maximum entropy; Mathematical analysis; Statistics","score_opus":0.014315544408462125,"score_gpt":0.3067710694712107,"score_spread":0.2924555250627486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090961668","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005863286,0.000006695765,0.9962819,0.0011146016,0.00016105284,0.00097369944,0.000006138625,0.00042033414,0.00044924245],"genre_scores_gemma":[0.26984343,0.0000012483074,0.7289727,0.0006232277,0.000017705846,0.00046322754,0.0000100943535,0.0000117651625,0.000056604218],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823934,0.00010663567,0.00067390944,0.0003373027,0.0003360456,0.00030674587],"domain_scores_gemma":[0.9986218,0.00025698397,0.0003036305,0.00029470687,0.00041466719,0.00010819688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006083171,0.00019484397,0.00020331607,0.0004926508,0.00021990886,0.00091474515,0.00033126466,0.000097011674,0.00007002813],"category_scores_gemma":[0.00012400628,0.00019766482,0.00007806669,0.0005373328,0.0000734466,0.006732766,0.0000025395484,0.00026956407,0.00006073576],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028458644,0.00013419623,0.000002778676,0.00016041203,0.000005938799,0.0000015280199,0.0016438266,0.0006457701,0.122343026,0.000028207538,0.0005195455,0.8744863],"study_design_scores_gemma":[0.00050973607,0.00008845971,0.00002819245,0.00015373674,0.0000069283396,0.0000050613476,0.00015027371,0.6320975,0.3662809,0.0004932107,0.000028361406,0.00015766283],"about_ca_topic_score_codex":0.00018613279,"about_ca_topic_score_gemma":0.000016381271,"teacher_disagreement_score":0.8743287,"about_ca_system_score_codex":0.00014478959,"about_ca_system_score_gemma":0.0002266496,"threshold_uncertainty_score":0.88209087},"labels":[],"label_agreement":null},{"id":"W2091982638","doi":"10.1016/s0933-3657(01)00101-4","title":"Bounded-depth threshold circuits for computer-assisted CT image classification","year":2002,"lang":"en","type":"article","venue":"Artificial Intelligence in Medicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Hong Kong Government; Chinese University of Hong Kong; University of Hong Kong; Ryerson University","keywords":"Perceptron; Algorithm; Electronic circuit; Computer science; Image (mathematics); Bounded function; Image processing; Artificial intelligence; Pattern recognition (psychology); Mathematics; Artificial neural network","score_opus":0.1711571020295055,"score_gpt":0.37698855384234103,"score_spread":0.20583145181283555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091982638","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022143861,0.00011812051,0.98724467,0.00628767,0.0007405051,0.00073743355,0.0000018288515,0.0003703305,0.0022850519],"genre_scores_gemma":[0.7523308,0.00017172289,0.24419343,0.0024332285,0.0004982305,0.00020833751,0.000020671921,0.000025718706,0.00011788233],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99722564,0.00009889338,0.0009686583,0.00066658016,0.0005804776,0.00045976538],"domain_scores_gemma":[0.9982271,0.00045847794,0.00022630539,0.0006750699,0.00022331046,0.00018971211],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010622998,0.00022864445,0.00035128853,0.00036938736,0.00014558404,0.00014169846,0.0010711022,0.0000732367,0.00028649493],"category_scores_gemma":[0.00046946015,0.00020496527,0.00006993585,0.0009807606,0.0004173397,0.0006149606,0.000098255754,0.00027292583,0.0001562398],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000036519841,0.00022503067,0.00008710596,0.000027489841,0.0000079069705,0.00003823681,0.0013352687,0.000008858579,0.013174455,0.036388323,0.0052629802,0.9434407],"study_design_scores_gemma":[0.0002098183,0.00057563244,0.0011222361,0.0002394196,0.0000124652715,0.000047713464,0.0005607875,0.86172545,0.07377084,0.060392056,0.00094328116,0.00040031262],"about_ca_topic_score_codex":0.00007436177,"about_ca_topic_score_gemma":0.000082109254,"teacher_disagreement_score":0.9430404,"about_ca_system_score_codex":0.00014076491,"about_ca_system_score_gemma":0.000033336328,"threshold_uncertainty_score":0.8358241},"labels":[],"label_agreement":null},{"id":"W2092851779","doi":"10.1117/12.770579","title":"Area prior constrained level set evolution for medical image segmentation","year":2008,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"London Health Sciences Centre; St Joseph's Health Care; CARE Canada","funders":"","keywords":"Prior probability; Segmentation; Image segmentation; Artificial intelligence; Computer science; Level set (data structures); Image (mathematics); Stability (learning theory); Noise (video); Set (abstract data type); Pattern recognition (psychology); Computer vision; Contrast (vision); Medical imaging; Mathematics; Algorithm; Machine learning; Bayesian probability","score_opus":0.02314005027411495,"score_gpt":0.2666002090500453,"score_spread":0.24346015877593033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092851779","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75113726,0.000029022774,0.24350439,0.003399433,0.00022551534,0.0009590395,0.000067592984,0.00020116448,0.00047656274],"genre_scores_gemma":[0.094404966,0.00007028192,0.904251,0.0003226503,0.00028440994,0.00042308687,0.000026850492,0.000040031373,0.00017675904],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99686855,3.7401154e-8,0.00086194795,0.00049422367,0.0013618985,0.0004133448],"domain_scores_gemma":[0.9968313,0.00027153612,0.00046016398,0.00008501853,0.0021258944,0.0002260702],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010707566,0.00029687918,0.00037459852,0.00013973362,0.00016355881,0.000114523355,0.0015558713,0.00022628176,0.000021646703],"category_scores_gemma":[0.0018631059,0.00025711575,0.00050879654,0.00037380043,0.00047032817,0.0010092852,0.00024294638,0.00027084386,0.0000016819661],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063782136,0.0001661644,0.00020181177,0.0003763534,0.00024292515,4.5711286e-7,0.00045039796,0.000010193804,0.6605179,0.31782842,0.018423054,0.0017185536],"study_design_scores_gemma":[0.0040773544,0.00088288117,0.0018501653,0.0005130769,0.00012947973,0.00020883663,0.0012976581,0.21887505,0.7652798,0.0053356835,0.0007863491,0.0007636595],"about_ca_topic_score_codex":0.0000070390984,"about_ca_topic_score_gemma":1.3731452e-7,"teacher_disagreement_score":0.6607466,"about_ca_system_score_codex":0.00024165044,"about_ca_system_score_gemma":0.00016151626,"threshold_uncertainty_score":0.9999881},"labels":[],"label_agreement":null},{"id":"W2094645117","doi":"10.1007/s10278-007-9091-y","title":"Automatic Delineation of the Diaphragm in Computed Tomographic Images","year":2008,"lang":"en","type":"review","venue":"Journal of Digital Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Diaphragm (acoustics); Computer science; Artificial intelligence; Computer vision; Computed tomographic; Image segmentation; Pattern recognition (psychology); Radiology; Medicine; Computed tomography; Physics","score_opus":0.02459037669306195,"score_gpt":0.3181476929999552,"score_spread":0.29355731630689325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094645117","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000018840337,0.6317217,0.36760417,0.000101735204,0.00022652012,0.00021067093,0.000004394557,0.000035567937,0.00007639886],"genre_scores_gemma":[0.000775716,0.9602392,0.038790897,0.000073868665,0.00007562303,0.0000051748884,0.0000040174746,0.000020940717,0.000014595984],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971236,0.0001752747,0.0016470301,0.00018001368,0.0006855731,0.00018852587],"domain_scores_gemma":[0.99698395,0.00033334445,0.001945654,0.00038399507,0.00027110172,0.00008198675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036726802,0.00023809203,0.00096542144,0.000782203,0.00003626434,0.00024576578,0.0015379953,0.00006078492,0.000003817372],"category_scores_gemma":[0.00037700092,0.00015071129,0.0005952409,0.0013295267,0.00015362931,0.0016855744,0.00029572943,0.00048974104,0.0000021684184],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.2947125e-7,0.000058761456,0.00013701648,0.00074017816,0.000025326968,0.00005137456,0.00006716059,0.0000019628956,0.0000021298943,0.000012930494,0.00077742955,0.9981255],"study_design_scores_gemma":[0.0060537653,0.0008015842,0.0059422106,0.40391922,0.0015961758,0.039143942,0.00027660464,0.10536276,0.0029479014,0.01007851,0.41820264,0.005674689],"about_ca_topic_score_codex":0.000002503592,"about_ca_topic_score_gemma":1.952498e-7,"teacher_disagreement_score":0.99245083,"about_ca_system_score_codex":0.00009800243,"about_ca_system_score_gemma":0.00034365954,"threshold_uncertainty_score":0.61458284},"labels":[],"label_agreement":null},{"id":"W2096029154","doi":"10.1109/iembs.2006.260658","title":"Towards Real-time Registration of 4D Ultrasound Images","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Feature (linguistics); Image registration; Tracking (education); Process (computing); Feature tracking; Image (mathematics); Feature extraction","score_opus":0.008725423940069844,"score_gpt":0.26652643765976375,"score_spread":0.2578010137196939,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096029154","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013499374,0.000008966098,0.88237655,0.00034984856,0.000028968547,0.00008620513,0.000001473105,0.00030961004,0.11548845],"genre_scores_gemma":[0.07922345,0.000019357562,0.91484046,0.00013107572,0.000041976153,0.000007721509,0.000012241773,0.000004557715,0.005719169],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990897,0.000039314597,0.00026654932,0.00017425197,0.00032314667,0.00010703514],"domain_scores_gemma":[0.9993684,0.00007568401,0.0001094478,0.0003169252,0.000093551964,0.000035972356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028038706,0.00006573709,0.00009496646,0.000055491208,0.000032228516,0.00009025949,0.00034600255,0.000034527282,0.00024881735],"category_scores_gemma":[0.00007108044,0.000056194353,0.00003291682,0.00018830181,0.00009791482,0.00041833532,0.00003970475,0.000036555095,0.000024016617],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013283241,0.00006302981,0.00008979282,0.000012057789,0.0000032899713,0.000004110429,0.000044301734,0.0000030091248,0.87454855,0.043876525,0.066348866,0.015005117],"study_design_scores_gemma":[0.00009266283,0.00004633425,0.0030910482,0.0000074707086,0.0000021841554,0.00000755091,0.0000061645214,0.00044319674,0.9859581,0.010202423,0.00006974701,0.000073112475],"about_ca_topic_score_codex":0.0010703108,"about_ca_topic_score_gemma":0.000007618767,"teacher_disagreement_score":0.11140954,"about_ca_system_score_codex":0.000020264537,"about_ca_system_score_gemma":0.00006034484,"threshold_uncertainty_score":0.27243754},"labels":[],"label_agreement":null},{"id":"W2096061584","doi":"10.1109/icassp.2008.4517940","title":"Segmentation of a speech spectrogram using mathematical morphology","year":2008,"lang":"en","type":"article","venue":"Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Spectrogram; Formant; Speech recognition; Computer science; Mathematical morphology; Segmentation; Artificial intelligence; Pattern recognition (psychology); Image (mathematics); Image processing","score_opus":0.0640269547212465,"score_gpt":0.32401465220228653,"score_spread":0.25998769748104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096061584","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33527464,0.000018083618,0.6614468,0.0004070939,0.00013672058,0.00023444065,0.0000051935217,0.00006841065,0.00240862],"genre_scores_gemma":[0.72600836,0.000042994605,0.27362683,0.00013301909,0.000079518846,0.000005507871,8.588738e-7,0.0000090999965,0.00009380554],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99808794,0.000015181021,0.00054811797,0.00033323557,0.0008101269,0.00020541387],"domain_scores_gemma":[0.99828297,0.00006333685,0.00058005383,0.00010003457,0.0008943661,0.00007924013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034902108,0.00018253643,0.00027261415,0.00019481944,0.00013742603,0.000107463085,0.0008689607,0.00009087817,0.00006850247],"category_scores_gemma":[0.00013915003,0.00014300505,0.00006988989,0.00023189544,0.00047392142,0.00051809556,0.00017338694,0.000243531,0.0000022115612],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003495177,0.00015435,0.00047822605,0.00016974322,0.000030308353,0.000007747293,0.00081811467,0.000020835863,0.94748664,0.0076842294,0.0001376313,0.04297725],"study_design_scores_gemma":[0.0003767638,0.00016957431,0.00021244504,0.00039492466,0.000028187633,0.0003360072,0.00042058816,0.185292,0.78527987,0.027309801,0.0000024786848,0.00017732086],"about_ca_topic_score_codex":0.000014589056,"about_ca_topic_score_gemma":1.8933477e-7,"teacher_disagreement_score":0.39073372,"about_ca_system_score_codex":0.00006682598,"about_ca_system_score_gemma":0.00012829232,"threshold_uncertainty_score":0.58315766},"labels":[],"label_agreement":null},{"id":"W2096470431","doi":"10.1109/ssiai.2008.4512321","title":"Region-Based Feature Extraction Using TRUS Images","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; University of Waterloo","funders":"","keywords":"Feature extraction; Computer science; Feature (linguistics); Artificial intelligence; Fuzzy set; Pattern recognition (psychology); Fuzzy logic; Fuzzy inference; Set (abstract data type); Data mining; Fuzzy inference system; Computer vision; Fuzzy control system; Adaptive neuro fuzzy inference system","score_opus":0.046650379780433585,"score_gpt":0.3171800321583187,"score_spread":0.2705296523778851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096470431","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011188027,0.000032835655,0.99479884,0.001318222,0.00013051275,0.00009748616,2.0342205e-7,0.00046780668,0.0020353184],"genre_scores_gemma":[0.1091602,0.000014134176,0.8873291,0.0018529005,0.00005249734,0.0000063161488,0.000001611188,0.000006011677,0.0015772467],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992333,0.00004758944,0.000109458146,0.00021806496,0.00025660085,0.00013498995],"domain_scores_gemma":[0.9994295,0.000062410625,0.000060350274,0.00030116094,0.000067398156,0.000079143094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008815365,0.0000787152,0.00007663784,0.00009187886,0.00012817353,0.000048679256,0.00027973013,0.00005386729,0.00006122646],"category_scores_gemma":[0.000051163348,0.000066826105,0.00004004964,0.00024959602,0.00006268975,0.0005743968,0.000033863016,0.00011446998,0.000018688668],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019472,0.00033094903,0.0023439485,0.000043716882,0.000021703605,0.0010782877,0.0005033787,0.00016364313,0.44302186,0.0019902312,0.34529004,0.20519277],"study_design_scores_gemma":[0.00025541533,0.00004112593,0.0016555972,0.000016841834,0.000003078346,0.00027289055,0.000012629846,0.04582515,0.9506561,0.00029357048,0.00080292707,0.00016466263],"about_ca_topic_score_codex":0.000044320423,"about_ca_topic_score_gemma":6.7560495e-7,"teacher_disagreement_score":0.5076342,"about_ca_system_score_codex":0.00004813886,"about_ca_system_score_gemma":0.00007792493,"threshold_uncertainty_score":0.27250895},"labels":[],"label_agreement":null},{"id":"W2096596434","doi":"10.1109/igarss.2002.1026145","title":"Shape preserving edge enhancement in remote sensing imagery","year":2003,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Enhanced Data Rates for GSM Evolution; Computer science; Boundary (topology); Orientation (vector space); Computer vision; Edge detection; Closing (real estate); Complement (music); Artificial intelligence; Tracing; Path (computing); Remote sensing; Image (mathematics); Geometry; Image processing; Mathematics; Geology","score_opus":0.024870351387340003,"score_gpt":0.2967052825205255,"score_spread":0.2718349311331855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096596434","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020380504,0.000041604053,0.956899,0.00037954588,0.00012365039,0.00014871587,4.5646452e-8,0.00019557217,0.040173825],"genre_scores_gemma":[0.034795824,0.000019408795,0.9621601,0.0017124494,0.000013450767,9.336671e-7,4.849002e-7,0.0000055491428,0.001291787],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99882525,0.00011977807,0.00026204297,0.00029051577,0.00026258422,0.0002398156],"domain_scores_gemma":[0.9993498,0.00008469902,0.000048782058,0.00039547854,0.000044800425,0.00007645017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063837686,0.00008649295,0.00010200029,0.00011189209,0.000036906084,0.000103834,0.00032785244,0.000032105592,0.00041574333],"category_scores_gemma":[0.0002526631,0.000080374,0.000024077692,0.00033151408,0.000024969417,0.0004661491,0.00016330762,0.00010903073,0.000055623917],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010197426,0.00003134642,0.000041952135,0.000020368501,0.0000035505054,0.00005755002,0.0003790828,0.0000015638664,0.06937984,0.0019413519,0.0056737387,0.92246866],"study_design_scores_gemma":[0.00020698446,0.000025939184,0.00012910078,0.000062606276,0.0000010490156,0.000011637665,0.000038726936,0.1580681,0.8347663,0.0053560603,0.0011675697,0.00016588121],"about_ca_topic_score_codex":0.00006425426,"about_ca_topic_score_gemma":0.00001024958,"teacher_disagreement_score":0.9223028,"about_ca_system_score_codex":0.000059599024,"about_ca_system_score_gemma":0.000047829046,"threshold_uncertainty_score":0.4552098},"labels":[],"label_agreement":null},{"id":"W2096781507","doi":"10.1109/icip.1998.999019","title":"Selective image diffusion: application to disparity estimation","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Regularization (linguistics); Computer science; Inverse problem; Term (time); A priori and a posteriori; Bayesian probability; Eigenvalues and eigenvectors; Algorithm; Computation; Transformation (genetics); Context (archaeology); Diffusion; Image processing; Image (mathematics); Artificial intelligence; Anisotropic diffusion; Mathematical optimization; Applied mathematics; Mathematics; Mathematical analysis; Physics","score_opus":0.01093091622261577,"score_gpt":0.2788137288571699,"score_spread":0.2678828126345541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096781507","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041106602,0.000004609889,0.9873991,0.002493608,0.00004257904,0.00034940004,4.6677164e-7,0.0006042963,0.0086948965],"genre_scores_gemma":[0.17587787,0.0000039587294,0.82148725,0.0017466238,0.000020530366,0.000118337695,0.0000025387062,0.000003948331,0.00073892926],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991462,0.000034234068,0.00015201527,0.00027600877,0.00025891134,0.00013262227],"domain_scores_gemma":[0.99936336,0.000045654164,0.000041160038,0.000337923,0.00008482386,0.00012710564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012590224,0.000073219206,0.000072527306,0.00007271288,0.000078971374,0.000099263925,0.00037011987,0.00003129183,0.00020990263],"category_scores_gemma":[0.00010283576,0.0000646298,0.000019968138,0.000459642,0.000021378652,0.0005394287,0.00013281104,0.00006798779,0.0006218552],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001194071,0.00015816937,0.00013235869,0.00000953839,0.0000033236854,0.0000014438368,0.0006355975,0.000013396316,0.043304093,0.009227638,0.060969517,0.8855437],"study_design_scores_gemma":[0.00013599217,0.00006497746,0.004176806,0.0000072487205,0.0000020750454,0.000003909774,0.000008289934,0.83577394,0.15526521,0.0039018667,0.00050618686,0.00015349737],"about_ca_topic_score_codex":0.000036482383,"about_ca_topic_score_gemma":0.00000472612,"teacher_disagreement_score":0.8853902,"about_ca_system_score_codex":0.000057135694,"about_ca_system_score_gemma":0.000005523007,"threshold_uncertainty_score":0.79928976},"labels":[],"label_agreement":null},{"id":"W2096786469","doi":"10.1109/rose.2008.4669190","title":"Evaluation of growing neural gas networks for selective 3D scanning","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial neural network; Context (archaeology); Sampling (signal processing); Artificial intelligence; Cloud computing; Neural gas; Selection (genetic algorithm); Machine learning; Computer vision; Real-time computing; Recurrent neural network; Geography","score_opus":0.059195289136395775,"score_gpt":0.3377631341712274,"score_spread":0.2785678450348316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096786469","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0069194334,0.000048573507,0.9909827,0.00013161932,0.000095997166,0.0003291569,1.48196e-7,0.00014207234,0.0013502898],"genre_scores_gemma":[0.6193661,0.0000033702368,0.38019007,0.000316709,0.000035327754,0.00004875743,0.00000155307,0.0000034557531,0.000034666933],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989079,0.0001038392,0.00017894947,0.00017051275,0.0005045455,0.00013424775],"domain_scores_gemma":[0.9991092,0.00012101914,0.00007684435,0.00014059576,0.0005110729,0.000041284446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011043475,0.00005642743,0.000087466986,0.00006201116,0.000081432554,0.000015273767,0.00022370825,0.000029263767,0.00001765046],"category_scores_gemma":[0.0002447861,0.00005100531,0.00003540591,0.00024278669,0.000038265443,0.00059893104,0.000049711332,0.000053827644,7.5771845e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005798813,0.000045963243,0.0003851845,0.000008771389,0.000026637046,0.0000015772222,0.001021863,0.0067832414,0.0067745126,0.0012543896,0.007284608,0.97640747],"study_design_scores_gemma":[0.0002422472,0.00007036966,0.00051825267,0.000008259281,0.000009902979,0.000006740611,0.000014217041,0.8990718,0.099304564,0.0006953217,0.0000065402955,0.0000517395],"about_ca_topic_score_codex":0.000021326907,"about_ca_topic_score_gemma":0.0000014339801,"teacher_disagreement_score":0.97635573,"about_ca_system_score_codex":0.00005235173,"about_ca_system_score_gemma":0.00008127752,"threshold_uncertainty_score":0.20799361},"labels":[],"label_agreement":null},{"id":"W2096888883","doi":"10.1109/igarss.1990.688916","title":"Texture Feature Extraction From Texture Spectrum","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Texture (cosmology); Feature extraction; Computer science; Image texture; Artificial intelligence; Pattern recognition (psychology); Extraction (chemistry); Texture compression; Texture filtering; Computer vision; Image segmentation; Image (mathematics)","score_opus":0.008106464233252602,"score_gpt":0.2766280374000719,"score_spread":0.2685215731668193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096888883","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022018934,0.00016998388,0.961186,0.024100365,0.00021204853,0.00013706028,0.0000029474113,0.0008833511,0.013088073],"genre_scores_gemma":[0.12404683,0.000045552184,0.85251033,0.009376238,0.0007605687,0.000015049233,0.000022286988,0.000012573034,0.013210567],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988577,0.00004256556,0.00014900045,0.00037595123,0.00036771194,0.00020708788],"domain_scores_gemma":[0.9992139,0.000063580046,0.000069023845,0.00048775508,0.00003256851,0.00013318023],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00011220315,0.00013470612,0.00011301234,0.000071547125,0.000074408796,0.0001824497,0.00059971295,0.00015056873,0.0016188234],"category_scores_gemma":[0.00003421439,0.00010596662,0.000054750748,0.00024110219,0.000029310362,0.0009902322,0.00010315637,0.00034353643,0.0004393373],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000028363265,0.00006469409,0.00007440152,0.000002432904,0.000010711639,0.000015474387,0.00023141954,0.000003886847,0.019053357,0.0038570561,0.32155105,0.6551327],"study_design_scores_gemma":[0.00062527595,0.00009625355,0.0078057637,0.000039422554,0.000014916243,0.000065297245,0.00008202171,0.031223306,0.688913,0.012034229,0.25851652,0.00058401277],"about_ca_topic_score_codex":0.000052640695,"about_ca_topic_score_gemma":0.00006289329,"teacher_disagreement_score":0.66985965,"about_ca_system_score_codex":0.00006652098,"about_ca_system_score_gemma":0.000031783995,"threshold_uncertainty_score":0.9992938},"labels":[],"label_agreement":null},{"id":"W2097344756","doi":"10.1109/tmi.2007.892510","title":"A Statistical Parts-Based Model of Anatomical Variability","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Artificial intelligence; Statistical model; Pattern recognition (psychology); Computer science; Invariant (physics); Active appearance model; Image (mathematics); Computer vision; Population; Set (abstract data type); Magnetic resonance imaging; Statistical analysis; Mathematics; Statistics","score_opus":0.018400016536653973,"score_gpt":0.32151596796027876,"score_spread":0.3031159514236248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097344756","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005838594,0.0000046848027,0.9969957,0.001242351,0.0002513549,0.00017216675,0.000014608513,0.0003381614,0.0003971017],"genre_scores_gemma":[0.65809363,0.000002822582,0.34022093,0.00163448,0.000014586531,0.000013212659,0.0000014770462,0.0000086820255,0.00001019188],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99705637,0.0001638531,0.0006350803,0.00044283713,0.0013138924,0.000388],"domain_scores_gemma":[0.9974693,0.001250018,0.00008515034,0.00050974835,0.0001294167,0.000556373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025686536,0.00016004409,0.00025090371,0.00020804652,0.00009250287,0.00003405406,0.0006347407,0.00010294787,0.00039734482],"category_scores_gemma":[0.00028639683,0.00014748037,0.00009756386,0.00037001772,0.000477532,0.0002582167,0.0000056471345,0.00054203783,0.000015189293],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006589636,0.0012398983,0.00012856566,0.000080278835,0.000025882544,0.00009975698,0.0002394061,0.0027689207,0.006385025,0.0054257032,0.00083181617,0.9827089],"study_design_scores_gemma":[0.00046256717,0.00003306716,0.00005040695,0.000044521385,0.000012165903,0.0000081608505,0.000010992162,0.8570179,0.13972376,0.0024890592,0.000018286328,0.00012913837],"about_ca_topic_score_codex":0.000027688882,"about_ca_topic_score_gemma":0.000009241733,"teacher_disagreement_score":0.9825797,"about_ca_system_score_codex":0.00010224848,"about_ca_system_score_gemma":0.00035908777,"threshold_uncertainty_score":0.60140747},"labels":[],"label_agreement":null},{"id":"W2097842599","doi":"10.1016/s1077-3142(03)00004-3","title":"An integrated range-sensing, segmentation and registration framework for the characterization of intra-surgical brain deformations in image-guided surgery","year":2003,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; McGill University","funders":"","keywords":"Artificial intelligence; Computer vision; Image registration; Segmentation; Computer science; Voxel; Transformation (genetics); Rigid transformation; Image (mathematics)","score_opus":0.04743858791097781,"score_gpt":0.3291403473260618,"score_spread":0.28170175941508396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097842599","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012234547,0.000022720866,0.98592633,0.0011223909,0.00013500005,0.0004561977,0.0000038830076,0.000075560216,0.000023343926],"genre_scores_gemma":[0.29728046,0.00012748111,0.7020357,0.0004459266,0.000032031974,0.000011088237,0.0000514687,0.000010627948,0.0000051802995],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986428,0.00025522095,0.0004736889,0.0002703225,0.00019982683,0.00015813507],"domain_scores_gemma":[0.9984893,0.0008876967,0.00022695959,0.00021360649,0.00010701739,0.00007537156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011616918,0.00013309484,0.00019661851,0.00022886769,0.00018973825,0.00040914083,0.00012318569,0.000075531476,0.000008175958],"category_scores_gemma":[0.000203793,0.000102013735,0.000036807247,0.00037418294,0.00015643303,0.0014130389,0.000034647815,0.00012602676,3.3844753e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014546418,0.00033606632,0.001088846,0.0004375593,0.00006091063,0.000036551246,0.008197631,0.000034966066,0.23665354,0.1579497,0.0031507353,0.59190804],"study_design_scores_gemma":[0.0018145181,0.00037868923,0.0024636043,0.00058267545,0.000023646307,0.00014723926,0.0016207707,0.8985573,0.054471087,0.03917721,0.00030144054,0.00046179345],"about_ca_topic_score_codex":0.0000075614353,"about_ca_topic_score_gemma":0.0000030275141,"teacher_disagreement_score":0.8985224,"about_ca_system_score_codex":0.000090171874,"about_ca_system_score_gemma":0.000045440273,"threshold_uncertainty_score":0.41599992},"labels":[],"label_agreement":null},{"id":"W2097921221","doi":"10.1109/icpr.2008.4761384","title":"An Approach for Dynamic Combination of Region and Boundary Information in Segmentation","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Segmentation; Scale-space segmentation; Weighting; Image segmentation; Boundary (topology); Computer science; Artificial intelligence; Segmentation-based object categorization; Pattern recognition (psychology); Bayesian probability; Computer vision; Mathematics","score_opus":0.016472809434249166,"score_gpt":0.27942899657283893,"score_spread":0.26295618713858976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097921221","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06715486,0.0000057861507,0.93208134,0.00007086451,0.000017422639,0.00035983653,5.0370954e-7,0.00006940902,0.00023995125],"genre_scores_gemma":[0.56554115,0.000018530616,0.4342178,0.0001268427,0.0000011960371,0.000036377547,0.000047098663,0.0000012794571,0.000009723136],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994498,0.000034142402,0.00021783244,0.0000948561,0.00014003011,0.00006332672],"domain_scores_gemma":[0.9996618,0.000026397724,0.0000921049,0.000116670984,0.00007471192,0.000028323335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020263679,0.000044864224,0.00006694406,0.00016550055,0.000045753364,0.000029348235,0.00011908929,0.000032237127,8.801253e-7],"category_scores_gemma":[0.00001968302,0.000043130476,0.000009815225,0.00015379064,0.0000546098,0.0023049703,0.000019596038,0.000029751955,2.593328e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004162603,0.0005094376,0.00799204,0.00028635023,0.000008516858,0.0000016409243,0.009936456,0.00007577624,0.018281471,0.019059736,0.00079266954,0.94301426],"study_design_scores_gemma":[0.0010763848,0.00023272587,0.019878868,0.000010197916,0.0000017403678,0.000020574884,0.00027972186,0.9266134,0.048495412,0.0032819635,0.000008764581,0.00010025723],"about_ca_topic_score_codex":0.000016615375,"about_ca_topic_score_gemma":0.0000024723136,"teacher_disagreement_score":0.942914,"about_ca_system_score_codex":0.000034795634,"about_ca_system_score_gemma":0.000028166856,"threshold_uncertainty_score":0.17588097},"labels":[],"label_agreement":null},{"id":"W2098467482","doi":"10.1109/imtc.2005.1604536","title":"A Psychometric Approach to Edge Detector Calibration in Grey-scale Images","year":2006,"lang":"en","type":"article","venue":"2005 IEEE Instrumentationand Measurement Technology Conference Proceedings","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Grey scale; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Edge detection; Detector; Filter (signal processing); Computer vision; Scale (ratio); Computer science; Pixel; Calibration; Intensity (physics); Image (mathematics); Pattern recognition (psychology); Image processing; Mathematics; Optics; Statistics; Physics; Telecommunications","score_opus":0.03184382978424995,"score_gpt":0.2563650219921989,"score_spread":0.22452119220794897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098467482","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09463006,0.00006854357,0.8971116,0.002145113,0.00021879072,0.0011621605,0.000004525509,0.00083629333,0.0038229302],"genre_scores_gemma":[0.77507514,0.000022586351,0.22369649,0.00032253066,0.00004793934,0.0006943466,0.000006528488,0.000015425412,0.000119037475],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99682593,0.000020806,0.0006855249,0.0008370247,0.0010441897,0.00058653235],"domain_scores_gemma":[0.998738,0.000012771842,0.00023380345,0.00026904204,0.00060078374,0.00014563561],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00075561955,0.00030481454,0.00030514802,0.002092335,0.00014118264,0.00032095585,0.0010167382,0.00022826783,0.000026656511],"category_scores_gemma":[0.00013724026,0.00030524685,0.000047821835,0.0028671683,0.00014992093,0.0013379944,0.00012008151,0.00030194694,0.00002921117],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032414024,0.00091109815,0.04724209,0.00011105375,0.000026955826,0.0000024742278,0.0005984462,0.000014737287,0.73291314,0.013981287,0.015899107,0.18826719],"study_design_scores_gemma":[0.0016745451,0.000211348,0.018624451,0.000134979,0.00001428288,0.000030186731,0.00039633474,0.0042021535,0.96341604,0.01013017,0.0005698432,0.00059566606],"about_ca_topic_score_codex":0.000069763584,"about_ca_topic_score_gemma":0.000046570374,"teacher_disagreement_score":0.6804451,"about_ca_system_score_codex":0.00043926446,"about_ca_system_score_gemma":0.0001971015,"threshold_uncertainty_score":0.99994},"labels":[],"label_agreement":null},{"id":"W2098502204","doi":"10.1109/icif.2007.4408109","title":"Contour-based multisensor image registration with rigid transformation","year":2007,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Artificial intelligence; Computer vision; Centroid; Image registration; Computer science; Transformation (genetics); Matching (statistics); Point set registration; Point (geometry); Pattern recognition (psychology); Image (mathematics); Mathematics; Geometry","score_opus":0.01307819646343365,"score_gpt":0.2833210165480913,"score_spread":0.2702428200846576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098502204","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016279912,0.000002704536,0.97557974,0.0010686743,0.000033664866,0.00028878587,6.979187e-7,0.00056377234,0.020833995],"genre_scores_gemma":[0.29309452,9.864051e-7,0.70568806,0.00096250867,0.000015863292,0.000008789828,0.000008446634,0.0000040497516,0.00021676855],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990155,0.000027221267,0.00024513135,0.00017664155,0.0003654547,0.00017002638],"domain_scores_gemma":[0.9993664,0.00007583552,0.0000816097,0.00024791207,0.00012804923,0.00010021246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005011366,0.00008656011,0.00007726548,0.000090170724,0.000060407267,0.00011370009,0.00022586982,0.000039241884,0.00007929403],"category_scores_gemma":[0.000029519308,0.00006533943,0.000023590657,0.00020484546,0.0000628538,0.00094766315,0.000006569966,0.00007367561,0.000030830794],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013113553,0.0003326616,0.00022473405,0.00009292635,0.00002136386,0.00012712796,0.0017777813,0.000031347932,0.30461934,0.043928057,0.007284867,0.64142865],"study_design_scores_gemma":[0.00081778306,0.00016692338,0.0010520999,0.000018031844,0.000003410064,0.000010545804,0.000066608074,0.027614865,0.9694233,0.0001923902,0.0004950062,0.00013906926],"about_ca_topic_score_codex":0.000055504548,"about_ca_topic_score_gemma":0.00008354012,"teacher_disagreement_score":0.6648039,"about_ca_system_score_codex":0.00004230712,"about_ca_system_score_gemma":0.00004640073,"threshold_uncertainty_score":0.26644647},"labels":[],"label_agreement":null},{"id":"W2098707698","doi":"10.1109/iembs.2005.1616573","title":"Intracranial Electrode Visualization in Invasive Pre-surgical Evaluation for Epilepsy","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital; Hôpital Notre-Dame","funders":"","keywords":"Epilepsy; Electroencephalography; Visualization; Surgical planning; Epilepsy surgery; Computer science; Brain stimulation; Medicine; Neuroscience; Radiology; Stimulation; Artificial intelligence; Psychology","score_opus":0.02037816664656384,"score_gpt":0.3569086418407376,"score_spread":0.3365304751941738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098707698","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015379783,0.000026407075,0.9816086,0.00091072946,0.000062967796,0.0008831765,5.8321035e-7,0.00019647951,0.0009312265],"genre_scores_gemma":[0.516154,0.000030245412,0.48216385,0.0008543335,0.00019972732,0.00039819628,0.000031124517,0.000008671873,0.00015987312],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986491,0.00013634653,0.00031337724,0.00029090352,0.0004135794,0.00019669712],"domain_scores_gemma":[0.9993094,0.00020469468,0.00007184131,0.00018382372,0.00016882858,0.00006139488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010292117,0.000083627034,0.00010619102,0.00013319647,0.0000376754,0.000073287854,0.00029755762,0.000066284476,0.00020640963],"category_scores_gemma":[0.00038862904,0.00007734624,0.000032086387,0.0003103287,0.000021381407,0.00072562724,0.000045575074,0.000067213994,0.00001740479],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038618404,0.00023661506,0.0004976957,0.000016937853,0.000006942055,0.0000027967824,0.0008158831,0.0002445653,0.010761054,0.038976297,0.0028780317,0.9455246],"study_design_scores_gemma":[0.0013972328,0.00019625957,0.0014985395,0.000016770478,0.000005756928,0.000011668626,0.000011001832,0.7734305,0.21634597,0.0062953006,0.0006393767,0.00015160609],"about_ca_topic_score_codex":0.000013960905,"about_ca_topic_score_gemma":0.00011677817,"teacher_disagreement_score":0.94537294,"about_ca_system_score_codex":0.00015196823,"about_ca_system_score_gemma":0.00013586428,"threshold_uncertainty_score":0.3154088},"labels":[],"label_agreement":null},{"id":"W2098972463","doi":"10.1109/igarss.1989.577831","title":"Digital Enhancement Of Star-1 Sar Imagery For Linear Feature Extraction","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Feature extraction; Computer science; Extraction (chemistry); Star (game theory); Artificial intelligence; Computer vision; Synthetic aperture radar; Pattern recognition (psychology); Remote sensing; Geology; Physics; Chemistry; Astrophysics","score_opus":0.01604662412189928,"score_gpt":0.3125469251069838,"score_spread":0.2965003009850845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098972463","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007318391,0.000031036358,0.9942662,0.0016786784,0.00007854876,0.00023923248,0.0000046019163,0.00014686186,0.0028230129],"genre_scores_gemma":[0.020854792,0.000014232442,0.9702185,0.0005952958,0.000094814146,0.000015989286,0.000011799113,0.0000047814187,0.008189843],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999295,0.000008598188,0.000182391,0.00017736993,0.0002145856,0.00012206693],"domain_scores_gemma":[0.9994622,0.000074659314,0.00008352946,0.00021735998,0.00010748527,0.000054763554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014233458,0.00006835792,0.000088606255,0.000053797026,0.000025952493,0.000056755704,0.00023677704,0.0000367577,0.00010371631],"category_scores_gemma":[0.00006275712,0.00005724821,0.000049748127,0.0000989649,0.000027915858,0.0010403089,0.000054774206,0.000057146997,0.000021562426],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009120872,0.00014919134,0.000010866552,0.000024876956,0.000009700207,8.4370663e-7,0.000097270764,0.0000022434724,0.076958485,0.0006786231,0.048941594,0.8731172],"study_design_scores_gemma":[0.00017964163,0.00010577796,0.00002286074,0.000011079753,0.000002229057,0.0000022062668,0.000016178641,0.014152277,0.96083546,0.00021330133,0.024384364,0.00007464973],"about_ca_topic_score_codex":0.0000016213115,"about_ca_topic_score_gemma":6.446393e-7,"teacher_disagreement_score":0.883877,"about_ca_system_score_codex":0.00003381852,"about_ca_system_score_gemma":0.000030324609,"threshold_uncertainty_score":0.23345143},"labels":[],"label_agreement":null},{"id":"W2099149651","doi":"10.1109/titb.2010.2052060","title":"Automatic Segmentation of Spinal Cord MRI Using Symmetric Boundary Tracing","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Institute of Neurological Disorders and Stroke","keywords":"Tracing; Segmentation; Spinal cord; Artificial intelligence; Computer vision; Computer science; Construct (python library); Boundary (topology); Surgical planning; Active contour model; Pattern recognition (psychology); Physical medicine and rehabilitation; Image segmentation; Medicine; Radiology; Psychology; Mathematics; Neuroscience","score_opus":0.013629298534413019,"score_gpt":0.30414722348589907,"score_spread":0.29051792495148604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099149651","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08643836,0.000013026462,0.91070867,0.0011118789,0.0007532102,0.0004041227,0.000006080843,0.0004925765,0.00007206776],"genre_scores_gemma":[0.7076632,0.000021379701,0.2919951,0.00025156524,0.000009255913,0.00004587336,0.000004576646,0.0000050699787,0.0000039826214],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981783,0.00003091385,0.00092007424,0.00017866872,0.0004565024,0.00023552559],"domain_scores_gemma":[0.9988872,0.00007007642,0.0003789001,0.00044114806,0.00015332326,0.000069384725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052772573,0.0001623571,0.00025408887,0.0045319735,0.00012176163,0.000036993944,0.00052396976,0.00033560183,0.000084735104],"category_scores_gemma":[0.000048983355,0.00015508359,0.000045583507,0.003753349,0.00046848255,0.0015980566,0.0000063259276,0.00075868773,0.000023248087],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022103628,0.00011928905,0.00004011346,0.00010249417,0.000015437823,0.0000034403483,0.00028405528,0.00006416773,0.07283263,0.0009525478,0.000043728705,0.92552],"study_design_scores_gemma":[0.0014261008,0.0011781707,0.0003798343,0.0002413453,0.000024046176,0.00014864601,0.00048268796,0.16292837,0.8306956,0.0022035812,0.00006395223,0.00022761323],"about_ca_topic_score_codex":0.000036560654,"about_ca_topic_score_gemma":0.0000054855773,"teacher_disagreement_score":0.9252924,"about_ca_system_score_codex":0.00012546382,"about_ca_system_score_gemma":0.000120018994,"threshold_uncertainty_score":0.6324125},"labels":[],"label_agreement":null},{"id":"W2099159162","doi":"10.1109/iembs.2007.4352229","title":"Fast B-Mode Ultrasound Image Simulation of Deformed Tissue","year":2007,"lang":"en","type":"article","venue":"Conference proceedings","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pixel; Voxel; Interpolation (computer graphics); Deformation (meteorology); Imaging phantom; Computer science; Computer vision; Finite element method; Artificial intelligence; Volume (thermodynamics); Projection (relational algebra); Image (mathematics); Algorithm; Optics; Physics","score_opus":0.02043535837115485,"score_gpt":0.33058851052174015,"score_spread":0.3101531521505853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099159162","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03207898,0.000008750338,0.9589145,0.00008975535,0.00006645479,0.0002256374,0.0000014719196,0.00025983603,0.008354604],"genre_scores_gemma":[0.7976503,0.000006975187,0.20203054,0.000120366225,0.000029715733,0.000007319092,0.0000028740474,0.0000063595553,0.00014558149],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986528,0.0000051393667,0.00037359796,0.00028660044,0.00041305233,0.0002687651],"domain_scores_gemma":[0.9987344,0.00013391113,0.00021243432,0.00015010177,0.0006356414,0.00013352012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048415977,0.0001271816,0.00016532646,0.00015154945,0.000056767578,0.00013492367,0.0006300954,0.000077587436,0.00008722391],"category_scores_gemma":[0.00043374297,0.000117554315,0.000030222463,0.00037155187,0.00014486934,0.001163436,0.000109178014,0.00012587199,0.000027316004],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009711295,0.000057505447,0.00072283135,0.000083420186,0.0000069075763,0.0000025045508,0.003886047,0.0000044753597,0.8290682,0.012456563,0.00038675702,0.15331511],"study_design_scores_gemma":[0.00020809885,0.000103625265,0.0020898834,0.000057896363,0.0000052164414,0.000009154343,0.00030006474,0.021658765,0.9704444,0.004790953,0.00016801829,0.00016391661],"about_ca_topic_score_codex":0.000019181483,"about_ca_topic_score_gemma":0.000002622766,"teacher_disagreement_score":0.7655713,"about_ca_system_score_codex":0.000045136407,"about_ca_system_score_gemma":0.0000629909,"threshold_uncertainty_score":0.4793726},"labels":[],"label_agreement":null},{"id":"W2099834727","doi":"10.1002/ccd.20357","title":"Real‐time image equalization for coronary X‐ray angiography","year":2005,"lang":"en","type":"review","venue":"Catheterization and Cardiovascular Interventions","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen Elizabeth II Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Medicine; Contrast (vision); Diaphragm (acoustics); Coronary arteries; Coronary angiography; Radiology; Equalization (audio); Angiography; Artificial intelligence; Computer vision; Channel (broadcasting); Cardiology; Artery; Computer science; Acoustics","score_opus":0.05492041044708552,"score_gpt":0.360196781097036,"score_spread":0.3052763706499505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099834727","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.3059226e-7,0.48148638,0.51714647,0.000008791689,0.0001150185,0.0008359462,0.00006204946,0.00025776777,0.00008743469],"genre_scores_gemma":[0.0000019762733,0.89804745,0.09925456,0.000035797268,0.00012566509,0.00084385014,0.0012617811,0.000053619933,0.00037527413],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974106,0.00043372138,0.0008408575,0.0007166773,0.00034365046,0.00025447912],"domain_scores_gemma":[0.9982873,0.00011164705,0.00031695695,0.0009281827,0.00020543126,0.00015047792],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00078043266,0.00036728667,0.0010576128,0.0005504949,0.00019467168,0.00033500357,0.0006169372,0.00024187811,0.00008084229],"category_scores_gemma":[0.00011012795,0.00034750288,0.0058842986,0.00062365667,0.00010851118,0.0005909353,0.0002259525,0.00012087495,0.00004187433],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.330589e-7,0.000059368012,1.6348423e-7,0.0031658562,0.0008956305,0.0000028303207,0.00005909565,0.0000013011937,0.000013884369,0.0009963496,0.00090540736,0.9938996],"study_design_scores_gemma":[0.0004268615,0.00015856478,0.000008771956,0.0060178456,0.0027180368,0.00006825489,0.000011815343,0.0005707531,0.000026848504,0.00019620013,0.9891025,0.00069354015],"about_ca_topic_score_codex":0.0000038500193,"about_ca_topic_score_gemma":2.6150258e-7,"teacher_disagreement_score":0.993206,"about_ca_system_score_codex":0.00006288723,"about_ca_system_score_gemma":0.000068993075,"threshold_uncertainty_score":0.9998977},"labels":[],"label_agreement":null},{"id":"W2100061857","doi":"10.1109/isie.2007.4374866","title":"A Robust, Feature-based Algorithm for Aerial Image Registration","year":2007,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Image registration; Translation (biology); Computer vision; Feature (linguistics); Aerial image; Feature extraction; Computer science; Rotation (mathematics); Noise (video); Scale-invariant feature transform; Feature detection (computer vision); Wavelet; Algorithm; Pattern recognition (psychology); Iterative closest point; RANSAC; Corner detection; Image (mathematics); Image processing; Point cloud","score_opus":0.02571592665791013,"score_gpt":0.2998912916785206,"score_spread":0.27417536502061046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100061857","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000068362697,0.000008960666,0.994875,0.0011555483,0.00024592437,0.0003923503,0.000002367125,0.00050398824,0.002809031],"genre_scores_gemma":[0.0002355955,9.998548e-7,0.99677545,0.0016599316,0.00018744946,0.000033547465,0.000018223593,0.000006255648,0.0010825394],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99906886,0.000019832507,0.00018293821,0.00025367158,0.0002634121,0.0002112777],"domain_scores_gemma":[0.9992801,0.0001271151,0.00007679463,0.00028075304,0.00013332086,0.000101926846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074052316,0.000085867454,0.00008458315,0.000078381774,0.000066016786,0.00014901813,0.000354551,0.00006612144,0.00004037668],"category_scores_gemma":[0.00008408676,0.00007523185,0.00005210211,0.00018525225,0.00004537519,0.00040137288,0.0000299003,0.0000668948,0.00000951409],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008637748,0.000049236052,0.0000015019098,0.000008543171,0.0000033175384,0.000009510341,0.000030391024,0.0000010997925,0.011982592,0.0040494464,0.06489962,0.9189561],"study_design_scores_gemma":[0.0009506263,0.00019169766,0.000061753344,0.000010679671,0.0000045764004,0.0000067224273,0.000013154214,0.25097933,0.74143654,0.0018563365,0.00430279,0.00018578114],"about_ca_topic_score_codex":0.000017867034,"about_ca_topic_score_gemma":0.00001027714,"teacher_disagreement_score":0.9187703,"about_ca_system_score_codex":0.000043416207,"about_ca_system_score_gemma":0.00007224686,"threshold_uncertainty_score":0.3067866},"labels":[],"label_agreement":null},{"id":"W2100145971","doi":"10.1109/ccece.2004.1349692","title":"Shape constrained discrete dynamic contours for noisy object segmentation","year":2004,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Active shape model; Point distribution model; Artificial intelligence; Computer vision; Computer science; Segmentation; Heat kernel signature; Shape analysis (program analysis); Active contour model; Pattern recognition (psychology); Normalization (sociology); Focus (optics); Boundary (topology); Image segmentation; Invariant (physics); Mathematics; Mathematical analysis","score_opus":0.012213829423631025,"score_gpt":0.30620651876130595,"score_spread":0.2939926893376749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100145971","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016125672,0.000015938807,0.99337804,0.0021258434,0.0001549492,0.0006898716,0.000008233153,0.00057442323,0.0014401373],"genre_scores_gemma":[0.25952217,0.000007168258,0.73827595,0.0017192044,0.000018879868,0.00011909879,0.000029116149,0.000008182443,0.00030024684],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989247,0.00002586021,0.00025318062,0.00031211047,0.0002524664,0.00023173164],"domain_scores_gemma":[0.99937356,0.00009709967,0.00008677777,0.00024366628,0.00008500378,0.000113893315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023799852,0.00012085126,0.00013023146,0.00008124463,0.00008838578,0.00013485113,0.00040627806,0.0000475195,0.000104930434],"category_scores_gemma":[0.00008180174,0.00010409939,0.000067828405,0.00016589607,0.000093038514,0.0005771459,0.00006008676,0.00006162346,0.000025904692],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030485306,0.00017291198,0.00006453451,0.000084102125,0.000073536314,0.00002426122,0.0018428826,0.000095748816,0.24055521,0.09842601,0.0023808756,0.65624946],"study_design_scores_gemma":[0.006494898,0.00094800914,0.0007990062,0.00010368959,0.000035160763,0.000049736445,0.0007814513,0.14980477,0.78352064,0.056420706,0.00022149303,0.0008204139],"about_ca_topic_score_codex":0.000031933047,"about_ca_topic_score_gemma":0.000026855727,"teacher_disagreement_score":0.655429,"about_ca_system_score_codex":0.0001144381,"about_ca_system_score_gemma":0.00011284608,"threshold_uncertainty_score":0.424505},"labels":[],"label_agreement":null},{"id":"W2100178215","doi":"10.1109/bmei.2008.108","title":"Multimodality Medical Image Registration Using Hybrid Optimization Algorithm","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Multimodality; Image registration; Metric (unit); Similarity (geometry); Computer science; Matching (statistics); Medical imaging; Mutual information; Optimization algorithm; Component (thermodynamics); Similarity measure; Artificial intelligence; Image (mathematics); Optimization problem; Hybrid algorithm (constraint satisfaction); Algorithm; Computer vision; Pattern recognition (psychology); Mathematical optimization; Mathematics","score_opus":0.03249991477267495,"score_gpt":0.31126136236134766,"score_spread":0.2787614475886727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100178215","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00039659842,0.0000085258225,0.99658614,0.00063593086,0.00012445392,0.00015729776,0.0000015411408,0.00058387214,0.0015056344],"genre_scores_gemma":[0.0072859945,0.00003488812,0.991477,0.0009438983,0.00006746195,0.00000782285,0.000017495822,0.000006596792,0.00015882785],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823254,0.00010905383,0.00033423014,0.0003190441,0.0008278336,0.00017729557],"domain_scores_gemma":[0.9991195,0.000054518212,0.000106358835,0.00036902318,0.00015513829,0.00019545881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048252146,0.00010198261,0.00011592679,0.000072073424,0.0001556364,0.00007058924,0.0004756471,0.0000593336,0.0005016024],"category_scores_gemma":[0.00030196115,0.00009325985,0.00004028961,0.0002237778,0.00015392901,0.0010798412,0.0001262533,0.00012395301,0.000019838897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010417975,0.0009107376,0.00035910998,0.000056952736,0.0000518036,0.0013520085,0.0010164384,0.00225696,0.013020624,0.0035517286,0.040700607,0.9367126],"study_design_scores_gemma":[0.0002082354,0.000020008112,0.0000699443,0.000008415314,0.0000016948243,0.00020941113,0.000005999195,0.9479916,0.051161624,0.00018616754,0.000029805207,0.00010706892],"about_ca_topic_score_codex":0.00019775207,"about_ca_topic_score_gemma":0.0000013631765,"teacher_disagreement_score":0.9457347,"about_ca_system_score_codex":0.00006533045,"about_ca_system_score_gemma":0.0001926363,"threshold_uncertainty_score":0.5492195},"labels":[],"label_agreement":null},{"id":"W2100217050","doi":"10.1109/iembs.2005.1616162","title":"3D TRUS Image Segmentation in Prostate Brachytherapy","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials","funders":"","keywords":"Brachytherapy; Imaging phantom; Prostate; Prostate brachytherapy; Ultrasound; Prostate cancer; Medicine; Radiation treatment planning; Radiology; 3D ultrasound; Image segmentation; Segmentation; Nuclear medicine; Computer science; Artificial intelligence; Radiation therapy; Cancer","score_opus":0.008721355646703953,"score_gpt":0.2901038038119102,"score_spread":0.28138244816520624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100217050","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007576574,0.000030371784,0.9848,0.0022052433,0.000049278726,0.00030137846,5.2603485e-7,0.00036942688,0.0046672267],"genre_scores_gemma":[0.032929283,0.000054032374,0.9626849,0.003140957,0.000032803622,0.000060000842,0.0000042168735,0.000006578197,0.0010871916],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99903023,0.000059941973,0.0002458005,0.00023960447,0.00024240505,0.00018203341],"domain_scores_gemma":[0.9995802,0.00003508368,0.000053342937,0.00023361197,0.00003420727,0.00006356072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028331162,0.000083980085,0.00008464916,0.00012366247,0.000028310145,0.00011521885,0.00030067938,0.000022502389,0.00034691094],"category_scores_gemma":[0.000018648627,0.00007371359,0.00001984597,0.00029310325,0.000032514297,0.0013109996,0.00005344155,0.00008541786,0.00015057235],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037729185,0.000076095515,0.0002625976,0.0000046294695,0.0000021917415,0.000015424936,0.00092426105,0.000006975742,0.042243835,0.00092911354,0.002181578,0.95334953],"study_design_scores_gemma":[0.0013776385,0.00013709557,0.0029807033,0.000019263025,0.0000014600897,0.000025664687,0.00008312988,0.051731702,0.9396747,0.001875741,0.001809652,0.00028324805],"about_ca_topic_score_codex":0.000035719033,"about_ca_topic_score_gemma":0.000025666272,"teacher_disagreement_score":0.9530663,"about_ca_system_score_codex":0.00007313664,"about_ca_system_score_gemma":0.00003054222,"threshold_uncertainty_score":0.37984315},"labels":[],"label_agreement":null},{"id":"W2100457326","doi":"10.1109/rcembs.1995.508692","title":"Mammographic image registration using a pinning function with elastic stretching","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg; University of Manitoba","funders":"","keywords":"Mammography; Image registration; Computer vision; Artificial intelligence; Computer science; Image (mathematics); Function (biology); Subtraction; Background subtraction; Algorithm; Pixel; Mathematics; Breast cancer; Medicine; Arithmetic","score_opus":0.0252479203754448,"score_gpt":0.24949275098834542,"score_spread":0.22424483061290063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100457326","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010891115,0.000021887034,0.98560905,0.00026413743,0.000057577116,0.00012303947,1.451838e-7,0.0005446649,0.0024883992],"genre_scores_gemma":[0.43333006,0.0000042207716,0.56610733,0.00038664564,0.000028438746,0.0000064555315,0.0000011340006,0.000006440859,0.00012927683],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898046,0.000047883634,0.00018735512,0.0002738863,0.00033767812,0.00017274311],"domain_scores_gemma":[0.99943656,0.000040937284,0.000104474406,0.00027307638,0.00006864185,0.00007630382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017223533,0.000100717516,0.000081462356,0.0001575922,0.00012682473,0.00024949928,0.00020972455,0.000033570715,0.00015477747],"category_scores_gemma":[0.000033761808,0.00008183039,0.000025011732,0.00050327025,0.000056229273,0.0012601642,0.000041214087,0.00012310351,0.000015933405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039203504,0.00051522173,0.003633857,0.00020976186,0.00015531361,0.00026750218,0.0030332285,0.00068535,0.35383004,0.040052682,0.013814675,0.5837632],"study_design_scores_gemma":[0.00047298442,0.00036081558,0.00088110025,0.0001261022,0.000025972344,0.000097915836,0.000115476716,0.970737,0.025196094,0.0015783862,0.00008140285,0.00032679643],"about_ca_topic_score_codex":0.000053980562,"about_ca_topic_score_gemma":0.000008288533,"teacher_disagreement_score":0.9700516,"about_ca_system_score_codex":0.00003139356,"about_ca_system_score_gemma":0.000013491234,"threshold_uncertainty_score":0.33369464},"labels":[],"label_agreement":null},{"id":"W2100635090","doi":"10.1007/s11548-010-0404-0","title":"Endocardial boundary extraction in left ventricular echocardiographic images using fast and adaptive B-spline snake algorithm","year":2010,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Sørensen–Dice coefficient; Segmentation; Algorithm; Computer science; Spline (mechanical); B-spline; MATLAB; Computational complexity theory; Image segmentation; Boundary (topology); Artificial intelligence; Computer vision; Mathematics","score_opus":0.01348211206049213,"score_gpt":0.28204849169254886,"score_spread":0.26856637963205676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100635090","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1332029,0.00047508243,0.8632982,0.00035905826,0.0025741432,0.00005280167,0.0000034408502,0.000022672488,0.000011693959],"genre_scores_gemma":[0.56614435,0.00043378634,0.43225822,0.00039029005,0.00075636286,0.0000016901026,0.0000058105697,0.000007254383,0.0000022423585],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9984765,0.0002547689,0.0005582036,0.0002221784,0.00032604908,0.0001622938],"domain_scores_gemma":[0.9985127,0.0006214432,0.00035607794,0.00010647061,0.00029090047,0.00011240743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010499147,0.00013390015,0.00035378992,0.00084115006,0.00006408014,0.00014502433,0.00028675393,0.00012415953,0.000009090754],"category_scores_gemma":[0.000071875496,0.00011947186,0.00015178251,0.00015454144,0.00022303975,0.0007243165,0.000109024346,0.0005440548,3.6826887e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059541075,0.00012232999,0.039618567,0.000006113443,0.0003608181,0.003058094,0.00016184372,0.00007332827,0.0056138826,0.00013225438,0.0008265267,0.9499667],"study_design_scores_gemma":[0.0021295932,0.00028040595,0.7425279,0.0002636053,0.000076258846,0.08170757,0.000050176066,0.16026334,0.0071335626,0.0034825893,0.0015103993,0.00057457096],"about_ca_topic_score_codex":0.0000104782375,"about_ca_topic_score_gemma":0.0000016619813,"teacher_disagreement_score":0.94939214,"about_ca_system_score_codex":0.000036652815,"about_ca_system_score_gemma":0.00011642759,"threshold_uncertainty_score":0.4871921},"labels":[],"label_agreement":null},{"id":"W2100646820","doi":"10.1109/icassp.2004.1326595","title":"Segmentation of prostate contours from ultrasound images","year":2004,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Queen's University","funders":"Nanyang Technological University; University of Pennsylvania","keywords":"Computer vision; Artificial intelligence; Image segmentation; Segmentation; Computer science; Ultrasound; Prostate; Radiology; Medicine","score_opus":0.009691389681206197,"score_gpt":0.2700350276894697,"score_spread":0.2603436380082635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100646820","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01630351,0.000034263347,0.9811732,0.00043597817,0.00007489326,0.00019509498,0.000007752588,0.000228469,0.0015468426],"genre_scores_gemma":[0.2696398,0.000028257193,0.72961307,0.0005088791,0.000013590216,0.000014376968,0.000013845182,0.000003916854,0.0001643055],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99918413,0.000028551507,0.00021804332,0.00018809817,0.00027235207,0.00010885227],"domain_scores_gemma":[0.9994386,0.000090461996,0.00009798937,0.00023494227,0.000076226286,0.00006182342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012631222,0.00007071179,0.000096657874,0.00004950557,0.00002562157,0.000059315258,0.00031260395,0.000024372257,0.00012349876],"category_scores_gemma":[0.000061339335,0.00005999846,0.000028147067,0.00014554673,0.00007145159,0.00059185986,0.000047314228,0.000046763555,0.000026034226],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037827404,0.000086179105,0.0008403904,0.000008412392,0.000015501284,0.0000062630215,0.0013240738,0.00001622421,0.95957625,0.0037041737,0.0016910107,0.03272776],"study_design_scores_gemma":[0.00044534326,0.000055970864,0.0023425014,0.000015398524,0.0000030853266,0.0000018816562,0.00012438605,0.000017304565,0.98259205,0.014325264,0.000008047441,0.00006875394],"about_ca_topic_score_codex":0.000507,"about_ca_topic_score_gemma":0.000010022604,"teacher_disagreement_score":0.25333628,"about_ca_system_score_codex":0.00003696935,"about_ca_system_score_gemma":0.000048779333,"threshold_uncertainty_score":0.24466662},"labels":[],"label_agreement":null},{"id":"W2100692842","doi":"10.1109/pacrim.1995.519599","title":"Image thresholding techniques","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Thresholding; Balanced histogram thresholding; Divergence (linguistics); Otsu's method; Artificial intelligence; Computer science; Pattern recognition (psychology); Minification; Image (mathematics); Histogram; Image segmentation; Function (biology); Computer vision; Discriminant function analysis; Mathematics; Machine learning; Histogram equalization","score_opus":0.026606427938607975,"score_gpt":0.28053776589539375,"score_spread":0.2539313379567858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100692842","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000041384064,0.000030497713,0.87881273,0.0013447348,0.00003989293,0.000093838884,1.3985152e-7,0.0018102349,0.117826544],"genre_scores_gemma":[0.0109323645,0.000042971784,0.9844799,0.0027821276,0.00003268553,0.000023163266,2.8939306e-7,0.0000053078857,0.0017011656],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99924135,0.000022013308,0.00014404408,0.00020570512,0.00022579855,0.00016111076],"domain_scores_gemma":[0.9994468,0.000033707158,0.000031662592,0.00036965482,0.000040666997,0.000077474775],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00016264152,0.00007161156,0.000071043396,0.00008337095,0.00005275382,0.00014439442,0.0006017785,0.000032221997,0.0014558034],"category_scores_gemma":[0.000043809694,0.000059516206,0.000030847375,0.00022335416,0.000045140707,0.00076532166,0.00016796928,0.00008220994,0.00027401675],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.7413599e-7,0.0000521403,0.000037847436,0.000006750915,0.0000034281447,0.000026694815,0.00018876228,1.4290897e-8,0.06699227,0.018490061,0.17964783,0.73455405],"study_design_scores_gemma":[0.000066658446,0.000044743767,0.0000349685,0.000012354705,0.0000011842519,0.000018455643,0.000010670133,0.01709539,0.97274524,0.003891912,0.0059249126,0.00015353019],"about_ca_topic_score_codex":0.0000072660287,"about_ca_topic_score_gemma":3.0709597e-7,"teacher_disagreement_score":0.90575296,"about_ca_system_score_codex":0.00002030959,"about_ca_system_score_gemma":0.000003480529,"threshold_uncertainty_score":0.999457},"labels":[],"label_agreement":null},{"id":"W2100822646","doi":"10.1109/ihmsc.2015.182","title":"A New Brain MRI Image Segmentation Strategy Based on K-means Clustering and SVM","year":2015,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; McGill University","keywords":"Artificial intelligence; Support vector machine; Pattern recognition (psychology); Cluster analysis; Computer science; Segmentation; Image segmentation; Noise (video); Segmentation-based object categorization; Pixel; Scale-space segmentation; Computer vision; Feature (linguistics); k-means clustering; Image (mathematics)","score_opus":0.03015396299135233,"score_gpt":0.31020976247833165,"score_spread":0.28005579948697934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100822646","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017008117,0.000006547368,0.98699224,0.0037944517,0.000068613765,0.0001999624,6.8876335e-7,0.0003617621,0.008405634],"genre_scores_gemma":[0.010316617,0.000002172256,0.9833802,0.005007039,0.000038679987,0.00001153912,0.0000055666687,0.000007907002,0.0012303025],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989848,0.000075092976,0.00016915752,0.00027558833,0.00034105527,0.00015429506],"domain_scores_gemma":[0.9992881,0.00008673729,0.000049498805,0.00024930647,0.0000474682,0.00027891147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035674882,0.00010247562,0.000088159955,0.00009809824,0.000035336147,0.00025501626,0.00024805672,0.00003495429,0.00010555282],"category_scores_gemma":[0.00006480406,0.00008959107,0.000017301543,0.00016979408,0.000028241078,0.00062314305,0.0000874011,0.00007666713,0.000038510683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027078595,0.00008732595,0.0001279079,0.000035606008,0.000010004625,0.000047006877,0.0013746183,0.00073894596,0.030841844,0.0028068768,0.18104628,0.7828565],"study_design_scores_gemma":[0.0013634462,0.0005081333,0.0002752477,0.000033482742,0.0000035438,0.000009379375,0.00023227726,0.89534336,0.0996124,0.0020352895,0.00035745034,0.00022600754],"about_ca_topic_score_codex":0.00012738812,"about_ca_topic_score_gemma":0.00002556377,"teacher_disagreement_score":0.8946044,"about_ca_system_score_codex":0.000051439147,"about_ca_system_score_gemma":0.000118333664,"threshold_uncertainty_score":0.36534178},"labels":[],"label_agreement":null},{"id":"W2100933760","doi":"10.1109/ccece.2000.849720","title":"Improving image segmentation using edge information","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Enhanced Data Rates for GSM Evolution; Edge detection; Image segmentation; Segmentation; Smoothness; Line (geometry); Line segment; Morphological gradient; Artificial intelligence; Computer science; Noise (video); Computer vision; Subtraction; Image (mathematics); Image processing; Algorithm; Mathematics; Pattern recognition (psychology); Scale-space segmentation; Geometry; Arithmetic","score_opus":0.023148413600914887,"score_gpt":0.2699517645367079,"score_spread":0.246803350935793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100933760","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011865443,0.000009105548,0.9926008,0.00017564562,0.0001231733,0.00016065752,5.083902e-7,0.0004558744,0.0052876812],"genre_scores_gemma":[0.018313054,0.000005800709,0.98010284,0.0013759081,0.000025669458,0.000009920321,0.0000036939748,0.0000032125406,0.0001598977],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923205,0.000028532131,0.00023135789,0.000112627946,0.00025455296,0.00014089378],"domain_scores_gemma":[0.9995213,0.000022088516,0.00010020252,0.00021086229,0.000079478225,0.000066049695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001610742,0.0000705993,0.000057930418,0.000120297525,0.000081416634,0.00028065214,0.0002718788,0.000029976722,0.00043527383],"category_scores_gemma":[0.000058861922,0.00006496247,0.000023404902,0.0002387513,0.000024746076,0.005176555,0.000103784776,0.000060876377,0.0002371134],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.096893e-7,0.00002234845,0.000036783556,0.000020242383,0.0000031147952,0.0000019952172,0.0008407292,0.0000046730916,0.11308927,0.0012456615,0.004904345,0.8798304],"study_design_scores_gemma":[0.00021498732,0.000026914699,0.0000567936,0.0000067195656,0.0000026992013,0.00001109548,0.00007945697,0.70005476,0.29906777,0.00019333171,0.00016515273,0.00012029007],"about_ca_topic_score_codex":0.00004685059,"about_ca_topic_score_gemma":5.001857e-7,"teacher_disagreement_score":0.87971014,"about_ca_system_score_codex":0.00006686787,"about_ca_system_score_gemma":0.000010753257,"threshold_uncertainty_score":0.47659433},"labels":[],"label_agreement":null},{"id":"W2101079880","doi":"10.1109/icip.2000.901105","title":"A simple and effective filter based on the rank difference","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Kernel adaptive filter; Mathematics; Filter (signal processing); Rank (graph theory); Kernel (algebra); Gaussian filter; Filter design; Adaptive filter; Algorithm; Edge-preserving smoothing; Composite image filter; Computer vision; Artificial intelligence; Computer science; Pixel; Bilateral filter; Image (mathematics); Combinatorics","score_opus":0.01953059456954055,"score_gpt":0.255529100088151,"score_spread":0.23599850551861046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101079880","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028394954,0.0000067979818,0.9882346,0.0025199861,0.000020090984,0.00024443681,4.875433e-7,0.00015706688,0.0059770346],"genre_scores_gemma":[0.95748633,0.0000039407246,0.029529255,0.012373327,0.000011547888,0.0000747995,3.1469793e-7,0.0000028325796,0.00051762303],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940336,0.000097562865,0.00006512333,0.0001663053,0.00017065261,0.00009701303],"domain_scores_gemma":[0.99901205,0.000636576,0.00001888093,0.00026736542,0.000017586175,0.000047562757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001394047,0.000060906164,0.000055782653,0.000028200866,0.00006102481,0.00008299827,0.00026369604,0.00001779863,0.0006803923],"category_scores_gemma":[0.00011496696,0.00003263577,0.000016213016,0.000103857376,0.000055290384,0.00007845296,0.00006441146,0.000072622366,0.000043677457],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053815666,0.00013725822,0.00075815164,0.000014343261,0.00000898854,0.000013134646,0.00070381496,0.000002595365,0.005516983,0.01307007,0.056333583,0.9234357],"study_design_scores_gemma":[0.00048626328,0.00030006637,0.0132906325,0.000023094015,0.0000031253028,0.0000027090352,0.000013039203,0.84052515,0.14006974,0.0046042474,0.0005187827,0.00016311921],"about_ca_topic_score_codex":0.000008463816,"about_ca_topic_score_gemma":8.726976e-7,"teacher_disagreement_score":0.95870537,"about_ca_system_score_codex":0.0000088991055,"about_ca_system_score_gemma":0.0000024963667,"threshold_uncertainty_score":0.7449818},"labels":[],"label_agreement":null},{"id":"W2101180054","doi":"10.1186/1475-925x-4-58","title":"A coarse-to-fine approach to prostate boundary segmentation in ultrasound images","year":2005,"lang":"en","type":"article","venue":"BioMedical Engineering OnLine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Segmentation; Computer science; Computer vision; Prostate; Dilation (metric space); Pattern recognition (psychology); Image segmentation; Contrast (vision); Similarity (geometry); Mathematics; Image (mathematics); Medicine; Geometry","score_opus":0.009963143030694213,"score_gpt":0.26817322009906397,"score_spread":0.25821007706836974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101180054","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008737353,0.000062579544,0.9867913,0.0030334636,0.00017456574,0.0005432434,0.000033875338,0.0005639214,0.000059676324],"genre_scores_gemma":[0.023487387,0.000013849744,0.9741342,0.0016633727,0.00022912711,0.0001287953,0.00013650539,0.000023563482,0.00018319354],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979682,0.000032363794,0.00047787192,0.0004738642,0.0006179263,0.00042979248],"domain_scores_gemma":[0.99895066,0.000102164406,0.000043633652,0.0003434166,0.000051520925,0.00050858466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046068436,0.00020440752,0.00022723168,0.0005216328,0.0000341603,0.00011929798,0.00061351317,0.0000796468,0.00002482825],"category_scores_gemma":[0.00042679094,0.00019413666,0.00003750257,0.0012258075,0.000052027404,0.00036751587,0.00019168032,0.00023740124,0.000057833033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025804431,0.0017092822,0.00007630226,0.00016758304,0.000030162259,0.00005165976,0.0027508454,0.006706078,0.49011716,0.000433532,0.017734341,0.48019725],"study_design_scores_gemma":[0.0049013738,0.0012947953,0.009509105,0.00069288956,0.000028278342,0.00023720438,0.00026840257,0.6966349,0.196977,0.00020274588,0.08688696,0.0023663423],"about_ca_topic_score_codex":0.000019968387,"about_ca_topic_score_gemma":0.0000045781685,"teacher_disagreement_score":0.6899288,"about_ca_system_score_codex":0.0001962963,"about_ca_system_score_gemma":0.00008197123,"threshold_uncertainty_score":0.7916663},"labels":[],"label_agreement":null},{"id":"W2101215114","doi":"10.1109/tmi.2005.863834","title":"Validation of bone segmentation and improved 3-D registration using contour coherency in CT data","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"National Center for Research Resources","keywords":"Segmentation; Artificial intelligence; Similarity (geometry); Computer science; Computer vision; Pattern recognition (psychology); Image segmentation; Level set (data structures); Scale-space segmentation; Parameterized complexity; Data set; Mathematics; Image (mathematics); Algorithm","score_opus":0.029134938815804475,"score_gpt":0.3179107110453602,"score_spread":0.2887757722295557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101215114","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025954403,0.000058155765,0.9726963,0.0006540106,0.0001754095,0.0002693028,0.000009248822,0.000101488054,0.00008167967],"genre_scores_gemma":[0.8709253,0.00003958344,0.12875646,0.00017887089,0.000028529093,0.000015870877,0.000024684117,0.000008566252,0.000022129056],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813837,0.00013828895,0.00058415387,0.00038379632,0.0005872746,0.0001681091],"domain_scores_gemma":[0.999085,0.00014414218,0.00019667941,0.00041749026,0.00006589625,0.00009077455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088857295,0.000117829186,0.00018173129,0.00023147865,0.00007190395,0.00007563132,0.00032496607,0.00003922247,0.000052900134],"category_scores_gemma":[0.000060852853,0.00011966281,0.000023845438,0.0003346352,0.00013122968,0.0011624737,0.000008889995,0.0002161445,0.0000012768437],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018235698,0.00042467687,0.0005054561,0.00009839976,0.000013520107,0.000059145637,0.00023409611,0.00033716916,0.34592026,0.0001688561,0.0002980407,0.65192217],"study_design_scores_gemma":[0.00090918154,0.000028950531,0.00027002854,0.00015055701,0.000016586717,0.000056771525,0.00008185713,0.59061116,0.4071882,0.00054029614,0.000008714189,0.00013767504],"about_ca_topic_score_codex":0.0012715379,"about_ca_topic_score_gemma":0.00009389601,"teacher_disagreement_score":0.8449709,"about_ca_system_score_codex":0.00006809514,"about_ca_system_score_gemma":0.00012561928,"threshold_uncertainty_score":0.48797077},"labels":[],"label_agreement":null},{"id":"W2101955990","doi":"10.1109/isbi.2008.4540944","title":"Image segmentation based on the Mumford-Shah model and its variations","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Image segmentation; Segmentation; Scale-space segmentation; Segmentation-based object categorization; Artificial intelligence; Computer science; Computer vision; Image (mathematics); Region growing; Minimum spanning tree-based segmentation; Pattern recognition (psychology)","score_opus":0.037177294839089894,"score_gpt":0.2835750865097427,"score_spread":0.24639779167065284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101955990","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014544023,0.0000042978018,0.98819137,0.0041313367,0.00002758949,0.00025328272,0.0000021871506,0.00024429677,0.0056912415],"genre_scores_gemma":[0.24224113,0.000016566251,0.7490315,0.007972586,0.000013511516,0.000071539485,0.0000047778935,0.0000057090133,0.0006426697],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920565,0.000058564798,0.0001385433,0.00019385356,0.0002917759,0.000111586916],"domain_scores_gemma":[0.9994086,0.00016992382,0.000046168167,0.00024749796,0.000064367894,0.000063438434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021558307,0.000076103985,0.000056408186,0.000063818,0.00021543079,0.00006310018,0.0002752692,0.000025363239,0.0001261718],"category_scores_gemma":[0.000092108894,0.00005062529,0.00002028014,0.00018299425,0.00005300315,0.00045215266,0.00006184439,0.000075617274,0.000036065656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002625332,0.00083007716,0.0002108705,0.00006155189,0.000049048504,0.000053003074,0.008042917,0.0036393865,0.29382536,0.49015668,0.12971893,0.07338592],"study_design_scores_gemma":[0.00015203809,0.000027155578,0.00017324953,0.0000040247332,0.0000018521952,0.0000031765815,0.00001387864,0.9544802,0.043422446,0.0016503324,0.000009349348,0.000062303414],"about_ca_topic_score_codex":0.000006779243,"about_ca_topic_score_gemma":0.0000012774192,"teacher_disagreement_score":0.95084083,"about_ca_system_score_codex":0.000023299188,"about_ca_system_score_gemma":0.000054036587,"threshold_uncertainty_score":0.20644395},"labels":[],"label_agreement":null},{"id":"W2102048908","doi":"10.1109/tmi.2006.873614","title":"Adaptive reproducing kernel particle method for extraction of the cortical surface","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McGill University; National Institute of Biomedical Imaging and Bioengineering; National Center for Research Resources; National Institute of Mental Health; Johns Hopkins University; Massachusetts General Hospital","keywords":"Curvature; Kernel (algebra); Polygon mesh; Surface (topology); Artificial intelligence; Intersection (aeronautics); Computer science; Mathematics; Algorithm; Computer vision; Geometry","score_opus":0.021314719906534975,"score_gpt":0.33304027068922304,"score_spread":0.31172555078268804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102048908","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019214693,0.00003113593,0.9925412,0.004262707,0.00057416473,0.00035023314,0.000004138657,0.00020841826,0.000106559],"genre_scores_gemma":[0.6209222,0.0000047198378,0.378305,0.00051107863,0.000049356324,0.0000368893,3.6824264e-7,0.00001054857,0.00015988442],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997782,0.00026041537,0.00046568987,0.00044699197,0.0007760776,0.00026883627],"domain_scores_gemma":[0.9981752,0.00088954065,0.00013448359,0.0005325457,0.00014020447,0.00012803728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013959706,0.00012348291,0.00017405408,0.000043231423,0.0002210502,0.000042727035,0.00048690065,0.00006067517,0.00007484982],"category_scores_gemma":[0.00018609838,0.00009381369,0.00014273859,0.00038530948,0.00018948253,0.00036319625,0.0000067122123,0.00038990748,0.000005986655],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000082463666,0.00091162353,0.00014106504,0.000059164948,0.000048027523,0.000017657356,0.0005665821,0.006958899,0.27366635,0.003117989,0.0032862711,0.7111439],"study_design_scores_gemma":[0.0002764154,0.00003060213,0.00016981855,0.000050051593,0.000017706116,0.000023350114,0.00004894532,0.44558424,0.55281574,0.0008593035,0.00005008126,0.0000737669],"about_ca_topic_score_codex":0.00021904356,"about_ca_topic_score_gemma":0.00001568233,"teacher_disagreement_score":0.7110701,"about_ca_system_score_codex":0.000068601716,"about_ca_system_score_gemma":0.000119706274,"threshold_uncertainty_score":0.38256112},"labels":[],"label_agreement":null},{"id":"W2102099319","doi":"10.1016/j.neuroimage.2008.12.037","title":"Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration","year":2009,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2304,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Mental Health; Biotechnology and Biological Sciences Research Council; Wellcome Trust","keywords":"Image registration; Computer science; Artificial intelligence; Segmentation; Normalization (sociology); Population; Algorithm; Spatial normalization; Ranking (information retrieval); Bayesian probability; Pattern recognition (psychology); Computer vision; Image (mathematics); Voxel; Medicine","score_opus":0.04386624692634696,"score_gpt":0.3480081950943739,"score_spread":0.304141948168027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102099319","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009245587,0.0000040584,0.9802106,0.0023482656,0.0000695951,0.00061470864,0.0000022327163,0.00022448464,0.0072804843],"genre_scores_gemma":[0.45955098,0.0000025018553,0.53594655,0.0042013,0.000095185576,0.000042805,0.000046844427,0.000009935279,0.00010389592],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99783856,0.00015836692,0.0003877995,0.00028274287,0.001188141,0.00014442224],"domain_scores_gemma":[0.9988534,0.00003707757,0.00018738778,0.0004752917,0.00036148648,0.000085346976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001832874,0.00010173449,0.00011886302,0.00016079925,0.000071815855,0.00008809669,0.00041468156,0.000044263426,0.000025816957],"category_scores_gemma":[0.0002855193,0.00010291451,0.000032886895,0.0003890327,0.000026222366,0.000563644,0.00004698116,0.00010090939,0.000029336285],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031736465,0.00009380982,0.0000034241332,0.000008279193,0.0000025184115,0.0000017504177,0.0005048718,0.00014663946,0.4810032,0.002080253,0.006886764,0.5092653],"study_design_scores_gemma":[0.0010106041,0.0006530433,0.011397139,0.00003118016,0.000028452021,0.000010585091,0.00003003039,0.2131709,0.763773,0.008984828,0.00062288647,0.0002873065],"about_ca_topic_score_codex":0.0000065260433,"about_ca_topic_score_gemma":0.0000026584853,"teacher_disagreement_score":0.508978,"about_ca_system_score_codex":0.000060136175,"about_ca_system_score_gemma":0.000064332315,"threshold_uncertainty_score":0.4196732},"labels":[],"label_agreement":null},{"id":"W2102503069","doi":"10.1109/igarss.2002.1026106","title":"Automatic registration of SAR and visible band remote sensing images","year":2003,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Image registration; Synthetic aperture radar; Matching (statistics); Remote sensing; Radar imaging; Image processing; Image (mathematics); Radar; Geography; Mathematics; Telecommunications","score_opus":0.014835121103863267,"score_gpt":0.28117776711665854,"score_spread":0.2663426460127953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102503069","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004582272,0.000039404742,0.9799448,0.00022252109,0.000028063792,0.00008385469,1.2844386e-7,0.00015010338,0.014948809],"genre_scores_gemma":[0.1889599,0.000012709499,0.8105007,0.00018714354,0.0000025339737,5.5437315e-8,3.0942974e-7,0.0000022340987,0.00033442106],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99936116,0.000063063875,0.00018753369,0.00013847226,0.0001719186,0.0000778279],"domain_scores_gemma":[0.9995586,0.000066958055,0.000078430196,0.00021172596,0.00004888866,0.000035385267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003654339,0.000050603958,0.0000820786,0.0000528067,0.000030348745,0.00005966574,0.00008292312,0.000023279057,0.000037529702],"category_scores_gemma":[0.00018764235,0.000043519423,0.000012397632,0.00010227893,0.000053019918,0.00029664772,0.000018233053,0.000034732617,0.000002634471],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.1399314e-7,0.000012039016,0.000024245008,0.00006499883,0.000005919429,0.000007751781,0.00022012752,7.106852e-7,0.14185622,0.0075868643,0.0026720352,0.8475486],"study_design_scores_gemma":[0.00012059337,0.000037036967,0.00020022292,0.00003319877,0.00000266634,0.000027452357,0.000024672921,0.044203077,0.9469402,0.008265665,0.00008447891,0.00006077499],"about_ca_topic_score_codex":0.00004155908,"about_ca_topic_score_gemma":0.0000014393664,"teacher_disagreement_score":0.8474878,"about_ca_system_score_codex":0.000008584451,"about_ca_system_score_gemma":0.000027391861,"threshold_uncertainty_score":0.17746705},"labels":[],"label_agreement":null},{"id":"W2102643595","doi":"10.1109/icpr.2006.1051","title":"Shape-Based Contour Interpolation and Extrapolation Using Distance Mapping","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Extrapolation; Interpolation (computer graphics); Active contour model; Contour line; Artificial intelligence; Computer vision; Computer science; Feature (linguistics); Iterative method; Multivariate interpolation; Iterative reconstruction; Set (abstract data type); Algorithm; Mathematics; Image (mathematics); Bilinear interpolation; Mathematical analysis; Image segmentation; Physics","score_opus":0.025052194251235508,"score_gpt":0.2770893706545245,"score_spread":0.252037176403289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102643595","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00969947,0.00004128329,0.9884148,0.00023000035,0.00005196563,0.00011561698,4.7633955e-7,0.0002470993,0.0011992712],"genre_scores_gemma":[0.5529813,5.174834e-7,0.4466996,0.00024383746,0.000019364626,0.0000025981576,0.0000025260572,0.000002305585,0.000047938418],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993473,0.00003318976,0.00018195217,0.00018292996,0.00015344028,0.0001011863],"domain_scores_gemma":[0.9996719,0.000050869487,0.000073076466,0.00012674027,0.00004264103,0.000034804143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015693139,0.00006501005,0.000064141146,0.00008311355,0.00006360329,0.0001378323,0.00011982312,0.0000288239,0.000045321114],"category_scores_gemma":[0.000016781376,0.00006125284,0.000015235958,0.00015720222,0.00003500521,0.00056055153,0.000031489544,0.000043007756,0.000001975226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011036592,0.00012064787,0.02622013,0.00008031088,0.0000098582905,0.000013786931,0.0005382042,0.00011601624,0.501001,0.08992315,0.001978849,0.379987],"study_design_scores_gemma":[0.00016427235,0.000010483226,0.004704662,0.000028395414,0.0000012740072,0.0000020154744,0.000014621525,0.97264266,0.019888967,0.002331522,0.00012766225,0.0000834643],"about_ca_topic_score_codex":0.0001410708,"about_ca_topic_score_gemma":0.000018286839,"teacher_disagreement_score":0.97252667,"about_ca_system_score_codex":0.000033316985,"about_ca_system_score_gemma":0.00001890519,"threshold_uncertainty_score":0.24978183},"labels":[],"label_agreement":null},{"id":"W2102721883","doi":"10.1109/tbme.2011.2105487","title":"Multiple-Object 2-D–3-D Registration for Noninvasive Pose Identification of Fracture Fragments","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Queen's University","funders":"Canadian Institutes of Health Research","keywords":"Artificial intelligence; Computer vision; Image registration; Metric (unit); Computer science; Similarity (geometry); Object (grammar); Mutual information; Identification (biology); Medical imaging; Patient registration; Pattern recognition (psychology); Image (mathematics); Engineering","score_opus":0.019710587320550012,"score_gpt":0.2544419967099774,"score_spread":0.2347314093894274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102721883","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011768473,0.000011186849,0.9972774,0.00012056024,0.0006797898,0.00040754123,0.000023602066,0.0002833217,0.000019778246],"genre_scores_gemma":[0.7824032,0.000021253607,0.21721046,0.000116738716,0.000036794514,0.00014084802,0.000010595444,0.000013910838,0.000046216308],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99861616,0.000021143356,0.0004803919,0.00029539785,0.00039459523,0.00019232732],"domain_scores_gemma":[0.9990621,0.00018297772,0.00014814387,0.0003554957,0.000106717955,0.00014457211],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028314206,0.00014184006,0.00015874217,0.0002735077,0.00006298417,0.000025942738,0.00039631265,0.00013972966,0.00003315018],"category_scores_gemma":[0.00008010566,0.00013729096,0.00010386472,0.00034882393,0.000064541426,0.00035411367,0.0000022398467,0.0001762084,0.0000075386743],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000842502,0.0013053686,0.000008554199,0.00034542245,0.00020270966,0.000019821244,0.003377133,0.0032123425,0.7818246,0.0005762036,0.0010173746,0.20802625],"study_design_scores_gemma":[0.00046226743,0.00020974544,0.00013015355,0.00005854628,0.00001607839,0.000007198706,0.000022126806,0.09885622,0.8998145,0.00011689856,0.00017101344,0.00013525717],"about_ca_topic_score_codex":0.00002550593,"about_ca_topic_score_gemma":0.0000013905575,"teacher_disagreement_score":0.78122634,"about_ca_system_score_codex":0.0000565792,"about_ca_system_score_gemma":0.000050294828,"threshold_uncertainty_score":0.5598563},"labels":[],"label_agreement":null},{"id":"W2102903558","doi":"10.1109/igarss.2001.977981","title":"Using the Canny edge detector for feature extraction and enhancement of remote sensing images","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":118,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Canny edge detector; Artificial intelligence; Computer vision; Computer science; Preprocessor; Feature extraction; Edge detection; Feature (linguistics); Image segmentation; Noise (video); Pattern recognition (psychology); Enhanced Data Rates for GSM Evolution; Detector; Segmentation; Image gradient; Image (mathematics); Image processing","score_opus":0.04633829167053216,"score_gpt":0.32741657532778257,"score_spread":0.2810782836572504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102903558","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019260144,0.0001952196,0.9964802,0.00076716574,0.00009490745,0.00022975622,9.0406076e-7,0.0000535531,0.00025225698],"genre_scores_gemma":[0.050085776,0.000032214346,0.94905174,0.00033261607,0.000026876183,6.317342e-7,3.2212952e-7,0.000003646262,0.0004662074],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99946696,0.000028875074,0.000121334255,0.00014463167,0.00013682205,0.0001013645],"domain_scores_gemma":[0.9995501,0.000085825006,0.00008315347,0.00018028232,0.00007408041,0.000026572767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019329535,0.00005849436,0.00007461027,0.000034966706,0.000083663384,0.000052461786,0.00011341605,0.000028416964,0.000019046975],"category_scores_gemma":[0.00006205087,0.000039436352,0.00002171327,0.000097660624,0.00004779956,0.0002090342,0.00004750342,0.000056730343,4.7632784e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.3237694e-7,0.0000038348026,7.3828124e-7,0.000010332839,0.00000333482,5.6608445e-7,0.00017006934,3.216662e-7,0.4333686,0.000020018839,0.0015311398,0.5648902],"study_design_scores_gemma":[0.000082277,0.000029665285,0.00002107149,0.00001656214,0.000004962707,0.000012096417,0.000030497176,0.15039952,0.84884155,0.00019949465,0.0003188344,0.000043455773],"about_ca_topic_score_codex":0.00006724085,"about_ca_topic_score_gemma":0.000005139661,"teacher_disagreement_score":0.56484675,"about_ca_system_score_codex":0.000024659486,"about_ca_system_score_gemma":0.000008211036,"threshold_uncertainty_score":0.16081677},"labels":[],"label_agreement":null},{"id":"W2102953959","doi":"10.1109/tip.2009.2039371","title":"Registering a MultiSensor Ensemble of Images","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Pairwise comparison; Artificial intelligence; Cluster analysis; Affine transformation; Pattern recognition (psychology); Computer science; Image (mathematics); Image registration; Image segmentation; Segmentation; Computer vision; Mathematics","score_opus":0.01756279870640615,"score_gpt":0.2970157589490909,"score_spread":0.27945296024268473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102953959","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037187838,0.000016377733,0.99397904,0.00021069159,0.0002816786,0.00015248943,0.0000035367727,0.0004255527,0.001211878],"genre_scores_gemma":[0.4937917,0.0000054315783,0.5057891,0.00010004017,0.0000150493,0.000022317776,2.05022e-7,0.000011022396,0.00026510676],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987587,0.000034336583,0.0003264717,0.00033613533,0.0003232627,0.00022111926],"domain_scores_gemma":[0.99903005,0.00007871201,0.00015523678,0.0004389672,0.00019009948,0.00010691748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022976343,0.00014521937,0.00017085797,0.0002080573,0.00016746769,0.00017505515,0.00048045517,0.00006853578,0.000053216037],"category_scores_gemma":[0.00003047178,0.00014067223,0.00007395628,0.00035215184,0.0001834074,0.0010826365,0.0000045066026,0.0003766108,0.00002041017],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000040748823,0.00006734992,0.0000011214892,0.000054143464,0.0000031311395,0.000006577493,0.00023855842,0.000010379456,0.63892084,0.0000074976647,0.000051141586,0.36063516],"study_design_scores_gemma":[0.00024649067,0.00004442859,0.000033512453,0.00008246209,0.000008376809,0.00003709262,0.000034566856,0.01231053,0.98688084,0.00014238895,0.00003845132,0.00014087003],"about_ca_topic_score_codex":0.000024936848,"about_ca_topic_score_gemma":0.000007686286,"teacher_disagreement_score":0.49007294,"about_ca_system_score_codex":0.00001841503,"about_ca_system_score_gemma":0.000076248645,"threshold_uncertainty_score":0.5736447},"labels":[],"label_agreement":null},{"id":"W2103007299","doi":"10.1109/ccece.2008.4564756","title":"Increasing segmentation accuracy in ultrasound imaging using filtering and snakes","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer vision; Speckle noise; Artificial intelligence; Computer science; Segmentation; Image segmentation; Noise (video); Contrast (vision); Speckle pattern; Ground truth; Ultrasound; Contrast-enhanced ultrasound; Boundary (topology); Pattern recognition (psychology); Image (mathematics); Mathematics; Radiology; Medicine","score_opus":0.02216356327537637,"score_gpt":0.23628457050276708,"score_spread":0.2141210072273907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103007299","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39698198,0.00007988207,0.6021139,0.00022735624,0.0000636772,0.00021245025,0.0000014541932,0.00015795429,0.00016132691],"genre_scores_gemma":[0.8988764,0.00017561256,0.100465834,0.00038312245,0.000054561522,0.000021391048,0.0000027629285,0.000014841406,0.0000054726597],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981959,0.000022550925,0.00033776206,0.0005904889,0.0002526445,0.00060066214],"domain_scores_gemma":[0.99896586,0.00017777192,0.000085908025,0.00011691227,0.00014736438,0.0005062129],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023692727,0.0002882202,0.0002786248,0.00063353067,0.00019816165,0.0005365284,0.000373139,0.00008220421,0.000012621552],"category_scores_gemma":[0.00017347875,0.00030770618,0.000024342597,0.00052552077,0.00008025063,0.001069325,0.000101605976,0.00037664335,0.0000017648899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039835024,0.0001277641,0.097000934,0.00031093054,0.00006370955,0.00049138785,0.009624703,0.00031013295,0.2513764,0.06406338,0.0002673484,0.5763235],"study_design_scores_gemma":[0.00033560637,0.00008341804,0.017692726,0.00023611767,0.000004885787,0.0006905301,0.000045606444,0.9738547,0.0061470317,0.00044709942,0.000029505838,0.00043274407],"about_ca_topic_score_codex":0.0037995377,"about_ca_topic_score_gemma":0.00017255622,"teacher_disagreement_score":0.9735446,"about_ca_system_score_codex":0.00021540272,"about_ca_system_score_gemma":0.00027785095,"threshold_uncertainty_score":0.9999375},"labels":[],"label_agreement":null},{"id":"W2103147398","doi":"10.1109/tip.2010.2045708","title":"Multivariate Image Segmentation Using Semantic Region Growing With Adaptive Edge Penalty","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":85,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Initialization; Image segmentation; Artificial intelligence; Pattern recognition (psychology); Scale-space segmentation; Computer science; Segmentation; Robustness (evolution); Segmentation-based object categorization; Multivariate statistics; Mathematics; Machine learning","score_opus":0.026505513887682915,"score_gpt":0.29990462454453803,"score_spread":0.2733991106568551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103147398","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009824527,0.000010260038,0.9882783,0.00026319196,0.0003164937,0.0004476521,0.0000028039997,0.0006591192,0.00019764157],"genre_scores_gemma":[0.4564726,0.0000028913541,0.54313123,0.00022950636,0.000038366506,0.00004164535,0.0000012373564,0.000026971782,0.000055544275],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99786174,0.00011362068,0.0003960386,0.0006634689,0.00055543776,0.00040966398],"domain_scores_gemma":[0.99867755,0.00009547937,0.00026391557,0.00043233344,0.00034400364,0.00018672044],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033941356,0.00030437246,0.00023684329,0.00034344743,0.0006648547,0.0005726012,0.00047960156,0.00010499639,0.000041461677],"category_scores_gemma":[0.000017840011,0.00027090593,0.00008007841,0.0007435111,0.00024199278,0.0052273953,0.0000073927877,0.0007119566,0.00003049435],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043390166,0.00016722015,0.0000045841625,0.00007652648,0.00002040861,0.00006588837,0.0011815298,0.00018422227,0.8018954,0.00001978118,0.000018048571,0.19632302],"study_design_scores_gemma":[0.0006738891,0.00011425954,0.000038707505,0.00022439034,0.00004701416,0.00016748151,0.0002434742,0.2324688,0.7655184,0.00017987816,0.000003846553,0.00031982487],"about_ca_topic_score_codex":0.00010208521,"about_ca_topic_score_gemma":0.000016596276,"teacher_disagreement_score":0.44664806,"about_ca_system_score_codex":0.00011707602,"about_ca_system_score_gemma":0.00021841127,"threshold_uncertainty_score":0.9999743},"labels":[],"label_agreement":null},{"id":"W2103902312","doi":"10.1109/cvpr.1999.784717","title":"Adaptive balloon models","year":2003,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Smoothness; Initialization; Computer science; Constant (computer programming); Boundary (topology); Slipping; Constraint (computer-aided design); Offset (computer science); Boundary value problem; Control theory (sociology); Algorithm; Mathematical optimization; Mathematics; Artificial intelligence; Mathematical analysis; Geometry","score_opus":0.03580490104319912,"score_gpt":0.26691132766971454,"score_spread":0.23110642662651543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103902312","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000019332185,0.00001673257,0.8361486,0.00010680904,0.000045973957,0.000060229366,8.022705e-8,0.00028646004,0.16331577],"genre_scores_gemma":[0.11022777,0.000005774617,0.88637406,0.0015497778,0.000004472114,0.000010219378,1.6836556e-7,0.0000024046785,0.0018253291],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994278,0.00004530654,0.00008753081,0.00015363748,0.00017805853,0.00010765408],"domain_scores_gemma":[0.99963015,0.000027814727,0.000018561017,0.0002162377,0.00003694372,0.00007030412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015890387,0.000046828245,0.000048441303,0.000033553362,0.000025514242,0.00003921669,0.00027133667,0.000021171967,0.00020207636],"category_scores_gemma":[0.000026735957,0.000038280763,0.000018624807,0.00013388623,0.000019512692,0.00051054213,0.00004073144,0.00004538291,0.000074236996],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.0074017e-7,0.00002272114,0.0000034659936,8.481526e-7,0.0000028701947,0.000004420866,0.00012772752,0.000018391673,0.0006594928,0.94768834,0.0071574515,0.04431398],"study_design_scores_gemma":[0.00029262342,0.00012147245,0.000022058137,0.000009212776,0.0000024157514,0.000014777951,0.00008373895,0.23681372,0.40864134,0.35233423,0.001425882,0.00023854415],"about_ca_topic_score_codex":0.00000934949,"about_ca_topic_score_gemma":9.179829e-7,"teacher_disagreement_score":0.5953541,"about_ca_system_score_codex":0.000015889615,"about_ca_system_score_gemma":0.000028557615,"threshold_uncertainty_score":0.22125946},"labels":[],"label_agreement":null},{"id":"W2104177337","doi":"10.1109/icpr.2006.600","title":"Generalizing inverse compositional image alignment","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Broyden–Fletcher–Goldfarb–Shanno algorithm; Inverse; Image (mathematics); Inverse problem; Computer science; Mutual information; Optimization problem; Class (philosophy); Algorithm; Mathematical optimization; Image registration; Mathematics; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.011174023261779064,"score_gpt":0.2577181815594743,"score_spread":0.24654415829769522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104177337","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012876723,0.000007954343,0.97388613,0.0012181192,0.0000727094,0.00007637859,9.2580257e-7,0.00037785235,0.023072274],"genre_scores_gemma":[0.016482623,0.0000021995938,0.9790507,0.0032964193,0.000067380315,0.000014326274,0.0000143045845,0.000003582796,0.001068461],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992024,0.000033994602,0.00015651001,0.0001798083,0.00029405646,0.00013325868],"domain_scores_gemma":[0.99965787,0.000021458452,0.000034194334,0.00019189475,0.00003818289,0.000056395056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012321598,0.00006457959,0.0000577368,0.000051894312,0.000059250506,0.00010524194,0.00029519657,0.000019862819,0.00032434266],"category_scores_gemma":[0.0000037899804,0.00005785094,0.000030187746,0.00011059135,0.000043646036,0.00040197236,0.00011911634,0.000039326493,0.00014088536],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.3512253e-7,0.000102485734,0.00013927594,0.000005671253,0.0000053373387,0.00003632074,0.00004700151,0.000016386595,0.5036947,0.16024381,0.32872716,0.006981021],"study_design_scores_gemma":[0.00026783615,0.000027306794,0.00073062896,0.000007684167,0.0000022315703,0.000017826307,0.0000088333745,0.02017463,0.96257216,0.013599611,0.002433181,0.00015809796],"about_ca_topic_score_codex":0.00013637646,"about_ca_topic_score_gemma":0.000005567071,"teacher_disagreement_score":0.45887744,"about_ca_system_score_codex":0.000046632213,"about_ca_system_score_gemma":0.000018611274,"threshold_uncertainty_score":0.35513246},"labels":[],"label_agreement":null},{"id":"W2104570405","doi":"10.1109/tip.2009.2017363","title":"Novel Approach for 3-D Reconstruction of Coronary Arteries From Two Uncalibrated Angiographic Images","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":90,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Toronto","keywords":"Artificial intelligence; Computer vision; Imaging phantom; Iterative reconstruction; Projection (relational algebra); Angiography; Coronary arteries; Biplane; Computer science; Medical imaging; Mathematics; Algorithm; Artery; Radiology; Medicine","score_opus":0.020712506136544018,"score_gpt":0.2762873065358332,"score_spread":0.2555748003992892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104570405","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016422045,0.00009128455,0.9968906,0.00018792955,0.00013807608,0.00035616415,0.00005778959,0.00048764085,0.00014830961],"genre_scores_gemma":[0.38610864,0.000012181919,0.6136111,0.00016841952,0.00002197337,0.00003831556,0.0000101208325,0.00000969025,0.000019548139],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986029,0.000042091353,0.00041792268,0.00044984228,0.00026357712,0.00022366695],"domain_scores_gemma":[0.99910355,0.00008113618,0.00020709887,0.00029667583,0.00022259713,0.00008893983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016700088,0.00019152937,0.00023665783,0.00031444588,0.0002166245,0.00019929063,0.00037678066,0.00007418362,0.000018597642],"category_scores_gemma":[0.000009892095,0.00018695695,0.00014101299,0.00067885313,0.0002209812,0.0018305177,0.0000019368838,0.000167907,0.0000011292645],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003623781,0.00018134738,0.0000061044516,0.000032547225,0.000015426342,9.109795e-7,0.00021074351,0.00012992717,0.37400925,0.00001232853,0.000039568597,0.6253256],"study_design_scores_gemma":[0.0009039786,0.00021237925,0.0002844192,0.00011544923,0.000039641312,0.000038302256,0.00012396593,0.09809625,0.8983551,0.0015964223,0.0000036930298,0.00023039238],"about_ca_topic_score_codex":0.00002221238,"about_ca_topic_score_gemma":0.0000010967104,"teacher_disagreement_score":0.6250952,"about_ca_system_score_codex":0.000028278546,"about_ca_system_score_gemma":0.00008591946,"threshold_uncertainty_score":0.76238835},"labels":[],"label_agreement":null},{"id":"W2104793605","doi":"10.1109/icassp.1994.389550","title":"Statistical morphological filters for binary image processing","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Binary number; Mathematical morphology; Dilation (metric space); Binary image; Image processing; Statistical analysis; Computer science; Class (philosophy); Pixel; Artificial intelligence; Noise (video); Pattern recognition (psychology); Image (mathematics); Algorithm; Mathematics; Statistics; Arithmetic; Combinatorics","score_opus":0.0445221141323483,"score_gpt":0.3181595245588077,"score_spread":0.2736374104264594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104793605","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001832632,0.000024449564,0.99393517,0.001555743,0.00004809578,0.00020979645,0.000004218886,0.0005090921,0.0035301982],"genre_scores_gemma":[0.027864968,0.000005390778,0.9688309,0.0022969241,0.000026194022,0.000067058805,0.0000050502504,0.000005372384,0.00089815224],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909097,0.000031812484,0.00017822297,0.00028157188,0.00019920597,0.00021820956],"domain_scores_gemma":[0.99945235,0.00015436529,0.000036195153,0.00018469294,0.000056547866,0.00011586184],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00016141155,0.00008214304,0.000098038756,0.000047740235,0.00007739339,0.00013656873,0.00040748608,0.000039775667,0.0011814337],"category_scores_gemma":[0.0001733703,0.000062778774,0.000027927174,0.00013185428,0.00010951827,0.00042930915,0.00010379318,0.00007273352,0.000090367794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004251738,0.00019478698,0.000026566715,0.00004451437,0.0000041957474,0.00013101814,0.00019382857,7.694463e-7,0.025287315,0.009053874,0.30942902,0.6556299],"study_design_scores_gemma":[0.0009698368,0.00077994046,0.00063791714,0.000033826662,0.000010529094,0.000121304234,0.00006827694,0.89882123,0.08324064,0.011291759,0.00351131,0.0005134072],"about_ca_topic_score_codex":0.0000019011726,"about_ca_topic_score_gemma":9.858587e-8,"teacher_disagreement_score":0.89882046,"about_ca_system_score_codex":0.00002029859,"about_ca_system_score_gemma":0.000009709314,"threshold_uncertainty_score":0.9997316},"labels":[],"label_agreement":null},{"id":"W2105049046","doi":"10.1109/ccece.2007.298","title":"Indirect Knowledge-Based Approach to Non-Rigid Multi-Modal Registration of Medical Images","year":2007,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Modalities; A priori and a posteriori; Computer science; Modal; Medical imaging; Artificial intelligence; Computer vision; Image registration; Medical diagnosis; Image (mathematics); Pattern recognition (psychology); Radiology; Medicine","score_opus":0.03494409462607209,"score_gpt":0.35021053582017203,"score_spread":0.31526644119409997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105049046","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007269725,0.000015527343,0.97143394,0.00053210487,0.00010798442,0.00031505956,9.167978e-7,0.00029360293,0.026573915],"genre_scores_gemma":[0.26678675,0.000001828003,0.73191196,0.0006750153,0.00004124801,0.000019200006,0.0000059281224,0.0000065446134,0.00055152574],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99787337,0.00006532135,0.0004755611,0.00036768115,0.0009667025,0.00025136076],"domain_scores_gemma":[0.99876857,0.00017869812,0.00011199644,0.000443092,0.00016535968,0.00033231202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00241164,0.000125848,0.00019099306,0.0002519889,0.00004434063,0.000056922716,0.0010210811,0.00012874205,0.00006829584],"category_scores_gemma":[0.00041742303,0.000105233405,0.000056905305,0.00060138694,0.00011778814,0.00024884907,0.00015194449,0.00015685386,0.00003406482],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058291662,0.0039282795,0.0012860772,0.00034262202,0.00005262185,0.000056801182,0.0034037253,0.000039918377,0.09992379,0.012158979,0.090903796,0.7878451],"study_design_scores_gemma":[0.0006339301,0.00012444046,0.003679756,0.000045965546,0.0000037210798,0.000006207941,0.000054183733,0.05159506,0.9433403,0.00006275442,0.0002758992,0.0001777883],"about_ca_topic_score_codex":0.00010252126,"about_ca_topic_score_gemma":0.000028737475,"teacher_disagreement_score":0.8434165,"about_ca_system_score_codex":0.000049601786,"about_ca_system_score_gemma":0.00024933103,"threshold_uncertainty_score":0.42912936},"labels":[],"label_agreement":null},{"id":"W2105365961","doi":"10.1109/icdsp.1997.628077","title":"Region growing and region merging image segmentation","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Pixel; Artificial intelligence; Image segmentation; Region growing; Computer science; Segmentation; Computer vision; Homogeneity (statistics); Range segmentation; Pattern recognition (psychology); Grey scale; Scale (ratio); Image texture; Image (mathematics); Geography; Cartography","score_opus":0.028213493384935964,"score_gpt":0.26622850459320613,"score_spread":0.23801501120827018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105365961","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018706862,0.000076787095,0.98955655,0.0029955718,0.000069372014,0.00012298556,3.4559424e-8,0.0004649501,0.0048430418],"genre_scores_gemma":[0.17289273,0.000293258,0.82176185,0.0032102116,0.000051665134,0.000023025512,0.0000015206734,0.0000104062,0.0017553458],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991744,0.000054739303,0.00016386708,0.00026036246,0.00020265601,0.00014394529],"domain_scores_gemma":[0.99955916,0.00004676571,0.00005920449,0.00021688789,0.00003525001,0.00008272397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011917626,0.00008133335,0.00007505817,0.00009937979,0.00009699423,0.00015157438,0.00019597416,0.000030782856,0.000050844934],"category_scores_gemma":[0.000038909555,0.000073981835,0.000020809559,0.00020071506,0.000046666566,0.0019310559,0.000100964164,0.00006846804,0.000024913179],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012417795,0.00003668566,0.00026365515,0.000026652939,0.0000076426,0.00009500805,0.0014549745,7.620199e-7,0.046873126,0.009255499,0.029885788,0.91209894],"study_design_scores_gemma":[0.0012162615,0.00020820554,0.00084782403,0.00013614947,0.000018267654,0.00046859222,0.00066906644,0.19254217,0.7950994,0.006677018,0.0014396225,0.00067745463],"about_ca_topic_score_codex":0.000017451544,"about_ca_topic_score_gemma":5.416088e-7,"teacher_disagreement_score":0.91142154,"about_ca_system_score_codex":0.000030221117,"about_ca_system_score_gemma":0.0000031531015,"threshold_uncertainty_score":0.30168918},"labels":[],"label_agreement":null},{"id":"W2105655359","doi":"10.3109/10929080500230320","title":"A point-selection algorithm based on spatial-stiffness analysis of rigid registration","year":2005,"lang":"en","type":"article","venue":"Computer Aided Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Algorithm; Computer science; Computation; Point (geometry); Selection (genetic algorithm); Artificial intelligence; Image registration; Set (abstract data type); Computer vision; Noise (video); Point cloud; Mathematics; Image (mathematics); Geometry","score_opus":0.016774607027823414,"score_gpt":0.2660548628692402,"score_spread":0.24928025584141678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105655359","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021199137,0.000009220674,0.9963671,0.00058340584,0.00027602765,0.00015069143,0.0000046038554,0.00036339296,0.000125624],"genre_scores_gemma":[0.3128885,0.000005390687,0.6847567,0.0019329644,0.00023442223,0.000028470744,0.00008718745,0.00001255762,0.00005379061],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978368,0.00022311197,0.00063743844,0.00047580365,0.00059709034,0.00022975465],"domain_scores_gemma":[0.9980153,0.0007361031,0.00034687406,0.0005613773,0.00022087329,0.000119503246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008149425,0.00017345691,0.0004241201,0.0010646633,0.00007231583,0.00011460346,0.00036909725,0.0000826577,0.00008801156],"category_scores_gemma":[0.000091205206,0.00017138064,0.000272351,0.0017502506,0.00005876739,0.00051243283,0.000060418923,0.00013308374,0.000015420022],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009738729,0.00018513868,0.00048716264,0.000013742526,0.000096766234,0.000007930939,0.00006709477,0.004358961,0.00047249027,0.00019124924,0.0070025995,0.9871071],"study_design_scores_gemma":[0.00014505173,0.000050658284,0.011801948,0.00004022431,0.000055779106,0.0000026040016,0.0000011993973,0.9629815,0.024400853,0.00009605781,0.000257463,0.00016662844],"about_ca_topic_score_codex":0.000109692075,"about_ca_topic_score_gemma":0.000021498938,"teacher_disagreement_score":0.9869405,"about_ca_system_score_codex":0.00010062426,"about_ca_system_score_gemma":0.0001316612,"threshold_uncertainty_score":0.69887},"labels":[],"label_agreement":null},{"id":"W2106033751","doi":"10.1016/j.media.2013.12.002","title":"Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge","year":2013,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":820,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials","funders":"National Institute of Biomedical Imaging and Bioengineering; National Cancer Institute; KWF Kankerbestrijding; National Institutes of Health; National Science Foundation","keywords":"Artificial intelligence; Segmentation; Computer science; Algorithm; Computer vision; Prostate; Pattern recognition (psychology); Medicine","score_opus":0.03657965750292922,"score_gpt":0.3547086912706948,"score_spread":0.3181290337677656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106033751","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001550177,0.00015240813,0.9877267,0.00855152,0.000061166975,0.0015051253,0.000004540532,0.000095710784,0.00035266374],"genre_scores_gemma":[0.18018322,0.00029809674,0.812542,0.0017107532,0.00018474051,0.004515063,0.00014772004,0.000026389192,0.0003919874],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99567354,0.00045061536,0.0005555052,0.00036847408,0.0027150633,0.00023680922],"domain_scores_gemma":[0.9973446,0.000293991,0.00027373113,0.0005536917,0.0013592794,0.0001747046],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005184345,0.0001302823,0.0002695637,0.00022323063,0.00010396221,0.00012269632,0.000812546,0.000074143514,0.0012905238],"category_scores_gemma":[0.0013197721,0.00008410465,0.0002076533,0.0010652986,0.00019561358,0.0007082358,0.00013515132,0.00012498305,0.000032857948],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020021416,0.00016598284,0.00007275564,0.000029898018,0.000523212,0.0000012270241,0.0011543231,0.000019126168,0.0021344975,0.00014473786,0.0045494833,0.9912028],"study_design_scores_gemma":[0.00074880396,0.00010605013,0.00078758324,0.000017237317,0.0009898231,0.0000011865905,0.00022574014,0.95205534,0.03980379,0.005059289,0.00007665533,0.00012852123],"about_ca_topic_score_codex":0.00013486753,"about_ca_topic_score_gemma":0.0000201749,"teacher_disagreement_score":0.9910742,"about_ca_system_score_codex":0.00007019517,"about_ca_system_score_gemma":0.00019057232,"threshold_uncertainty_score":0.9996224},"labels":[],"label_agreement":null},{"id":"W2106358106","doi":"10.1109/icpr.2006.117","title":"A New Hierarchical Image Segmentation Method","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Image segmentation; Computer science; Artificial intelligence; Segmentation; Piecewise; Image (mathematics); Scale-space segmentation; Segmentation-based object categorization; Computer vision; Pixel; Regular polygon; Pattern recognition (psychology); Image processing; Piecewise linear function; Mathematics","score_opus":0.010159759322932931,"score_gpt":0.31776897540701704,"score_spread":0.3076092160840841,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106358106","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000023605624,0.000009839953,0.96536213,0.0017633785,0.00007008435,0.00013804887,3.2924518e-7,0.00062783976,0.032004725],"genre_scores_gemma":[0.00066926016,0.000001691437,0.99377656,0.001181376,0.00008563083,0.000012487373,0.000006004748,0.000005186242,0.004261783],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989581,0.00009213851,0.00020743474,0.00025501978,0.00032256957,0.00016469996],"domain_scores_gemma":[0.99945813,0.000095769305,0.000044085948,0.000257558,0.000035541714,0.000108924476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002635059,0.00008124984,0.00008433956,0.00008561414,0.000043775548,0.0001599091,0.0003985132,0.000034320477,0.0006978764],"category_scores_gemma":[0.000026814616,0.00006885467,0.000038753675,0.00026887518,0.000021271268,0.0005909036,0.00011179618,0.00008780335,0.00013270075],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015196864,0.000034846704,0.000026862715,0.000004111135,0.0000032608132,0.00001526635,0.00008146919,0.0000019095119,0.13900039,0.061440777,0.09434833,0.7050413],"study_design_scores_gemma":[0.0003951343,0.00005351045,0.00068801094,0.0000052383093,0.0000036429792,0.00003197024,0.000011063786,0.012652592,0.923641,0.06139893,0.00095800706,0.00016087828],"about_ca_topic_score_codex":0.00030805665,"about_ca_topic_score_gemma":0.000004864688,"teacher_disagreement_score":0.7846406,"about_ca_system_score_codex":0.000032400825,"about_ca_system_score_gemma":0.000060034596,"threshold_uncertainty_score":0.7641257},"labels":[],"label_agreement":null},{"id":"W2106384870","doi":"10.1109/icpr.2002.1048467","title":"Image registration using virtual circles and edge direction","year":2003,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer vision; Computer science; Enhanced Data Rates for GSM Evolution; Similarity (geometry); Artificial intelligence; Image registration; Heuristic; Image (mathematics); Noise (video); Set (abstract data type); Smoothness; Algorithm; Mathematics","score_opus":0.023441171724921734,"score_gpt":0.29250902224111675,"score_spread":0.26906785051619503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106384870","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0074959444,0.000020046182,0.9839575,0.00010181447,0.000100529505,0.00007427349,1.3036669e-7,0.00023001301,0.0080197295],"genre_scores_gemma":[0.17008853,0.000016823396,0.82918966,0.0002877149,0.000018906783,0.000003992177,6.7775323e-7,0.0000036205086,0.00039007328],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99942905,0.00006265382,0.00011629674,0.00017377343,0.0001355383,0.000082693674],"domain_scores_gemma":[0.99969244,0.000030398916,0.000042399122,0.00014388346,0.000037938764,0.000052956013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000244598,0.000049891652,0.00004986555,0.00004890314,0.00007112167,0.0001354982,0.00008162322,0.000027214312,0.000036929687],"category_scores_gemma":[0.00011399392,0.00004756044,0.000011414815,0.0001309683,0.00004876035,0.000692071,0.000024092236,0.000042812146,0.000005741559],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014938794,0.000056776313,0.00022896093,0.000013452514,0.000006752011,0.000008945204,0.00035608863,0.0000014075979,0.62565863,0.076997675,0.0034881635,0.29318166],"study_design_scores_gemma":[0.00031108054,0.0001116713,0.0015823774,0.000020774105,0.0000061146097,0.000088998546,0.00013397321,0.04221809,0.9481743,0.006232032,0.00088062865,0.0002399795],"about_ca_topic_score_codex":0.000028465594,"about_ca_topic_score_gemma":0.0000034140376,"teacher_disagreement_score":0.32251564,"about_ca_system_score_codex":0.000023255698,"about_ca_system_score_gemma":0.000025733576,"threshold_uncertainty_score":0.19394584},"labels":[],"label_agreement":null},{"id":"W2106535234","doi":"","title":"Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials","year":2014,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Inference; Semidefinite programming; Polynomial; Mathematics; Regular polygon; Context (archaeology); Mathematical optimization; Markov chain; Algorithm; Applied mathematics; Computer science; Artificial intelligence","score_opus":0.0061467088673332485,"score_gpt":0.24553286291358636,"score_spread":0.2393861540462531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106535234","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017757146,0.0000069293405,0.97447103,0.00032342327,0.00009171341,0.00017785969,4.81666e-7,0.00015573885,0.0070156516],"genre_scores_gemma":[0.76208997,0.0000015517614,0.23710401,0.0004669966,0.000019421617,0.000008834577,6.096331e-7,0.0000028395127,0.00030576938],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989835,0.00009702752,0.00025110506,0.00020344877,0.0003136033,0.00015128923],"domain_scores_gemma":[0.9990968,0.00025070045,0.00011387461,0.000353738,0.000103332815,0.00008155423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004417357,0.000085816704,0.00020044252,0.00006763107,0.000030299934,0.000050032697,0.00047001417,0.0000449097,0.00016178351],"category_scores_gemma":[0.00018021163,0.000058789512,0.00003617778,0.0001441897,0.00008913314,0.00007196343,0.00012184995,0.00007083455,0.000010559025],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024579823,0.00042797756,0.0014619009,0.00008669284,0.000068419824,0.000017127966,0.0010578287,0.00052617193,0.038939,0.012343521,0.015281755,0.9295438],"study_design_scores_gemma":[0.005611754,0.00105142,0.0024711045,0.00011637588,0.00002505512,0.000019776982,0.000054302607,0.18574189,0.80343443,0.0005702282,0.00045100492,0.00045262976],"about_ca_topic_score_codex":0.00008809964,"about_ca_topic_score_gemma":0.000006013808,"teacher_disagreement_score":0.92909116,"about_ca_system_score_codex":0.0000074140767,"about_ca_system_score_gemma":0.000039802424,"threshold_uncertainty_score":0.23973668},"labels":[],"label_agreement":null},{"id":"W2107050683","doi":"10.1109/tmi.2003.819283","title":"Intensity-based 2-D-3-D registration of cerebral angiograms","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":144,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Thomas Hospital","funders":"","keywords":"Imaging phantom; Digital subtraction angiography; Similarity (geometry); Segmentation; Standard deviation; Artificial intelligence; Nuclear medicine; Subtraction; Magnetic resonance angiography; Computed radiography; Image registration; Similarity measure; Mathematics; Magnetic resonance imaging; Computer science; Angiography; Medicine; Radiology; Image quality; Image (mathematics)","score_opus":0.015543092727364021,"score_gpt":0.278472194895236,"score_spread":0.26292910216787196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107050683","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00042692103,0.000020001431,0.9953317,0.0019375595,0.0005119932,0.00014253242,0.000001616778,0.00031409165,0.0013135885],"genre_scores_gemma":[0.82732767,0.000007970561,0.16985413,0.002689666,0.000016240045,0.000021753762,0.0000018339695,0.000010577168,0.0000701829],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99793607,0.00015115723,0.00041616586,0.0003187465,0.000939344,0.00023853272],"domain_scores_gemma":[0.9988758,0.0001719578,0.000114202536,0.00043369722,0.00014564575,0.0002587059],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007260982,0.0001353446,0.00018544159,0.00023032847,0.00009780585,0.000053522348,0.00042878272,0.00007224377,0.00030278537],"category_scores_gemma":[0.0001349695,0.00012699555,0.00014320343,0.0005321586,0.00024543333,0.00034355177,0.0000016923368,0.00033226184,0.00001960104],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000323756,0.0011223872,0.00029712231,0.00010792244,0.000060954804,0.00014428065,0.00048463486,0.00040756134,0.011217688,0.0045083263,0.0055682585,0.97604847],"study_design_scores_gemma":[0.0011206629,0.00013092718,0.000084201456,0.00022821955,0.00002778951,0.0000797785,0.000099617864,0.13248292,0.8627376,0.0020474463,0.0006539714,0.00030689946],"about_ca_topic_score_codex":0.000051909967,"about_ca_topic_score_gemma":0.0000067110886,"teacher_disagreement_score":0.97574157,"about_ca_system_score_codex":0.000046373243,"about_ca_system_score_gemma":0.00019558863,"threshold_uncertainty_score":0.51787287},"labels":[],"label_agreement":null},{"id":"W2107159423","doi":"10.1109/fuzz.2001.1007236","title":"Fuzzy integral based region merging for watershed image segmentation","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Fuzzy logic; Watershed; Artificial intelligence; Image segmentation; Preprocessor; Pattern recognition (psychology); Computer science; Segmentation; Image fusion; Image (mathematics); Fuzzy set; Computer vision; Mathematics; Data mining","score_opus":0.03487611454850921,"score_gpt":0.2844641448600572,"score_spread":0.24958803031154803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107159423","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002772604,0.00000936132,0.99141914,0.0034605737,0.00012083972,0.00037125597,6.613589e-7,0.00059169065,0.003749197],"genre_scores_gemma":[0.042987544,0.0000055784467,0.9528071,0.0026393665,0.000036572845,0.00018258132,0.000013574854,0.000009534055,0.0013180932],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99900615,0.000051417406,0.00022165547,0.00028455813,0.00022362085,0.0002125796],"domain_scores_gemma":[0.99939334,0.00008394494,0.000068487476,0.0002775095,0.00008930471,0.00008739304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019721602,0.00010627683,0.0000998631,0.0001249924,0.00008495842,0.00015053038,0.00037480146,0.000037615355,0.0002019358],"category_scores_gemma":[0.00006463254,0.00008626603,0.00006463055,0.00019007773,0.000041351803,0.0007891721,0.000046270714,0.00006208682,0.000047694564],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000133547355,0.00017358294,0.00009600883,0.0000758764,0.000018096565,0.00002285319,0.0013746638,0.00000943569,0.12523961,0.008192043,0.21012814,0.65465635],"study_design_scores_gemma":[0.00075821625,0.00012664938,0.000027765915,0.000019615953,0.000006232291,0.000005451579,0.00009528191,0.23376763,0.7618129,0.002591525,0.0006038597,0.00018484564],"about_ca_topic_score_codex":0.000016675758,"about_ca_topic_score_gemma":0.0000013493103,"teacher_disagreement_score":0.65447146,"about_ca_system_score_codex":0.000059994312,"about_ca_system_score_gemma":0.000008997471,"threshold_uncertainty_score":0.35178268},"labels":[],"label_agreement":null},{"id":"W2107290706","doi":"10.1109/cvpr.2005.284","title":"Quantitative Evaluation of a Novel Image Segmentation Algorithm","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Segmentation; Image segmentation; Computer science; Artificial intelligence; Ground truth; Precision and recall; Embedding; Scale-space segmentation; Pattern recognition (psychology); Range (aeronautics); Segmentation-based object categorization; Algorithm; Computer vision","score_opus":0.0672843648863743,"score_gpt":0.3928497549076738,"score_spread":0.32556539002129953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107290706","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018271396,0.000030204306,0.9943158,0.00041008327,0.00005436263,0.0003227653,0.0000027228016,0.00014453153,0.0028924006],"genre_scores_gemma":[0.011708211,0.000006188152,0.9878335,0.00028774794,0.000019355937,0.00004259509,0.000008344835,0.0000039647202,0.000090117814],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985392,0.00008809592,0.00027330726,0.00019525712,0.0008018546,0.00010230102],"domain_scores_gemma":[0.99903023,0.000073972085,0.0001338137,0.00021779993,0.000495815,0.0000483686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010882627,0.00007095035,0.00008972416,0.00011367192,0.0000288771,0.000040412604,0.00027388628,0.000026715034,0.00027421585],"category_scores_gemma":[0.00012382554,0.00006346398,0.000031813357,0.0002720596,0.00005254805,0.0010928686,0.00006371929,0.000046422796,0.00003661656],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010849135,0.00013349643,0.000004944612,0.0000039155993,0.0000084495005,1.4449259e-7,0.00059466483,0.000022554776,0.20154661,0.0049759103,0.00074435613,0.7919639],"study_design_scores_gemma":[0.00048096583,0.000058515947,0.00018620102,0.000007045095,0.000008033344,0.0000015458545,0.00008896317,0.5226043,0.47614467,0.00035700807,0.000009250731,0.000053487423],"about_ca_topic_score_codex":0.000019054847,"about_ca_topic_score_gemma":0.0000042615407,"teacher_disagreement_score":0.7919104,"about_ca_system_score_codex":0.00007929983,"about_ca_system_score_gemma":0.00008125596,"threshold_uncertainty_score":0.30024713},"labels":[],"label_agreement":null},{"id":"W2107618208","doi":"10.1109/fuzz.2003.1206566","title":"Image segmentation with GA optimized fuzzy reasoning","year":2004,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Image segmentation; Computer vision; Pattern recognition (psychology); Computer science; Fuzzy logic; Image texture; Image gradient; Scale-space segmentation; Segmentation-based object categorization; Segmentation; Mathematics","score_opus":0.009638484528932454,"score_gpt":0.26972071169940887,"score_spread":0.2600822271704764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107618208","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000961139,0.000009280171,0.9827191,0.00085919525,0.000042263568,0.00021929234,3.101404e-7,0.0007231668,0.014466228],"genre_scores_gemma":[0.008531497,0.00000981816,0.98992616,0.0011863755,0.000018579307,0.000039235998,0.0000052659047,0.000007993602,0.00027505867],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99900335,0.000033463806,0.0001648856,0.00026952077,0.00034720937,0.0001815431],"domain_scores_gemma":[0.99941605,0.00002754725,0.00007065959,0.00029828207,0.00007924569,0.00010819767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020245842,0.000101179634,0.00010062809,0.00007470553,0.000074844495,0.00018465637,0.0003508648,0.000027767293,0.000108404325],"category_scores_gemma":[0.000034383935,0.0000759631,0.000024050465,0.0002858536,0.000057325888,0.0010241569,0.00007913513,0.00008265176,0.00007363598],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015986696,0.00084411557,0.00037528505,0.000122655,0.0001676844,0.00059989933,0.007853276,0.0016915055,0.41949534,0.15615536,0.0137399705,0.398795],"study_design_scores_gemma":[0.003458381,0.00031459815,0.00033509574,0.0000930155,0.000012019355,0.00007938554,0.00027434883,0.0063494053,0.9831271,0.0055346917,0.000046097517,0.00037583313],"about_ca_topic_score_codex":0.0000644256,"about_ca_topic_score_gemma":0.0000035446226,"teacher_disagreement_score":0.5636318,"about_ca_system_score_codex":0.000076611585,"about_ca_system_score_gemma":0.00007246085,"threshold_uncertainty_score":0.30976853},"labels":[],"label_agreement":null},{"id":"W2107713977","doi":"10.1109/iembs.2007.4353604","title":"Segmentation of Lung Lobes in Isotropic CT Images Using Wavelet Transformation","year":2007,"lang":"en","type":"article","venue":"Conference proceedings","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Foothills Medical Centre; University of Calgary","funders":"","keywords":"Fissure; Oblique case; Wavelet; Isotropy; Artificial intelligence; Segmentation; Computer vision; Wavelet transform; Transformation (genetics); Computer science; Geology; Mathematics; Optics; Physics","score_opus":0.02445070465377807,"score_gpt":0.30824486904839987,"score_spread":0.2837941643946218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107713977","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.304366,0.000018388715,0.69438744,0.00009251355,0.000039623632,0.00020871809,7.434913e-7,0.000076258366,0.00081034173],"genre_scores_gemma":[0.80272275,0.000026030968,0.1971324,0.00008115984,0.000011901397,0.0000075017815,0.000002418655,0.000004321796,0.000011528079],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987843,0.000009366753,0.00044447943,0.00021190537,0.00032624413,0.00022368746],"domain_scores_gemma":[0.9994102,0.0000302932,0.00017743728,0.000079837155,0.00023987172,0.00006235557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053787907,0.0001098465,0.00015333216,0.00021140347,0.00003843875,0.00009320725,0.00035828367,0.00003585906,0.000032997596],"category_scores_gemma":[0.000061667604,0.0001087343,0.000028158982,0.00046145363,0.00007174188,0.0016420978,0.000050530216,0.000117872,0.0000025489683],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013203353,0.00008793967,0.01422085,0.0003309687,0.00000898011,0.000007224808,0.00988749,0.0000012416153,0.6959655,0.013225669,0.00013059196,0.2661203],"study_design_scores_gemma":[0.00036189883,0.000048925904,0.008250634,0.00014731303,0.0000054701827,0.00001248803,0.00068506517,0.02901273,0.9596431,0.0016897446,0.000009443428,0.00013316519],"about_ca_topic_score_codex":0.0000648869,"about_ca_topic_score_gemma":0.0000039061033,"teacher_disagreement_score":0.49835676,"about_ca_system_score_codex":0.00009376192,"about_ca_system_score_gemma":0.000050099352,"threshold_uncertainty_score":0.4434056},"labels":[],"label_agreement":null},{"id":"W2107759483","doi":"10.1109/cbms.1991.128987","title":"A parallel approach to tubule grading in breast cancer lesions and its VLSI implementation","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; Technical University of Nova Scotia","funders":"","keywords":"Tubule; Very-large-scale integration; Computer science; Subtraction; Grading (engineering); Artificial intelligence; Pattern recognition (psychology); Computer vision; Mathematics; Arithmetic; Medicine; Embedded system; Biology","score_opus":0.049653469460376375,"score_gpt":0.3313135034193329,"score_spread":0.28166003395895656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107759483","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010666216,0.00007908414,0.98225653,0.0043415143,0.0000327861,0.0003691311,0.0000036787862,0.00015769931,0.0020933817],"genre_scores_gemma":[0.5771005,0.00015727997,0.4192084,0.0027754828,0.0000255483,0.00025239258,0.000002580006,0.0000072247576,0.00047055117],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917984,0.0000373665,0.0001693585,0.00025588254,0.00017989986,0.00017762723],"domain_scores_gemma":[0.999681,0.000020916423,0.000026717566,0.00012082458,0.00002618134,0.00012433977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014038959,0.00007255588,0.000081317354,0.00013284624,0.000051632986,0.00007583192,0.00021540056,0.000021945814,0.00027820098],"category_scores_gemma":[0.00000737583,0.00006403318,0.000011825683,0.00034750157,0.000010235948,0.00043106783,0.00011862149,0.000059201528,0.000015738093],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037849754,0.00030962322,0.006005859,0.000058329235,0.000015078205,0.000010414949,0.0071374625,0.00008420364,0.012391882,0.033351913,0.048272893,0.89235854],"study_design_scores_gemma":[0.003233787,0.00017858982,0.11528905,0.00014179574,0.000021138076,0.00015457135,0.0018391194,0.8310283,0.043375496,0.0024906723,0.0010610869,0.0011863834],"about_ca_topic_score_codex":0.00025154726,"about_ca_topic_score_gemma":0.00004236227,"teacher_disagreement_score":0.8911722,"about_ca_system_score_codex":0.000048497837,"about_ca_system_score_gemma":0.000009926346,"threshold_uncertainty_score":0.30461055},"labels":[],"label_agreement":null},{"id":"W2108201369","doi":"10.1109/cgiv.2007.49","title":"Improved Co-occurrence Matrix as a Feature Space for Relative Entropy-based Image Thresholding","year":2007,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Thresholding; Pattern recognition (psychology); Artificial intelligence; Entropy (arrow of time); Co-occurrence matrix; Computer science; Feature (linguistics); Kullback–Leibler divergence; Image (mathematics); Feature vector; Computer vision; Mathematics; Image segmentation; Image texture; Physics","score_opus":0.014709490167426868,"score_gpt":0.3479556842705924,"score_spread":0.3332461941031656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108201369","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003066526,0.000039553724,0.99352396,0.0026461335,0.00016363774,0.0008078476,0.00002130464,0.0006531514,0.001837736],"genre_scores_gemma":[0.013048989,0.0000039591505,0.9835595,0.0017602233,0.000054325308,0.00005225991,0.000033321732,0.000010500561,0.0014769206],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984108,0.000038146653,0.00025605917,0.00048216013,0.00036895557,0.0004438824],"domain_scores_gemma":[0.9984428,0.0005369293,0.00016361379,0.00044354252,0.00019301928,0.00022004613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008740141,0.00018647037,0.00017951544,0.00015789153,0.00014134539,0.00019705252,0.00068251655,0.000111879956,0.000108689186],"category_scores_gemma":[0.00043819414,0.000157073,0.00010632125,0.00036124664,0.00010671357,0.00082050095,0.00009670437,0.0002477899,0.000039540653],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016241547,0.00025270294,0.00028847175,0.00014435677,0.000051380783,0.00008934364,0.0011556001,0.0000030618496,0.69110733,0.15162598,0.11612321,0.03899613],"study_design_scores_gemma":[0.0008518758,0.00025701476,0.000059029342,0.000034911045,0.00000919873,0.000007287022,0.00007310181,0.023745613,0.9678315,0.0035980123,0.0032742848,0.0002582023],"about_ca_topic_score_codex":0.000017535429,"about_ca_topic_score_gemma":0.0000031851378,"teacher_disagreement_score":0.2767241,"about_ca_system_score_codex":0.000103183236,"about_ca_system_score_gemma":0.0001362163,"threshold_uncertainty_score":0.6405251},"labels":[],"label_agreement":null},{"id":"W2108584285","doi":"10.1109/tmi.2009.2012888","title":"Prostate Cancer Segmentation With Simultaneous Estimation of Markov Random Field Parameters and Class","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":135,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; Mount Sinai Hospital","funders":"","keywords":"Multispectral image; Markov random field; Artificial intelligence; Computer science; Segmentation; Prostate cancer; Fuzzy logic; Pattern recognition (psychology); Image segmentation; Pixel; Cluster analysis; Magnetic resonance imaging; Computer vision; Cancer; Medicine; Radiology","score_opus":0.006524925755081631,"score_gpt":0.2800909569477525,"score_spread":0.27356603119267087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108584285","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008893318,0.000046768895,0.985244,0.0051315455,0.00012185039,0.00033581606,0.0000028283757,0.00016617005,0.0000577449],"genre_scores_gemma":[0.8210959,0.00016071051,0.17616089,0.0024892925,0.0000077155355,0.000042696578,0.0000014975188,0.000006367197,0.000034981604],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985091,0.000084361774,0.00030181557,0.00027942684,0.00064378255,0.00018149047],"domain_scores_gemma":[0.99892086,0.00053966406,0.00010625718,0.00018591076,0.00006972485,0.000177615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002691257,0.00013144178,0.00017746087,0.00014134451,0.00008912613,0.00006321257,0.00019610494,0.00004789928,0.00005818348],"category_scores_gemma":[0.000065951,0.00010623723,0.000033241595,0.0002573166,0.00012261134,0.00044649676,0.000001832009,0.00024124015,0.000001545994],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008619884,0.000076357755,0.000017138163,0.000023736784,0.000013649926,0.000028778211,0.0004671376,0.0045893663,0.001950088,0.000009846811,0.0000954179,0.9926423],"study_design_scores_gemma":[0.0015889917,0.00023762937,0.000032782944,0.00024103632,0.000028614626,0.00003775649,0.000071337156,0.7441778,0.25322047,0.00021416342,0.000007385316,0.00014199589],"about_ca_topic_score_codex":0.00006972072,"about_ca_topic_score_gemma":0.00000862771,"teacher_disagreement_score":0.9925003,"about_ca_system_score_codex":0.000041776457,"about_ca_system_score_gemma":0.00008009944,"threshold_uncertainty_score":0.43322286},"labels":[],"label_agreement":null},{"id":"W2108607725","doi":"10.1109/tmi.2007.908124","title":"A Statistical Model for Point-Based Target Registration Error With Anisotropic Fiducial Localizer Error","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":101,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Northern Digital (Canada); Robarts Clinical Trials; Western University","funders":"","keywords":"Fiducial marker; Computer science; Artificial intelligence; Algorithm; Image registration; Mean squared error; Point (geometry); Computer vision; Mathematics; Image (mathematics); Statistics; Geometry","score_opus":0.02870991148665506,"score_gpt":0.3055852594469484,"score_spread":0.27687534796029334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108607725","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012872112,0.000011877073,0.9930335,0.0054388293,0.00021659162,0.00050334196,0.000031793727,0.00052385847,0.000111443886],"genre_scores_gemma":[0.26837415,0.000006084472,0.72680724,0.004276824,0.000053569103,0.00022167398,0.000016716613,0.000026701815,0.00021702689],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99712837,0.00011200503,0.000479081,0.0006077506,0.0012227681,0.00045000398],"domain_scores_gemma":[0.9984913,0.00032143737,0.00011121623,0.00043194328,0.00018033668,0.00046376087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038834623,0.00024545076,0.00026937033,0.00018558219,0.0003891784,0.00007541064,0.0005230416,0.0001042607,0.00032597582],"category_scores_gemma":[0.000092849405,0.00021030683,0.00009698613,0.00029689193,0.00049829716,0.00056367245,0.0000033036742,0.00042919692,0.000019832876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014023759,0.0051823896,0.00018142587,0.00060815644,0.00026049916,0.0019138709,0.004485535,0.10318961,0.006351761,0.008318089,0.109940305,0.75816596],"study_design_scores_gemma":[0.0014713777,0.00018742312,0.000016094493,0.00007219189,0.000021680993,0.00008609138,0.000025172609,0.98052734,0.016342692,0.0007800026,0.00022142203,0.00024849034],"about_ca_topic_score_codex":0.00004086181,"about_ca_topic_score_gemma":0.00001823556,"teacher_disagreement_score":0.87733775,"about_ca_system_score_codex":0.00012171898,"about_ca_system_score_gemma":0.00079647964,"threshold_uncertainty_score":0.85760635},"labels":[],"label_agreement":null},{"id":"W2108637262","doi":"10.1109/iembs.2008.4649831","title":"3D prostate segmentation based on ellipsoid fitting, image tapering and warping","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Image warping; Segmentation; Artificial intelligence; Image segmentation; Ellipse; Computer vision; Computer science; Ellipsoid; Prostate; Mathematics; Medicine; Geometry; Physics","score_opus":0.014159512144675403,"score_gpt":0.2581730895735627,"score_spread":0.24401357742888732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108637262","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01433228,0.000010777983,0.9813175,0.00050037686,0.000055055585,0.00024493004,5.353992e-7,0.00050239847,0.0030361123],"genre_scores_gemma":[0.10527613,0.000034103352,0.89157313,0.0025291806,0.000022333137,0.00003493772,0.0000042350116,0.000009269308,0.00051667955],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989467,0.000052335214,0.00020067145,0.00030997442,0.0003070502,0.00018321612],"domain_scores_gemma":[0.99946606,0.00009119805,0.00006934961,0.0002292561,0.000046286128,0.00009787172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023688114,0.00010812757,0.000092347684,0.0001033205,0.00016575777,0.00009855177,0.00019110258,0.000027412181,0.000072901785],"category_scores_gemma":[0.00006578525,0.000095697265,0.000020522179,0.00017036048,0.000072259965,0.0005775283,0.000079760546,0.00009511639,0.000030141447],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025186337,0.00023876988,0.0044826455,0.00015906974,0.000017346423,0.00022040786,0.0048859655,0.00028184292,0.25700456,0.0007289335,0.009359176,0.7225961],"study_design_scores_gemma":[0.00055871217,0.00017993514,0.0013781635,0.000056829507,0.00000235764,0.00002181681,0.0000526112,0.35268202,0.6445244,0.00010143318,0.00022149978,0.0002202235],"about_ca_topic_score_codex":0.00001901486,"about_ca_topic_score_gemma":7.3441214e-7,"teacher_disagreement_score":0.72237587,"about_ca_system_score_codex":0.00003547321,"about_ca_system_score_gemma":0.000033896024,"threshold_uncertainty_score":0.39024213},"labels":[],"label_agreement":null},{"id":"W2109185541","doi":"10.1016/j.media.2006.06.008","title":"Intra-subject elastic registration of 3D ultrasound images","year":2006,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Voxel; Artificial intelligence; Computer vision; 3D ultrasound; Image registration; Process (computing); Ultrasound; Speckle pattern; Image (mathematics); Radiology; Medicine","score_opus":0.006560609823574037,"score_gpt":0.2707165386954212,"score_spread":0.2641559288718472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109185541","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026067581,0.00010729905,0.9914423,0.0007878555,0.00005885399,0.00009816659,0.00000564927,0.00023344383,0.004659642],"genre_scores_gemma":[0.5960137,0.000068582034,0.40233728,0.0005588098,0.00015404177,0.000025949763,0.00008961879,0.0000124351745,0.000739529],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9966976,0.00021072889,0.0007917824,0.0004553237,0.0015560945,0.00028848008],"domain_scores_gemma":[0.9980068,0.0005890627,0.00030954625,0.000660108,0.00023513133,0.00019930956],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011457341,0.00017172284,0.0004176429,0.00043300155,0.000071899965,0.000139282,0.00090976787,0.00011862304,0.0012626888],"category_scores_gemma":[0.0018283917,0.00014731825,0.00021933006,0.0022339763,0.00041291377,0.0005946544,0.000106837935,0.00022375044,0.000041131443],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003808843,0.0022136827,0.01512404,0.0003435887,0.0022827012,0.0009942062,0.0007101663,0.0002058641,0.541667,0.007989342,0.14026296,0.2881684],"study_design_scores_gemma":[0.0011923704,0.00027416623,0.037081443,0.00011434686,0.0015987429,0.000079216356,0.000084522915,0.06030051,0.8903494,0.007647916,0.00040501275,0.0008723376],"about_ca_topic_score_codex":0.0006695145,"about_ca_topic_score_gemma":0.00010293513,"teacher_disagreement_score":0.593407,"about_ca_system_score_codex":0.000046139518,"about_ca_system_score_gemma":0.00012847308,"threshold_uncertainty_score":0.9996503},"labels":[],"label_agreement":null},{"id":"W2109213053","doi":"10.5555/602099.602104","title":"Direct surface extraction from 3D freehand ultrasound images","year":2002,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Voxel; Artificial intelligence; Computer science; Pixel; Computer vision; 3D ultrasound; Noise (video); Data set; Sampling (signal processing); Ultrasound; Image resolution; Feature extraction; Surface (topology); Pattern recognition (psychology); Image (mathematics); Mathematics; Acoustics; Geometry","score_opus":0.010700172220669567,"score_gpt":0.1951282265233755,"score_spread":0.18442805430270592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109213053","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43011123,0.0003356381,0.5624393,0.00010500179,0.00019167441,0.00013168,0.00011059332,0.0003989587,0.0061759003],"genre_scores_gemma":[0.8705679,0.00043973632,0.1265389,0.00007276143,0.000024322606,2.7732764e-7,0.000012146245,0.000007620737,0.0023363368],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9988496,0.000086258304,0.00011808707,0.00041154775,0.00034820798,0.00018631459],"domain_scores_gemma":[0.9990598,0.0001834865,0.00012946263,0.00037910583,0.000119567834,0.00012857873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001466375,0.000042655014,0.00018195066,0.000029251965,0.00018359988,0.00024478204,0.0006158962,0.0000819118,0.00091084],"category_scores_gemma":[0.00006300091,0.00015032424,0.00007148115,0.00027529115,0.00020266726,0.0011944665,0.00012387325,0.00012279314,0.00007406343],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012595247,0.00015700661,0.0027199704,0.000014873767,0.000028311373,0.0001791707,0.00028157356,0.000004466371,0.031096814,4.49463e-7,0.042666852,0.92284924],"study_design_scores_gemma":[0.0007702147,0.00008483212,0.9942539,0.000114090064,0.000029409419,0.00005989432,0.00026520083,0.0016871776,0.0015529254,0.00023412489,0.0006329553,0.00031531078],"about_ca_topic_score_codex":0.078363866,"about_ca_topic_score_gemma":0.02245611,"teacher_disagreement_score":0.9915339,"about_ca_system_score_codex":0.000060516504,"about_ca_system_score_gemma":0.00001753566,"threshold_uncertainty_score":0.9973059},"labels":[],"label_agreement":null},{"id":"W2109622329","doi":"10.1109/icarcv.2006.345482","title":"3D Boundary Reconstruction of Mouse Brain Cells","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Confocal; Boundary (topology); Visualization; Stack (abstract data type); Computer vision; Artificial intelligence; Computer science; 3D reconstruction; Confocal microscopy; Iterative reconstruction; Feature extraction; Surface reconstruction; Identification (biology); Surface (topology); Pattern recognition (psychology); Optics; Physics; Biology; Geometry; Mathematics","score_opus":0.007305376989140333,"score_gpt":0.23923583891821407,"score_spread":0.23193046192907374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109622329","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007624768,0.000010432985,0.9804597,0.00033238603,0.00012095609,0.00007377391,9.135107e-7,0.00024209532,0.01113501],"genre_scores_gemma":[0.013351506,0.0000038037906,0.97999185,0.0005844764,0.000029190136,0.000004455217,0.0000019042074,0.0000035298542,0.006029271],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993753,0.000034841207,0.00019702369,0.0001369715,0.00016905922,0.00008679577],"domain_scores_gemma":[0.9995918,0.000047520316,0.00006753619,0.0002178223,0.000045690565,0.000029603103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016554595,0.000046933434,0.00006931236,0.00006834444,0.000026781308,0.00004256549,0.00023244054,0.00003004263,0.00020656291],"category_scores_gemma":[0.000015215432,0.00004201114,0.000023873865,0.00014518134,0.000081000246,0.00033979354,0.0000534261,0.000044578897,0.000026919412],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011213358,0.000048150112,0.000104621155,0.000012779238,0.0000029056005,0.0000022215604,0.000043014308,0.00000392295,0.5652657,0.0059555927,0.059061866,0.36949807],"study_design_scores_gemma":[0.00009254461,0.000026545642,0.00008114759,0.0000048078587,6.173166e-7,0.0000070999977,0.000007068041,0.0024747055,0.993855,0.0025621161,0.0008367849,0.00005157549],"about_ca_topic_score_codex":0.00014909296,"about_ca_topic_score_gemma":0.000008602563,"teacher_disagreement_score":0.42858925,"about_ca_system_score_codex":0.000017599215,"about_ca_system_score_gemma":0.00003409024,"threshold_uncertainty_score":0.22617191},"labels":[],"label_agreement":null},{"id":"W2110593503","doi":"10.1109/iccv.2009.5459263","title":"Globally optimal segmentation of multi-region objects","year":2009,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":128,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Computer science; Artificial intelligence; Image segmentation; Boundary (topology); Object (grammar); ENCODE; Computer vision; Domain (mathematical analysis); Scale-space segmentation; Pattern recognition (psychology); Graph; Topology (electrical circuits); Mathematics; Theoretical computer science; Combinatorics","score_opus":0.022525665244047623,"score_gpt":0.3053756206244485,"score_spread":0.28284995538040086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110593503","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013488347,0.00002270988,0.99428666,0.00036388612,0.00004716665,0.00016048054,2.3849154e-7,0.00028073884,0.0034892901],"genre_scores_gemma":[0.26985177,0.000010218764,0.72872496,0.0011923541,0.000008991085,0.0000029789417,0.0000018094788,0.0000015823005,0.00020534947],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991554,0.00003681971,0.00021047382,0.00018140086,0.00029458755,0.00012130659],"domain_scores_gemma":[0.9995114,0.000020294343,0.000088287925,0.00023079364,0.00008189027,0.00006731267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001311839,0.00007159871,0.000090128124,0.00006626694,0.00002457205,0.00004299468,0.00042380413,0.000034717945,0.00002587627],"category_scores_gemma":[0.00003551776,0.0000622951,0.00003438129,0.00023062744,0.0000281433,0.0004847456,0.00005062625,0.000041821153,0.000014253882],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000817865,0.00031989632,0.0001798217,0.000011330717,0.000010061196,0.0000224086,0.0009269773,0.000074578886,0.12845244,0.015939387,0.005766195,0.8482887],"study_design_scores_gemma":[0.0005959887,0.00033378787,0.003572886,0.000022225899,0.000004009519,0.000016256436,0.00008306815,0.033111174,0.9610022,0.0010997442,0.000020378096,0.00013832128],"about_ca_topic_score_codex":0.000016907774,"about_ca_topic_score_gemma":0.0000015096289,"teacher_disagreement_score":0.84815043,"about_ca_system_score_codex":0.000029995825,"about_ca_system_score_gemma":0.00003318736,"threshold_uncertainty_score":0.25403205},"labels":[],"label_agreement":null},{"id":"W2110720619","doi":"10.1109/icassp.2008.4518020","title":"Non-negative sparse image coder via simulated annealing and pseudo-inversion","year":2008,"lang":"en","type":"article","venue":"Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Simulated annealing; Computer science; Inversion (geology); Computation; Algorithm; Coding (social sciences); Inverse; Neural coding; Artificial intelligence; Pattern recognition (psychology); Mathematics","score_opus":0.03757194424632792,"score_gpt":0.2915057597714066,"score_spread":0.25393381552507865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110720619","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36784258,0.00001593186,0.6286903,0.00076673843,0.00017566688,0.00025940305,0.0000061518103,0.000107340325,0.002135887],"genre_scores_gemma":[0.92067724,0.000106595544,0.07852745,0.00042398288,0.000079906626,0.0000039594115,0.0000012328055,0.000012219378,0.00016741031],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828714,0.000010789797,0.0003806438,0.00042829194,0.0006799179,0.00021322617],"domain_scores_gemma":[0.99821293,0.00006900722,0.0003508781,0.00008295692,0.0011613414,0.00012290059],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028358007,0.00021374944,0.00022761807,0.0001658716,0.00029555213,0.00024257408,0.0007048516,0.00009441887,0.0000260303],"category_scores_gemma":[0.0001469789,0.00016784866,0.00004433577,0.00019447242,0.0004208569,0.00095539814,0.00023862661,0.00030754568,0.0000029728499],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000115912066,0.00014499165,0.0009794489,0.00023848149,0.000054427288,0.000019491417,0.0033441565,0.00021743133,0.955926,0.0007968896,0.00084915286,0.037313614],"study_design_scores_gemma":[0.0003913739,0.00008772003,0.00022323923,0.00034160193,0.000014255958,0.000047993366,0.00029281294,0.7792141,0.21356925,0.0056488444,0.0000018157276,0.0001669898],"about_ca_topic_score_codex":0.00003729553,"about_ca_topic_score_gemma":4.3039145e-7,"teacher_disagreement_score":0.77899665,"about_ca_system_score_codex":0.00005201964,"about_ca_system_score_gemma":0.0000879731,"threshold_uncertainty_score":0.68446696},"labels":[],"label_agreement":null},{"id":"W2110991707","doi":"10.1109/tvcg.2010.234","title":"Visual Comparability of 3D Regular Sampling and Reconstruction","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Comparability; Artificial intelligence; Computer science; Lattice (music); Cartesian coordinate system; Metric (unit); Visualization; Tetrahedron; Computer vision; Pattern recognition (psychology); Mathematics; Geometry; Physics; Combinatorics","score_opus":0.021193303631145497,"score_gpt":0.30752934010348043,"score_spread":0.2863360364723349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110991707","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11993,0.000009330313,0.8791962,0.000027108406,0.00047795565,0.00016190209,0.0000027219573,0.00018386904,0.000010878358],"genre_scores_gemma":[0.8894554,0.00009899425,0.11009117,0.0002967044,0.000029273135,0.000011577443,0.0000026593846,0.000008721679,0.000005551561],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885803,0.00009749836,0.00034607016,0.0003420078,0.00023732905,0.00011906559],"domain_scores_gemma":[0.9992358,0.00012851038,0.00012162881,0.00024012526,0.0001614117,0.0001124905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003207458,0.00013224235,0.00017989984,0.00027713514,0.00017521858,0.00009623945,0.00014641565,0.00011013673,0.000015525657],"category_scores_gemma":[0.0000061751475,0.00013350659,0.000039695125,0.00042375794,0.00027192145,0.00039544943,0.0000060242264,0.00022204743,7.2978116e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000307362,0.00053177314,0.0012355464,0.00016398422,0.000062498686,0.0000014204431,0.001284899,0.00007124127,0.008387844,0.2515211,0.000036468846,0.73667246],"study_design_scores_gemma":[0.0005099686,0.000265619,0.0025373823,0.000052435997,0.000018410285,0.000058105557,0.00002527635,0.92001027,0.07385233,0.0023888003,0.00006717766,0.00021423686],"about_ca_topic_score_codex":0.000017960989,"about_ca_topic_score_gemma":0.000016145636,"teacher_disagreement_score":0.91993904,"about_ca_system_score_codex":0.0000072226812,"about_ca_system_score_gemma":0.00002666785,"threshold_uncertainty_score":0.54442406},"labels":[],"label_agreement":null},{"id":"W2111170990","doi":"10.1109/isit.2008.4595365","title":"Stochastic optimization approach for entropic image alignment","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convexity; Divergence (linguistics); Degenerate energy levels; Image (mathematics); Computer science; Property (philosophy); Tsallis entropy; Image registration; Artificial intelligence; Mathematics; Mathematical optimization; Algorithm; Pattern recognition (psychology); Physics","score_opus":0.02234562812938833,"score_gpt":0.2635756315473135,"score_spread":0.24123000341792517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111170990","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00002138749,0.000010177641,0.9974198,0.00021035495,0.000056653782,0.000472994,8.8006436e-7,0.00034773067,0.001460073],"genre_scores_gemma":[0.007524504,0.0000065768586,0.99095416,0.00062916696,0.00003304597,0.00015734648,0.000015299374,0.0000058815826,0.0006740124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991943,0.000022649081,0.0001623557,0.00023531119,0.00022886936,0.0001564623],"domain_scores_gemma":[0.9995209,0.000043014268,0.000046740275,0.00024356667,0.000066520726,0.00007928927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009996801,0.0000755388,0.000084412044,0.000055231536,0.00008664698,0.000042490592,0.00033404725,0.00002717923,0.00006938834],"category_scores_gemma":[0.000052937252,0.00006428935,0.000034989476,0.00011944381,0.00004820862,0.00034319144,0.00007325692,0.000032309163,0.000006651356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006120447,0.0028063855,0.000121321085,0.0003101372,0.00019150587,0.000050645107,0.006047249,0.4194002,0.040680416,0.1242852,0.23944096,0.16660479],"study_design_scores_gemma":[0.0003074574,0.0000621686,0.000008771848,0.0000020782845,0.0000025489542,0.000012048634,0.00001372009,0.98028547,0.01900922,0.00018916474,0.000021626678,0.00008573343],"about_ca_topic_score_codex":0.0000038177645,"about_ca_topic_score_gemma":3.0643832e-8,"teacher_disagreement_score":0.56088525,"about_ca_system_score_codex":0.000039076152,"about_ca_system_score_gemma":0.000030554867,"threshold_uncertainty_score":0.26216438},"labels":[],"label_agreement":null},{"id":"W2111952116","doi":"10.1109/icip.2008.4712004","title":"Statistical fusion and sampling of scientific images","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Porous medium; Sampling (signal processing); Resolution (logic); Computer science; Scale (ratio); Iterative reconstruction; Image resolution; Fusion; Artificial intelligence; Computer vision; Pattern recognition (psychology); Porosity; Geology; Physics","score_opus":0.042686977861164654,"score_gpt":0.3174551791289261,"score_spread":0.27476820126776147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111952116","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019736938,0.000023571009,0.9791309,0.00009704172,0.000054367578,0.00005042194,0.0000017047873,0.00009374605,0.0008112919],"genre_scores_gemma":[0.253066,0.000015000853,0.7465933,0.00006938196,0.0000042594156,0.0000014179448,0.0000014378392,0.0000013113481,0.00024789455],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993741,0.00002083689,0.0001297805,0.00016845502,0.00022424232,0.00008261126],"domain_scores_gemma":[0.99958044,0.00010596012,0.000028614526,0.0001628204,0.000058142963,0.00006399012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020244747,0.00003702964,0.00006404889,0.000066053035,0.000089755755,0.000042540065,0.00016896676,0.000014341842,0.00009435734],"category_scores_gemma":[0.00010329086,0.000029685247,0.000008024666,0.00014054056,0.00028791296,0.00021521615,0.00014730234,0.000034982477,0.000006594151],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035195892,0.00011481387,0.0034200985,0.000048705366,0.00000538792,0.000029110008,0.00088861625,8.144389e-7,0.5256907,0.044050675,0.01960258,0.406145],"study_design_scores_gemma":[0.00022961323,0.00008899074,0.027886325,0.000020547643,0.000002329708,0.00005456865,0.000029078912,0.004181725,0.960455,0.006664731,0.00025366957,0.0001333895],"about_ca_topic_score_codex":0.0000146523935,"about_ca_topic_score_gemma":4.530135e-7,"teacher_disagreement_score":0.43476436,"about_ca_system_score_codex":0.000004750633,"about_ca_system_score_gemma":0.000028214523,"threshold_uncertainty_score":0.12105292},"labels":[],"label_agreement":null},{"id":"W2112548769","doi":"10.1109/iscas.1992.230680","title":"Feature oriented image sequence processing and 3D adaptive morphology-formulation and properties","year":2003,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Feature (linguistics); Sequence (biology); TRACE (psycholinguistics); Frame (networking); Computer science; Image processing; Image (mathematics); Simple (philosophy); Artificial intelligence; Computer vision; Mathematical morphology; Algorithm; Pattern recognition (psychology)","score_opus":0.03010896439921792,"score_gpt":0.27720751984823133,"score_spread":0.2470985554490134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112548769","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017907565,0.00020657084,0.9796452,0.0004972205,0.000023304032,0.00020293542,4.275772e-7,0.00022272923,0.0012940579],"genre_scores_gemma":[0.34308356,0.000016461527,0.65602267,0.00044462332,0.000004818039,0.000013726818,8.233308e-7,0.0000035056391,0.00040982958],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99930805,0.000055873537,0.000093126364,0.00026406843,0.0001460569,0.00013283367],"domain_scores_gemma":[0.99963516,0.000017215592,0.000051237344,0.00011963983,0.0001072297,0.00006954054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001863771,0.00008924958,0.00008499145,0.000054778127,0.000107838685,0.00011602838,0.00009262462,0.000050420706,0.000010357592],"category_scores_gemma":[0.00010421163,0.00006460735,0.0000056798162,0.00016962364,0.00013080618,0.0010632018,0.000064623746,0.00009786224,0.000002184449],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018239709,0.000058294263,0.0009672864,0.0001079004,0.000013327408,0.0000431821,0.003521067,0.0000017236899,0.6426981,0.030557012,0.0016424647,0.32037142],"study_design_scores_gemma":[0.0007640943,0.00035443573,0.0022787245,0.00014532328,0.000013396784,0.00035246692,0.00049863016,0.11029903,0.88064015,0.00353874,0.00064625306,0.00046875855],"about_ca_topic_score_codex":0.000008658276,"about_ca_topic_score_gemma":0.0000013844791,"teacher_disagreement_score":0.32517597,"about_ca_system_score_codex":0.000019919646,"about_ca_system_score_gemma":0.000041522315,"threshold_uncertainty_score":0.26346114},"labels":[],"label_agreement":null},{"id":"W2112594336","doi":"10.1109/iccv.1998.710727","title":"Contagion-driven image segmentation and labeling","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Army Research Laboratory","keywords":"Segmentation; Artificial intelligence; Image segmentation; Synthetic aperture radar; Pixel; Pattern recognition (psychology); Computer science; Homogeneous; Computer vision; Image texture; Maximum likelihood; Mathematics; Statistics","score_opus":0.019704532259916543,"score_gpt":0.2632579664598507,"score_spread":0.2435534341999342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112594336","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018003728,0.000056379657,0.9914586,0.0011760788,0.00005001901,0.00011673006,3.6178497e-7,0.00032872506,0.0050127115],"genre_scores_gemma":[0.045201503,0.00010765435,0.951898,0.0019514787,0.000018648554,0.000013844832,0.0000014434505,0.0000040630853,0.0008033834],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99939567,0.000030143216,0.00012890238,0.00017838462,0.00016223585,0.000104678045],"domain_scores_gemma":[0.9996554,0.000047508507,0.00003523162,0.00014800573,0.000039582203,0.000074239964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000092432456,0.000058322843,0.000061024348,0.00005076011,0.000055492943,0.00012702604,0.00016979694,0.00002161087,0.0003819284],"category_scores_gemma":[0.000029332696,0.000051217477,0.00001225785,0.000107171785,0.000038872106,0.0006598871,0.00008562636,0.00004983133,0.00008888529],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.799353e-7,0.000075437056,0.00070364296,0.000022175802,0.000014008423,0.000026691254,0.0017981696,0.0000010867136,0.3012787,0.010376547,0.030093022,0.65560955],"study_design_scores_gemma":[0.0012832701,0.00018520969,0.00078374945,0.00004001986,0.00001073127,0.000053055046,0.00026074066,0.30563927,0.6872306,0.0032042335,0.00087835966,0.00043078198],"about_ca_topic_score_codex":0.000014069576,"about_ca_topic_score_gemma":0.000001607406,"teacher_disagreement_score":0.6551788,"about_ca_system_score_codex":0.00001595928,"about_ca_system_score_gemma":0.0000031390157,"threshold_uncertainty_score":0.41818482},"labels":[],"label_agreement":null},{"id":"W2113065449","doi":"10.1109/icip.2005.1529748","title":"An automatic segmentation of color images by using a combination of mixture modelling and adaptive region information: a level set approach","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Image segmentation; Segmentation; Scale-space segmentation; Artificial intelligence; Mixture model; Computer science; Segmentation-based object categorization; Pattern recognition (psychology); Region growing; Boundary (topology); Computer vision; Gaussian; Minification; Mathematics","score_opus":0.05136056307698174,"score_gpt":0.29307129130783927,"score_spread":0.24171072823085754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113065449","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033641744,0.000022787846,0.96565527,0.00009431906,0.000010402634,0.0003435836,0.000008152019,0.000081267644,0.00014249369],"genre_scores_gemma":[0.43785536,0.000006592537,0.5620003,0.000090704336,0.0000031500103,0.000008570687,0.000024457337,0.0000021458468,0.000008745522],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902505,0.00008395439,0.00037247568,0.00012968967,0.00030372938,0.00008508925],"domain_scores_gemma":[0.9992674,0.000037424743,0.00030513457,0.00016556014,0.00017414463,0.00005031721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028743083,0.00008715145,0.00013563728,0.00013746928,0.000044801116,0.000051207782,0.00018190364,0.00005429814,0.0000042455217],"category_scores_gemma":[0.000013459907,0.00008031351,0.000018050026,0.00019335852,0.00007046599,0.0024236492,0.00004440885,0.00005246036,3.7715728e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007248644,0.0011330488,0.00028487525,0.00089132646,0.00011085596,0.0000011395174,0.04002572,0.06332033,0.115376025,0.013920736,0.007153587,0.75770986],"study_design_scores_gemma":[0.0003384739,0.00012822983,0.000033397566,0.000023851451,0.0000073451697,0.0000075266234,0.0005491863,0.8647816,0.13382307,0.00023636689,0.0000022362203,0.00006873389],"about_ca_topic_score_codex":0.000049571994,"about_ca_topic_score_gemma":4.844599e-7,"teacher_disagreement_score":0.8014613,"about_ca_system_score_codex":0.000042169362,"about_ca_system_score_gemma":0.000039024144,"threshold_uncertainty_score":0.32750902},"labels":[],"label_agreement":null},{"id":"W2113087971","doi":"10.1007/978-3-642-40760-4_68","title":"Adaptive Voxel, Texture and Temporal Conditional Random Fields for Detection of Gad-Enhancing Multiple Sclerosis Lesions in Brain MRI","year":2013,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"NeuroRx Research (Canada); Montreal Neurological Institute and Hospital; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Voxel; Conditional random field; Pattern recognition (psychology); Multiple sclerosis; Artificial intelligence; Probabilistic logic; Computer science; Medicine","score_opus":0.023090805883596764,"score_gpt":0.265599434616264,"score_spread":0.24250862873266724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113087971","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03161725,0.000052112457,0.96619016,0.0012596805,0.00017266697,0.0006463568,0.0000031062364,0.000056989807,0.0000016945387],"genre_scores_gemma":[0.5947875,0.0000030726023,0.40445024,0.00066933496,0.00002794204,0.000057225785,0.000001430918,0.000002805612,4.8510094e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846375,0.00008369062,0.00034198456,0.0004940168,0.00033989953,0.00027665254],"domain_scores_gemma":[0.9979187,0.0014522964,0.00012431378,0.00024506362,0.00016974042,0.000089848574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072386686,0.00013317635,0.00020950423,0.0003546103,0.00013212311,0.00013454976,0.00053479825,0.000098020384,0.000007690265],"category_scores_gemma":[0.0005745542,0.00011594961,0.00003870009,0.00085255626,0.00033320708,0.0008702844,0.00022982384,0.00021036345,0.0000012196901],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021011978,0.00007549703,0.0016345545,0.000032939657,0.000003944894,0.0000021919952,0.001735611,0.004462683,0.12783465,0.00009612435,0.00006820863,0.86403257],"study_design_scores_gemma":[0.00086130167,0.00016773041,0.0094893975,0.00010113538,0.000001012849,0.000005445885,0.000005025047,0.6995321,0.2785641,0.011153792,0.0000018431317,0.000117158845],"about_ca_topic_score_codex":0.0002699314,"about_ca_topic_score_gemma":0.00042775538,"teacher_disagreement_score":0.86391544,"about_ca_system_score_codex":0.000065131506,"about_ca_system_score_gemma":0.00010527167,"threshold_uncertainty_score":0.4728288},"labels":[],"label_agreement":null},{"id":"W2113204136","doi":"10.1109/iembs.2007.4352541","title":"Invariant SPHARM Shape Descriptors for Complex Geometry in MR Region of Interest Analysis","year":2007,"lang":"en","type":"article","venue":"Conference proceedings","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Shape analysis (program analysis); Invariant (physics); Spherical harmonics; Artificial intelligence; Concentric; Region of interest; Computer science; Pattern recognition (psychology); Computer vision; Mathematics; Geometry; Mathematical analysis","score_opus":0.16037569780227612,"score_gpt":0.3396319846837548,"score_spread":0.17925628688147865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113204136","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1639082,0.000015189985,0.83492273,0.00025283048,0.000051180603,0.00029751094,0.0000014958022,0.000098859025,0.00045198132],"genre_scores_gemma":[0.83367914,0.000014643619,0.1659155,0.00028936993,0.000018970666,0.000025347317,0.0000055087685,0.0000058669443,0.000045654455],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99851507,0.000008360318,0.00054729055,0.00040155937,0.0002177241,0.00030998327],"domain_scores_gemma":[0.99883616,0.00011132749,0.0002793242,0.0001589425,0.00047651958,0.00013775537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091433566,0.00014013144,0.00031999263,0.00087545416,0.000037892027,0.00011485993,0.00089671783,0.00008363002,0.00007454797],"category_scores_gemma":[0.0004193283,0.00013464548,0.000090898444,0.0018354646,0.00012304973,0.00058903126,0.0001987633,0.00014114505,0.0000026395658],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014422569,0.0005070388,0.10721094,0.0006430206,0.00026975528,0.00003526376,0.008637713,0.000001050272,0.30509254,0.16936427,0.0045295446,0.40356463],"study_design_scores_gemma":[0.0018670849,0.00067075295,0.10270837,0.00045637187,0.0001936056,0.000027237867,0.0026517129,0.24998099,0.620894,0.019025832,0.0006479738,0.0008760588],"about_ca_topic_score_codex":0.000052235464,"about_ca_topic_score_gemma":0.000035364796,"teacher_disagreement_score":0.66977096,"about_ca_system_score_codex":0.00007144653,"about_ca_system_score_gemma":0.000056295296,"threshold_uncertainty_score":0.5490684},"labels":[],"label_agreement":null},{"id":"W2113918006","doi":"10.1109/icdar.1993.395734","title":"Thresholding using an illumination model","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Thresholding; Pixel; Artificial intelligence; Computer vision; Raster graphics; Computer science; Image (mathematics); Pattern recognition (psychology); Mathematics","score_opus":0.1202705896887505,"score_gpt":0.328203941821004,"score_spread":0.2079333521322535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113918006","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059404518,0.000009351077,0.98744684,0.00017611588,0.000035638317,0.00005805923,1.12804734e-7,0.00039191285,0.0059415344],"genre_scores_gemma":[0.32151192,0.0000035556895,0.6773861,0.0007548973,0.00001263646,0.0000021938856,3.4668383e-7,0.0000027985816,0.00032554407],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994006,0.000018302602,0.00010489612,0.00016444651,0.00020685555,0.000104855964],"domain_scores_gemma":[0.9996238,0.000010332262,0.00002841951,0.0002302476,0.00004048607,0.0000667644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013739412,0.000047098107,0.000043576634,0.00006829497,0.00006041922,0.00009162742,0.00030582162,0.000024836929,0.00017988597],"category_scores_gemma":[0.000019039431,0.00004278552,0.000013645088,0.00014448872,0.000021706917,0.0012611309,0.00006426943,0.00004272156,0.00001713794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.787994e-7,0.00026523342,0.000070158676,0.000011708249,0.0000057204634,0.0000129832515,0.0030690501,0.004822886,0.13372652,0.07647649,0.0051705427,0.77636826],"study_design_scores_gemma":[0.000044068693,0.000012056846,0.0000060990396,0.00000316581,7.624935e-7,0.00000352308,0.000013418573,0.9311183,0.06638814,0.002344795,0.000009802139,0.000055847664],"about_ca_topic_score_codex":0.000008922781,"about_ca_topic_score_gemma":7.5999026e-7,"teacher_disagreement_score":0.92629546,"about_ca_system_score_codex":0.000029893317,"about_ca_system_score_gemma":0.000005626734,"threshold_uncertainty_score":0.19696254},"labels":[],"label_agreement":null},{"id":"W2114218028","doi":"10.1109/42.906425","title":"Three-dimensional multimodal brain warping using the Demons algorithm and adaptive intensity corrections","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":214,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Image warping; Image registration; Computer science; Transformation (genetics); Context (archaeology); Artificial intelligence; Mutual information; Algorithm; Computer vision; Iterated function; Intensity (physics); Image (mathematics); Mathematics; Physics; Mathematical analysis","score_opus":0.024028029916767727,"score_gpt":0.2945248068574551,"score_spread":0.27049677694068736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114218028","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017644407,0.000057229892,0.98846173,0.008071178,0.0009616406,0.00025279718,0.000004225498,0.00039020507,0.00003656097],"genre_scores_gemma":[0.3482747,0.00004242913,0.6410209,0.010349546,0.00016241222,0.00005088293,0.0000017446245,0.000029531788,0.0000678464],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978401,0.0001409607,0.00033562846,0.00048006105,0.0008324568,0.00037078545],"domain_scores_gemma":[0.99835986,0.00069801137,0.00007996431,0.0003516401,0.00015674855,0.0003538033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007113977,0.0002036531,0.00020395748,0.00019051987,0.0007993553,0.00012035504,0.0004450895,0.00008539116,0.0001338911],"category_scores_gemma":[0.00010538576,0.00015823389,0.00009313116,0.00052512664,0.0005249681,0.00055042026,0.000021481048,0.0008192998,0.000013434792],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012035917,0.00012932009,0.00008007799,0.0000027933156,0.000034460158,0.000112332535,0.00033887496,0.00044928223,0.0009410118,0.000033456552,0.0006713814,0.99719495],"study_design_scores_gemma":[0.00039618154,0.0000338104,0.00018960389,0.000106147454,0.000022125216,0.0006145765,0.00017165944,0.9918223,0.0059280493,0.00046298545,0.00007435962,0.0001782073],"about_ca_topic_score_codex":0.00061902765,"about_ca_topic_score_gemma":0.00009328445,"teacher_disagreement_score":0.9970168,"about_ca_system_score_codex":0.00010925308,"about_ca_system_score_gemma":0.00015203313,"threshold_uncertainty_score":0.645259},"labels":[],"label_agreement":null},{"id":"W2114315529","doi":"10.1109/isbi.2009.5193235","title":"Reduced-dimensionality matching for 3-D reconstruction of prostate brachytherapy implants from incomplete data","year":2009,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"National Cancer Institute","keywords":"Brachytherapy; Prostate brachytherapy; Curse of dimensionality; Matching (statistics); Computer science; Prostate; Artificial intelligence; Medical physics; Computer vision; Medicine; Radiology; Radiation therapy; Internal medicine; Pathology; Cancer","score_opus":0.057551318937591196,"score_gpt":0.3439237845270273,"score_spread":0.28637246558943613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114315529","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.079386264,0.0000237981,0.9186367,0.00096814614,0.00016186447,0.00033418793,0.00011114803,0.00019076135,0.00018716951],"genre_scores_gemma":[0.15457855,0.000015932015,0.84411263,0.0010731899,0.000037955666,0.0000063394295,0.00014158832,0.000003669482,0.000030168007],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988229,0.00005743239,0.00036407093,0.00038856428,0.00022759255,0.00013940377],"domain_scores_gemma":[0.9987644,0.00014934035,0.00017921673,0.0007584031,0.00008483748,0.000063826024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047459762,0.000087901724,0.00016314814,0.00005205241,0.00006130804,0.000050879393,0.00072429643,0.000036788213,0.00004515542],"category_scores_gemma":[0.000048627953,0.00007499133,0.00002739456,0.0001121636,0.000036597274,0.0008852539,0.00013532604,0.00006511398,0.0000030396316],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000198292,0.000037223967,0.00005105538,0.000005919727,0.000010727114,9.1896976e-7,0.0001563304,9.816076e-7,0.1704653,0.0017769341,0.0022991635,0.82517564],"study_design_scores_gemma":[0.00095544424,0.00023910764,0.008719572,0.00009870294,0.000008881036,0.00004517149,0.000050002927,0.020218264,0.7036545,0.26548398,0.00025974607,0.0002665953],"about_ca_topic_score_codex":0.00015678126,"about_ca_topic_score_gemma":0.0000068127633,"teacher_disagreement_score":0.82490903,"about_ca_system_score_codex":0.000018056593,"about_ca_system_score_gemma":0.00005284504,"threshold_uncertainty_score":0.30580577},"labels":[],"label_agreement":null},{"id":"W2114407479","doi":"10.1109/tip.2006.877511","title":"Image and Texture Segmentation Using Local Spectral Histograms","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":122,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Army Research Office; Air Force Office of Scientific Research; University of Waterloo","keywords":"Pattern recognition (psychology); Artificial intelligence; Histogram; Segmentation; Region growing; Image segmentation; Scale-space segmentation; Range segmentation; Image texture; Segmentation-based object categorization; Computer vision; Mathematics; Boundary (topology); Computer science; Image (mathematics)","score_opus":0.012208320610576646,"score_gpt":0.27595482519074854,"score_spread":0.2637465045801719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114407479","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026032394,0.00012339198,0.99574554,0.00027783922,0.00014693064,0.00022711615,0.0000040438103,0.0005232972,0.00034860522],"genre_scores_gemma":[0.35373026,0.000010233919,0.64587355,0.00021951359,0.000042448963,0.000020205567,0.0000024362953,0.000018302333,0.00008307206],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984652,0.00006057234,0.00032344693,0.00046195,0.00038230256,0.00030655562],"domain_scores_gemma":[0.99940234,0.000035725436,0.0001234172,0.00021963184,0.00011104943,0.00010783865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017599916,0.00021115929,0.00016488388,0.00021868624,0.00039254437,0.0005044741,0.0002452675,0.00008110237,0.00004360712],"category_scores_gemma":[0.000003330175,0.00020958243,0.000059335733,0.00045974535,0.0002871678,0.0018397759,0.0000039564275,0.00028804294,0.000012104185],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007879065,0.00012636157,0.000003977592,0.00006751237,0.0000051482207,0.000023577013,0.0002618796,0.00014613922,0.29808855,0.000016880547,0.00012453311,0.7011276],"study_design_scores_gemma":[0.00048846554,0.0000727327,0.000043900436,0.00009363457,0.000030490677,0.00011185223,0.0001402836,0.29124963,0.7063439,0.0010987367,0.000027685308,0.00029868115],"about_ca_topic_score_codex":0.0001407906,"about_ca_topic_score_gemma":0.00001669798,"teacher_disagreement_score":0.7008289,"about_ca_system_score_codex":0.00018068112,"about_ca_system_score_gemma":0.00008335104,"threshold_uncertainty_score":0.85465235},"labels":[],"label_agreement":null},{"id":"W2114913871","doi":"10.1023/b:matg.0000041179.79093.87","title":"Identification of Mineral Grains in a Petrographic Thin Section Using Phi- and Max-Images","year":2004,"lang":"en","type":"article","venue":"Mathematical Geology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Brock University","keywords":"Petrography; Thin section; Edge detection; Artificial intelligence; Rotation (mathematics); Geology; Image segmentation; Segmentation; Image (mathematics); Polarizer; Computer vision; Birefringence; Image processing; Computer science; Mineralogy; Mathematics; Optics; Physics","score_opus":0.01866829887877061,"score_gpt":0.292760868058199,"score_spread":0.27409256917942837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114913871","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30544895,0.000021363068,0.6937754,0.00046627055,0.000039247134,0.00010972613,3.1622386e-7,0.000053170777,0.00008552842],"genre_scores_gemma":[0.76958287,0.000007766156,0.23023501,0.00011712311,0.000014251491,0.000014018862,9.0552896e-7,0.0000038050835,0.000024233053],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99902916,0.000070473136,0.00038954054,0.00020800471,0.0001573916,0.00014545125],"domain_scores_gemma":[0.9994888,0.00010461943,0.000116382056,0.00020325126,0.000042159878,0.000044788827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046901553,0.00007536494,0.00017493682,0.0002368592,0.00002590224,0.000025565849,0.0001863947,0.00007210191,0.000017936381],"category_scores_gemma":[0.00028360286,0.00006834726,0.000028542314,0.00033257026,0.0002196658,0.00020671196,0.000095842304,0.00010616694,0.0000037287252],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016139124,0.00096937164,0.005344424,0.0005978703,0.00003857435,0.00006691417,0.0056527574,0.00029943284,0.55385727,0.41871768,0.00011694746,0.014322626],"study_design_scores_gemma":[0.00095567387,0.00019640519,0.014128236,0.00010300408,0.000015353724,0.00019048742,0.00016564359,0.07044439,0.080191016,0.83338964,0.0000050381936,0.00021508751],"about_ca_topic_score_codex":0.000045631667,"about_ca_topic_score_gemma":0.000012045613,"teacher_disagreement_score":0.47366622,"about_ca_system_score_codex":0.000023303224,"about_ca_system_score_gemma":0.000019858151,"threshold_uncertainty_score":0.27871203},"labels":[],"label_agreement":null},{"id":"W2115235891","doi":"10.1109/ism.2008.79","title":"Robust Edge Detection Based on Non-local Contribution of Local Frequency Characteristics","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Edge detection; Enhanced Data Rates for GSM Evolution; Pixel; Artificial intelligence; Computer science; Computer vision; Noise (video); Image (mathematics); Deriche edge detector; Morphological gradient; Canny edge detector; Pattern recognition (psychology); Image processing","score_opus":0.01878710694902251,"score_gpt":0.24219681138479998,"score_spread":0.22340970443577746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115235891","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045975344,0.0000030052647,0.9939306,0.00012741637,0.00020691863,0.00018514619,0.0000030867332,0.00023543468,0.00071087986],"genre_scores_gemma":[0.911267,0.000005637428,0.08798502,0.00063606957,0.000034843113,0.000017634675,0.0000121432795,0.000005287838,0.000036370835],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99878734,0.000066812296,0.00033299363,0.00024489185,0.00039051843,0.00017745321],"domain_scores_gemma":[0.99911314,0.0000956977,0.00012956734,0.00032815072,0.00022716692,0.00010624738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023959731,0.0001093004,0.00016705858,0.00012072878,0.000084772146,0.000016589018,0.0002972675,0.00009293861,0.000085722764],"category_scores_gemma":[0.000095797994,0.00009722483,0.000052592044,0.00028329578,0.0001976402,0.00024508263,0.000040600513,0.00014201902,0.000039686158],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007625516,0.00088732905,0.0014754832,0.00006636987,0.000024446703,0.00013184312,0.00021076495,0.00046731834,0.11444485,0.0052832747,0.0025459207,0.87438613],"study_design_scores_gemma":[0.0003539505,0.00031550354,0.0069326703,0.000022612723,0.0000030096548,0.000010552543,0.0000064595083,0.383712,0.60842127,0.00010139856,0.000020410722,0.000100159275],"about_ca_topic_score_codex":0.000065330794,"about_ca_topic_score_gemma":0.000005540455,"teacher_disagreement_score":0.90666944,"about_ca_system_score_codex":0.00011662862,"about_ca_system_score_gemma":0.000091696704,"threshold_uncertainty_score":0.39647135},"labels":[],"label_agreement":null},{"id":"W2115718371","doi":"10.1016/s0031-3203(99)00181-8","title":"Designing Gabor filters for optimal texture separability","year":2000,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":351,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Gabor filter; Artificial intelligence; Computer vision; Computer science; Pattern recognition (psychology); Texture filtering; Texture (cosmology); Feature extraction; Gabor transform; Segmentation; Bandwidth (computing); Image texture; Filter (signal processing); Orientation (vector space); Mathematics; Image segmentation; Image (mathematics); Time–frequency analysis; Telecommunications","score_opus":0.03163246357106438,"score_gpt":0.29350065602912295,"score_spread":0.2618681924580586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115718371","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015877053,0.000011887265,0.9823187,0.00045734242,0.000092162794,0.0004566776,0.000027651562,0.00034299775,0.00041550057],"genre_scores_gemma":[0.197837,0.000019479718,0.79880226,0.002502229,0.00013720918,0.0003597614,0.00013943817,0.00001559433,0.00018706164],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99895287,0.000085286345,0.0002164182,0.00034464477,0.00018485908,0.00021591072],"domain_scores_gemma":[0.9993974,0.00014672757,0.00005828369,0.00022404738,0.00008450791,0.000089052555],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00027462706,0.00010910525,0.000108148255,0.00005042936,0.00008863735,0.00011087047,0.00027908603,0.000063697844,0.0010843858],"category_scores_gemma":[0.000058062968,0.00010613086,0.00006599738,0.00012096991,0.00003449165,0.00050546404,0.000024823688,0.00009680006,0.00018893105],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000074979203,0.000037076767,0.000087356835,0.00002292715,0.000005222305,0.0000031332759,0.00021655955,0.000003924036,0.0021719618,8.603244e-7,0.0021403646,0.9953031],"study_design_scores_gemma":[0.0020583412,0.0007125508,0.0031308928,0.00027931554,0.000048091846,0.00008092723,0.00010463585,0.10462747,0.8785381,0.006980795,0.0024525048,0.0009864036],"about_ca_topic_score_codex":0.0000127221665,"about_ca_topic_score_gemma":0.0000017034806,"teacher_disagreement_score":0.9943167,"about_ca_system_score_codex":0.00003813783,"about_ca_system_score_gemma":0.000020267727,"threshold_uncertainty_score":0.99982876},"labels":[],"label_agreement":null},{"id":"W2115922709","doi":"10.1109/tip.2006.881961","title":"Unsupervised Variational Image Segmentation/Classification Using a Weibull Observation Model","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Weibull distribution; Image segmentation; Artificial intelligence; Pattern recognition (psychology); Segmentation; Mathematics; Scale-space segmentation; Computer science; Gaussian; Statistics","score_opus":0.04563796258482002,"score_gpt":0.3024029108559979,"score_spread":0.2567649482711779,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115922709","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025722622,0.000022699254,0.9949364,0.0007329236,0.00015859037,0.00042111485,0.000018665014,0.00074348896,0.00039387838],"genre_scores_gemma":[0.18552338,0.0000066488524,0.8135333,0.0004738879,0.000058105128,0.00011794814,0.000028282117,0.000030082268,0.00022836566],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975385,0.000106518506,0.0006327175,0.0006211481,0.00076321774,0.00033788988],"domain_scores_gemma":[0.99863607,0.00007916773,0.0002825614,0.0003945261,0.0005014011,0.00010629747],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037391164,0.00026954513,0.00019420817,0.0003617835,0.00060569466,0.00066472666,0.0004750097,0.00011473839,0.00006993788],"category_scores_gemma":[0.00001533142,0.00028997363,0.00009561015,0.00089039427,0.00011596694,0.003899558,0.0000049765918,0.00027306876,0.00003895127],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017816057,0.00041551606,0.000010686305,0.0000877513,0.000013543653,0.000006113386,0.0003934248,0.018726764,0.87881774,0.00041102976,0.0002088943,0.10089074],"study_design_scores_gemma":[0.00042570173,0.000020955786,0.00013357415,0.000049183578,0.000023865956,0.000011393293,0.000040762126,0.72826076,0.26820317,0.002605431,0.0000053640947,0.00021985179],"about_ca_topic_score_codex":0.000066567976,"about_ca_topic_score_gemma":0.0000058746004,"teacher_disagreement_score":0.709534,"about_ca_system_score_codex":0.00033032632,"about_ca_system_score_gemma":0.00034281288,"threshold_uncertainty_score":0.99995524},"labels":[],"label_agreement":null},{"id":"W2116060502","doi":"10.1109/icip.2004.1418814","title":"Joint dense 3D interpretation and multiple motion segmentation of temporal image sequences: a variational framework with active curve evolution and level sets","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Joint (building); Segmentation; Interpretation (philosophy); Image segmentation; Artificial intelligence; Motion (physics); Computer vision; Computer science; Mathematics","score_opus":0.023143103839070943,"score_gpt":0.2845443865560426,"score_spread":0.26140128271697166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116060502","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060737137,0.000020018031,0.9382424,0.00042545897,0.000030440011,0.0003578819,0.000013760065,0.00010398938,0.00006893938],"genre_scores_gemma":[0.49810082,0.0000056175736,0.5017336,0.00010610152,0.000010676095,0.000014136256,0.00001628894,0.0000034320478,0.000009293033],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874294,0.000130171,0.00030746104,0.00032352834,0.0003731554,0.00012276777],"domain_scores_gemma":[0.99912155,0.00017390144,0.0002495785,0.00014637374,0.00022906685,0.0000795599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003333135,0.00012616969,0.00013969059,0.00015237225,0.00007587987,0.00008785819,0.00010032711,0.00007174882,0.000031804426],"category_scores_gemma":[0.00019367451,0.00010593761,0.00001749111,0.00021071569,0.00014361208,0.0018879516,0.00007099457,0.000117574666,0.0000026931984],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030300926,0.00046134536,0.018661175,0.00021533458,0.00014970596,0.000009482011,0.022658268,0.00063824054,0.12279595,0.01848315,0.00025556766,0.8153688],"study_design_scores_gemma":[0.0010677613,0.0003374547,0.0904002,0.00017536043,0.000023806375,0.000045522178,0.00061951054,0.7673026,0.13041419,0.009351765,0.0000021977894,0.0002596669],"about_ca_topic_score_codex":0.00018734316,"about_ca_topic_score_gemma":0.000055635275,"teacher_disagreement_score":0.81510913,"about_ca_system_score_codex":0.0001336817,"about_ca_system_score_gemma":0.00006705333,"threshold_uncertainty_score":0.43200102},"labels":[],"label_agreement":null},{"id":"W2116120292","doi":"10.1109/isbi.2010.5490303","title":"Spatio-temporal segmentation of the heart in 4D MRI images using graph cuts with motion cues","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Segmentation; Computer science; Computer vision; Artificial intelligence; Visualization; Image segmentation; Graph; Ranging; Cut; Pattern recognition (psychology); Theoretical computer science","score_opus":0.01384550829470448,"score_gpt":0.2828425758621863,"score_spread":0.2689970675674818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116120292","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.268578,0.0000030656558,0.73041856,0.0004985397,0.00009337273,0.00021299027,6.831359e-7,0.00006730199,0.00012746599],"genre_scores_gemma":[0.51878995,0.0000010886893,0.48097336,0.00018667776,0.000009345647,0.000006692347,0.000002138431,0.0000032936123,0.000027451802],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990448,0.00008089777,0.00022606696,0.0001884702,0.0003443978,0.000115407274],"domain_scores_gemma":[0.999419,0.000032864482,0.00011834957,0.00030635096,0.00008619868,0.000037285856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031268282,0.000080335805,0.000095829375,0.000121016594,0.00005899677,0.00007556844,0.00030293452,0.000036809553,0.000047539852],"category_scores_gemma":[0.000024685261,0.000051560757,0.000028450158,0.00045422537,0.00014441521,0.00069897866,0.00008071497,0.00013564597,0.0000013804033],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011600746,0.00022132674,0.3964215,0.000047588906,0.000010991215,0.000003888231,0.0012550737,0.00016201707,0.57408476,0.0038790929,0.0012737397,0.02262846],"study_design_scores_gemma":[0.00024466956,0.00004409457,0.03560846,0.000030916864,0.0000031927136,0.000009035795,0.00005637522,0.011554313,0.9507805,0.0015729561,0.000011807381,0.0000836391],"about_ca_topic_score_codex":0.0007143886,"about_ca_topic_score_gemma":0.00053420727,"teacher_disagreement_score":0.3766958,"about_ca_system_score_codex":0.000018459179,"about_ca_system_score_gemma":0.0000583733,"threshold_uncertainty_score":0.21025868},"labels":[],"label_agreement":null},{"id":"W2116578356","doi":"10.1109/iccv.2011.6126330","title":"Recursive MDL via graph cuts: Application to segmentation","year":2011,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Minimum description length; Segmentation; Maxima and minima; Hierarchy; Image (mathematics); Image segmentation; Algorithm; Representation (politics); Pattern recognition (psychology); Computer science; Mathematics; Artificial intelligence","score_opus":0.019482078714556202,"score_gpt":0.27603958932360106,"score_spread":0.25655751060904486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116578356","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025157112,0.000006480482,0.98833144,0.00040984224,0.00010107637,0.0004856807,5.5899403e-7,0.0004969765,0.009916355],"genre_scores_gemma":[0.029041383,0.0000075099088,0.9665322,0.0038283593,0.000022371507,0.00022028005,0.0000068508266,0.000006576825,0.0003344852],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990376,0.000042292322,0.00020148627,0.0003107785,0.00025511582,0.00015269117],"domain_scores_gemma":[0.99925524,0.000023005568,0.00006888802,0.00039514387,0.000106168045,0.00015154546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019090183,0.00008670395,0.00007511034,0.00014352254,0.000052644864,0.00003801732,0.0005213078,0.000037397196,0.00018432189],"category_scores_gemma":[0.000022460898,0.00007918227,0.000028580062,0.0004764742,0.000026633996,0.00052708574,0.000106696054,0.000054753727,0.00041078523],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007443168,0.00012722133,0.00027606316,0.000009079083,0.000012213923,0.0000035908238,0.0040900796,0.0000011203659,0.062522836,0.031507984,0.018025815,0.88341653],"study_design_scores_gemma":[0.00015801168,0.00016881674,0.0012520016,0.0000086315695,0.0000048706916,0.0000059189683,0.0001228078,0.0010452355,0.95807606,0.038478896,0.00049065263,0.00018807294],"about_ca_topic_score_codex":0.0001337033,"about_ca_topic_score_gemma":0.000010863821,"teacher_disagreement_score":0.89555323,"about_ca_system_score_codex":0.000041282623,"about_ca_system_score_gemma":0.000016408114,"threshold_uncertainty_score":0.527995},"labels":[],"label_agreement":null},{"id":"W2116657046","doi":"10.1109/tuffc.2010.1613","title":"Information tracking approach to segmentation of ultrasound imagery of the prostate","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Image segmentation; Computer vision; Prostate; Ultrasound; Rendering (computer graphics); Prior probability; Palpation; Pattern recognition (psychology); Bayesian probability; Radiology; Medicine; Cancer","score_opus":0.007253873843285957,"score_gpt":0.22720307730836156,"score_spread":0.2199492034650756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116657046","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027332222,0.0000203843,0.97094274,0.00016996768,0.00028074108,0.00080241915,0.000042089236,0.00006595853,0.0003434824],"genre_scores_gemma":[0.92845047,0.000047037927,0.07099777,0.00039346394,0.000008154977,0.00008093764,0.0000026613545,0.000007878236,0.000011614122],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985273,0.00008219604,0.0005286292,0.000185718,0.0004561228,0.0002200227],"domain_scores_gemma":[0.99859834,0.00032993147,0.0002753306,0.00039494192,0.00030023998,0.00010123849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051590044,0.00015365351,0.00020849273,0.00025404146,0.00014728506,0.000093517796,0.0004487545,0.00009451812,0.0000073721676],"category_scores_gemma":[0.00012935397,0.000121049794,0.0000926307,0.000844416,0.000102766375,0.0008491849,0.0000017655847,0.00040969424,0.0000014370793],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023222969,0.00022303895,0.00017409716,0.000058953938,0.00003899937,1.8271159e-7,0.0018473082,0.0006408298,0.78999096,0.0025445537,0.0000482614,0.20440958],"study_design_scores_gemma":[0.0010672287,0.00029700669,0.0015375149,0.000030988926,0.000050801053,0.000031104057,0.00008981483,0.019403176,0.9757877,0.0014604089,0.00001777529,0.00022646776],"about_ca_topic_score_codex":0.00006197035,"about_ca_topic_score_gemma":0.000009546196,"teacher_disagreement_score":0.9011183,"about_ca_system_score_codex":0.00003742275,"about_ca_system_score_gemma":0.0001356992,"threshold_uncertainty_score":0.49362674},"labels":[],"label_agreement":null},{"id":"W2117210007","doi":"10.1109/titb.2009.2035693","title":"An Adaptive Monte Carlo Approach to Phase-Based Multimodal Image Registration","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Initialization; Computer science; Monte Carlo method; Image registration; Algorithm; Sampling (signal processing); Artificial intelligence; Adaptive sampling; Importance sampling; Residual; Mathematical optimization; Image (mathematics); Computer vision; Mathematics; Statistics","score_opus":0.013091529638309354,"score_gpt":0.29712154433137034,"score_spread":0.284030014693061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117210007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002496326,0.000004358645,0.9881729,0.0066866665,0.00015908688,0.00081316376,0.000018713108,0.0011826298,0.00046615137],"genre_scores_gemma":[0.6259627,0.0000041335093,0.37166026,0.0021943462,0.000010704293,0.00014035567,0.000014293398,0.0000040969476,0.000009132455],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981896,0.000052005642,0.00068724476,0.00031151265,0.00045910556,0.00030052484],"domain_scores_gemma":[0.998738,0.000033763798,0.00018403179,0.0006861588,0.00019661905,0.00016142741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041391165,0.0002139904,0.00023996037,0.0025472883,0.00012639302,0.00006539301,0.00072888884,0.00033717562,0.000012517325],"category_scores_gemma":[0.000027653616,0.00020229902,0.00004164382,0.0022710038,0.00018715671,0.0021210874,0.0000020929779,0.0005373679,0.00004078873],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001694318,0.0013682535,0.0000023169882,0.000019228955,0.000012420661,0.000008271096,0.0013280142,0.004524924,0.034344774,0.0017123955,0.0009839244,0.95552605],"study_design_scores_gemma":[0.0035857216,0.0045165312,0.000104032224,0.000077071796,0.00001137855,0.000024460673,0.0005875881,0.6360336,0.35379487,0.00076215924,0.00017484889,0.00032772092],"about_ca_topic_score_codex":0.000048798734,"about_ca_topic_score_gemma":0.0000044518424,"teacher_disagreement_score":0.95519835,"about_ca_system_score_codex":0.00021113649,"about_ca_system_score_gemma":0.000092164046,"threshold_uncertainty_score":0.82495147},"labels":[],"label_agreement":null},{"id":"W2117260013","doi":"10.1109/icip.2005.1530280","title":"A generalized Mumford-Shah model for roof-edge detection","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Piecewise; Segmentation; Roof; Contrast (vision); Artificial intelligence; Edge detection; Enhanced Data Rates for GSM Evolution; Computer science; Minification; Image segmentation; Constant (computer programming); Energy (signal processing); Image (mathematics); Mathematics; Computer vision; Algorithm; Pattern recognition (psychology); Mathematical optimization; Image processing; Statistics; Mathematical analysis; Engineering; Structural engineering","score_opus":0.036195350053919675,"score_gpt":0.31265134705046016,"score_spread":0.2764559969965405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117260013","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009710064,0.000018306782,0.9958227,0.0009560365,0.000076432516,0.00033296156,0.000001108847,0.0006989782,0.0011224617],"genre_scores_gemma":[0.07310229,0.0000067959504,0.9194196,0.0028845156,0.000086718894,0.00016730353,0.0000024212043,0.000007882638,0.004322477],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991652,0.000019570465,0.0001978549,0.00024933598,0.00017939456,0.00018864387],"domain_scores_gemma":[0.9994659,0.000034152115,0.00004770347,0.0002849576,0.000072858034,0.00009441901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021948107,0.000085497915,0.000092051756,0.00007751267,0.0000763949,0.00007547496,0.00038062964,0.000050834627,0.000051962903],"category_scores_gemma":[0.000047540798,0.00007376837,0.00006066931,0.00013113605,0.000025271835,0.0005760571,0.00007989998,0.000052178868,0.000030223875],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000054185175,0.000041747287,0.000001276701,0.000007117743,0.000005433522,3.106016e-7,0.00025004728,0.00040387464,0.03054608,0.00786383,0.010014336,0.9508605],"study_design_scores_gemma":[0.00028549277,0.000027458746,0.0000026777006,0.0000018984473,0.0000019929303,0.0000021249818,0.0000030023548,0.7220009,0.27357256,0.002740228,0.001288439,0.000073191884],"about_ca_topic_score_codex":0.000010295888,"about_ca_topic_score_gemma":0.000057748035,"teacher_disagreement_score":0.9507873,"about_ca_system_score_codex":0.000055512024,"about_ca_system_score_gemma":0.000032529097,"threshold_uncertainty_score":0.3008187},"labels":[],"label_agreement":null},{"id":"W2117632376","doi":"10.1109/ciisp.2007.369175","title":"X-ray image segmentation using active contour model with global constraints","year":2007,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Artificial intelligence; Active contour model; Computer vision; Computer science; Image segmentation; Constraint (computer-aided design); Scale-space segmentation; Noise (video); Computation; Image (mathematics); Process (computing); Geodesic; Measure (data warehouse); Pattern recognition (psychology); Mathematics; Algorithm; Geometry","score_opus":0.02309084285737132,"score_gpt":0.3258652963302629,"score_spread":0.30277445347289156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117632376","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008133953,0.0000027003703,0.97920036,0.000112677226,0.000043599204,0.00027129689,0.0000047755548,0.00033048677,0.011900125],"genre_scores_gemma":[0.17229648,8.905897e-7,0.82667166,0.0009260963,0.000015058357,0.000003952322,0.000002907763,0.0000043760433,0.000078582576],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987903,0.00003075693,0.00021202379,0.00029002235,0.00041510482,0.0002617754],"domain_scores_gemma":[0.9993192,0.000042882253,0.00010444583,0.00021674846,0.00016161706,0.00015508897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033206042,0.0001219496,0.000112877344,0.000052797364,0.000074683965,0.00010151248,0.00030511926,0.000044486413,0.00008327272],"category_scores_gemma":[0.000025533536,0.00009810021,0.000025333191,0.00026267706,0.00019682659,0.0010541618,0.00007492051,0.00007847029,0.000013178862],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006764316,0.00019223224,0.00046949257,0.000018649682,0.00006366828,0.00013956563,0.0011330832,0.000444885,0.2739465,0.016520401,0.00090034545,0.70610356],"study_design_scores_gemma":[0.0011055067,0.000120751305,0.0009149336,0.000032299442,0.0000149181,0.000058655336,0.0007468758,0.49914882,0.49512908,0.002415315,0.000004305974,0.00030855395],"about_ca_topic_score_codex":0.00005359247,"about_ca_topic_score_gemma":0.000019423955,"teacher_disagreement_score":0.705795,"about_ca_system_score_codex":0.00020773073,"about_ca_system_score_gemma":0.00013270028,"threshold_uncertainty_score":0.40004104},"labels":[],"label_agreement":null},{"id":"W2117661333","doi":"10.1109/tbme.2008.918580","title":"Performing Accurate Joint Kinematics From 3-D <i>In Vivo</i> Image Sequences Through Consensus-Driven Simultaneous Registration","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Robustness (evolution); Image registration; Computer vision; Artificial intelligence; Computer science; Kinematics; Segmentation; Image segmentation; Trajectory; Object (grammar); Algorithm; Image (mathematics)","score_opus":0.025735484512171874,"score_gpt":0.25959924429788894,"score_spread":0.23386375978571708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117661333","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02008117,0.000018686764,0.9778255,0.0006878506,0.0004833675,0.00023986842,0.000025753307,0.0005509961,0.00008680308],"genre_scores_gemma":[0.56440514,0.0001285894,0.43508294,0.00023861484,0.000048608472,0.000035227233,0.0000041735616,0.00001516246,0.000041561685],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792796,0.000047793386,0.0006304097,0.00041398167,0.00063634454,0.0003435117],"domain_scores_gemma":[0.9987581,0.0005065105,0.00010938826,0.00037560627,0.00006133064,0.00018909237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017940706,0.0002344368,0.0002816933,0.00020961944,0.00013520474,0.000065625856,0.00040259218,0.00015018822,0.00009814785],"category_scores_gemma":[0.00011212855,0.00022312843,0.00008653407,0.0005628702,0.00020972837,0.00045428693,0.0000055490746,0.00044431502,0.000028279434],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017539356,0.00041124184,0.0000018588526,0.000117173106,0.00006240189,0.0013161544,0.003263021,0.11694517,0.85941005,0.00015766898,0.00075582316,0.017541911],"study_design_scores_gemma":[0.0003412511,0.0000895352,0.000004050244,0.00014572067,0.0000068660984,0.00008872273,0.000044641987,0.6794373,0.31937656,0.00008333751,0.00017295407,0.00020905265],"about_ca_topic_score_codex":0.00016718061,"about_ca_topic_score_gemma":0.000008505242,"teacher_disagreement_score":0.56249213,"about_ca_system_score_codex":0.00014240715,"about_ca_system_score_gemma":0.00010885571,"threshold_uncertainty_score":0.9098913},"labels":[],"label_agreement":null},{"id":"W2118173174","doi":"10.5430/jbgc.v2n2p72","title":"Compensation of the non-uniformity of back-lighting sources when digitising X-ray films for video-densitometric measures","year":2012,"lang":"en","type":"article","venue":"Journal of Biomedical Graphics and Computing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Subtraction; Compensation (psychology); Backlight; Standard deviation; Position (finance); Computer science; Artificial intelligence; Computer vision; Optics; Mathematics; Physics; Liquid-crystal display; Statistics; Arithmetic","score_opus":0.022622660241189224,"score_gpt":0.2706569421670727,"score_spread":0.24803428192588348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118173174","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25409314,0.00028610442,0.74499017,0.00020999406,0.00029117905,0.00010051246,0.000001408629,0.000008333603,0.000019172667],"genre_scores_gemma":[0.82385236,0.00002080793,0.1758153,0.00017777001,0.00012626489,3.4771884e-7,4.9351064e-7,0.0000048010866,0.0000018778849],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787086,0.00010492674,0.0008627723,0.00011111482,0.0008289954,0.00022132798],"domain_scores_gemma":[0.9972984,0.00076068897,0.0012382895,0.00015750606,0.0003689737,0.0001761415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002718104,0.00010542491,0.00032716434,0.00043309355,0.00014651076,0.00006315762,0.0005204651,0.00007296998,0.0000031259237],"category_scores_gemma":[0.0006200185,0.00006855076,0.0001678385,0.00081034756,0.00028204723,0.00035482203,0.00021875305,0.00022224415,9.8641216e-8],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007022355,0.0010523176,0.14745417,0.0013243422,0.0004879081,0.000007920096,0.010119466,0.00008244132,0.103589505,0.008183598,0.003995944,0.72363216],"study_design_scores_gemma":[0.005258863,0.0020916308,0.24900842,0.0050150063,0.00033161452,0.0006425845,0.001786858,0.43901706,0.27827436,0.015158903,0.002387542,0.0010271586],"about_ca_topic_score_codex":0.000011737332,"about_ca_topic_score_gemma":3.2158258e-7,"teacher_disagreement_score":0.722605,"about_ca_system_score_codex":0.000013504886,"about_ca_system_score_gemma":0.0000646148,"threshold_uncertainty_score":0.2795419},"labels":[],"label_agreement":null},{"id":"W2118192797","doi":"10.1109/ccece.2009.5090191","title":"Scale-Space Random Walks","year":2009,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Random walk; Image segmentation; Random walker algorithm; Segmentation; Computer science; Noise (video); Image (mathematics); Space (punctuation); Artificial intelligence; Image texture; Scale (ratio); Mathematics; Statistics; Physics","score_opus":0.008441609605379926,"score_gpt":0.26971449081478144,"score_spread":0.2612728812094015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118192797","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020977328,0.00001559851,0.95675194,0.0079971375,0.00006356402,0.00010076499,7.4776395e-8,0.00066575536,0.03419541],"genre_scores_gemma":[0.07254945,0.000015302025,0.9137626,0.008428453,0.000036669637,0.0000056101458,6.802453e-7,0.000002347426,0.0051988387],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993174,0.00003256635,0.00011517971,0.00017327855,0.00022562183,0.00013593167],"domain_scores_gemma":[0.9995049,0.000041517917,0.000025204594,0.00029723343,0.00003340953,0.000097738994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019793698,0.000058404898,0.00008133779,0.000046314173,0.00003642593,0.0000858641,0.00044866206,0.000027992417,0.00018052745],"category_scores_gemma":[0.000038565246,0.00003850253,0.00003141216,0.0001765582,0.000021714295,0.00034497422,0.000045553166,0.00006244717,0.00012757491],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033277463,0.00003756335,0.000019516612,0.0000012441832,0.000001550623,0.00000742745,0.00017114225,4.1021386e-7,0.009580928,0.0067663454,0.04645099,0.93695956],"study_design_scores_gemma":[0.001690277,0.00019862523,0.0016983944,0.000018971976,0.0000035559342,0.000022046508,0.000024809226,0.010744919,0.95247954,0.025738832,0.007103374,0.0002766405],"about_ca_topic_score_codex":0.0000063999614,"about_ca_topic_score_gemma":9.2709354e-7,"teacher_disagreement_score":0.94289863,"about_ca_system_score_codex":0.00001342541,"about_ca_system_score_gemma":0.00001626675,"threshold_uncertainty_score":0.1976649},"labels":[],"label_agreement":null},{"id":"W2118434484","doi":"10.1109/pacrim.1991.160842","title":"Application of mathematical morphology to the extraction of craniofacial landmarks","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Artificial intelligence; Craniofacial; Computer science; Mathematical morphology; Computer vision; Pattern recognition (psychology); Skull; Feature extraction; Grayscale; Scale (ratio); Image processing; Image (mathematics); Biology; Anatomy; Geography; Cartography","score_opus":0.01921132752728124,"score_gpt":0.297867424173513,"score_spread":0.27865609664623175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118434484","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022753128,0.0000030549857,0.99196255,0.0015944212,0.00002849011,0.0001811015,5.7813526e-7,0.000048111833,0.0039064023],"genre_scores_gemma":[0.6779355,0.000004263107,0.32140252,0.00040296718,0.000016639879,0.000030320436,4.8441933e-7,0.0000020058878,0.00020534579],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99944293,0.00003317151,0.00019658808,0.000096178446,0.00017100856,0.000060125003],"domain_scores_gemma":[0.9994987,0.000089239285,0.00006814694,0.00026216338,0.000051509698,0.000030242652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020441564,0.000034759672,0.00007612666,0.000038984297,0.000015817885,0.000006538661,0.00031263736,0.000030141433,0.0004157316],"category_scores_gemma":[0.000071375885,0.000022501281,0.000021593658,0.00016373718,0.000038508766,0.00008560114,0.000052299594,0.00004219805,0.00006912652],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000551845,0.0003501166,0.000339551,0.000043022625,0.000012311081,0.0000015591477,0.0016246383,0.000019662293,0.26166594,0.096740015,0.020669352,0.6185283],"study_design_scores_gemma":[0.00041370868,0.0002911666,0.005848202,0.000020397058,0.000014462706,0.000056981033,0.000121282086,0.2149562,0.7588906,0.016919741,0.0022796413,0.00018757259],"about_ca_topic_score_codex":0.000017301134,"about_ca_topic_score_gemma":0.0000012791759,"teacher_disagreement_score":0.67566013,"about_ca_system_score_codex":0.000006780867,"about_ca_system_score_gemma":0.0000033394217,"threshold_uncertainty_score":0.45519695},"labels":[],"label_agreement":null},{"id":"W2118692366","doi":"10.1109/tbme.2010.2055865","title":"Optimizing the Use of Radiologist Seed Points for Improved Multiple Sclerosis Lesion Segmentation","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Segmentation; Heuristics; Sørensen–Dice coefficient; Lesion; Computer science; Artificial intelligence; Spearman's rank correlation coefficient; Correlation; Pattern recognition (psychology); Ground truth; Classifier (UML); Image segmentation; Medicine; Mathematics; Machine learning; Pathology","score_opus":0.05760486849705833,"score_gpt":0.2647805450410744,"score_spread":0.20717567654401609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118692366","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040757847,0.0000041947587,0.99350446,0.00075092906,0.00088346575,0.00047108732,0.000026000822,0.00028329526,7.848015e-7],"genre_scores_gemma":[0.29061532,0.000031935648,0.7089703,0.0001944953,0.000028114277,0.00012805428,0.000006591485,0.000013061483,0.000012151183],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989381,0.000028076294,0.00033750944,0.0002497424,0.00023154292,0.0002150637],"domain_scores_gemma":[0.99867284,0.00070985354,0.00008370054,0.00033581824,0.00006834142,0.000129456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034045964,0.00013563629,0.00015437545,0.0001734676,0.00012090841,0.00006491664,0.00039009043,0.00012017712,0.000010618959],"category_scores_gemma":[0.00013518381,0.00010167062,0.00010429405,0.00032914872,0.0001324525,0.00035375863,0.00000513564,0.00030470567,0.0000019359634],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011450379,0.00007460722,5.7943106e-7,0.00001859804,0.000017980981,3.2145027e-7,0.00014259743,0.0029069735,0.8850084,0.000026914591,0.0000747409,0.11171687],"study_design_scores_gemma":[0.00041007443,0.00009312161,0.00005199405,0.000027732596,0.000008535729,0.0000029558005,0.0000090259955,0.5223282,0.4767432,0.000006926352,0.00023894473,0.00007932119],"about_ca_topic_score_codex":0.00002960584,"about_ca_topic_score_gemma":0.0000044802564,"teacher_disagreement_score":0.5194212,"about_ca_system_score_codex":0.000036683465,"about_ca_system_score_gemma":0.000030280122,"threshold_uncertainty_score":0.4146008},"labels":[],"label_agreement":null},{"id":"W2118853360","doi":"10.1117/12.654160","title":"Brain extraction using geodesic active contours","year":2006,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Preprocessor; Voxel; Geodesic; Computer vision; Pattern recognition (psychology); Cortex (anatomy); Mathematics; Neuroscience","score_opus":0.014443834685809186,"score_gpt":0.26584148746047903,"score_spread":0.25139765277466986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118853360","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94932526,0.00004312108,0.045172486,0.0028509547,0.00024589506,0.00052395643,0.000015412554,0.00019114281,0.0016317749],"genre_scores_gemma":[0.18366078,0.000023727136,0.8150428,0.00033127633,0.00049702014,0.00012088988,0.000007803699,0.000048781847,0.0002669532],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976202,2.514635e-8,0.00067034696,0.00043989674,0.00088212936,0.00038738758],"domain_scores_gemma":[0.9976186,0.0002288021,0.0005209465,0.000074855925,0.0014447145,0.00011205061],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069518277,0.00027596607,0.00033267043,0.00013968632,0.00010369207,0.00020148496,0.0012714214,0.00017553379,0.000011829693],"category_scores_gemma":[0.000498944,0.00024376287,0.00044743434,0.00042056368,0.00022314223,0.0015650484,0.00019687551,0.00031163957,0.0000013552925],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002225792,0.000092623464,0.00006629327,0.00010724278,0.00009930852,1.413294e-7,0.000093246956,0.00006424983,0.68966126,0.3038217,0.0045557274,0.001415924],"study_design_scores_gemma":[0.00084194896,0.00019124556,0.00097408757,0.00021524998,0.000067484994,0.000030402547,0.00046177921,0.10153737,0.88838655,0.005933646,0.0010046004,0.00035564956],"about_ca_topic_score_codex":0.000053927466,"about_ca_topic_score_gemma":2.3622108e-7,"teacher_disagreement_score":0.7698703,"about_ca_system_score_codex":0.00028177266,"about_ca_system_score_gemma":0.000052391017,"threshold_uncertainty_score":0.99403614},"labels":[],"label_agreement":null},{"id":"W2118932613","doi":"10.1109/cvprw.2006.134","title":"Multi-Scale Contour Extraction Based on Natural Image Statistics","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Clutter; Computer science; Artificial intelligence; Robustness (evolution); Pattern recognition (psychology); Computer vision; Scale (ratio); Bayesian probability; Segmentation; Image segmentation; Probabilistic logic; Prior probability; Object (grammar); Feature extraction; Radar","score_opus":0.011143330628365162,"score_gpt":0.3028211770634809,"score_spread":0.29167784643511574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118932613","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001225322,0.000005499107,0.99548924,0.00039570843,0.00022768545,0.00014571543,0.000006690142,0.00048567276,0.0031212433],"genre_scores_gemma":[0.057380266,8.312186e-7,0.9385864,0.0013886652,0.0000436671,0.00001272988,0.000022022163,0.0000059439103,0.0025594526],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990322,0.00005444457,0.00018990997,0.00023612728,0.0003277346,0.00015955788],"domain_scores_gemma":[0.99936867,0.0001438484,0.00006677118,0.0002644081,0.00009838257,0.00005789839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016040662,0.000093327515,0.00008231486,0.00007520675,0.000059140733,0.00013554576,0.00026307776,0.000035459216,0.00016351699],"category_scores_gemma":[0.000053891847,0.00007878718,0.000026529988,0.000120560835,0.000044904627,0.00037581514,0.000029117486,0.00013244928,0.00010788495],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001867447,0.00077895605,0.00037399592,0.000030082532,0.000005776468,0.000110579895,0.00006529291,0.000069914444,0.5495972,0.005280668,0.24696082,0.19670805],"study_design_scores_gemma":[0.00046160433,0.00005398373,0.0050822794,0.000008039974,0.0000021146739,0.0000028701554,0.0000069517055,0.69616586,0.29728013,0.00042284784,0.00038815502,0.00012517022],"about_ca_topic_score_codex":0.00018579066,"about_ca_topic_score_gemma":0.000058321082,"teacher_disagreement_score":0.69609594,"about_ca_system_score_codex":0.000053898726,"about_ca_system_score_gemma":0.00003079613,"threshold_uncertainty_score":0.3212848},"labels":[],"label_agreement":null},{"id":"W2118979849","doi":"10.1016/j.compmedimag.2011.06.001","title":"Quick detection of brain tumors and edemas: A bounding box method using symmetry","year":2011,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":108,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Minimum bounding box; Computer science; Artificial intelligence; Bounding overwatch; Pattern recognition (psychology); Magnetic resonance imaging; Histogram; Sørensen–Dice coefficient; Image segmentation; Computer vision; Image (mathematics); Medicine; Radiology","score_opus":0.028607222027919404,"score_gpt":0.31644253616642337,"score_spread":0.28783531413850394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118979849","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02307828,0.00043540026,0.9753152,0.0004710608,0.00029085262,0.00013575694,8.562286e-7,0.00022734811,0.000045238],"genre_scores_gemma":[0.22537461,0.00012452294,0.7724992,0.0019173041,0.000060471757,0.000005866917,0.000001113762,0.000013526782,0.000003351615],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99796486,0.0003030085,0.00047583494,0.00044835545,0.0005213405,0.00028658545],"domain_scores_gemma":[0.9986248,0.0004013139,0.00020515922,0.00028693507,0.00009205505,0.00038973565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017538335,0.00018091724,0.00034877806,0.00037550283,0.00015948538,0.000095668554,0.00039799127,0.000097425465,0.000008124486],"category_scores_gemma":[0.0004747328,0.00016632106,0.000062057974,0.0005515069,0.00046744398,0.00042159384,0.00044046078,0.0003134645,3.1694321e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021533271,0.000119410906,0.0019260301,0.0003035532,0.00005518832,0.000117053,0.0026337365,1.6758223e-7,0.051852018,0.011246668,0.00015567773,0.931569],"study_design_scores_gemma":[0.0017045278,0.00018233624,0.003281585,0.00087633106,0.00004858824,0.0009619468,0.00021842273,0.88304514,0.08463644,0.024322249,0.00019896161,0.00052346085],"about_ca_topic_score_codex":0.00016064564,"about_ca_topic_score_gemma":0.0000020289647,"teacher_disagreement_score":0.93104553,"about_ca_system_score_codex":0.000016350077,"about_ca_system_score_gemma":0.00007954909,"threshold_uncertainty_score":0.6782376},"labels":[],"label_agreement":null},{"id":"W2118980630","doi":"10.1109/tip.2004.828435","title":"A Maximum Likelihood Approach for Image Registration Using Control Point And Intensity","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Image registration; Image processing; Maximum likelihood; Intensity (physics); Image segmentation; Mathematics; Pattern recognition (psychology); Image (mathematics); Statistics; Optics","score_opus":0.020524809734147706,"score_gpt":0.2821390551606165,"score_spread":0.26161424542646877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118980630","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006287148,0.000043922337,0.9973458,0.00071301893,0.00009612781,0.00063510676,0.000009193557,0.00040951552,0.00011857134],"genre_scores_gemma":[0.35873663,0.000005555319,0.6406197,0.00052191893,0.000024324952,0.000067496134,0.0000016299012,0.000014429147,0.000008327527],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99851716,0.000036881484,0.00034565385,0.0005147464,0.00027752598,0.00030800948],"domain_scores_gemma":[0.9990567,0.00004158531,0.00016595428,0.00027350403,0.00031611268,0.00014615257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042209562,0.00019942617,0.0002233232,0.00017672451,0.00042128,0.00050812785,0.00024973924,0.00008445538,0.000004046263],"category_scores_gemma":[0.000030775514,0.00019563978,0.000077632685,0.0002838012,0.0001835326,0.0018760916,0.0000034288848,0.00024069732,0.0000023404132],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013100888,0.0005921057,0.0000016287852,0.00044709255,0.000040256375,0.000021694188,0.0016703515,0.0006572417,0.5168399,0.000073152834,0.000073845185,0.47945172],"study_design_scores_gemma":[0.0022328775,0.00020904263,0.000011345594,0.00014990995,0.00006233422,0.00019143602,0.00023787579,0.32843414,0.6595763,0.008549882,0.0000044113754,0.00034041135],"about_ca_topic_score_codex":0.00003914795,"about_ca_topic_score_gemma":0.0000029484997,"teacher_disagreement_score":0.47911128,"about_ca_system_score_codex":0.00013900774,"about_ca_system_score_gemma":0.00017575198,"threshold_uncertainty_score":0.79779583},"labels":[],"label_agreement":null},{"id":"W2119023565","doi":"10.1109/icpr.2006.734","title":"Joint Image Segmentation and Interpretation Using Iterative Semantic Region Growing on SAR Sea Ice Imagery","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Segmentation; Computer science; Markov random field; Artificial intelligence; Image segmentation; Synthetic aperture radar; Scale-space segmentation; Interpretation (philosophy); Radar imaging; Computer vision; Segmentation-based object categorization; Pattern recognition (psychology); Semantic interpretation; Radar","score_opus":0.020876188780663836,"score_gpt":0.2844226047498505,"score_spread":0.26354641596918665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119023565","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05064668,0.000022560343,0.94659495,0.00072347105,0.00010570671,0.0002954645,7.430578e-7,0.00030428453,0.0013061562],"genre_scores_gemma":[0.49703866,0.0000072600096,0.50092256,0.0018308245,0.000050264716,0.000008649379,0.000012611136,0.000011012886,0.000118148404],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99855316,0.00016653308,0.00035051606,0.0004118847,0.00032676032,0.00019115058],"domain_scores_gemma":[0.9993322,0.00011038093,0.00015578579,0.00022788737,0.000109762,0.00006397648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002801493,0.00015763387,0.00015326007,0.0002258025,0.00013937494,0.0003883375,0.00015099639,0.000045521665,0.000016259364],"category_scores_gemma":[0.00005152903,0.00014628559,0.000042973086,0.00025962232,0.00007810043,0.0025893566,0.00010428854,0.00012065128,0.000014249474],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002624591,0.00014879959,0.00045214372,0.00011556133,0.000023777826,0.00012762783,0.0021436082,0.00013909463,0.8789617,0.005612934,0.0040608575,0.108187646],"study_design_scores_gemma":[0.0003129509,0.00009735931,0.0009581845,0.00015256484,0.000011312548,0.00004906306,0.00016614613,0.3737346,0.6211736,0.0031293873,0.000006351484,0.00020846126],"about_ca_topic_score_codex":0.00017931555,"about_ca_topic_score_gemma":0.0000035990356,"teacher_disagreement_score":0.446392,"about_ca_system_score_codex":0.000120478384,"about_ca_system_score_gemma":0.000025444726,"threshold_uncertainty_score":0.5965353},"labels":[],"label_agreement":null},{"id":"W2119076421","doi":"10.1109/tmi.2003.812263","title":"Registration and fusion of retinal images-an evaluation study","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":154,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Computer Research Institute of Montréal","funders":"","keywords":"Artificial intelligence; Computer vision; Affine transformation; Computer science; Grayscale; Image registration; Image fusion; Pixel; Fusion; Transformation (genetics); Pattern recognition (psychology); Control point; Mathematics; Image (mathematics)","score_opus":0.02792124040778408,"score_gpt":0.3430248436430776,"score_spread":0.3151036032352935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119076421","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021282997,0.00003219162,0.976817,0.00045845043,0.0002708173,0.00041770368,0.0000010162362,0.0001403581,0.0005794549],"genre_scores_gemma":[0.9518785,0.000026542515,0.047719073,0.000269921,0.000015347565,0.00004904988,0.0000013351219,0.000008295677,0.000031925607],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9969216,0.0006245985,0.0004184534,0.0003937751,0.0014789277,0.00016262813],"domain_scores_gemma":[0.99891376,0.00014504627,0.00012241055,0.00039922819,0.00019362949,0.00022591377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028166745,0.00012369615,0.00015269338,0.00018377145,0.00014789336,0.000071493814,0.00027517564,0.000048517337,0.00030366908],"category_scores_gemma":[0.00021547968,0.00011441074,0.000035809702,0.00030950637,0.00015328854,0.0007053676,0.0000028016696,0.0002748975,0.0000044467934],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013114205,0.00087695674,0.0003058371,0.000019468776,0.00001355388,0.00002323748,0.0011191868,0.000042813022,0.011607476,0.00020966437,0.00018946186,0.98557925],"study_design_scores_gemma":[0.004225802,0.0012379696,0.0042185956,0.00027688174,0.00016920548,0.000244758,0.0023118348,0.35796106,0.62558854,0.003089714,0.000070258364,0.0006053667],"about_ca_topic_score_codex":0.00005418359,"about_ca_topic_score_gemma":0.000012471085,"teacher_disagreement_score":0.98497385,"about_ca_system_score_codex":0.000047840287,"about_ca_system_score_gemma":0.0001624953,"threshold_uncertainty_score":0.46655345},"labels":[],"label_agreement":null},{"id":"W2119245818","doi":"10.1007/s11265-008-0202-x","title":"Robust Multimodal Registration Using Local Phase-Coherence Representations","year":2008,"lang":"en","type":"article","venue":"Journal of Signal Processing Systems","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Artificial intelligence; Computer science; Robustness (evolution); Subpixel rendering; Residual; Image registration; Computer vision; Pattern recognition (psychology); Coherence (philosophical gambling strategy); Image (mathematics); Mathematics; Algorithm; Pixel; Statistics","score_opus":0.09840738497082493,"score_gpt":0.34384115141689625,"score_spread":0.24543376644607132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119245818","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008737176,0.0005010352,0.99000406,0.00009930634,0.00022618823,0.00015358052,8.5737247e-7,0.000078128236,0.00019964254],"genre_scores_gemma":[0.7778992,0.000008284135,0.22171594,0.00005000512,0.00022806908,0.0000038452185,8.9276693e-7,0.000008356757,0.000085411004],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997556,0.00016227561,0.00095074374,0.00021565576,0.0009205085,0.00019485803],"domain_scores_gemma":[0.9975832,0.000090056375,0.0012227689,0.00018238029,0.00075248734,0.00016908026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007874328,0.00013191633,0.00026457486,0.00021354164,0.00028372207,0.00026272744,0.00057359645,0.00007674852,0.0000116359715],"category_scores_gemma":[0.00009280516,0.00011273516,0.000076452925,0.00045027141,0.00017983036,0.0021131453,0.000040043946,0.00027870835,0.0000033918818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014959852,0.0014142564,0.0013662705,0.00070156,0.00014233157,0.0019370788,0.008167873,0.12563287,0.4028821,0.00058658887,0.010461132,0.44655833],"study_design_scores_gemma":[0.0011552274,0.0003105811,0.00010931047,0.0006364996,0.000021702466,0.0039510136,0.00055014016,0.9520682,0.040778145,0.00015337266,0.00005990695,0.00020589028],"about_ca_topic_score_codex":0.00007319287,"about_ca_topic_score_gemma":6.132652e-7,"teacher_disagreement_score":0.8264353,"about_ca_system_score_codex":0.00014213719,"about_ca_system_score_gemma":0.0005664271,"threshold_uncertainty_score":0.45972064},"labels":[],"label_agreement":null},{"id":"W2119255561","doi":"10.1109/tmi.2007.899180","title":"Real-Time Vessel Segmentation and Tracking for Ultrasound Imaging Applications","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":140,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer vision; Ellipse; Segmentation; Artificial intelligence; Kalman filter; Computer science; Tracking (education); Feature (linguistics); Image segmentation; Transverse plane; 3D ultrasound; Ultrasound; Mathematics; Engineering; Acoustics; Physics","score_opus":0.011049614635369734,"score_gpt":0.308905252891492,"score_spread":0.29785563825612227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119255561","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041660186,0.000051513915,0.9960188,0.0015155374,0.00023520585,0.0006874243,0.0000083336145,0.00066108134,0.0004054852],"genre_scores_gemma":[0.3261619,0.0003329303,0.66935563,0.0031090232,0.00022668099,0.00050851173,0.000021086753,0.000058173515,0.00022606773],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99776584,0.000060710987,0.0004971011,0.0005436559,0.0007006593,0.000432006],"domain_scores_gemma":[0.9977768,0.0012146657,0.00011973115,0.0003402056,0.00013801375,0.0004106072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013961572,0.00019974679,0.0001901012,0.00029422514,0.00041794742,0.00021590656,0.00044316603,0.00006639611,0.00010677428],"category_scores_gemma":[0.00007172209,0.00020390298,0.00008094773,0.00040491045,0.0002264711,0.0008752791,0.0000050503586,0.00029808548,0.000023848359],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008374291,0.000115146264,0.000039510127,0.000028777027,0.000013862375,0.000011259504,0.0003609627,0.000008729993,0.101289615,0.00020344055,0.00053594937,0.89738435],"study_design_scores_gemma":[0.0019266828,0.00006785676,0.00042855777,0.00021846214,0.00008068854,0.00032683503,0.0006089495,0.10268547,0.88926697,0.0026139123,0.0010900671,0.00068553863],"about_ca_topic_score_codex":0.000034098644,"about_ca_topic_score_gemma":0.0000039603296,"teacher_disagreement_score":0.89669883,"about_ca_system_score_codex":0.000112896334,"about_ca_system_score_gemma":0.00008907812,"threshold_uncertainty_score":0.83149225},"labels":[],"label_agreement":null},{"id":"W2119300483","doi":"10.1007/s11263-006-7934-5","title":"Graph Cuts and Efficient N-D Image Segmentation","year":2006,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1904,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Segmentation; Cut; Image segmentation; Computer science; Artificial intelligence; Graph; Graph partition; Segmentation-based object categorization; Scale-space segmentation; Mathematics; Algorithm; Pattern recognition (psychology); Theoretical computer science","score_opus":0.004883711073049776,"score_gpt":0.29079230183203986,"score_spread":0.28590859075899006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119300483","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041382395,0.00008105438,0.955906,0.0013095714,0.0010199131,0.00006698618,0.0000010901177,0.000046397967,0.00018662955],"genre_scores_gemma":[0.30768326,0.000033867454,0.69137293,0.00048764344,0.00038628,0.0000011711231,0.0000036750178,0.000005734038,0.00002545004],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838203,0.00006794229,0.00048081507,0.00015659521,0.0008101353,0.000102453334],"domain_scores_gemma":[0.99884754,0.00008973611,0.00035752874,0.0001108252,0.0005207721,0.000073575844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039146392,0.00009670605,0.00012136738,0.00035075244,0.0000393351,0.0003281622,0.000625964,0.000032157284,0.00001677535],"category_scores_gemma":[0.000014544706,0.00008069628,0.000070483526,0.00012701491,0.000055788776,0.0007026026,0.00020148793,0.00011728394,0.000007789928],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006710381,0.00056062214,0.00066058704,0.000017289238,0.00008520474,0.00049378,0.00052327133,0.0013983076,0.14282873,0.008712319,0.03279069,0.8118621],"study_design_scores_gemma":[0.0070556896,0.0021577484,0.0632174,0.0008075425,0.000048916463,0.0039570103,0.00006074159,0.44288,0.42751858,0.046446547,0.0049195364,0.0009302964],"about_ca_topic_score_codex":0.000010510522,"about_ca_topic_score_gemma":5.147807e-7,"teacher_disagreement_score":0.8109318,"about_ca_system_score_codex":0.000057910478,"about_ca_system_score_gemma":0.00003081253,"threshold_uncertainty_score":0.32906988},"labels":[],"label_agreement":null},{"id":"W2119531662","doi":"10.1109/tpami.2009.96","title":"TurboPixels: Fast Superpixels Using Geometric Flows","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1140,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Toronto","funders":"","keywords":"Compact space; Constraint (computer-aided design); Image (mathematics); Limiting; Speedup; Artificial intelligence; Computer science; Computer vision; Algorithm; Pattern recognition (psychology); Mathematics; Geometry","score_opus":0.02889710000689543,"score_gpt":0.30100753875346264,"score_spread":0.2721104387465672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119531662","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004850323,0.00010858232,0.99422395,0.0003035713,0.0001238535,0.00013215443,0.000014567729,0.0001909244,0.000052057312],"genre_scores_gemma":[0.9562425,0.00027882072,0.04193811,0.001410054,0.00001968086,0.000007329239,0.0000030317974,0.000007187116,0.00009330256],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99820876,0.000092819886,0.00044285704,0.00055068766,0.0004294887,0.0002754016],"domain_scores_gemma":[0.9989891,0.000105529805,0.00009232988,0.0005243899,0.00008650798,0.00020214633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028312107,0.00022728585,0.0003266098,0.0012935854,0.00019332579,0.0002127652,0.00054118555,0.000072303716,0.00029051164],"category_scores_gemma":[0.000008214784,0.00019831749,0.00022934384,0.0028993313,0.000050240964,0.00044072865,0.0000051512325,0.0002426358,0.000020346966],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000022152358,0.00013534051,0.00009198488,0.00000498407,0.000122356,0.000011571759,0.00016213574,0.004517522,0.0028641985,0.000013204384,0.0000058768833,0.9920686],"study_design_scores_gemma":[0.00007484186,0.0001753612,0.0005432475,0.000020926498,0.00024016196,0.000020342075,0.000026545658,0.45630953,0.5420416,0.0002522081,0.000018770645,0.00027647836],"about_ca_topic_score_codex":0.0004042713,"about_ca_topic_score_gemma":0.00011310295,"teacher_disagreement_score":0.99179214,"about_ca_system_score_codex":0.00005219286,"about_ca_system_score_gemma":0.000020499507,"threshold_uncertainty_score":0.8087152},"labels":[],"label_agreement":null},{"id":"W2120090673","doi":"10.1109/crv.2007.64","title":"The importance of scale when selecting pixels for image registration","year":2007,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Pixel; Computer science; Transformation (genetics); Image (mathematics); Scale (ratio); Set (abstract data type); Artificial intelligence; Measure (data warehouse); Reliability (semiconductor); Computer vision; Selection (genetic algorithm); Position (finance); Image registration; Algorithm; Data mining","score_opus":0.017541084422096672,"score_gpt":0.3182596382608441,"score_spread":0.3007185538387474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120090673","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015791155,0.000019166613,0.9916673,0.0008657137,0.000054319735,0.00023976083,3.135927e-7,0.00012686968,0.005447496],"genre_scores_gemma":[0.03798271,0.000004205586,0.9598954,0.00041964132,0.000035259454,0.000014863408,0.0000011022696,0.0000036096592,0.0016432148],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99921644,0.000015334688,0.00028556195,0.00013819293,0.0001950751,0.00014941937],"domain_scores_gemma":[0.9990681,0.00032894415,0.00015674162,0.00025366436,0.00015450014,0.000038065424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015043153,0.00004564878,0.000057817084,0.000023775488,0.00010655003,0.00009009371,0.000367512,0.000022970002,0.0000078828625],"category_scores_gemma":[0.0002464605,0.00003135034,0.00002987359,0.0001274133,0.000059750597,0.00047649472,0.000037952697,0.000043964767,0.0000010915438],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018050616,0.00006401872,0.002294075,0.000049104743,0.000014275286,0.0000027624224,0.0011374669,4.4772372e-7,0.32341143,0.08093863,0.04025403,0.5518157],"study_design_scores_gemma":[0.00011156121,0.000060374856,0.00071463047,0.0000075093176,0.0000017201072,0.0000040164987,0.00009234161,0.0022463365,0.97882783,0.017237432,0.0006404171,0.000055811055],"about_ca_topic_score_codex":0.000013344889,"about_ca_topic_score_gemma":0.000094285024,"teacher_disagreement_score":0.6554164,"about_ca_system_score_codex":0.000019769517,"about_ca_system_score_gemma":0.000032768545,"threshold_uncertainty_score":0.12784298},"labels":[],"label_agreement":null},{"id":"W2120247265","doi":"10.1109/ismvl.2003.1201389","title":"Automated finding of the Willis ring in MR angiography images using fuzzy knowledge base","year":2004,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Atlantic Canada Opportunities Agency","keywords":"Artificial intelligence; Computer science; Circle of Willis; Fuzzy logic; Fuzzy set; Computer vision; Radiology; Medicine","score_opus":0.0253899352009432,"score_gpt":0.3098509351768193,"score_spread":0.2844609999758761,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120247265","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.114001825,0.00012472391,0.88266546,0.00017899853,0.00011840734,0.00018370686,0.000001186841,0.0005546093,0.002171099],"genre_scores_gemma":[0.6346531,0.00000405569,0.36520246,0.00010805275,0.0000074195104,0.0000040434215,3.1691883e-7,0.0000044830026,0.00001609702],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990724,0.000069652524,0.00025935407,0.00019962035,0.00019766312,0.00020132224],"domain_scores_gemma":[0.9994178,0.000059307487,0.00008704186,0.0003313219,0.000051751547,0.000052793595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035733683,0.00008912005,0.00011499886,0.000300621,0.000060569975,0.00005167079,0.0005783978,0.000038031245,0.000013615789],"category_scores_gemma":[0.00007464154,0.00006400847,0.00010431226,0.0014490845,0.00009193117,0.00044864093,0.00025659683,0.00009023855,0.0000031449697],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000069897524,0.00089237053,0.06611741,0.00027493882,0.00009660413,0.00007287249,0.009677081,0.0022710045,0.83172,0.020873822,0.0025855356,0.065411344],"study_design_scores_gemma":[0.00047863,0.000023616034,0.013783712,0.00027365223,0.000006914897,0.000011561394,0.00007439397,0.043155733,0.9394602,0.0025680515,0.0000074706154,0.00015603901],"about_ca_topic_score_codex":0.00024741513,"about_ca_topic_score_gemma":0.000026128804,"teacher_disagreement_score":0.5206513,"about_ca_system_score_codex":0.000065468535,"about_ca_system_score_gemma":0.00007262362,"threshold_uncertainty_score":0.26101896},"labels":[],"label_agreement":null},{"id":"W2120309401","doi":"10.1109/pacrim.2007.4313174","title":"An Improved Multiscale Normal-Mesh-Based Image Coder","year":2007,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Bicubic interpolation; Interpolation (computer graphics); Computer science; Artificial intelligence; Image (mathematics); Coding (social sciences); Image scaling; Computer vision; Image quality; Representation (politics); Mesh generation; Algorithm; Pattern recognition (psychology); Mathematics; Image processing; Linear interpolation; Finite element method; Engineering","score_opus":0.009330449131729781,"score_gpt":0.3035945243189385,"score_spread":0.29426407518720876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120309401","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012219789,0.0000047643366,0.99199134,0.00039643937,0.000116016585,0.00020907163,0.0000012631427,0.0009518219,0.0051073004],"genre_scores_gemma":[0.10342053,8.456269e-7,0.8919236,0.0040334645,0.000038642193,0.000011127431,0.0000067579203,0.000008859066,0.00055616506],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99876606,0.000040437782,0.00026656376,0.0003273838,0.00027845678,0.00032108818],"domain_scores_gemma":[0.9988818,0.00008817095,0.00006230752,0.0005910687,0.00010989026,0.00026672365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006780723,0.00011470478,0.00010418699,0.000105914434,0.0000719215,0.00014583906,0.00072555523,0.000061238374,0.00042454567],"category_scores_gemma":[0.00004612263,0.000098025695,0.000042202606,0.00022794647,0.00009296906,0.00092923857,0.00008026429,0.00011740698,0.00009186771],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011437357,0.00027914904,0.0003084688,0.000014072978,0.0000054016054,0.000045498455,0.00021842423,0.0000016331367,0.747393,0.0011809935,0.004787467,0.24575442],"study_design_scores_gemma":[0.0004216307,0.00011487518,0.0012901247,0.0000038141015,0.0000016521403,0.0000032387056,0.000022068984,0.14473757,0.852836,0.00012697194,0.00029455396,0.00014746249],"about_ca_topic_score_codex":0.00009325064,"about_ca_topic_score_gemma":0.00004277535,"teacher_disagreement_score":0.24560696,"about_ca_system_score_codex":0.00003516154,"about_ca_system_score_gemma":0.000048159516,"threshold_uncertainty_score":0.4648477},"labels":[],"label_agreement":null},{"id":"W2120405244","doi":"10.1109/nafips.2000.877392","title":"An adaptive deterministic annealing approach for medical image segmentation","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McGill University","keywords":"Simulated annealing; Cluster analysis; Segmentation; Image segmentation; Computer science; Adaptive simulated annealing; Artificial intelligence; Mathematical optimization; Scale-space segmentation; Algorithm; Pattern recognition (psychology); Mathematics","score_opus":0.039784488424335414,"score_gpt":0.3227332135737313,"score_spread":0.2829487251493959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120405244","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017828881,0.000015634569,0.99516845,0.00021982373,0.000068406334,0.0004130149,0.0000026879266,0.0004937752,0.0034399196],"genre_scores_gemma":[0.07637855,0.000007947033,0.9217744,0.0014459299,0.000075621225,0.00016008937,0.000017082544,0.000009693764,0.00013067738],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851286,0.00008383866,0.00027901708,0.00038231426,0.0005118983,0.00023008556],"domain_scores_gemma":[0.99911505,0.0001419564,0.00007268143,0.0003157239,0.00009514707,0.0002594432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045427185,0.000112572445,0.00012387888,0.000081457874,0.00010858457,0.00015065116,0.00066308747,0.000072097166,0.00037538685],"category_scores_gemma":[0.00015702253,0.00009800599,0.000042813605,0.00016450026,0.000083095634,0.0008353813,0.000064047366,0.00008923564,0.000021747035],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011635071,0.00042750878,0.000025418954,0.000059064834,0.000019720392,0.00002694971,0.0014574869,0.000017309545,0.0133727295,0.008383566,0.010999586,0.96519905],"study_design_scores_gemma":[0.00035449312,0.00027082147,0.000022704664,0.000007681579,0.000004429345,0.000013402796,0.00013728486,0.9632297,0.035219736,0.0005827668,0.00002169683,0.00013527727],"about_ca_topic_score_codex":0.000008736811,"about_ca_topic_score_gemma":0.0000010279632,"teacher_disagreement_score":0.96506375,"about_ca_system_score_codex":0.000036283294,"about_ca_system_score_gemma":0.000024418858,"threshold_uncertainty_score":0.41102228},"labels":[],"label_agreement":null},{"id":"W2120431813","doi":"10.1016/j.ultrasmedbio.2011.06.006","title":"Automatic Adaptive Parameterization in Local Phase Feature-Based Bone Segmentation in Ultrasound","year":2011,"lang":"en","type":"article","venue":"Ultrasound in Medicine & Biology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Feature (linguistics); Artificial intelligence; Computer science; Ultrasound; Pattern recognition (psychology); Phase (matter); Computer vision; Radiology; Medicine; Physics","score_opus":0.03629412039859929,"score_gpt":0.3243408047171521,"score_spread":0.2880466843185528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120431813","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15565932,0.0001461644,0.84261703,0.00026139204,0.00022536835,0.000590975,0.0000037703826,0.00014002774,0.00035593566],"genre_scores_gemma":[0.8389828,0.000072493356,0.15904844,0.001565917,0.000031305048,0.00014602249,0.0001324965,0.00001152506,0.000008963195],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9973707,0.0006092038,0.000767966,0.00056809105,0.0002546532,0.0004293393],"domain_scores_gemma":[0.997983,0.0012336696,0.00023764026,0.0003733533,0.00005875919,0.00011359317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011675151,0.0002471436,0.00046838756,0.00078966416,0.000031112646,0.000016735754,0.00047467492,0.00019753996,0.00029111782],"category_scores_gemma":[0.0012511312,0.00021076741,0.000032300366,0.0011832829,0.0005093102,0.0003835913,0.00003617542,0.0004240643,0.000014248008],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024241437,0.0018489452,0.03891813,0.000111914655,0.000025517395,0.00024465204,0.014853697,0.00007077222,0.3001984,0.0049253856,0.0009731684,0.637587],"study_design_scores_gemma":[0.04898996,0.016588977,0.20470524,0.0025448247,0.000090121175,0.000405252,0.005946346,0.26535982,0.36119497,0.090805344,0.000440396,0.002928756],"about_ca_topic_score_codex":0.0007157604,"about_ca_topic_score_gemma":0.0003792846,"teacher_disagreement_score":0.6835686,"about_ca_system_score_codex":0.00027331308,"about_ca_system_score_gemma":0.00011426895,"threshold_uncertainty_score":0.85948455},"labels":[],"label_agreement":null},{"id":"W2120763784","doi":"10.1109/ijcnn.2006.246725","title":"A Reinforcement Learning Framework for Medical Image Segmentation","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reinforcement learning; Computer science; Thresholding; Artificial intelligence; Segmentation; Image segmentation; Structuring element; Computer vision; Machine learning; Image (mathematics); Image processing; Mathematical morphology","score_opus":0.03457277230092933,"score_gpt":0.31471739117421715,"score_spread":0.28014461887328784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120763784","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033522197,0.000011734638,0.97399604,0.014556429,0.00095740246,0.0006487091,0.000002036275,0.00041501314,0.0060604224],"genre_scores_gemma":[0.79533327,0.000068059984,0.19438355,0.005843139,0.0020555442,0.0004826109,0.000044886037,0.000032713564,0.0017562234],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971264,0.0000391994,0.00062145956,0.00046289936,0.0013095466,0.00044048435],"domain_scores_gemma":[0.9985015,0.0002738554,0.0004042608,0.00016752271,0.00052906515,0.00012381344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088103744,0.00024813545,0.0002062854,0.00010839757,0.0002851238,0.00057337596,0.0012759565,0.0001217947,0.00027734824],"category_scores_gemma":[0.0003314121,0.0001858597,0.00012185624,0.00026370614,0.00015038613,0.0006534164,0.00017311434,0.00054469035,0.00005304599],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012176678,0.00013481772,0.00021580522,0.00004107379,0.00006396564,0.00001436536,0.0004036417,0.0055965353,0.012837136,0.8238743,0.10184432,0.054852284],"study_design_scores_gemma":[0.0007680348,0.00040531252,0.00034994324,0.00035257437,0.000016173375,0.000044472938,0.00008613698,0.8437667,0.036021244,0.11642869,0.0013543458,0.00040638942],"about_ca_topic_score_codex":0.000036582027,"about_ca_topic_score_gemma":0.0000029779285,"teacher_disagreement_score":0.8381702,"about_ca_system_score_codex":0.00012208421,"about_ca_system_score_gemma":0.00007133925,"threshold_uncertainty_score":0.7579138},"labels":[],"label_agreement":null},{"id":"W2120829706","doi":"10.1007/11596448_131","title":"A Novel Multi-stage 3D Medical Image Segmentation: Methodology and Validation","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Centroid; Image segmentation; Scale-space segmentation; Computer vision; Segmentation-based object categorization; Measure (data warehouse); Pattern recognition (psychology); Data mining","score_opus":0.08461493616461994,"score_gpt":0.36923633067801553,"score_spread":0.2846213945133956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120829706","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000123423615,0.00020951877,0.9960763,0.0016408912,0.0007052732,0.00050606125,0.0000073275114,0.00029078027,0.00055149035],"genre_scores_gemma":[0.00028559854,0.00013069627,0.9945924,0.0041034915,0.0003172474,0.000022574419,0.000014497365,0.000026994612,0.0005064839],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955276,0.00013121641,0.0007243636,0.0015102356,0.0015929677,0.0005136012],"domain_scores_gemma":[0.9970866,0.001077308,0.00037112832,0.00085949927,0.00025117004,0.00035429432],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0028069678,0.00045957614,0.000523618,0.00076733454,0.00019413223,0.0004553424,0.0021429562,0.00045558612,0.00027194442],"category_scores_gemma":[0.00068070693,0.00042545272,0.0000739989,0.00042007095,0.0013019902,0.0010767813,0.0014234197,0.0008727128,0.000030193043],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030572412,0.000044238503,0.000011113811,0.000041253348,0.0000098142145,0.00006700873,0.00074853795,0.00025317192,0.005352761,0.0025787472,0.00002383312,0.9908665],"study_design_scores_gemma":[0.0016356918,0.000293344,0.00013440578,0.00049522094,0.000023874733,0.0004930323,0.0000013335435,0.8885167,0.093101144,0.012996436,0.0010387264,0.0012700361],"about_ca_topic_score_codex":0.000033837372,"about_ca_topic_score_gemma":0.000055260432,"teacher_disagreement_score":0.9895964,"about_ca_system_score_codex":0.00025249683,"about_ca_system_score_gemma":0.0005594021,"threshold_uncertainty_score":0.99981976},"labels":[],"label_agreement":null},{"id":"W2121062299","doi":"10.1007/s10278-008-9124-1","title":"Prostate Tissue Texture Feature Extraction for Suspicious Regions Identification on TRUS Images","year":2008,"lang":"en","type":"article","venue":"Journal of Digital Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Region of interest; Gabor filter; Segmentation; Pattern recognition (psychology); Feature (linguistics); Identification (biology); Filter (signal processing); Computer vision; Wavelet; Feature extraction","score_opus":0.01742328483747841,"score_gpt":0.31381994320727086,"score_spread":0.29639665836979245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121062299","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025435693,0.00018313776,0.98922974,0.006738426,0.0003810477,0.00026034968,0.000009243185,0.00010403277,0.0005504597],"genre_scores_gemma":[0.9189103,0.000095157935,0.07749114,0.0005696863,0.0002910345,0.000015081398,0.000011906251,0.00002171031,0.0025939874],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99876255,0.000025960077,0.00040529686,0.00019554407,0.00042509142,0.00018555102],"domain_scores_gemma":[0.998562,0.00013017209,0.0005496662,0.00023069828,0.0004096027,0.00011788597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022986054,0.00012602717,0.00016260694,0.00023976502,0.00015964253,0.0004639492,0.0004052147,0.000036938603,0.0000032429548],"category_scores_gemma":[0.00034232755,0.00010523207,0.0001043971,0.0002199611,0.00007477775,0.0034689386,0.000036048335,0.000257498,0.000008908265],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005777532,0.00028489408,0.00060084736,0.000041515064,0.000033419663,0.00039862955,0.0010762479,0.000022374163,0.08424526,0.0005375981,0.15780877,0.75489265],"study_design_scores_gemma":[0.002156251,0.0008851267,0.008710428,0.00038751477,0.000053906748,0.007846348,0.00035163172,0.0034513995,0.9290719,0.017517524,0.028890403,0.0006775434],"about_ca_topic_score_codex":8.389182e-7,"about_ca_topic_score_gemma":1.2263718e-7,"teacher_disagreement_score":0.91636676,"about_ca_system_score_codex":0.00008798427,"about_ca_system_score_gemma":0.00007614354,"threshold_uncertainty_score":0.44738728},"labels":[],"label_agreement":null},{"id":"W2121141217","doi":"10.1109/tpami.2010.24","title":"Self-Validated Labeling of Markov Random Fields for Image Segmentation","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":83,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Alberta","keywords":"Cut; Markov random field; Initialization; Image segmentation; Artificial intelligence; Computer science; Pattern recognition (psychology); Markov chain; Segmentation; Maxima and minima; Robustness (evolution); Connected-component labeling; Algorithm; Mathematics; Scale-space segmentation; Machine learning","score_opus":0.013624021759611343,"score_gpt":0.29784644653444453,"score_spread":0.28422242477483317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121141217","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024461076,0.000011730503,0.99668425,0.00023509197,0.00016709875,0.0002762135,0.00003352463,0.00011996551,0.000026039088],"genre_scores_gemma":[0.6892925,0.00010980724,0.31025052,0.00023288302,0.000009386415,0.00004763032,0.000009068247,0.000005858499,0.000042374984],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988418,0.000060260732,0.00041389329,0.00032075416,0.00022226467,0.00014100122],"domain_scores_gemma":[0.99902236,0.0002719579,0.00014443949,0.0003276893,0.00014719661,0.00008634172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042018577,0.00013564865,0.00024826315,0.00034977088,0.0001052197,0.00008460645,0.00031316365,0.00006858289,0.0001712403],"category_scores_gemma":[0.000015079117,0.00011870867,0.0001743768,0.00055852364,0.000052061077,0.00026124122,0.0000042485904,0.00020790455,0.00000337867],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022024184,0.0001855624,0.000082392675,0.000058822683,0.00034552807,0.0000016131801,0.0004918266,0.00028233897,0.08217028,0.000027349954,0.000019889107,0.9163124],"study_design_scores_gemma":[0.00021910397,0.000079268946,0.000023034752,0.000008170024,0.00023343098,0.0000021460567,0.000023278977,0.14775516,0.8513939,0.0001490444,0.000008166473,0.00010531168],"about_ca_topic_score_codex":0.0002544938,"about_ca_topic_score_gemma":0.00030573324,"teacher_disagreement_score":0.9162071,"about_ca_system_score_codex":0.000010053339,"about_ca_system_score_gemma":0.000016660266,"threshold_uncertainty_score":0.4840799},"labels":[],"label_agreement":null},{"id":"W2121814445","doi":"10.1109/iembs.2008.4649855","title":"Improved interactive medical image segmentation using Enhanced Intelligent Scissors (EIS)","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Image segmentation; Pruning; Boundary (topology); Computer vision; Market segmentation; Tracing; Viterbi algorithm; Pattern recognition (psychology); Hidden Markov model; Mathematics","score_opus":0.028220952629675326,"score_gpt":0.335990562534863,"score_spread":0.30776960990518765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121814445","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028959494,0.000012999401,0.9681117,0.0003415422,0.00032191558,0.00031846936,6.684636e-7,0.00047036773,0.0014628452],"genre_scores_gemma":[0.27224082,0.0000619075,0.7256435,0.0016103629,0.00006190535,0.000031993073,0.000005552717,0.000012364185,0.00033156155],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99791014,0.00012428827,0.00045208624,0.0004539199,0.00076828216,0.00029125702],"domain_scores_gemma":[0.99884313,0.0001549829,0.00015376051,0.0003632039,0.00019602184,0.00028888494],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003879375,0.00016967651,0.00018606702,0.00017599443,0.00015461106,0.00009157067,0.0007446043,0.00008519956,0.0011493884],"category_scores_gemma":[0.00037480032,0.0001464295,0.00007495003,0.00040247315,0.00019891033,0.0013756477,0.00033361878,0.00023181525,0.000089136505],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017042363,0.00019679338,0.000027758268,0.00001433818,0.000033741355,0.000075089214,0.0033228917,0.000004064013,0.7614962,0.00030091146,0.0014116508,0.23309954],"study_design_scores_gemma":[0.0002558924,0.000076851364,0.000047525544,0.000027605387,0.0000033964836,0.00006257286,0.00018630095,0.11111779,0.88784266,0.00019897243,0.000021165997,0.00015925567],"about_ca_topic_score_codex":0.00014765168,"about_ca_topic_score_gemma":0.000009078084,"teacher_disagreement_score":0.24328132,"about_ca_system_score_codex":0.0001876311,"about_ca_system_score_gemma":0.00020850869,"threshold_uncertainty_score":0.99976367},"labels":[],"label_agreement":null},{"id":"W2121979828","doi":"10.1017/s143192761300161x","title":"Selection and Tuning of a Fast and Simple Phase-Contrast Microscopy Image Segmentation Algorithm for Measuring Myoblast Growth Kinetics in an Automated Manner","year":2013,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Phase contrast microscopy; Segmentation; Contrast (vision); Simple (philosophy); Kinetics; Phase (matter); Algorithm; Materials science; Biological system; Microscopy; Artificial intelligence; Computer science; Pattern recognition (psychology); Chemistry; Optics; Physics; Biology","score_opus":0.01106619625002076,"score_gpt":0.30270194633313885,"score_spread":0.2916357500831181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121979828","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4182469,0.00010861878,0.58118457,0.000038779417,0.000012485578,0.00030665807,0.000016462469,0.000083469204,0.0000020578264],"genre_scores_gemma":[0.34767756,0.000090233705,0.6519703,0.000107490654,0.000013676702,0.00006461001,0.000047681733,0.00001493422,0.000013450011],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985329,0.00010025082,0.00046607072,0.00048981805,0.0001306888,0.00028029724],"domain_scores_gemma":[0.99921167,0.00007386116,0.00020261775,0.00015076628,0.00023185626,0.00012922272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031508,0.00020753517,0.00034934605,0.00038465392,0.00013199559,0.00034556037,0.00016132022,0.000084573934,0.0000130365515],"category_scores_gemma":[0.000030135328,0.00020285998,0.000041682462,0.00044577883,0.00013775588,0.0009604456,0.00009100346,0.00009819734,6.72861e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008626031,0.00010849683,0.00081524637,0.00006685054,0.000038120983,8.1189927e-7,0.0008401078,0.000001155472,0.9071579,0.000005009157,0.00009659512,0.090861104],"study_design_scores_gemma":[0.0011995618,0.00022179712,0.0013946431,0.000040257964,0.0000584008,0.000010353791,0.00028333024,0.22557913,0.77094215,0.00010648014,0.0000036151655,0.00016027881],"about_ca_topic_score_codex":0.00040379565,"about_ca_topic_score_gemma":0.00008964861,"teacher_disagreement_score":0.22557797,"about_ca_system_score_codex":0.00004664916,"about_ca_system_score_gemma":0.000019927385,"threshold_uncertainty_score":0.827239},"labels":[],"label_agreement":null},{"id":"W2122108494","doi":"10.1109/cvpr.2007.383010","title":"Segmenting Images on the Tensor Manifold","year":2007,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bhattacharyya distance; Intrinsic dimension; Tensor (intrinsic definition); Manifold (fluid mechanics); Artificial intelligence; Image segmentation; Segmentation; Mathematics; Metric (unit); Riemannian manifold; Computer science; Market segmentation; Pattern recognition (psychology); Space (punctuation); Mathematical analysis; Geometry; Curse of dimensionality","score_opus":0.0195429106902412,"score_gpt":0.2784481375915804,"score_spread":0.25890522690133916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122108494","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015152501,0.000006618833,0.92543447,0.0029570956,0.00008022162,0.00012167599,1.5503657e-7,0.0003977177,0.06948681],"genre_scores_gemma":[0.2563158,0.000007498816,0.7143753,0.020430773,0.00010443243,0.000013608364,6.7116207e-7,0.000009771412,0.008742131],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991239,0.000030100251,0.0001536361,0.00017882373,0.0003112587,0.00020233411],"domain_scores_gemma":[0.9992153,0.00028896966,0.000044822522,0.00035588807,0.000038062473,0.000056938785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096127315,0.000067898465,0.00005032888,0.000051762585,0.000100404504,0.000121464356,0.00059056876,0.000021246991,0.00027941517],"category_scores_gemma":[0.000108167704,0.000038727845,0.00002953157,0.00017700656,0.00002750652,0.00018749134,0.00013416671,0.000100321544,0.00021567104],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005050294,0.0001347931,0.0006394722,0.000011267446,0.000020819583,0.00009909425,0.0004781254,0.0000010775428,0.0754538,0.32876408,0.16590269,0.4284897],"study_design_scores_gemma":[0.00010691199,0.000045446854,0.0022121093,0.000012798607,0.0000015052833,0.000007702319,0.000110981404,0.0008380142,0.9937002,0.0016470861,0.0012213787,0.00009585187],"about_ca_topic_score_codex":0.000011659205,"about_ca_topic_score_gemma":0.0000013586949,"teacher_disagreement_score":0.9182464,"about_ca_system_score_codex":0.000019179748,"about_ca_system_score_gemma":0.000008180571,"threshold_uncertainty_score":0.30594},"labels":[],"label_agreement":null},{"id":"W2122126162","doi":"10.1016/j.media.2011.05.009","title":"Max-flow segmentation of the left ventricle by recovering subject-specific distributions via a bound of the Bhattacharyya measure","year":2011,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CARE Canada; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bhattacharyya distance; Segmentation; Metric (unit); Measure (data warehouse); Active contour model; Kernel density estimation; Algorithm; Mathematics; Similarity measure; Computer science; Flow (mathematics); Artificial intelligence; Active shape model; Kernel (algebra); Similarity (geometry); Image segmentation; Mathematical optimization; Image (mathematics); Data mining; Geometry","score_opus":0.014707241027463076,"score_gpt":0.250092132108051,"score_spread":0.2353848910805879,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122126162","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016090669,0.00019061781,0.9823595,0.0006523383,0.00013111642,0.00020101751,0.000038251794,0.0000449876,0.00029147565],"genre_scores_gemma":[0.9508714,0.000090079564,0.048498314,0.00032263546,0.000025641883,0.000024300492,0.00004478508,0.000010617721,0.00011222584],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9971167,0.00037048687,0.00059467286,0.00031451805,0.0013724826,0.00023114815],"domain_scores_gemma":[0.99829787,0.00012635173,0.00036658096,0.0008594092,0.0002041699,0.00014559996],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00095827965,0.00013723466,0.0002964936,0.00012083593,0.00016712883,0.00004579586,0.0014793227,0.00008648929,0.0015865118],"category_scores_gemma":[0.00042651856,0.0000859481,0.00038042982,0.0020213323,0.00043657934,0.00037069336,0.0003650682,0.00024635505,0.000007245846],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003852507,0.0020243844,0.059217606,0.00015925585,0.0023033647,0.000041802257,0.007176028,0.000016292366,0.42924318,0.00054752966,0.03842894,0.4608031],"study_design_scores_gemma":[0.00042802095,0.000050230934,0.035675786,0.00006860707,0.0004485684,0.00001083619,0.00011581403,0.016682424,0.94537604,0.00075991463,0.00019405165,0.00018969105],"about_ca_topic_score_codex":0.00024608284,"about_ca_topic_score_gemma":0.000045546938,"teacher_disagreement_score":0.9347807,"about_ca_system_score_codex":0.00008315241,"about_ca_system_score_gemma":0.0000931681,"threshold_uncertainty_score":0.99932617},"labels":[],"label_agreement":null},{"id":"W2122266115","doi":"10.1109/iembs.1995.575170","title":"A method to match human sulci in 3D-space","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Noise (video); Deformation (meteorology); Pattern recognition (psychology); Transformation (genetics); Space (punctuation); Image (mathematics); Geology; Biology","score_opus":0.03897189740719519,"score_gpt":0.35880162236295,"score_spread":0.3198297249557548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122266115","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00046430656,0.000008410287,0.9711317,0.0041816817,0.000025311314,0.00016505226,1.4989779e-7,0.00033654095,0.023686862],"genre_scores_gemma":[0.00820615,0.000001987678,0.9804825,0.0039363005,0.000014433118,0.000030940595,2.4612083e-7,0.000004596043,0.0073228194],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99905354,0.000095570285,0.00017033331,0.0002572585,0.00023667117,0.0001866192],"domain_scores_gemma":[0.99944097,0.000062180734,0.000023309201,0.00032730133,0.000026498994,0.00011974086],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004097015,0.0000707006,0.000100978075,0.00014659048,0.000029415007,0.000080392536,0.0005453231,0.00003224633,0.0011727043],"category_scores_gemma":[0.000032198925,0.00006269267,0.00001811632,0.000489483,0.000012437794,0.00025140712,0.00018949516,0.00008602153,0.00033291354],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.237812e-7,0.0001901397,0.0004132671,0.000015376952,0.0000051575353,0.000065370055,0.003696261,0.000009685719,0.05168875,0.047642667,0.1540003,0.7422721],"study_design_scores_gemma":[0.0010567829,0.00040728925,0.0036928738,0.0000849534,0.000004450338,0.00003575917,0.00020582677,0.086015455,0.86998767,0.013062266,0.024625178,0.0008214974],"about_ca_topic_score_codex":0.00018229599,"about_ca_topic_score_gemma":0.00003465592,"teacher_disagreement_score":0.81829894,"about_ca_system_score_codex":0.0000386402,"about_ca_system_score_gemma":0.0000049936334,"threshold_uncertainty_score":0.99974036},"labels":[],"label_agreement":null},{"id":"W2122321062","doi":"10.1090/qam/1788425","title":"Shape recognition via Wasserstein distance","year":2000,"lang":"lv","type":"article","venue":"Quarterly of Applied Mathematics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":95,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Science Foundation","keywords":"Absolute continuity; Mathematics; Omega; Lebesgue measure; Domain (mathematical analysis); Combinatorics; Regular polygon; Image (mathematics); Lebesgue integration; Concave function; Convex function; Unit (ring theory); Convex conjugate; Mathematical analysis; Convex body; Geometry; Convex optimization; Physics; Computer science","score_opus":0.01795774777811982,"score_gpt":0.2524282196693236,"score_spread":0.2344704718912038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122321062","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027048338,0.000063921805,0.9532035,0.00016189794,0.00011872484,0.00078888115,0.000027717922,0.00029326932,0.018293759],"genre_scores_gemma":[0.09086522,0.000092517126,0.90791684,0.00024796513,0.000104057406,0.00009448025,0.00003427699,0.000047950627,0.0005966694],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969452,0.000039924988,0.0011853311,0.00054194045,0.00084576965,0.00044181882],"domain_scores_gemma":[0.9979718,0.00022517351,0.00054486725,0.00091783516,0.00012952283,0.00021082249],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005738095,0.00035691992,0.0005820876,0.00012862423,0.00010723824,0.00015757643,0.00093202,0.00020314199,0.0033738662],"category_scores_gemma":[0.000009741646,0.0003645528,0.00014125324,0.00049475726,0.00028094678,0.0003976503,0.000025290716,0.00027292877,0.0013305707],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002280467,0.0005379545,2.565929e-7,0.0006624259,0.00003858486,0.0000076009396,0.011489958,0.0000010593311,0.0087907035,0.0031011717,0.0010438003,0.97430366],"study_design_scores_gemma":[0.0025616644,0.0036001818,0.000028261973,0.0017650425,0.0003033455,0.000051650935,0.004532186,0.2723739,0.22412561,0.48757306,0.0011518579,0.0019332295],"about_ca_topic_score_codex":0.000009143222,"about_ca_topic_score_gemma":0.0000036625422,"teacher_disagreement_score":0.97237045,"about_ca_system_score_codex":0.00006827034,"about_ca_system_score_gemma":0.0000581919,"threshold_uncertainty_score":0.9998807},"labels":[],"label_agreement":null},{"id":"W2122352893","doi":"10.1109/iembs.2005.1616500","title":"Slice-Based Prostate Segmentation in 3D US Images Using Continuity Constraint","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Artificial Intelligence in Medicine (Canada); Robarts Clinical Trials","funders":"","keywords":"Constraint (computer-aided design); Computer science; Segmentation; Computer vision; Artificial intelligence; Image segmentation; Prostate; Pattern recognition (psychology); Mathematics; Medicine","score_opus":0.018072669187640774,"score_gpt":0.3032247075260192,"score_spread":0.28515203833837843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122352893","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054939922,0.000014087631,0.9419495,0.0006145492,0.00004958785,0.00041666834,0.000002811078,0.00030981493,0.0017030851],"genre_scores_gemma":[0.30315238,0.0000030739022,0.6947841,0.0019213678,0.000017385317,0.000020336853,0.00000410252,0.0000047383933,0.000092484406],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986893,0.000109130175,0.0003524874,0.00030143742,0.00030549444,0.00024217057],"domain_scores_gemma":[0.9994045,0.000074714735,0.000109935645,0.00023872576,0.00008061127,0.00009149818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004995674,0.00011674906,0.0001342322,0.00015604962,0.00004601389,0.00016056228,0.00029013984,0.000041409665,0.00017717376],"category_scores_gemma":[0.000055857847,0.0001080611,0.000029948933,0.00028665783,0.000108184744,0.00078685203,0.00007338107,0.00011579944,0.000025585005],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010437678,0.00022040441,0.008538267,0.000031090833,0.000007346094,0.000034647517,0.00082506833,0.00046331753,0.20418221,0.001052224,0.0007258441,0.78390914],"study_design_scores_gemma":[0.0009847268,0.000046690053,0.004687041,0.000039858394,0.0000033839642,0.000008364724,0.00005453378,0.17132741,0.8223254,0.00018382813,0.00014157651,0.00019720786],"about_ca_topic_score_codex":0.00018912747,"about_ca_topic_score_gemma":0.00007876694,"teacher_disagreement_score":0.78371197,"about_ca_system_score_codex":0.00015985641,"about_ca_system_score_gemma":0.00012266185,"threshold_uncertainty_score":0.4406604},"labels":[],"label_agreement":null},{"id":"W2122643004","doi":"10.1007/s11263-009-0249-6","title":"A Statistical Overlap Prior for Variational Image Segmentation","year":2009,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"London Health Sciences Centre","funders":"","keywords":"Bhattacharyya distance; Segmentation; Image segmentation; Artificial intelligence; Pattern recognition (psychology); Scale-space segmentation; Nonparametric statistics; Mathematics; Gaussian; Computer science; Algorithm; Image (mathematics); Segmentation-based object categorization; Statistics","score_opus":0.01000484510054001,"score_gpt":0.35073963879234166,"score_spread":0.34073479369180165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122643004","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008334357,0.000023111586,0.99350905,0.003970056,0.0013432337,0.00018031364,0.000011568406,0.00005380299,0.00007540234],"genre_scores_gemma":[0.043213993,0.000019378578,0.9537758,0.0022405754,0.0006967141,0.0000034007392,0.000022303318,0.00000585471,0.00002192618],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978848,0.00007449521,0.00066482596,0.0001959241,0.0010372933,0.00014267582],"domain_scores_gemma":[0.9978158,0.0003428,0.0004764258,0.00013444701,0.0011069978,0.0001235362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006062577,0.00011918994,0.00017292917,0.00022633065,0.000054295764,0.00036997115,0.0009490044,0.000048367994,0.000048647657],"category_scores_gemma":[0.00013510315,0.000103988714,0.00011621493,0.00008447949,0.000030213469,0.001435045,0.00008591926,0.00013514019,0.000011015157],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014130345,0.000339526,0.000035613666,0.0000061670594,0.00006338184,0.00008069289,0.00027402837,0.00011212446,0.020350192,0.032030422,0.026083935,0.92048264],"study_design_scores_gemma":[0.0105348285,0.007711182,0.053247742,0.0004912388,0.00006928907,0.0014357966,0.00003535771,0.6449319,0.078198485,0.1911429,0.011331472,0.00086977985],"about_ca_topic_score_codex":0.0000014827813,"about_ca_topic_score_gemma":1.3124301e-7,"teacher_disagreement_score":0.9196128,"about_ca_system_score_codex":0.00013933537,"about_ca_system_score_gemma":0.00012760877,"threshold_uncertainty_score":0.42405367},"labels":[],"label_agreement":null},{"id":"W2123464138","doi":"10.1109/ciip.2009.4937877","title":"2D ultrasound image segmentation using graph cuts and local image features","year":2009,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Artificial intelligence; Computer science; Image segmentation; Segmentation; Cut; Computer vision; Graph; Energy minimization; Inference; Modality (human–computer interaction); Pattern recognition (psychology); Minification; Theoretical computer science","score_opus":0.011244396742229566,"score_gpt":0.29620539255573036,"score_spread":0.2849609958135008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123464138","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006595194,0.000073395066,0.98981625,0.00048982736,0.00006626538,0.00020380791,0.0000013862262,0.00039803714,0.0023558647],"genre_scores_gemma":[0.11208585,0.000045957866,0.8849929,0.0026323353,0.000032058942,0.0000039981005,0.0000066639845,0.0000062773797,0.00019395989],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987976,0.00006834684,0.00021109833,0.0003535456,0.0003404849,0.00022892312],"domain_scores_gemma":[0.99935734,0.00007890683,0.00007191592,0.00026727858,0.00007757539,0.000146972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002207559,0.00014489466,0.00012717707,0.00013586032,0.00012317435,0.00037580042,0.00029867954,0.0000560396,0.000071374125],"category_scores_gemma":[0.000046843954,0.00012572548,0.000037603662,0.00030161996,0.00015826423,0.001393604,0.000061710096,0.00012510689,0.000010719721],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000045990837,0.00006297504,0.00005735202,0.000011855809,0.000008032965,0.000031564632,0.00060006185,0.000002362088,0.8093918,0.0019495478,0.009162667,0.17871721],"study_design_scores_gemma":[0.00042615389,0.0001383149,0.004826049,0.000024437273,0.000012221014,0.00016656598,0.00024298485,0.0028026449,0.981165,0.009894076,0.0000309196,0.0002706204],"about_ca_topic_score_codex":0.000059447942,"about_ca_topic_score_gemma":0.0000043904492,"teacher_disagreement_score":0.17844659,"about_ca_system_score_codex":0.000042497755,"about_ca_system_score_gemma":0.000026865127,"threshold_uncertainty_score":0.5126936},"labels":[],"label_agreement":null},{"id":"W2123532216","doi":"10.1007/11566465_2","title":"Bone Enhancement Filtering: Application to Sinus Bone Segmentation and Simulation of Pituitary Surgery","year":2005,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Segmentation; Measure (data warehouse); Sinus (botany); Bone structure; Computation; Artificial intelligence; Computer vision; Biomedical engineering; Medicine; Data mining; Algorithm","score_opus":0.014710865874149718,"score_gpt":0.29115812438604916,"score_spread":0.27644725851189944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123532216","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05650681,0.00007644383,0.9416225,0.0011915357,0.00013163293,0.0003824933,6.9497355e-7,0.00008434121,0.000003576681],"genre_scores_gemma":[0.5265465,0.0000065612644,0.47256243,0.0008201634,0.00004087159,0.000018593115,0.0000017584002,0.0000027652845,3.7211234e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981454,0.000048401256,0.000441477,0.0005492258,0.0005603518,0.0002551561],"domain_scores_gemma":[0.9987995,0.0003851171,0.00015500608,0.0004153407,0.0001272146,0.00011782142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008827323,0.00013200488,0.00019436772,0.00045107334,0.00008422941,0.00010652441,0.0003965017,0.000042027976,0.0000069722064],"category_scores_gemma":[0.00013991246,0.00012935273,0.000025293812,0.0011587904,0.00014816853,0.0008429146,0.00032203784,0.00008807292,0.000005622095],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034348434,0.000045113466,0.00016697311,0.000015707134,0.0000010090512,0.000002064156,0.000659152,0.09199947,0.1073143,0.000015700078,0.000008461857,0.7997686],"study_design_scores_gemma":[0.00008053198,0.00005599821,0.0011038947,0.000036356432,0.0000011067973,0.00000630166,5.2887435e-7,0.59394115,0.40398023,0.0006804315,0.000014028786,0.00009948301],"about_ca_topic_score_codex":0.000024127572,"about_ca_topic_score_gemma":0.0000126876575,"teacher_disagreement_score":0.79966915,"about_ca_system_score_codex":0.00012978408,"about_ca_system_score_gemma":0.0000770103,"threshold_uncertainty_score":0.52748513},"labels":[],"label_agreement":null},{"id":"W2123889588","doi":"10.1109/titb.2006.875665","title":"Novel Multistage Three-Dimensional Medical Image Segmentation: Methodology and Validation","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Segmentation; Computer science; Dilation (metric space); Artificial intelligence; Image segmentation; Centroid; Computer vision; Fast marching method; Scale-space segmentation; Pattern recognition (psychology); Mathematics","score_opus":0.02057845995460659,"score_gpt":0.3023059608971348,"score_spread":0.2817275009425282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123889588","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047131553,0.000023449273,0.9833439,0.010362343,0.00039342733,0.00042144093,0.00001593225,0.0006274866,0.00009884625],"genre_scores_gemma":[0.18290655,0.000032761476,0.8151433,0.0015765863,0.000031313313,0.00020334474,0.000056599412,0.000009736882,0.000039791885],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978944,0.000067919216,0.0008375659,0.00028460144,0.00064352143,0.00027198208],"domain_scores_gemma":[0.99891114,0.00028041395,0.00022055252,0.0003433559,0.00014653138,0.00009801326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094217766,0.0001980294,0.00026061828,0.0018747199,0.00014374749,0.000044965676,0.00041870112,0.0004911349,0.00016707894],"category_scores_gemma":[0.00008496788,0.00018199369,0.000033404966,0.0013448346,0.00052969606,0.0014044313,0.00001378265,0.00059416104,0.000049548264],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006760721,0.00060220656,0.00024688488,0.00011933614,0.000050273575,0.000046720084,0.00046996315,0.0004023342,0.13717724,0.01303037,0.0017989398,0.8459881],"study_design_scores_gemma":[0.006026527,0.0005900708,0.0015338038,0.00018649647,0.000027671602,0.00060035195,0.00025333505,0.09392968,0.884867,0.011080805,0.00038801797,0.0005162289],"about_ca_topic_score_codex":0.00021761339,"about_ca_topic_score_gemma":0.00006517404,"teacher_disagreement_score":0.8454719,"about_ca_system_score_codex":0.000121158046,"about_ca_system_score_gemma":0.000086516826,"threshold_uncertainty_score":0.7421487},"labels":[],"label_agreement":null},{"id":"W2124289575","doi":"10.5430/jbgc.v3n3p29","title":"Solving the over segmentation problem in applications of Watershed Transform","year":2013,"lang":"en","type":"article","venue":"Journal of Biomedical Graphics and Computing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Watershed; Artificial intelligence; Segmentation; Fuzzy logic; Cluster analysis; Computer science; Image segmentation; Pattern recognition (psychology); Image (mathematics); Computer vision; Scale-space segmentation; Segmentation-based object categorization","score_opus":0.008105029847496515,"score_gpt":0.2589677807576537,"score_spread":0.2508627509101572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124289575","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035871286,0.00012125452,0.96116126,0.0025314796,0.000037917773,0.00023070727,2.5871202e-7,0.000011378267,0.00003446064],"genre_scores_gemma":[0.85591865,0.000104931285,0.14354362,0.00037720535,0.00004211622,0.0000071160516,8.3790167e-7,0.0000032140574,0.000002327614],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987125,0.00005853963,0.000600571,0.000095831216,0.00040375468,0.00012880136],"domain_scores_gemma":[0.9992353,0.00016778712,0.00029961762,0.000093892355,0.00011222168,0.00009120646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009527053,0.00006520127,0.00014460785,0.00024049441,0.00006362331,0.000070827504,0.00036321487,0.000045357512,0.0000105195695],"category_scores_gemma":[0.0000197632,0.000039858474,0.000051346226,0.00054068025,0.00016347566,0.00027709277,0.000067053894,0.00021947635,2.3581039e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032726023,0.00016341245,0.0016644598,0.00010001389,0.00003309554,0.000004275845,0.0029721602,0.000006561729,0.021669911,0.004901999,0.0005378447,0.967943],"study_design_scores_gemma":[0.005632063,0.0016781832,0.054426037,0.0014226417,0.00008870437,0.00039495216,0.0032770673,0.6536862,0.06537791,0.21143083,0.001780023,0.00080535945],"about_ca_topic_score_codex":0.000040134706,"about_ca_topic_score_gemma":0.0000014187636,"teacher_disagreement_score":0.96713763,"about_ca_system_score_codex":0.000013679986,"about_ca_system_score_gemma":0.000036933467,"threshold_uncertainty_score":0.16253814},"labels":[],"label_agreement":null},{"id":"W2124471971","doi":"10.1007/978-3-642-04271-3_71","title":"Multiple Sclerosis Lesion Segmentation Using an Automatic Multimodal Graph Cuts","year":2009,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Graph; Cut; Image segmentation; Pattern recognition (psychology); Computer vision; Theoretical computer science","score_opus":0.048247356477776546,"score_gpt":0.31362364812890453,"score_spread":0.26537629165112797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124471971","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17936002,0.000021058984,0.81926686,0.00027990987,0.00033948472,0.00031898383,6.4163294e-7,0.0004104039,0.0000026469538],"genre_scores_gemma":[0.49386954,0.000003474437,0.5049005,0.0011839754,0.000032890995,0.000004008283,0.0000016286914,0.00000390068,9.357848e-8],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971771,0.00015491813,0.00041207374,0.000856382,0.0008904686,0.0005090185],"domain_scores_gemma":[0.9985633,0.00019705508,0.00015978348,0.0007187668,0.0001404463,0.00022064637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008848362,0.00022890346,0.00021393034,0.0005985921,0.00032178833,0.00054057926,0.0015993284,0.00008719096,0.0000074456325],"category_scores_gemma":[0.00017626848,0.00020815892,0.00005129362,0.0021929503,0.00025823642,0.0025198346,0.00024763012,0.00022650717,0.0000062400486],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015187078,0.00012290837,0.0005126922,0.000005508145,9.146464e-7,0.00000772641,0.0010219848,0.016667424,0.17827198,0.00001630427,0.000002801458,0.8033682],"study_design_scores_gemma":[0.0003039722,0.0001710807,0.010855609,0.0000799372,0.000001900747,0.000014655365,0.0000012780849,0.75364846,0.23092568,0.0038067633,2.6125772e-7,0.00019040187],"about_ca_topic_score_codex":0.00008630644,"about_ca_topic_score_gemma":0.000020451282,"teacher_disagreement_score":0.80317783,"about_ca_system_score_codex":0.00020928778,"about_ca_system_score_gemma":0.00015702967,"threshold_uncertainty_score":0.84884745},"labels":[],"label_agreement":null},{"id":"W2124498492","doi":"10.1109/icsmc.2007.4414078","title":"Invariant feature set in convex hull for fast image registration","year":2007,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Windsor","funders":"","keywords":"Convex hull; Mathematics; Artificial intelligence; Computer vision; Convex set; Quadrilateral; Diagonal; Regular polygon; Computer science; Geometry; Convex optimization","score_opus":0.02161485351463198,"score_gpt":0.3186812333584569,"score_spread":0.2970663798438249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124498492","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00035179613,0.0000073513424,0.9895348,0.00272262,0.00008560477,0.00036599085,0.0000022584707,0.00019468523,0.0067348843],"genre_scores_gemma":[0.036528718,0.0000037585996,0.9587096,0.0024476834,0.000047370162,0.000026777736,0.000021578795,0.0000054455295,0.0022090885],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991496,0.000023970257,0.00020571775,0.00023459831,0.00019408239,0.00019201731],"domain_scores_gemma":[0.9994134,0.0001148698,0.00006697261,0.00025597613,0.0000736155,0.00007520176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008770937,0.00007488044,0.000090983274,0.00009734366,0.000031528925,0.00010369347,0.00034395745,0.00006999406,0.00004406387],"category_scores_gemma":[0.00013377107,0.00006516367,0.000025805668,0.00023055584,0.000038330327,0.0005100001,0.000052751566,0.000102223144,0.0000135948785],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000494745,0.00019774938,0.0003613942,0.00010876259,0.0000134707,0.00015001575,0.0027190247,0.0000012798386,0.21342632,0.17625904,0.36940214,0.2373113],"study_design_scores_gemma":[0.0013715026,0.00024498245,0.0034979098,0.00004941778,0.000004044317,0.000033839435,0.00040188804,0.018379003,0.95720685,0.0114758,0.006966503,0.00036827818],"about_ca_topic_score_codex":0.000045026805,"about_ca_topic_score_gemma":0.00013235954,"teacher_disagreement_score":0.7437805,"about_ca_system_score_codex":0.000043211898,"about_ca_system_score_gemma":0.00004399711,"threshold_uncertainty_score":0.26572976},"labels":[],"label_agreement":null},{"id":"W2124643149","doi":"10.1109/tbme.2011.2161987","title":"A Model-Based Validation Scheme for Organ Segmentation in CT Scan Volumes","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Segmentation; Artificial intelligence; Computer science; Scale-space segmentation; Image segmentation; Pattern recognition (psychology); Computer vision; Process (computing); Similarity (geometry); Image (mathematics)","score_opus":0.030395602169360124,"score_gpt":0.26232724827424997,"score_spread":0.23193164610488984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124643149","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043988023,0.0000053113135,0.9943007,0.00015559951,0.00028813857,0.00037472742,0.000009301511,0.0004482021,0.000019223051],"genre_scores_gemma":[0.46748224,0.0000044883755,0.5321367,0.00012329892,0.00001208473,0.00019850885,0.0000075662333,0.00001361678,0.000021523687],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878323,0.000019646568,0.0003201404,0.00030445325,0.00032266785,0.0002498345],"domain_scores_gemma":[0.9994411,0.0000709726,0.000045884186,0.00023305853,0.000043980843,0.00016499723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027934095,0.00014497645,0.0001385006,0.00046010406,0.000053715128,0.000036150024,0.00031267243,0.00006347554,0.000047537706],"category_scores_gemma":[0.000023725072,0.00015023732,0.000064275875,0.000561161,0.000043257467,0.00036631033,0.0000020448836,0.00016965462,0.00000878378],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055676966,0.0012515294,0.000027951335,0.00025877036,0.000061941995,0.000026432832,0.0017584091,0.057126038,0.34554273,0.0006077771,0.0003561309,0.5929266],"study_design_scores_gemma":[0.00042253805,0.00007380048,0.000012749945,0.00003566989,0.0000039923325,0.0000017442275,0.000008100269,0.6124374,0.38681802,0.000064299405,0.000020910686,0.000100728736],"about_ca_topic_score_codex":0.000025169655,"about_ca_topic_score_gemma":0.0000030469091,"teacher_disagreement_score":0.5928259,"about_ca_system_score_codex":0.0001481232,"about_ca_system_score_gemma":0.00008833593,"threshold_uncertainty_score":0.61265},"labels":[],"label_agreement":null},{"id":"W2124763288","doi":"10.1016/j.neuroimage.2012.10.081","title":"A direct morphometric comparison of five labeling protocols for multi-atlas driven automatic segmentation of the hippocampus in Alzheimer's disease","year":2012,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Sciences Centre; Heart and Stroke Foundation; Sunnybrook Health Science Centre; University of Toronto","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Servier; Bayer HealthCare; Pfizer; Novartis Pharmaceuticals Corporation; GE Healthcare; Abbott Laboratories; BioClinica; Takeda Pharmaceutical Company; Eli Lilly and Company; Genentech; Campbell Foundation; Alzheimer's Drug Discovery Foundation; Merck; National Institute on Aging; Alzheimer's Association; Roche","keywords":"Atlas (anatomy); Hippocampus; Segmentation; Neuroscience; Disease; Cartography; Computer science; Artificial intelligence; Pattern recognition (psychology); Biology; Medicine; Pathology; Anatomy; Geography","score_opus":0.07876497040148014,"score_gpt":0.382567372923757,"score_spread":0.30380240252227686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124763288","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23533702,0.00028736095,0.7401109,0.0001709593,0.00027304728,0.0235037,0.000037130187,0.00021538163,0.00006453362],"genre_scores_gemma":[0.7426173,0.0000016898597,0.2544073,0.000101275444,0.000013202472,0.0028333494,0.0000048205548,0.000011763991,0.0000093336],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984005,0.00024353953,0.00051888044,0.00021777424,0.00039106887,0.0002282404],"domain_scores_gemma":[0.998632,0.00032075588,0.00041647398,0.00043158754,0.000095266885,0.000103928214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003981426,0.000121656245,0.00025511847,0.0002351231,0.00004577234,0.000028723915,0.00060551416,0.000031852273,0.000009896208],"category_scores_gemma":[0.00055081624,0.00009540062,0.00008655659,0.00085983315,0.00008392734,0.00047020515,0.00018135304,0.00010303959,0.0000025977142],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007439086,0.0042062458,0.27362928,0.0009846514,0.00009709339,0.000006872388,0.008791272,0.0006766493,0.31984574,0.00026789244,0.0030847592,0.38833514],"study_design_scores_gemma":[0.0013728185,0.00014537625,0.13234514,0.00020098241,0.000052758667,0.0000010197264,0.000071567105,0.34246308,0.5230271,0.00013311217,0.000019877847,0.0001671569],"about_ca_topic_score_codex":0.000022441853,"about_ca_topic_score_gemma":0.0000025905144,"teacher_disagreement_score":0.5072803,"about_ca_system_score_codex":0.00003274269,"about_ca_system_score_gemma":0.00006049584,"threshold_uncertainty_score":0.38903242},"labels":[],"label_agreement":null},{"id":"W2124833802","doi":"10.1109/mwscas.2004.1354405","title":"X-ray image segmentation using auto adaptive fuzzy index measure","year":2004,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Artificial intelligence; Image segmentation; Computer vision; Computer science; Pixel; Histogram; Image processing; Pattern recognition (psychology); Scale-space segmentation; Segmentation; Image texture; Fuzzy logic; Tracing; Segmentation-based object categorization; Entropy (arrow of time); Grayscale; Image (mathematics)","score_opus":0.029852191431048832,"score_gpt":0.29580551623223955,"score_spread":0.2659533248011907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124833802","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001877622,0.000018697227,0.9926793,0.0004183868,0.00013793375,0.0002917786,9.667083e-7,0.00064277014,0.003932565],"genre_scores_gemma":[0.22506742,0.0000026279117,0.77374834,0.0010202248,0.000034287226,0.000014924863,0.0000019674414,0.000008445412,0.00010176723],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99852884,0.000069957045,0.0002508786,0.0003434563,0.00057582493,0.00023105046],"domain_scores_gemma":[0.99925375,0.000028265647,0.00009872349,0.000324991,0.00015869389,0.00013557496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003216448,0.00013667454,0.00012301725,0.00013347017,0.00010870655,0.00016862621,0.00046036782,0.000062894105,0.00008445332],"category_scores_gemma":[0.000049332855,0.00012204661,0.00004776844,0.00041341892,0.00007483516,0.0013950277,0.00013933633,0.00013588343,0.00007652453],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020195403,0.00030950466,0.00029828533,0.000029045135,0.000066404245,0.000108321874,0.0034464626,0.0012868802,0.7630198,0.024702257,0.0016245957,0.20508823],"study_design_scores_gemma":[0.002198575,0.00029528633,0.0028618649,0.00012909969,0.000023097266,0.000044982888,0.0007287375,0.07372966,0.8903599,0.02886438,0.00004624478,0.0007181886],"about_ca_topic_score_codex":0.00019956191,"about_ca_topic_score_gemma":0.000009588898,"teacher_disagreement_score":0.2231898,"about_ca_system_score_codex":0.00023919785,"about_ca_system_score_gemma":0.00016385848,"threshold_uncertainty_score":0.49769163},"labels":[],"label_agreement":null},{"id":"W2125593607","doi":"10.1109/icip.2004.1421666","title":"Image partioning by level set multiregion competition","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Image segmentation; Context (archaeology); Segmentation; Competition (biology); Algorithm; Computer science; Image (mathematics); Domain (mathematical analysis); Level set (data structures); Representation (politics); Set (abstract data type); Partition (number theory); Mathematics; Minification; Artificial intelligence; Theoretical computer science; Mathematical optimization; Pattern recognition (psychology); Combinatorics; Mathematical analysis","score_opus":0.040142405245967246,"score_gpt":0.3122583602445898,"score_spread":0.27211595499862257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125593607","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007292298,0.000013832565,0.9926792,0.0031887263,0.00004449713,0.000096574564,0.000003283805,0.00045038958,0.0027942855],"genre_scores_gemma":[0.1105907,0.000013013038,0.8850245,0.0026205266,0.00005167866,0.00001983808,0.000027733215,0.000004954843,0.0016470444],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992151,0.00004512691,0.00016231182,0.00020390064,0.00023021393,0.00014333829],"domain_scores_gemma":[0.9995655,0.000031247662,0.000048277285,0.00021988712,0.000052602736,0.000082502906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017938732,0.000069808164,0.00006173914,0.000041579748,0.00007714311,0.000116484494,0.00027967102,0.00003078956,0.0002128645],"category_scores_gemma":[0.000032999214,0.00006496597,0.000024491395,0.00012008178,0.000043836135,0.0006679036,0.00008056914,0.000065530774,0.00024376613],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000026414893,0.00012537182,0.000077497,0.000010492836,0.0000071433715,0.000003866106,0.00054079416,0.0000063176503,0.16595177,0.015909078,0.42805016,0.38931486],"study_design_scores_gemma":[0.0005843834,0.00006687525,0.00044372896,0.000028028746,0.0000029953524,0.000016489132,0.00005861236,0.18078856,0.7951999,0.00052155205,0.022018107,0.00027081356],"about_ca_topic_score_codex":0.000028111937,"about_ca_topic_score_gemma":0.00000879809,"teacher_disagreement_score":0.6292481,"about_ca_system_score_codex":0.00003842797,"about_ca_system_score_gemma":0.000013838692,"threshold_uncertainty_score":0.31332016},"labels":[],"label_agreement":null},{"id":"W2125630043","doi":"10.1002/hbm.20453","title":"Optimization of the SNR‐resolution tradeoff for registration of magnetic resonance images","year":2007,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children; Toronto Centre for Phenogenomics; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; National Cancer Institute; National Institutes of Health; Canadian Institutes of Health Research; Hospital for Sick Children; Ontario Innovation Trust","keywords":"Magnetic resonance imaging; Resolution (logic); Image registration; Nuclear magnetic resonance; Functional magnetic resonance imaging; Computer vision; Artificial intelligence; Computer science; Physics; Image (mathematics); Psychology; Neuroscience; Medicine; Radiology","score_opus":0.031052191025261234,"score_gpt":0.29018889682696675,"score_spread":0.2591367058017055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125630043","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011225292,0.00016508972,0.9965337,0.0008073634,0.000059221973,0.0004064553,0.0000022401446,0.00006199547,0.000841384],"genre_scores_gemma":[0.2796139,0.000008384004,0.71922815,0.00040300476,0.00005707512,0.000023418857,0.000008299348,0.000009031577,0.000648698],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989238,0.0000632779,0.00041884804,0.0001754461,0.00028126748,0.00013737583],"domain_scores_gemma":[0.9990519,0.00018850395,0.00029260808,0.00032166127,0.00012053917,0.000024784924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011782241,0.000067733876,0.00010030728,0.00009284725,0.00011942787,0.00002374543,0.00041920433,0.000044925633,0.000015848103],"category_scores_gemma":[0.00032807817,0.000059316328,0.000054311848,0.0002908848,0.00013165711,0.00023275109,0.000052504438,0.000058005062,2.1003315e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013392109,0.00010700414,0.00028603038,0.00026347474,0.000006765027,0.0000011188023,0.0022963476,0.0011692815,0.83965266,0.056392685,0.014005344,0.08580592],"study_design_scores_gemma":[0.0020025338,0.000623439,0.16562028,0.0009035937,0.000022319651,0.000013361057,0.00048759248,0.20107704,0.6006087,0.024379998,0.0037302533,0.00053089956],"about_ca_topic_score_codex":0.000014378446,"about_ca_topic_score_gemma":0.0000052571418,"teacher_disagreement_score":0.27849138,"about_ca_system_score_codex":0.00003088381,"about_ca_system_score_gemma":0.000027978911,"threshold_uncertainty_score":0.24188496},"labels":[],"label_agreement":null},{"id":"W2126171091","doi":"10.1109/iembs.2008.4649851","title":"Object contour extraction in medical images by fast adaptive B-Snake","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Image segmentation; Spline (mechanical); Object detection; Segmentation; Edge detection; B-spline; Image processing; Medical imaging; Pattern recognition (psychology); Image (mathematics); Mathematics","score_opus":0.019422976919166095,"score_gpt":0.30136466231041564,"score_spread":0.28194168539124953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126171091","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015931542,0.00007147864,0.98294324,0.0011943459,0.00011875861,0.0001526865,0.0000015483698,0.00036397195,0.01356084],"genre_scores_gemma":[0.6898441,0.00026867088,0.30132756,0.00398983,0.000086246546,0.00007134376,0.000008316724,0.000013005866,0.0043909866],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983563,0.000116925155,0.00028283804,0.0003135506,0.0007090203,0.00022138939],"domain_scores_gemma":[0.99930423,0.00016397264,0.00006303115,0.00022768277,0.000059191116,0.00018186253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037910198,0.00010766269,0.00014560924,0.00010580019,0.000057270358,0.000035220284,0.00049349485,0.00009274828,0.0007271464],"category_scores_gemma":[0.00019774697,0.00009122541,0.000035113324,0.00027690065,0.00012894637,0.0007696398,0.000105334766,0.00024526467,0.00008296478],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002046474,0.00051751913,0.0015013783,0.0000095681635,0.000018757417,0.0008809677,0.0012414206,0.0000014613383,0.024602922,0.0021832767,0.41892153,0.55010074],"study_design_scores_gemma":[0.0028332286,0.00060434436,0.020601546,0.00011911042,0.000005940131,0.00082083314,0.0005906817,0.037271,0.9300868,0.0016108317,0.0045633293,0.0008923347],"about_ca_topic_score_codex":0.0003487711,"about_ca_topic_score_gemma":0.000048497997,"teacher_disagreement_score":0.9054839,"about_ca_system_score_codex":0.00006576316,"about_ca_system_score_gemma":0.00012298714,"threshold_uncertainty_score":0.79617435},"labels":[],"label_agreement":null},{"id":"W2126175023","doi":"10.1109/crv.2006.42","title":"Image Thresholding Using Ant Colony Optimization","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Thresholding; Ant colony optimization algorithms; Computer science; Artificial intelligence; ANT; Image (mathematics); Computer vision; Ant colony; Pattern recognition (psychology)","score_opus":0.017207561125858476,"score_gpt":0.2866701728225589,"score_spread":0.26946261169670044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126175023","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009558047,0.00001432196,0.99087864,0.00021681465,0.000079577774,0.00010724893,2.8615997e-7,0.0004504669,0.0072968216],"genre_scores_gemma":[0.016079498,0.0000030637002,0.98315763,0.00050014985,0.000042820957,0.000004018373,0.0000027811889,0.0000049360237,0.00020507965],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992898,0.000025273985,0.00016389893,0.00017959288,0.00020289172,0.00013854959],"domain_scores_gemma":[0.9996266,0.000027177368,0.000052422438,0.00019489622,0.00006119012,0.00003771556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016737625,0.00006187398,0.00006516699,0.00007215837,0.00007506542,0.00016959567,0.0002694526,0.000028278133,0.00015916012],"category_scores_gemma":[0.000022153348,0.000055309523,0.000020707339,0.0002440474,0.000034704353,0.0006996805,0.00010469857,0.000044400986,0.00001109873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000069532193,0.00038928277,0.0013074129,0.000056389505,0.000019007577,0.00015854667,0.0003291249,0.046572756,0.7755598,0.07048051,0.065418296,0.039701916],"study_design_scores_gemma":[0.0000934196,0.000012002005,0.00006473814,0.0000073527735,0.0000015858711,0.000007771367,0.0000054715165,0.86622626,0.1326098,0.0008470138,0.000048921927,0.00007567078],"about_ca_topic_score_codex":0.00014156236,"about_ca_topic_score_gemma":0.0000019586196,"teacher_disagreement_score":0.8196535,"about_ca_system_score_codex":0.000048327733,"about_ca_system_score_gemma":0.00002698085,"threshold_uncertainty_score":0.22554569},"labels":[],"label_agreement":null},{"id":"W2126520869","doi":"10.1109/icip.2008.4711949","title":"Robust snake convergence based on dynamic programming","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Convergence (economics); Hidden Markov model; Boundary (topology); Computer vision; Viterbi algorithm; Algorithm; Dynamic programming; Object (grammar); Noise (video); Active contour model; Pattern recognition (psychology); Image segmentation; Image (mathematics); Mathematics","score_opus":0.035858528643503466,"score_gpt":0.2701679945955919,"score_spread":0.2343094659520884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126520869","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006452802,0.000006142314,0.9954099,0.00055928243,0.00012687795,0.00018135076,2.730824e-7,0.0007682957,0.0023025633],"genre_scores_gemma":[0.22395429,0.000005567127,0.7732,0.001955546,0.000008560338,0.00003115678,0.0000027081924,0.000005184423,0.0008369872],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989545,0.000039236395,0.00015863284,0.00028176952,0.00037113187,0.00019472853],"domain_scores_gemma":[0.99933964,0.00006647847,0.00004331481,0.00038645393,0.000051974523,0.00011211626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015632568,0.000089042514,0.00008201521,0.000080263315,0.0000970779,0.0000394233,0.0005247639,0.00003441937,0.00027068402],"category_scores_gemma":[0.000058014703,0.00007425917,0.000035789257,0.00029218048,0.00008253851,0.00023779407,0.00006133624,0.00009522004,0.00013063736],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015379766,0.000761312,0.00291215,0.000069928756,0.000017705563,0.00049571274,0.00068149547,0.00085414207,0.006754455,0.009337213,0.02646383,0.9516367],"study_design_scores_gemma":[0.00033201018,0.00023792502,0.0015164262,0.000030057185,0.0000017674345,0.00002318744,0.000013881717,0.942188,0.054158892,0.00014738888,0.0011040606,0.00024637696],"about_ca_topic_score_codex":0.000015813013,"about_ca_topic_score_gemma":0.000002048889,"teacher_disagreement_score":0.9513903,"about_ca_system_score_codex":0.00003936553,"about_ca_system_score_gemma":0.00006649424,"threshold_uncertainty_score":0.30282012},"labels":[],"label_agreement":null},{"id":"W2126729316","doi":"10.1109/ijcnn.2011.6033488","title":"Neural image thresholding with SIFT-Controlled gabor features","year":2011,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Thresholding; Artificial intelligence; Scale-invariant feature transform; Computer science; Computer vision; Pattern recognition (psychology); Image (mathematics)","score_opus":0.01965431197931189,"score_gpt":0.2525863854778191,"score_spread":0.2329320734985072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126729316","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029602377,0.000038261125,0.9502124,0.0004430222,0.000086103624,0.00032401833,3.072222e-7,0.00074586336,0.045189757],"genre_scores_gemma":[0.26863888,0.0000043463197,0.7277434,0.0023099058,0.000031562682,0.000050340874,7.436258e-7,0.000009222907,0.0012116203],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99894464,0.0000392497,0.00017677249,0.00029095873,0.00030858486,0.0002397722],"domain_scores_gemma":[0.99925846,0.000050175488,0.00007216337,0.00040777956,0.00008457904,0.00012681782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021123403,0.00012982167,0.00018643618,0.00008841691,0.00006953784,0.00014392588,0.00066698005,0.00003600884,0.00042864113],"category_scores_gemma":[0.000041282572,0.00007985944,0.00004891289,0.000212851,0.00008092288,0.00079695985,0.00012514071,0.000132443,0.000031273466],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013319822,0.0011882014,0.005904672,0.00011668519,0.00039476732,0.0016787855,0.013975491,0.0000061873643,0.17401691,0.21369573,0.15136519,0.4363254],"study_design_scores_gemma":[0.007131667,0.0007652269,0.01285763,0.000049281698,0.000035724966,0.00014077812,0.00025042729,0.015411308,0.9572713,0.0052271336,0.00014272553,0.0007168321],"about_ca_topic_score_codex":0.00006558064,"about_ca_topic_score_gemma":0.0000105944955,"teacher_disagreement_score":0.7832544,"about_ca_system_score_codex":0.000013442823,"about_ca_system_score_gemma":0.000028795213,"threshold_uncertainty_score":0.46933198},"labels":[],"label_agreement":null},{"id":"W2126835830","doi":"10.1109/icdar.1993.395669","title":"Segmentation of handwritten digits using contour features","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence; Curvature; Chain code; sort; Pattern recognition (psychology); String (physics); Path (computing); Segmentation; Binary number; Image segmentation; Boundary (topology); Image (mathematics); Computer vision; Mathematics; Arithmetic; Geometry; Information retrieval","score_opus":0.036832974535477354,"score_gpt":0.28794513381068765,"score_spread":0.2511121592752103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126835830","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040936316,0.0000778186,0.9895662,0.00024385788,0.00006094682,0.000114842966,7.194696e-7,0.0001571227,0.0056848503],"genre_scores_gemma":[0.31600547,0.000019756802,0.6816341,0.0007779084,0.000024530464,0.0000049204223,0.0000013516839,0.0000044145568,0.0015274956],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99930423,0.00003104649,0.00017088235,0.00013947672,0.00025264485,0.00010173411],"domain_scores_gemma":[0.99958074,0.00004003745,0.00007574753,0.00018394992,0.00006652147,0.000053022985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008997819,0.00005958862,0.0000885823,0.00006863491,0.00003123169,0.00006315086,0.0002655771,0.000029187337,0.00023802409],"category_scores_gemma":[0.000037120506,0.000049790462,0.000027782291,0.00017146074,0.00003553053,0.00052898476,0.0000646309,0.00004201394,0.000011038952],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021322448,0.00013663883,0.0006112618,0.00003321136,0.00002444115,0.000011201635,0.0013216989,0.0000138433525,0.28660446,0.0053500356,0.027145602,0.6787455],"study_design_scores_gemma":[0.0003041839,0.000055113294,0.0007850662,0.000023903713,0.0000044866783,0.000013151488,0.00006744801,0.025437383,0.97251344,0.00061295426,0.000078642144,0.000104240826],"about_ca_topic_score_codex":0.00004101485,"about_ca_topic_score_gemma":0.0000022996524,"teacher_disagreement_score":0.685909,"about_ca_system_score_codex":0.000019659445,"about_ca_system_score_gemma":0.00000710806,"threshold_uncertainty_score":0.2606197},"labels":[],"label_agreement":null},{"id":"W2127412835","doi":"10.1016/j.media.2014.02.009","title":"Dual optimization based prostate zonal segmentation in 3D MR images","year":2014,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Canadian Institutes of Health Research","keywords":"Prostate; Segmentation; Consistency (knowledge bases); Computer science; Artificial intelligence; Prostate gland; Image segmentation; Relaxation (psychology); Algorithm; Pattern recognition (psychology); Mathematics; Medicine","score_opus":0.006099888535110403,"score_gpt":0.2740379779660794,"score_spread":0.26793808943096903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127412835","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007397684,0.000018896942,0.9950391,0.0029646344,0.00006298937,0.00019729536,0.000004142053,0.0002720568,0.00070110173],"genre_scores_gemma":[0.077582516,0.000032959684,0.91827035,0.0035383406,0.00007648865,0.00009547274,0.00021494324,0.000015048035,0.00017390547],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963808,0.00050305884,0.0006491647,0.00059002714,0.0015169634,0.00035998406],"domain_scores_gemma":[0.9985044,0.00030553882,0.00019197764,0.0004906149,0.0001828385,0.00032463766],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0019137901,0.00019654984,0.00036730358,0.0007976204,0.00008549285,0.00023555793,0.0005959815,0.00011368763,0.001694189],"category_scores_gemma":[0.0012026551,0.00017657263,0.00014854461,0.0025179535,0.00019139863,0.00090759003,0.00016116971,0.00026395798,0.00005182799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006262857,0.0016277668,0.02416587,0.00019060085,0.00069368194,0.0006405374,0.0014871045,0.07191706,0.014271711,0.00049818994,0.012246386,0.87219846],"study_design_scores_gemma":[0.00070465275,0.00005930031,0.0016601466,0.000023976683,0.00010317053,0.000002687849,0.000020853067,0.9794311,0.017631173,0.000112699876,0.000053134583,0.00019711566],"about_ca_topic_score_codex":0.000121582925,"about_ca_topic_score_gemma":0.00003822545,"teacher_disagreement_score":0.90751404,"about_ca_system_score_codex":0.000091321825,"about_ca_system_score_gemma":0.00012982715,"threshold_uncertainty_score":0.9992184},"labels":[],"label_agreement":null},{"id":"W2127643825","doi":"10.1109/tbme.2007.903520","title":"Mosaicing of Bladder Endoscopic Image Sequences: Distortion Calibration and Registration Algorithm","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":96,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre d'expertise et de recherche en infrastructures urbaines","funders":"","keywords":"Computer vision; Artificial intelligence; Image registration; Rigid transformation; Computer science; Distortion (music); Transformation (genetics); Ground truth; Pixel; Similarity (geometry); Process (computing); Coordinate system; Image (mathematics)","score_opus":0.011968859312873767,"score_gpt":0.2336847777589437,"score_spread":0.22171591844606992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127643825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028594038,0.000025939691,0.99623215,0.00017985763,0.00026997304,0.00013476143,0.0000073643355,0.00027259957,0.000017956969],"genre_scores_gemma":[0.5700374,0.00013783158,0.4296401,0.000062339604,0.0000340213,0.00003066442,0.000006717724,0.000009492482,0.000041394753],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988494,0.00002691719,0.00033357975,0.00023978564,0.00040192687,0.00014839415],"domain_scores_gemma":[0.99947983,0.000078674304,0.00007358845,0.00019066394,0.000039912644,0.00013732223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014205418,0.000116739015,0.00013986535,0.00020137822,0.000085928186,0.000024549665,0.0001550931,0.000084242514,0.000026409012],"category_scores_gemma":[0.000019489504,0.00011291238,0.00003781354,0.00035867578,0.00015779982,0.0005778463,0.0000023755235,0.00016855972,0.0000020185018],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007122849,0.00029980356,0.000006815584,0.00015246127,0.00006785842,0.000070772876,0.001437342,0.0021292046,0.6150832,0.00025543026,0.0003835431,0.38010645],"study_design_scores_gemma":[0.00025565363,0.00014588218,0.000068373134,0.000060551083,0.000008189891,0.000040977196,0.000014642013,0.52177066,0.4774418,0.000040158717,0.000043742188,0.00010937281],"about_ca_topic_score_codex":0.000052642703,"about_ca_topic_score_gemma":0.0000011114851,"teacher_disagreement_score":0.567178,"about_ca_system_score_codex":0.00005538362,"about_ca_system_score_gemma":0.000051990068,"threshold_uncertainty_score":0.46044332},"labels":[],"label_agreement":null},{"id":"W2127754037","doi":"10.1118/1.598942","title":"Intraoperative ultrasound for guidance and tissue shift correction in image‐guided neurosurgery","year":2000,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":250,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Imaging phantom; Modality (human–computer interaction); Image-guided surgery; Medicine; Ultrasound; Computer vision; Medical imaging; Neurosurgery; Artificial intelligence; Craniotomy; Intraoperative MRI; Computer science; Radiology; Magnetic resonance imaging; Interventional magnetic resonance imaging","score_opus":0.014934530016071004,"score_gpt":0.3052819134355345,"score_spread":0.29034738341946353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127754037","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012049087,0.000038162518,0.9852553,0.0011788737,0.0003476095,0.00029027805,0.0000024286046,0.00015599828,0.0006822466],"genre_scores_gemma":[0.8984115,0.00034502672,0.08907074,0.009998346,0.0007260486,0.00032659533,0.000032107935,0.000038436112,0.0010511619],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998624,0.00009472956,0.00030318322,0.00034836572,0.00040450526,0.00022516615],"domain_scores_gemma":[0.99894947,0.0005778848,0.000046743462,0.00020925715,0.00004551577,0.000171157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004130799,0.00012013311,0.00018527579,0.000033570257,0.00006747424,0.00009305351,0.0002949176,0.000069986476,0.00018530214],"category_scores_gemma":[0.00069558475,0.000110515924,0.000026362495,0.00028598544,0.00019069934,0.0004993619,0.000042911997,0.0002046561,0.000020002928],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008607579,0.00012358496,0.00036063496,0.0000244785,0.000004118206,0.000023455723,0.00082939496,0.0000035477792,0.0022111926,0.0003358187,0.025993118,0.97008204],"study_design_scores_gemma":[0.0020478975,0.0004702876,0.012419309,0.000319106,0.000013593479,0.00008922231,0.000031852505,0.031474683,0.9059694,0.03995621,0.006418427,0.0007900475],"about_ca_topic_score_codex":0.000042420645,"about_ca_topic_score_gemma":0.000007949323,"teacher_disagreement_score":0.969292,"about_ca_system_score_codex":0.000032156684,"about_ca_system_score_gemma":0.00008210303,"threshold_uncertainty_score":0.45067084},"labels":[],"label_agreement":null},{"id":"W2127912520","doi":"10.1109/iembs.2007.4352410","title":"Airway Segmentation and Measurement in CT Images","year":2007,"lang":"en","type":"article","venue":"Conference proceedings","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Vector flow; Segmentation; Artificial intelligence; Computer vision; Computer science; Image segmentation; Edge detection; Computed tomography; Airway; Cone beam ct; Volume (thermodynamics); 3d model; Pattern recognition (psychology); Heuristic; Image (mathematics); Image processing; Radiology; Medicine; Physics","score_opus":0.03425797640120814,"score_gpt":0.28682285334095187,"score_spread":0.2525648769397437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127912520","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042565055,0.000077382196,0.95238096,0.0005753681,0.00007944709,0.00027032112,2.5793312e-7,0.0001954075,0.003855794],"genre_scores_gemma":[0.8786882,0.0000474942,0.1208238,0.00033499856,0.000015859363,0.000024579089,5.122612e-7,0.000004491277,0.000060036386],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99872863,0.0000073077344,0.00025293216,0.00031258457,0.00046296144,0.00023555288],"domain_scores_gemma":[0.9994402,0.000022685866,0.00008472717,0.00007828406,0.00026338166,0.00011071441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013115079,0.00010736262,0.00010983759,0.00017700176,0.000045743353,0.0002016816,0.0003041386,0.00002507957,0.00002438458],"category_scores_gemma":[0.0001567247,0.00010180862,0.000013000531,0.00027315257,0.000071560826,0.0008740443,0.000123472,0.00012237797,0.000009379231],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000061130672,0.00006013496,0.012939088,0.00007106647,0.000005065199,0.000015982076,0.002737214,4.4824883e-8,0.3384815,0.006522022,0.0015622131,0.6375996],"study_design_scores_gemma":[0.000469526,0.00009247764,0.031980198,0.00012228075,0.0000034177863,0.000021623922,0.00072949525,0.0010421075,0.96095383,0.004211204,0.00015827503,0.00021558354],"about_ca_topic_score_codex":0.000025751997,"about_ca_topic_score_gemma":0.000008271753,"teacher_disagreement_score":0.83612317,"about_ca_system_score_codex":0.00009092444,"about_ca_system_score_gemma":0.0000465926,"threshold_uncertainty_score":0.41516352},"labels":[],"label_agreement":null},{"id":"W2127927483","doi":"10.1109/titb.2006.864476","title":"High-Performance Medical Image Registration Using New Optimization Techniques","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Computer science; Image registration; Metric (unit); Implementation; Image (mathematics); Bounded function; Computation; Similarity (geometry); Parallel computing; Algorithm; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.00797374032554557,"score_gpt":0.2587452523931287,"score_spread":0.2507715120675831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127927483","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025580341,0.000011094429,0.9877678,0.0069690426,0.00037176284,0.00045435678,0.0000041073863,0.0015639426,0.00029983072],"genre_scores_gemma":[0.280768,0.00012312416,0.7179416,0.0009096193,0.0000642578,0.000079262645,0.00003344972,0.000011419145,0.00006925622],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975791,0.000039315302,0.0010280669,0.00026282988,0.0007772984,0.00031341254],"domain_scores_gemma":[0.9988336,0.000049361577,0.00032879313,0.00051344745,0.00016815834,0.000106629035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005420585,0.0002232746,0.00025440223,0.0023859853,0.00015000807,0.000071186565,0.000694451,0.00061107497,0.00015130514],"category_scores_gemma":[0.00004730778,0.00021285469,0.000034876466,0.0025013355,0.00030780156,0.0027787113,0.000008505021,0.00065030565,0.000037177248],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005007382,0.0003547148,0.00005969525,0.000109488254,0.00002336224,0.00002644255,0.00021160241,0.018677788,0.017813971,0.0056818738,0.004453759,0.95253724],"study_design_scores_gemma":[0.0012031936,0.0003474006,0.000062586354,0.00025198754,0.000011274369,0.00012115575,0.00004721453,0.31122968,0.6842616,0.0019178229,0.00025818704,0.00028787245],"about_ca_topic_score_codex":0.00037331777,"about_ca_topic_score_gemma":0.000013178978,"teacher_disagreement_score":0.95224935,"about_ca_system_score_codex":0.00027086693,"about_ca_system_score_gemma":0.0002463573,"threshold_uncertainty_score":0.8679962},"labels":[],"label_agreement":null},{"id":"W2128089874","doi":"10.1109/tpami.2002.1114849","title":"Flux maximizing geometric flows","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":381,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; IBM (Canada)","funders":"","keywords":"Image segmentation; Surface (topology); Artificial intelligence; Segmentation; Computer science; Computer vision; Image (mathematics); Set (abstract data type); Field (mathematics); Level set (data structures); Algorithm; Mathematics; Geometry","score_opus":0.03171995903845451,"score_gpt":0.27753714757311465,"score_spread":0.24581718853466014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128089874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041968294,0.00016982111,0.9983094,0.00039185988,0.00013533694,0.000104874685,0.000010070178,0.0002185459,0.00024046337],"genre_scores_gemma":[0.9651566,0.0006002423,0.032718197,0.00077735324,0.000014103759,0.000026026366,0.0000023963557,0.000009025449,0.00069601554],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984162,0.00008010031,0.00038067467,0.0004941318,0.0003906438,0.00023825503],"domain_scores_gemma":[0.99901706,0.00014871922,0.00008713129,0.00050771673,0.00006066849,0.00017869499],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00023829505,0.0001870281,0.00026368018,0.0011846952,0.00017845709,0.00017988947,0.00051232765,0.000058219386,0.0019650187],"category_scores_gemma":[0.0000107076285,0.00016455463,0.00020220972,0.0026589017,0.000050701532,0.00033359288,0.00000698269,0.00026367515,0.00015840486],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.7050387e-7,0.00011388815,0.00005771604,0.0000073625574,0.0001542098,0.000017519335,0.0001618643,0.0013675594,0.00033018627,0.00001011162,0.000059490827,0.9977192],"study_design_scores_gemma":[0.000079037054,0.00011025222,0.00016055552,0.00001522201,0.00021723523,0.000026204872,0.00002161122,0.60347915,0.39542997,0.00011033434,0.000085591135,0.0002648119],"about_ca_topic_score_codex":0.00030761314,"about_ca_topic_score_gemma":0.00010421394,"teacher_disagreement_score":0.9974544,"about_ca_system_score_codex":0.000034418114,"about_ca_system_score_gemma":0.0000047646918,"threshold_uncertainty_score":0.9989473},"labels":[],"label_agreement":null},{"id":"W2128182638","doi":"10.1109/icme.2003.1220983","title":"Unsupervised model based image segmentation using domain knowledge based fuzzy logic and edge enhancement","year":2003,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Artificial intelligence; Image segmentation; Segmentation; Computer vision; Scale-space segmentation; Boundary (topology); Fuzzy logic; Segmentation-based object categorization; Edge detection; Image (mathematics); Process (computing); Domain (mathematical analysis); Pattern recognition (psychology); Image processing; Mathematics","score_opus":0.03686724213071014,"score_gpt":0.31508197320486453,"score_spread":0.2782147310741544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128182638","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005472348,0.00007278429,0.9875413,0.00027992704,0.000083234445,0.0004888475,0.0000018376978,0.00027165722,0.0057880166],"genre_scores_gemma":[0.060657535,0.0000055496903,0.9367189,0.0023665803,0.000010554032,0.000056195462,0.00000883554,0.000012858934,0.00016301943],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823356,0.0002414381,0.00036605314,0.0005093657,0.0003380673,0.0003115094],"domain_scores_gemma":[0.99906427,0.00009522243,0.00009424662,0.00041879996,0.00013416682,0.00019331207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007186861,0.00021002328,0.00018339271,0.00018271561,0.0001575487,0.00018973844,0.00031360806,0.00006840015,0.00024231647],"category_scores_gemma":[0.000058799596,0.00018874994,0.000049814098,0.00036595966,0.000104711144,0.00061012275,0.00007612142,0.00010082306,0.000024349854],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010341616,0.00044639574,0.00007884052,0.000113080234,0.000012855173,0.0000119993165,0.00054206175,0.00042241838,0.9577877,0.01894071,0.0009927852,0.020640828],"study_design_scores_gemma":[0.0007540145,0.000055386492,0.000009442198,0.000019454048,0.0000061284413,0.0000015200204,0.000040071878,0.53856283,0.45582992,0.0045244703,0.000029422019,0.0001673374],"about_ca_topic_score_codex":0.000008816678,"about_ca_topic_score_gemma":0.0000025379975,"teacher_disagreement_score":0.5381404,"about_ca_system_score_codex":0.00014464978,"about_ca_system_score_gemma":0.00023653703,"threshold_uncertainty_score":0.7696999},"labels":[],"label_agreement":null},{"id":"W2128370414","doi":"10.1142/s0218001403002307","title":"NEW BINARY MORPHOLOGICAL OPERATIONS FOR EFFECTIVE LOW-COST BOUNDARY DETECTION","year":2003,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Boundary (topology); Computer science; Binary number; Implementation; FLOPS; Artificial intelligence; Algorithm; Parallel computing; Arithmetic; Mathematics; Programming language","score_opus":0.06445734321966223,"score_gpt":0.34633871300233293,"score_spread":0.2818813697826707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128370414","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028244622,0.00004285652,0.969343,0.00070917537,0.0012692756,0.000301965,0.000012983716,0.000033564767,0.000042526393],"genre_scores_gemma":[0.8900776,0.00009724379,0.108273104,0.0011440323,0.00032416833,0.0000413216,0.000011262613,0.000008055533,0.000023251832],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986899,0.00013001954,0.000522988,0.00019970782,0.0003297282,0.00012767664],"domain_scores_gemma":[0.9986885,0.00024263252,0.00019367681,0.00007728548,0.00065436907,0.00014349517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054484495,0.000110098736,0.00013296664,0.00022803181,0.000106375344,0.0003140375,0.00032497785,0.00006504671,0.00020681412],"category_scores_gemma":[0.0005519413,0.000097916985,0.000094244344,0.0001229422,0.00006665277,0.0006949878,0.000040454423,0.00018287772,0.000036187404],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031453994,0.00008831185,0.00002758304,0.00000428921,0.000026868922,0.000037219135,0.0002197104,0.000037653244,0.018888168,0.00034181532,0.000107014734,0.9801899],"study_design_scores_gemma":[0.00024890283,0.00049148255,0.00025526417,0.000129248,0.000017439015,0.00056136586,0.00022124765,0.010142607,0.94069666,0.046486974,0.00054255675,0.00020624045],"about_ca_topic_score_codex":0.000016261603,"about_ca_topic_score_gemma":0.000016529168,"teacher_disagreement_score":0.9799837,"about_ca_system_score_codex":0.000065804095,"about_ca_system_score_gemma":0.00008167641,"threshold_uncertainty_score":0.39929387},"labels":[],"label_agreement":null},{"id":"W2128804547","doi":"10.1109/iembs.2008.4649290","title":"Shape-guided active contour based segmentation and tracking of lumbar vertebrae in video fluoroscopy using complex wavelets","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wavelet; Artificial intelligence; Computer vision; Segmentation; Computer science; Fluoroscopy; Curvature; Vertebra; Tracking (education); Lumbar vertebrae; Pattern recognition (psychology); Lumbar; Mathematics; Radiology; Medicine; Anatomy","score_opus":0.08947295229275415,"score_gpt":0.33682608027090455,"score_spread":0.2473531279781504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128804547","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32612243,0.000015647967,0.67320746,0.0000900743,0.000028815433,0.0002493813,0.0000017570829,0.00007977917,0.0002046431],"genre_scores_gemma":[0.58752805,0.000012053103,0.41179094,0.0006377823,0.000006721943,0.0000072924677,0.0000042656748,0.000005308523,0.0000075897365],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99864584,0.00012024788,0.00039887754,0.0003024157,0.00034565973,0.0001869621],"domain_scores_gemma":[0.9993618,0.000097460565,0.0001510292,0.00019365216,0.000109203094,0.000086836655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023408279,0.00012265687,0.00022517168,0.00018726752,0.00007338631,0.000035523877,0.00024349014,0.000053296546,0.00015824824],"category_scores_gemma":[0.00007451467,0.00012167916,0.00003082795,0.00030187058,0.00012385727,0.00079884316,0.00007571717,0.000090808775,0.0000018904342],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008261634,0.00009052222,0.0023934522,0.000031755484,0.000008578126,0.000026008947,0.0012155988,0.000021130034,0.9103722,0.00016740391,0.00037814755,0.085286915],"study_design_scores_gemma":[0.00083437545,0.000052543768,0.03105939,0.000041322997,0.0000030237745,0.000014810391,0.000078278455,0.33845517,0.6292451,0.0001072363,0.0000025317324,0.00010623239],"about_ca_topic_score_codex":0.0002930906,"about_ca_topic_score_gemma":0.00002268797,"teacher_disagreement_score":0.33843404,"about_ca_system_score_codex":0.00009318902,"about_ca_system_score_gemma":0.000095718584,"threshold_uncertainty_score":0.4961932},"labels":[],"label_agreement":null},{"id":"W2129232242","doi":"10.1016/s1053-8119(03)00143-5","title":"Extraction of epileptogenic foci from PET and SPECT images by fuzzy modeling and data fusion","year":2003,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"","keywords":"Partial volume; Artificial intelligence; Computer science; Single-photon emission computed tomography; Nuclear medicine; Modality (human–computer interaction); Ictal; Fuzzy logic; Gold standard (test); Image fusion; Ictal-Interictal SPECT Analysis by SPM; Emission computed tomography; Voxel; Positron emission tomography; Pattern recognition (psychology); Computer vision; Radiology; Medicine; Epilepsy; Image (mathematics)","score_opus":0.038234268984843306,"score_gpt":0.31476449165064807,"score_spread":0.27653022266580474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129232242","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14101674,0.00044509547,0.8572656,0.00018035404,0.000086044325,0.00013328255,0.000058586025,0.0001045911,0.0007097446],"genre_scores_gemma":[0.6957309,0.0009108891,0.3029183,0.00030279654,0.000022263981,0.0000038011926,0.000044745215,0.000013177196,0.000053152922],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987533,0.00014069611,0.00022647697,0.00051020255,0.0002390356,0.00013025051],"domain_scores_gemma":[0.99899656,0.00013948834,0.00009479629,0.0006514349,0.00003187895,0.00008582501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030793104,0.000106118016,0.00013740065,0.00005517469,0.00006225784,0.00009998094,0.00037779642,0.000022841292,0.000035784637],"category_scores_gemma":[0.00023574736,0.00010338009,0.000015141242,0.000110633155,0.000069925554,0.00094539253,0.0003163957,0.00013769671,0.0000033162182],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004848085,0.00005317182,0.00025207057,0.000017919034,0.000005804543,0.0000357654,0.000083006686,0.000001911237,0.94911015,0.00015810206,0.006272405,0.04400484],"study_design_scores_gemma":[0.0011278244,0.00021770723,0.0022812053,0.000061807856,0.00004558177,0.00020550888,0.00008943364,0.2981642,0.6880233,0.008297564,0.0010347745,0.00045106924],"about_ca_topic_score_codex":0.00007860932,"about_ca_topic_score_gemma":0.000002051159,"teacher_disagreement_score":0.55471414,"about_ca_system_score_codex":0.0000074718264,"about_ca_system_score_gemma":0.000018774539,"threshold_uncertainty_score":0.42157176},"labels":[],"label_agreement":null},{"id":"W2129320806","doi":"10.1109/imtc.1994.352165","title":"Physical significance of measurements in images","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Binary image; Binary number; Artificial intelligence; Curvature; Robustness (evolution); Computer science; Image processing; Image plane; Computer vision; Mathematical morphology; Image segmentation; Segmentation; Mathematics; Image (mathematics); Algorithm; Geometry","score_opus":0.05891699261596787,"score_gpt":0.3040501144980661,"score_spread":0.24513312188209826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129320806","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00396145,0.000021797563,0.97982204,0.00032161255,0.000027652153,0.00011495233,2.5801575e-7,0.00010189665,0.01562834],"genre_scores_gemma":[0.81728715,0.000005094358,0.18210189,0.000213212,0.00000902827,0.0000133013755,1.10023045e-7,0.0000020534642,0.00036815178],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992901,0.000040356314,0.00013433782,0.00014075822,0.00029778059,0.00009663065],"domain_scores_gemma":[0.9996512,0.000036460435,0.00003735766,0.00020237548,0.000037610076,0.00003498628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001349752,0.000045400622,0.000085636115,0.00005437357,0.0000074961463,0.000014139117,0.00035862593,0.000012100006,0.00013015959],"category_scores_gemma":[0.00005174745,0.000038403225,0.00001978064,0.00023553192,0.000042097105,0.00025837778,0.000052678122,0.000045652258,0.000032654163],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012777936,0.0006265572,0.003183985,0.000027709417,0.000006747573,0.000006433148,0.0009831338,0.000006492775,0.6578192,0.0023883875,0.015167283,0.31978276],"study_design_scores_gemma":[0.00014059654,0.00003361667,0.0013566244,0.000010003504,5.6533105e-7,3.7962894e-7,0.0000075172657,0.006144854,0.9913905,0.00084807776,0.000015903192,0.000051361323],"about_ca_topic_score_codex":0.000024895578,"about_ca_topic_score_gemma":0.0000016789323,"teacher_disagreement_score":0.8133257,"about_ca_system_score_codex":0.000016365448,"about_ca_system_score_gemma":0.000005517586,"threshold_uncertainty_score":0.15660381},"labels":[],"label_agreement":null},{"id":"W2129633956","doi":"10.1109/isccsp.2008.4537346","title":"Modifying weber fraction law to postprocessing and edge detection applications","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Sobel operator; Pixel; Thresholding; Histogram; Edge detection; Mathematics; Artificial intelligence; Boundary (topology); Enhanced Data Rates for GSM Evolution; Block (permutation group theory); Computer vision; Line (geometry); Sensitivity (control systems); Algorithm; Computer science; Image (mathematics); Image processing; Geometry; Mathematical analysis","score_opus":0.024848441452997094,"score_gpt":0.29185138812066325,"score_spread":0.26700294666766616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129633956","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003111445,0.000023697761,0.9907733,0.0003846358,0.00003114237,0.00019722752,1.5739661e-7,0.00043196735,0.0050463984],"genre_scores_gemma":[0.58850044,0.000011179988,0.4092373,0.0019679912,0.000035575933,0.00008525977,6.2084956e-7,0.0000040051914,0.00015763265],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993933,0.000016231055,0.00011614945,0.00022216355,0.00015230535,0.000099844816],"domain_scores_gemma":[0.99960077,0.00003198172,0.00003287521,0.00016787459,0.00006590839,0.00010060318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000918687,0.00005700915,0.000055556342,0.00007250563,0.0002760811,0.00007874025,0.0001317029,0.000034062265,0.000012519935],"category_scores_gemma":[0.000016036882,0.00005390439,0.00001105797,0.00026061284,0.000037937127,0.0006798461,0.00007277497,0.00007123334,0.000039731494],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002688271,0.000042921412,0.00011065901,0.000018650591,0.0000041374465,0.000002789269,0.00047823304,0.000005573915,0.13029794,0.006447906,0.00046876408,0.86211973],"study_design_scores_gemma":[0.0001867419,0.000072541836,0.001638649,0.000017640823,0.0000041637013,0.000115737406,0.000062389576,0.016325295,0.97131205,0.0036788806,0.0063541355,0.00023180225],"about_ca_topic_score_codex":0.00008574192,"about_ca_topic_score_gemma":0.0000141018745,"teacher_disagreement_score":0.86188793,"about_ca_system_score_codex":0.00003404419,"about_ca_system_score_gemma":0.00001456132,"threshold_uncertainty_score":0.21981573},"labels":[],"label_agreement":null},{"id":"W2129985596","doi":"10.1117/1.2194018","title":"Multiscale model-based feature extraction in structural texture images","year":2006,"lang":"en","type":"article","venue":"Journal of Electronic Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Artificial intelligence; Texture filtering; Image texture; Texture compression; Computer science; Bidirectional texture function; Pattern recognition (psychology); Texture (cosmology); Segmentation; Computer vision; Projective texture mapping; Feature extraction; Orientation (vector space); Image segmentation; Mathematics; Image (mathematics); Geometry","score_opus":0.0037619207831801576,"score_gpt":0.2746280228840208,"score_spread":0.2708661021008406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129985596","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0077220937,0.0010595754,0.9874306,0.0033170215,0.00011200108,0.00008349037,5.1832785e-7,0.00005386285,0.00022089013],"genre_scores_gemma":[0.81346524,0.00001208666,0.18589327,0.0003699233,0.000107579355,0.0000020614273,0.0000015616638,0.000009004765,0.00013928101],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861676,0.00007346626,0.00036760423,0.00017003222,0.00039958503,0.00037254827],"domain_scores_gemma":[0.99923396,0.00005788612,0.00033877665,0.00016934406,0.00014527641,0.00005472878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044611478,0.00012931289,0.00017628341,0.00029778623,0.00005714603,0.00013710161,0.00045645976,0.000046424833,0.000012311602],"category_scores_gemma":[0.000040539944,0.000108688386,0.0000893508,0.00028977636,0.000039984345,0.0012426574,0.000030110848,0.00073203625,0.0000011661145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008280231,0.0002436021,0.0062969713,0.000058369922,0.00002548918,0.00045957335,0.00029330293,0.04906565,0.48376015,0.0030943428,0.028698128,0.42792162],"study_design_scores_gemma":[0.0008886041,0.000051436367,0.0038111524,0.0000626834,0.000008618771,0.00031054488,0.000013126622,0.8601558,0.12529457,0.009063105,0.00018224523,0.00015812369],"about_ca_topic_score_codex":0.00002856929,"about_ca_topic_score_gemma":0.000015154599,"teacher_disagreement_score":0.8110902,"about_ca_system_score_codex":0.00033875802,"about_ca_system_score_gemma":0.00027727353,"threshold_uncertainty_score":0.44321838},"labels":[],"label_agreement":null},{"id":"W2130160942","doi":"10.1109/icip.2007.4379515","title":"Edge Sensitive Variational Image Thresholding","year":2007,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Energy functional; Minimax; Regularization (linguistics); Computer science; Thresholding; Energy minimization; Variational method; Weighting; Artificial intelligence; Smoothing; Image segmentation; Energy (signal processing); Fidelity; Image (mathematics); Algorithm; Mathematical optimization; Mathematics; Computer vision","score_opus":0.01528684749122643,"score_gpt":0.3000587533706988,"score_spread":0.2847719058794724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130160942","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002579092,0.0000031306342,0.94402117,0.0005945556,0.00013269825,0.00008002324,4.638756e-7,0.00040100914,0.054509047],"genre_scores_gemma":[0.06193359,0.0000014152121,0.934411,0.0029304218,0.000079659774,0.000002234586,0.000002601817,0.0000036649774,0.0006354037],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99915,0.000020752044,0.0001618209,0.00019452159,0.00029707424,0.00017583834],"domain_scores_gemma":[0.9993976,0.00017034831,0.000040872943,0.00018754718,0.00011062354,0.00009301287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073795207,0.00006193244,0.0000599951,0.00008696368,0.00006161636,0.000085293505,0.00025510305,0.000031174783,0.00020866646],"category_scores_gemma":[0.00009246622,0.000054651355,0.000026033527,0.00022210991,0.000038949947,0.00057275343,0.00012945327,0.00007962425,0.00012241793],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005801158,0.00010232036,0.00020166478,0.0000083383575,0.000021302978,0.00017170627,0.001124573,0.0000016817868,0.18152413,0.5643817,0.03455542,0.21790135],"study_design_scores_gemma":[0.00021433015,0.00003198255,0.00635734,0.000008910258,0.0000022171525,0.000030281604,0.00006432539,0.012072903,0.97123724,0.009272664,0.00054097857,0.00016681047],"about_ca_topic_score_codex":0.000013627069,"about_ca_topic_score_gemma":0.0000021038818,"teacher_disagreement_score":0.78971314,"about_ca_system_score_codex":0.00004071051,"about_ca_system_score_gemma":0.00003101476,"threshold_uncertainty_score":0.22847514},"labels":[],"label_agreement":null},{"id":"W2130167729","doi":"10.1109/cvpr.2008.4587789","title":"SMRFI: Shape matching via registration of vector-valued feature images","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Mitacs","keywords":"Artificial intelligence; Image registration; Computer vision; Feature vector; Computer science; Pattern recognition (psychology); Feature (linguistics); Point set registration; Matching (statistics); Vertex (graph theory); Feature extraction; Image (mathematics); Point (geometry); Mathematics; Geometry","score_opus":0.019674749982134556,"score_gpt":0.2790374629856562,"score_spread":0.25936271300352165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130167729","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004770832,0.00004022175,0.9884803,0.0011956467,0.00008059468,0.0001254759,7.801101e-7,0.00035106792,0.0049550743],"genre_scores_gemma":[0.35202438,0.000017623928,0.6457811,0.0005090271,0.000032002514,0.0000067723454,0.0000041473113,0.000005245567,0.0016196732],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99898183,0.000056146913,0.00021434782,0.00021890341,0.00040168405,0.00012709515],"domain_scores_gemma":[0.9993213,0.000048176953,0.00012541897,0.00034431028,0.000095798736,0.00006499827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021567417,0.000088194414,0.000122027785,0.000067046385,0.000070828784,0.00003418866,0.0004444289,0.000055226734,0.00012998896],"category_scores_gemma":[0.00005735703,0.00007468167,0.00004386247,0.00023214731,0.00007984893,0.00057742896,0.00008371139,0.000113545764,0.000016730579],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011697638,0.00018673016,0.0003273165,0.00009293269,0.000030648374,0.00009618558,0.002836634,0.0000089786345,0.7903391,0.02218566,0.089992,0.093892135],"study_design_scores_gemma":[0.0002499579,0.00010412639,0.011480313,0.000031999123,0.000004100092,0.00008510146,0.000031399955,0.008543774,0.97636837,0.0028093075,0.00011210486,0.00017942917],"about_ca_topic_score_codex":0.00007729231,"about_ca_topic_score_gemma":0.0000031711445,"teacher_disagreement_score":0.34725356,"about_ca_system_score_codex":0.000021628675,"about_ca_system_score_gemma":0.00004598337,"threshold_uncertainty_score":0.30454305},"labels":[],"label_agreement":null},{"id":"W2130506971","doi":"10.1109/ccece.2004.1349634","title":"Evaluation of hierarchical elastic medical image registration method","year":2004,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Histogram; Computer science; Artificial intelligence; Mutual information; Image registration; Resampling; Computer vision; Partition (number theory); Image (mathematics); Enhanced Data Rates for GSM Evolution; Spline (mechanical); Pattern recognition (psychology); Mathematics","score_opus":0.03921933951754129,"score_gpt":0.39581667126986464,"score_spread":0.35659733175232333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130506971","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004495024,0.000013844644,0.98527867,0.0024959038,0.00008233597,0.00019640975,3.4306882e-7,0.00017941774,0.011303578],"genre_scores_gemma":[0.12213746,0.0000050888248,0.8773938,0.0003738582,0.000029748357,0.00002312716,0.0000033146496,0.000003536398,0.000030111647],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9962053,0.00036855173,0.0003653691,0.00024490608,0.0026905376,0.00012536322],"domain_scores_gemma":[0.99883026,0.00017866038,0.000103657636,0.00032910617,0.0004135423,0.00014474655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052712206,0.000070947885,0.00011472454,0.00009466778,0.00003112235,0.00003687321,0.00048637314,0.00007637888,0.00050580606],"category_scores_gemma":[0.0026996275,0.00006013714,0.00004066342,0.0002833821,0.000104968945,0.00042018274,0.00009127159,0.0001335327,0.000025455678],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003586686,0.00020565985,0.000006199405,0.000019684925,0.000018535093,0.000012420177,0.00034819305,0.00008845381,0.032063883,0.1668982,0.00078097096,0.7995542],"study_design_scores_gemma":[0.0013510745,0.0001998196,0.00053342426,0.00006316632,0.000038376824,0.0000733917,0.000024618435,0.24134018,0.51371515,0.24248563,0.000022426299,0.00015278436],"about_ca_topic_score_codex":0.00004815433,"about_ca_topic_score_gemma":0.000010026153,"teacher_disagreement_score":0.7994014,"about_ca_system_score_codex":0.000087474415,"about_ca_system_score_gemma":0.00063840364,"threshold_uncertainty_score":0.55382216},"labels":[],"label_agreement":null},{"id":"W2130541310","doi":"10.1109/icip.2007.4379878","title":"Segmentation of Medical Ultrasound Images using Active Contours","year":2007,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Image segmentation; Segmentation; Medical ultrasound; Ultrasound; Active contour model; Medical imaging; Radiology; Medicine","score_opus":0.02434074534883581,"score_gpt":0.3596687221142167,"score_spread":0.3353279767653809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130541310","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017535146,0.000017210252,0.97936034,0.00021537108,0.00011504831,0.0001301389,0.0000010813283,0.00014410785,0.0024815826],"genre_scores_gemma":[0.2807311,0.000018976727,0.7184019,0.00071579294,0.00004093356,0.0000023608216,0.0000030490503,0.0000051483817,0.000080705504],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99837816,0.000049251106,0.00029463787,0.0001808002,0.0009269936,0.00017015486],"domain_scores_gemma":[0.9990773,0.00035111725,0.00012416914,0.00018917407,0.000110873916,0.00014731938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010101326,0.00007487497,0.00011477811,0.00010903253,0.000036634912,0.000032060696,0.0004842535,0.00006872784,0.00041079786],"category_scores_gemma":[0.00028241987,0.00006428596,0.000034860455,0.00024715558,0.00012718195,0.00053538394,0.00008709187,0.00009580121,0.0000055682426],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009769145,0.00011969213,0.0007044725,0.000018162715,0.000026614827,0.000028479615,0.000988284,0.0000012177262,0.7030863,0.0069994,0.0014116734,0.28660592],"study_design_scores_gemma":[0.0002451886,0.000036819667,0.0016294777,0.0000195757,0.0000038419084,0.000022682598,0.00033171216,0.000628819,0.99626905,0.00073036633,0.000010290198,0.000072181305],"about_ca_topic_score_codex":0.00019200904,"about_ca_topic_score_gemma":0.000013087751,"teacher_disagreement_score":0.29318273,"about_ca_system_score_codex":0.000056321547,"about_ca_system_score_gemma":0.00008451172,"threshold_uncertainty_score":0.44979483},"labels":[],"label_agreement":null},{"id":"W2130572687","doi":"10.1007/11566489_21","title":"Maximum a Posteriori Local Histogram Estimation for Image Registration","year":2005,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Maximum a posteriori estimation; Artificial intelligence; Histogram; Image registration; Estimator; Pattern recognition (psychology); Principle of maximum entropy; Computer science; A priori and a posteriori; Image histogram; Mathematics; Computer vision; Histogram matching; Context (archaeology); Image (mathematics); Maximum likelihood; Image processing; Statistics; Image texture; Geography","score_opus":0.012797552459452731,"score_gpt":0.29649503772293184,"score_spread":0.2836974852634791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130572687","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00059305097,0.00003420476,0.99380356,0.004354112,0.0004024553,0.0004785445,0.0000011313947,0.0003115332,0.00002138384],"genre_scores_gemma":[0.3467569,0.0000014830646,0.6515597,0.0015461446,0.00009321163,0.000033021643,0.0000032204675,0.000004949276,0.0000013393054],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979721,0.000044092794,0.00036816727,0.00066632294,0.00055003876,0.00039928523],"domain_scores_gemma":[0.99875176,0.00021554052,0.00013917199,0.00057727576,0.00019231664,0.00012391043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009065663,0.00016408619,0.00015776203,0.00029864293,0.0001760747,0.0004583919,0.0012398282,0.00007336654,0.000006995713],"category_scores_gemma":[0.0002540333,0.0001535913,0.00004940722,0.0009812447,0.00044847003,0.0017248804,0.00022589536,0.00015800768,0.000013721861],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000038300664,0.0000410128,0.00000899975,0.000012917351,8.292375e-7,0.0000040599234,0.00045350977,0.0058401,0.0063829613,0.00037769182,0.00007057364,0.98680353],"study_design_scores_gemma":[0.0002536466,0.00015817973,0.00014777739,0.0000322603,0.0000015714212,0.0000344453,4.4740742e-7,0.8689256,0.11381116,0.016352335,0.00011939755,0.00016318215],"about_ca_topic_score_codex":0.00002542447,"about_ca_topic_score_gemma":0.000034764373,"teacher_disagreement_score":0.98664033,"about_ca_system_score_codex":0.00032257612,"about_ca_system_score_gemma":0.00021208273,"threshold_uncertainty_score":0.62632716},"labels":[],"label_agreement":null},{"id":"W2130748174","doi":"10.1109/nfsi-icfbi.2007.4387709","title":"Medical Image Segmentation: Methods and Software","year":2007,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":109,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Segmentation; Image segmentation; Scale-space segmentation; Segmentation-based object categorization; Computer science; Artificial intelligence; Region growing; Process (computing); Software; Computer vision; Minimum spanning tree-based segmentation; Pattern recognition (psychology)","score_opus":0.017509472174303178,"score_gpt":0.4001116249803628,"score_spread":0.38260215280605964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130748174","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016039902,0.00006551069,0.9945943,0.0010627387,0.00011731926,0.00010323028,1.808526e-7,0.0005614879,0.003334847],"genre_scores_gemma":[0.00037701192,0.000025046074,0.99507767,0.004086121,0.000038654776,0.00000652195,0.0000015072739,0.000004615463,0.00038283068],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987782,0.00008700945,0.00023602383,0.00024631128,0.00046921388,0.00018323344],"domain_scores_gemma":[0.99897647,0.00041354305,0.00004148127,0.00022676552,0.00005914893,0.00028262014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021499803,0.000078247365,0.00009234275,0.00008154875,0.000060705683,0.000088886234,0.00037841467,0.00006209544,0.00076332816],"category_scores_gemma":[0.00052235456,0.00006384957,0.000020363259,0.00021640476,0.00011225591,0.00048832496,0.00023852332,0.00011129584,0.0000266789],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.555523e-7,0.000015603624,0.00013739082,0.000007152602,0.0000037460168,0.000031529282,0.00015994556,6.5150054e-9,0.0056001465,0.0029355942,0.0033768434,0.98773116],"study_design_scores_gemma":[0.00053341675,0.000092729126,0.0027476603,0.000021636646,0.000005255238,0.00014854834,0.00015282624,0.0022900559,0.98346144,0.008037953,0.0022528768,0.00025559365],"about_ca_topic_score_codex":0.000014478868,"about_ca_topic_score_gemma":0.000004471884,"teacher_disagreement_score":0.9874756,"about_ca_system_score_codex":0.000020259027,"about_ca_system_score_gemma":0.00004138451,"threshold_uncertainty_score":0.8357908},"labels":[],"label_agreement":null},{"id":"W2131077461","doi":"10.1109/ccece.2002.1013070","title":"A Markov random fields model for hybrid edge- and region-based color image segmentation","year":2003,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Image segmentation; Artificial intelligence; Computer science; Markov random field; Computer vision; Scale-space segmentation; Segmentation; Enhanced Data Rates for GSM Evolution; Pattern recognition (psychology); Region growing; Markov chain; Markov process; Edge detection; Image (mathematics); Segmentation-based object categorization; Image processing; Mathematics; Machine learning; Statistics","score_opus":0.019078273850347507,"score_gpt":0.27892032144409795,"score_spread":0.25984204759375046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131077461","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008156255,0.00002041143,0.99577534,0.00109445,0.00006732972,0.0006892011,0.0000020250977,0.00021460565,0.00132098],"genre_scores_gemma":[0.0767916,0.000012112162,0.9178647,0.0038384022,0.00001050688,0.00023384178,0.0000066181624,0.000007233661,0.0012349829],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911845,0.000064476255,0.00020338884,0.00028266612,0.00016454938,0.00016649999],"domain_scores_gemma":[0.99923253,0.0002754837,0.000065487475,0.00023024094,0.00009586956,0.00010038577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033367844,0.00010118995,0.00012499785,0.00007091583,0.000089419045,0.00012365812,0.00018648086,0.00003735277,0.00003165955],"category_scores_gemma":[0.00020105283,0.00008808962,0.000044267286,0.00008993811,0.000056098677,0.00037874086,0.00002788628,0.00005485817,0.0000028582808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004991924,0.000606134,0.00021459814,0.0004544739,0.00008351985,0.00006853268,0.001631347,0.0007767605,0.05006054,0.039434317,0.45279783,0.45337275],"study_design_scores_gemma":[0.002058227,0.000067393834,0.0000033036774,0.000007958164,0.000006024915,0.0000066600037,0.00001521993,0.7730896,0.22011839,0.004436734,0.00008638953,0.000104117025],"about_ca_topic_score_codex":0.0000057399866,"about_ca_topic_score_gemma":0.0000024935446,"teacher_disagreement_score":0.7723128,"about_ca_system_score_codex":0.000027185164,"about_ca_system_score_gemma":0.00008360672,"threshold_uncertainty_score":0.35921904},"labels":[],"label_agreement":null},{"id":"W2131104747","doi":"10.1016/j.neuroimage.2011.09.012","title":"BEaST: Brain extraction based on nonlocal segmentation technique","year":2011,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":494,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Medical Research Council; University of California, Los Angeles; Canadian Institutes of Health Research; Genentech; U.S. Food and Drug Administration; National Institutes of Health; Eisai; University of California, San Diego; Alzheimer’s Research UK; National Institute on Aging; National Institute for Health and Care Research; Northern California Institute for Research and Education; F. Hoffmann-La Roche; Elan; Novartis; Medpace; GlaxoSmithKline; AstraZeneca; Eli Lilly and Company; Bristol-Myers Squibb; Alzheimer's Disease Neuroimaging Initiative; Pfizer; Synarc; Alzheimer's Association","keywords":"Prior probability; Computer science; Robustness (evolution); Segmentation; Sørensen–Dice coefficient; Artificial intelligence; Neuroimaging; Pattern recognition (psychology); Dice; Margin (machine learning); Image segmentation; Machine learning; Bayesian probability; Statistics; Mathematics; Medicine","score_opus":0.03659903092988445,"score_gpt":0.30044937623098267,"score_spread":0.2638503453010982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131104747","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022994923,0.000002041369,0.98613256,0.0006947103,0.00020086394,0.00048759254,0.0000024918547,0.0008002357,0.011449524],"genre_scores_gemma":[0.120552376,0.000002803337,0.8694258,0.009354917,0.000057756264,0.00022713505,0.000012432493,0.0000274363,0.00033935002],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984244,0.00020440682,0.0002521287,0.00045321853,0.00044225436,0.00022356835],"domain_scores_gemma":[0.9989965,0.00013670405,0.000118736396,0.00055908103,0.00006157021,0.0001274143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040161418,0.0001559354,0.00011019696,0.00021122073,0.00008915757,0.00008061613,0.0005098321,0.0000710023,0.00022187739],"category_scores_gemma":[0.0001478809,0.00015308683,0.000058783087,0.00033093357,0.00006774016,0.0007040771,0.00007043317,0.00025623364,0.00012616716],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036345387,0.0004545479,0.00015518494,0.000026554664,0.000003926364,0.0002002274,0.0002854253,0.000008483659,0.7625654,0.0012607179,0.014452448,0.22055079],"study_design_scores_gemma":[0.00030426262,0.00041295067,0.0022908903,0.000023285967,0.0000035394223,0.000025136227,0.00000938267,0.024122907,0.9717368,0.00048994797,0.00040399635,0.00017688623],"about_ca_topic_score_codex":0.000022264745,"about_ca_topic_score_gemma":0.000001317441,"teacher_disagreement_score":0.2203739,"about_ca_system_score_codex":0.00005415205,"about_ca_system_score_gemma":0.00004818242,"threshold_uncertainty_score":0.62427},"labels":[],"label_agreement":null},{"id":"W2131125043","doi":"10.1109/icip.2009.5414187","title":"Multi-sensor image registration based-on local phase coherence","year":2009,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Image registration; Computer vision; Coherence (philosophical gambling strategy); Computer science; Contrast (vision); Mutual information; Pattern recognition (psychology); Image (mathematics); Noise (video); Mathematics; Statistics","score_opus":0.03017192023053094,"score_gpt":0.3464183609985401,"score_spread":0.3162464407680092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131125043","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013687219,0.000003467806,0.98993874,0.0023396728,0.000052611515,0.00020945816,0.0000012020198,0.00061611674,0.0067018527],"genre_scores_gemma":[0.18473266,0.0000013309183,0.8082772,0.0062929792,0.000020076313,0.000009399663,0.0000057765087,0.0000030634865,0.0006575247],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885374,0.000061855615,0.00021858054,0.00032458425,0.00036768988,0.0001735404],"domain_scores_gemma":[0.99918157,0.000061406616,0.000071648,0.0004699744,0.00008423304,0.00013118598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023606676,0.00010837851,0.00009299595,0.00007287215,0.000057626246,0.00014089866,0.00043544552,0.000046443172,0.00018431139],"category_scores_gemma":[0.00010283588,0.00009144215,0.00003577097,0.00021098721,0.000079340236,0.0004634826,0.00001940073,0.00011117568,0.00013097125],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018220193,0.0008287312,0.0000069281186,0.0000070862293,0.0000018746489,0.000081219216,0.00007463802,0.000035125442,0.15360191,0.002903133,0.02209597,0.82034516],"study_design_scores_gemma":[0.00091378397,0.00054120977,0.00017908835,0.000017251727,0.0000015813596,0.000004392048,0.000013927308,0.56269616,0.43498677,0.00031121026,0.00020255095,0.00013208437],"about_ca_topic_score_codex":0.00001527391,"about_ca_topic_score_gemma":0.0000022402455,"teacher_disagreement_score":0.8202131,"about_ca_system_score_codex":0.000047080506,"about_ca_system_score_gemma":0.000053982447,"threshold_uncertainty_score":0.37289026},"labels":[],"label_agreement":null},{"id":"W2131230393","doi":"10.1007/s11548-006-0001-4","title":"3D Segmentation of Medical Images Using a Fast Multistage Hybrid Algorithm","year":2006,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials","funders":"National Natural Science Foundation of China","keywords":"Computer science; Robustness (evolution); Segmentation; Fast marching method; Computer vision; Algorithm; Artificial intelligence; Dilation (metric space); Image segmentation; Pattern recognition (psychology); Mathematics","score_opus":0.01641674329643051,"score_gpt":0.2992375017257806,"score_spread":0.2828207584293501,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131230393","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038695823,0.00030086038,0.9588378,0.0007175489,0.001352402,0.000043446977,0.0000072305115,0.000026705004,0.000018174016],"genre_scores_gemma":[0.18433118,0.00018114674,0.81421775,0.00060376606,0.0006269042,0.0000016534783,0.000017122598,0.000007678649,0.000012786275],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99771637,0.00027236587,0.0008844559,0.00017085376,0.0008162698,0.00013966738],"domain_scores_gemma":[0.9979752,0.0007040079,0.00068400404,0.00010498821,0.00043280434,0.000099022756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011365761,0.000118749915,0.00035017106,0.0004537984,0.0000405553,0.0000745457,0.00057468977,0.000086241445,0.000039137187],"category_scores_gemma":[0.00007957225,0.0001024541,0.00013075277,0.000109287605,0.00020711712,0.00048890396,0.00013865037,0.00020885069,6.704817e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027632823,0.00021039601,0.0057629044,0.000016786667,0.00021883794,0.0012811569,0.00009461029,0.00009381346,0.005519568,0.00026930484,0.0065222755,0.97998273],"study_design_scores_gemma":[0.0038385242,0.00037704298,0.100434504,0.00088473223,0.00009369118,0.04200871,0.00006111824,0.7487485,0.0988187,0.0028911312,0.0010853242,0.0007580351],"about_ca_topic_score_codex":0.00004285984,"about_ca_topic_score_gemma":7.373505e-7,"teacher_disagreement_score":0.9792247,"about_ca_system_score_codex":0.000056255663,"about_ca_system_score_gemma":0.00017194617,"threshold_uncertainty_score":0.4177957},"labels":[],"label_agreement":null},{"id":"W2131339155","doi":"10.1016/j.neuroimage.2005.03.036","title":"Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification","year":2005,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":840,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Partial volume; Artificial intelligence; Segmentation; Computer science; Pattern recognition (psychology); Volume (thermodynamics); Surface (topology); Computer vision; Algorithm; Mathematics; Physics; Geometry","score_opus":0.044100796343031747,"score_gpt":0.3487428204217074,"score_spread":0.30464202407867563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131339155","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9196406,0.00004827362,0.078923896,0.000837562,0.000085956795,0.000309256,0.0000012506133,0.00012366923,0.000029551835],"genre_scores_gemma":[0.9851299,0.000007617157,0.014664709,0.00014875403,0.000020073508,0.000009029712,0.0000010692493,0.0000050094354,0.000013820261],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986539,0.00048057034,0.0001886056,0.0002360145,0.00034548595,0.00009544777],"domain_scores_gemma":[0.9994744,0.000106891996,0.00010973979,0.0001836487,0.000075586664,0.000049734674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008091621,0.00007778392,0.000092534836,0.000044118056,0.00009254904,0.0001097212,0.00008941265,0.00004456341,0.000009018685],"category_scores_gemma":[0.00019229669,0.000058108515,0.000012655394,0.0001047082,0.00015357522,0.0005454147,0.000086083935,0.000107416345,0.0000016493358],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016951657,0.00005255685,0.020075243,0.00005038992,0.00000843097,0.0000022610875,0.000497665,0.00003677399,0.78921103,0.00011627992,0.0004511729,0.18948126],"study_design_scores_gemma":[0.00025644075,0.000043499895,0.21482104,0.000012857582,0.00002368317,0.000017718947,0.0000052917517,0.73586506,0.048842527,0.000020295738,0.000047869933,0.00004372634],"about_ca_topic_score_codex":0.000013961202,"about_ca_topic_score_gemma":0.0000034418479,"teacher_disagreement_score":0.7403685,"about_ca_system_score_codex":0.000021759924,"about_ca_system_score_gemma":0.000024005034,"threshold_uncertainty_score":0.23695964},"labels":[],"label_agreement":null},{"id":"W2131546334","doi":"10.1109/isbi.2010.5490162","title":"Wavelet-based variational deformable registration for ultrasound","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Image registration; Artificial intelligence; Computer vision; Computer science; Wavelet; Discrete wavelet transform; Pyramid (geometry); Energy (signal processing); Wavelet transform; Multiresolution analysis; Pattern recognition (psychology); Image (mathematics); Mathematics","score_opus":0.01225563990767804,"score_gpt":0.2748404721255818,"score_spread":0.2625848322179038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131546334","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030649008,7.052299e-7,0.99309325,0.0011346207,0.00022204398,0.00022633531,0.000002671952,0.00028194409,0.0047319597],"genre_scores_gemma":[0.055428103,3.2259692e-7,0.94181436,0.0017223675,0.00006354809,0.000071586874,0.00003391238,0.0000036209553,0.000862201],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992992,0.000011530431,0.0001668722,0.00017091862,0.00022384559,0.00012760726],"domain_scores_gemma":[0.99924463,0.00023391008,0.000066658045,0.0002601025,0.00012883506,0.00006586823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041088997,0.00005877155,0.000052385814,0.0000472339,0.00008852227,0.00013916129,0.00031672814,0.000050150295,0.00021341478],"category_scores_gemma":[0.00029159154,0.000050252835,0.00003172771,0.000111042675,0.000030073172,0.0005118421,0.000014884151,0.00008091989,0.000019895104],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000073891138,0.0001148951,0.00008555919,0.000023355122,0.000008076056,9.794227e-7,0.00008418872,0.000011948474,0.23365594,0.64719474,0.03998309,0.078829795],"study_design_scores_gemma":[0.00064111216,0.00010291804,0.0011670518,0.0000042627335,0.0000039506326,0.000011922454,0.0000059982267,0.34197816,0.61621094,0.033162218,0.006518376,0.00019312896],"about_ca_topic_score_codex":0.000018681212,"about_ca_topic_score_gemma":0.000021250638,"teacher_disagreement_score":0.61403257,"about_ca_system_score_codex":0.000016113545,"about_ca_system_score_gemma":0.00012873935,"threshold_uncertainty_score":0.23367421},"labels":[],"label_agreement":null},{"id":"W2131621605","doi":"10.1109/iembs.2001.1017329","title":"Dynamic edge tracing for 2D image segmentation","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Computer vision; Segmentation; Image segmentation; Computer science; Pixel; Scale-space segmentation; Tracing; Tracking (education); Segmentation-based object categorization; Region growing; Enhanced Data Rates for GSM Evolution; Morphological gradient; Kalman filter; Pattern recognition (psychology); Cluster analysis","score_opus":0.013411435124087923,"score_gpt":0.31688107446572816,"score_spread":0.30346963934164023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131621605","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007433227,0.000019140401,0.9940006,0.0018281657,0.00008906907,0.0003526067,0.0000015679478,0.00051036826,0.0024551696],"genre_scores_gemma":[0.033155166,0.0000071780937,0.9635977,0.0016503885,0.00003525085,0.00008253239,0.000009524631,0.0000069199345,0.0014553014],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992017,0.000021541184,0.00020152146,0.00022826507,0.00017616662,0.00017079852],"domain_scores_gemma":[0.99952203,0.00007725909,0.00005458411,0.00021268427,0.00006229607,0.00007117385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024860312,0.000077763674,0.00007638011,0.000075504,0.0000707377,0.00012413567,0.00032647827,0.000029907125,0.00012964461],"category_scores_gemma":[0.000041542633,0.00007032559,0.000042820233,0.00012744962,0.00002572273,0.0010520525,0.000051118008,0.00004857249,0.00007526021],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018427145,0.000044584023,0.0000050570675,0.00001653829,0.000004756376,9.2635617e-7,0.00041331793,0.000006840431,0.12782422,0.0020403222,0.008393808,0.8612478],"study_design_scores_gemma":[0.00047556456,0.00007023014,0.00012326217,0.000011699312,0.0000049707483,0.000006574694,0.00007774478,0.28262347,0.71397626,0.0016994115,0.00077021983,0.00016056237],"about_ca_topic_score_codex":0.000004312479,"about_ca_topic_score_gemma":0.000008493695,"teacher_disagreement_score":0.8610872,"about_ca_system_score_codex":0.00007727728,"about_ca_system_score_gemma":0.000025262983,"threshold_uncertainty_score":0.28677943},"labels":[],"label_agreement":null},{"id":"W2132032064","doi":"10.1109/iccv.1995.466823","title":"Annular symmetry operators: a method for locating and describing objects","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Computer vision; Segmentation; Enhanced Data Rates for GSM Evolution; Object (grammar); Graph; Set (abstract data type); Image segmentation; Pattern recognition (psychology); Theoretical computer science","score_opus":0.053029542396554635,"score_gpt":0.3094251632080964,"score_spread":0.25639562081154177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132032064","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00036752637,0.0002073718,0.9970197,0.0004182001,0.000048570928,0.00023765626,4.1746605e-7,0.00030723886,0.0013933214],"genre_scores_gemma":[0.038403925,0.000012238343,0.9589156,0.0023541378,0.000028635443,0.00003994068,4.3962848e-7,0.000006526727,0.0002385409],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917865,0.000059733415,0.00016327623,0.000276796,0.00014447753,0.00017707446],"domain_scores_gemma":[0.99936515,0.00021120351,0.00003595212,0.0002136221,0.000071771094,0.00010228549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004931742,0.000080561746,0.000104116065,0.00007822603,0.000099395205,0.00018543411,0.0002605417,0.000037208032,0.000039184266],"category_scores_gemma":[0.00032278302,0.00006897833,0.000022079264,0.00022954139,0.000022499566,0.00049985584,0.00012674172,0.00006060578,0.000006274251],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.223246e-7,0.000031854917,0.00013318913,0.000038171656,0.000010157094,0.000005225147,0.0013711231,0.0000012927057,0.013151056,0.011566799,0.0052156774,0.9684749],"study_design_scores_gemma":[0.00031183948,0.000098855395,0.000023821252,0.000049975082,0.0000052711634,0.000023752942,0.00025574293,0.58555365,0.41190943,0.0013317416,0.00025700152,0.00017889727],"about_ca_topic_score_codex":0.000022457396,"about_ca_topic_score_gemma":0.0000013897475,"teacher_disagreement_score":0.96829605,"about_ca_system_score_codex":0.000014526729,"about_ca_system_score_gemma":0.000009505889,"threshold_uncertainty_score":0.28128546},"labels":[],"label_agreement":null},{"id":"W2132240568","doi":"10.1109/wacv.2009.5403049","title":"An interactive graph cut method for brain tumor segmentation","year":2009,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Cut; Graph; Image segmentation; Regularization (linguistics); Computer vision; Medical imaging; Pattern recognition (psychology); Theoretical computer science","score_opus":0.018178482053345282,"score_gpt":0.38481329147646104,"score_spread":0.36663480942311577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132240568","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002798847,0.0000032791984,0.99494034,0.002864719,0.00009147255,0.00044396197,0.000001829953,0.0004982511,0.00087627384],"genre_scores_gemma":[0.009691788,9.052117e-7,0.9724228,0.017496439,0.00003556777,0.00005912223,0.000012615203,0.0000045028223,0.0002762774],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990057,0.00013060351,0.00019902604,0.00031065886,0.00019426075,0.00015976893],"domain_scores_gemma":[0.9991966,0.00023522186,0.00008452463,0.00027557646,0.00009772191,0.000110366505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005102284,0.0000935597,0.0001038403,0.00013029418,0.00006215974,0.00014390361,0.00044431153,0.000023793848,0.000066529064],"category_scores_gemma":[0.00010239774,0.0000809377,0.000049691236,0.00021856013,0.000015276632,0.001276044,0.000024013008,0.00006546346,0.00000894926],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012887134,0.00012809636,0.00000693768,0.000004547309,0.000007542575,0.000003615941,0.0010309274,0.0000048813513,0.17091921,0.016328434,0.016900547,0.79465234],"study_design_scores_gemma":[0.00038960125,0.0006214903,0.00023917617,0.000008762724,0.000004019998,0.000010233766,0.00021040418,0.060575385,0.8955295,0.04206672,0.00020007284,0.00014464906],"about_ca_topic_score_codex":0.000021829279,"about_ca_topic_score_gemma":0.00000570736,"teacher_disagreement_score":0.79450774,"about_ca_system_score_codex":0.000037049864,"about_ca_system_score_gemma":0.00002694762,"threshold_uncertainty_score":0.33005437},"labels":[],"label_agreement":null},{"id":"W2132322720","doi":"10.1016/s1361-8415(03)00037-9","title":"A fully automatic and robust brain MRI tissue classification method","year":2003,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":336,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Parametric statistics; Classifier (UML); Data set; Set (abstract data type); Magnetic resonance imaging; Mathematics; Statistics; Medicine","score_opus":0.01855722862763469,"score_gpt":0.3401510110432345,"score_spread":0.32159378241559977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132322720","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023915927,0.00012025692,0.9875069,0.010014971,0.000043268767,0.00015479741,0.0000011833697,0.0003617809,0.0015576573],"genre_scores_gemma":[0.0063737365,0.00005887064,0.9892604,0.00352294,0.000028514205,0.000051765328,0.000014087315,0.000010519718,0.0006792076],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966839,0.0007893838,0.0005276833,0.0005960699,0.0011003952,0.00030257434],"domain_scores_gemma":[0.997906,0.00061509863,0.00016603236,0.0006806951,0.00013165695,0.000500511],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002850471,0.00017492715,0.00039956707,0.00044639647,0.00012046903,0.00024593654,0.0006474261,0.00013873575,0.0019377952],"category_scores_gemma":[0.0032053336,0.0001488885,0.00011524649,0.0021754778,0.00019323509,0.0005202226,0.00013292755,0.00024836077,0.000071623675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001357883,0.00021034511,0.00046869545,0.00006572242,0.0005132916,0.00017794527,0.00070455467,0.000013143892,0.0090042865,0.00701326,0.025059042,0.95676833],"study_design_scores_gemma":[0.0004562471,0.000075076285,0.0024188822,0.00002923261,0.0004415053,0.00006009806,0.00013618096,0.96878934,0.021883013,0.002372942,0.0029836632,0.00035383663],"about_ca_topic_score_codex":0.00005179056,"about_ca_topic_score_gemma":0.000021697044,"teacher_disagreement_score":0.96877617,"about_ca_system_score_codex":0.000048660106,"about_ca_system_score_gemma":0.00011974026,"threshold_uncertainty_score":0.99897456},"labels":[],"label_agreement":null},{"id":"W2132525931","doi":"10.1109/isbi.2009.5193049","title":"Automatic contrast enhancement of white matter lesions in FLAIR MRI","year":2009,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sunnybrook Health Science Centre; University of Toronto","funders":"","keywords":"Fluid-attenuated inversion recovery; White matter; Contrast (vision); Hyperintensity; Contrast enhancement; Image contrast; Robustness (evolution); Artificial intelligence; Magnetic resonance imaging; Pattern recognition (psychology); Computer science; Nuclear medicine; Medicine; Radiology; Chemistry","score_opus":0.01100249492163507,"score_gpt":0.28127840804284737,"score_spread":0.2702759131212123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132525931","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013176558,0.000010086864,0.9758018,0.0039062398,0.00003555254,0.00018390654,2.6857356e-7,0.00011746484,0.0067680697],"genre_scores_gemma":[0.5716137,0.0000056600525,0.42436168,0.0033775312,0.0000046884193,0.000010822722,8.477378e-7,0.0000016947414,0.0006233138],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991335,0.000040535204,0.00030833302,0.00015440429,0.0002225499,0.00014065951],"domain_scores_gemma":[0.99955195,0.000033384622,0.000061890765,0.0002696308,0.00003009742,0.000053022333],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00019219321,0.00006542033,0.00012439684,0.000103002436,0.000023436329,0.00001958816,0.00034863365,0.000025089077,0.001377727],"category_scores_gemma":[0.000012991529,0.000053392454,0.000024028785,0.0002108338,0.000026439548,0.00024165357,0.000072208466,0.000054899403,0.00008133917],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004409038,0.00092699926,0.0055244863,0.00007462577,0.000014040512,0.000031019088,0.0041096136,0.00000942003,0.083916605,0.010983188,0.08527802,0.80912757],"study_design_scores_gemma":[0.00075495715,0.00028982278,0.113413386,0.00019519139,0.0000043579075,0.0000064254446,0.00007817589,0.057157718,0.8220892,0.005610679,0.00014203426,0.00025805368],"about_ca_topic_score_codex":0.000010843816,"about_ca_topic_score_gemma":0.000004651648,"teacher_disagreement_score":0.80886954,"about_ca_system_score_codex":0.00002664104,"about_ca_system_score_gemma":0.000024916453,"threshold_uncertainty_score":0.99953514},"labels":[],"label_agreement":null},{"id":"W2132855892","doi":"10.1109/tip.2009.2032940","title":"Effective Level Set Image Segmentation With a Kernel Induced Data Term","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Electric (Canada); Institut National de la Recherche Scientifique","funders":"","keywords":"Piecewise; Image segmentation; Mathematics; Kernel (algebra); Scale-space segmentation; Segmentation; Artificial intelligence; Algorithm; Pattern recognition (psychology); Computer science; Mathematical analysis","score_opus":0.05165852706854316,"score_gpt":0.3424181079295539,"score_spread":0.2907595808610107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132855892","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032483276,0.000017084403,0.9944641,0.0005298187,0.00009092405,0.00066929095,0.000035571757,0.0006072337,0.00033763726],"genre_scores_gemma":[0.5011478,0.0000059498843,0.4978859,0.0007552613,0.000030403731,0.0000749066,0.00001705648,0.000017960998,0.00006474296],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997872,0.000119291886,0.00031349645,0.00078301947,0.0005788677,0.00033335268],"domain_scores_gemma":[0.99854016,0.000074005206,0.00017730583,0.0008526811,0.0001993021,0.00015654786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036954315,0.00027223927,0.00021023455,0.00025732812,0.0003568997,0.00061823125,0.0010009811,0.000078241595,0.000022458567],"category_scores_gemma":[0.000019167894,0.00023881122,0.000039529004,0.0006835456,0.00010559024,0.00353589,0.000009460673,0.00038953524,0.000033769786],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034152814,0.00018083258,0.0000020182936,0.000038711358,0.000014845097,0.000024288798,0.00093905436,0.000011556214,0.284072,0.0000030947942,0.00015414745,0.7145253],"study_design_scores_gemma":[0.0010797658,0.00050557335,0.00054801133,0.00023675336,0.00004493989,0.0000745335,0.00016175005,0.023679182,0.97306484,0.0002216658,0.0000048019874,0.00037816685],"about_ca_topic_score_codex":0.00001559089,"about_ca_topic_score_gemma":0.000009698848,"teacher_disagreement_score":0.71414715,"about_ca_system_score_codex":0.000118756325,"about_ca_system_score_gemma":0.00017693768,"threshold_uncertainty_score":0.97384393},"labels":[],"label_agreement":null},{"id":"W2132927595","doi":"10.1109/iembs.2008.4650433","title":"Fast feature based multi slice to volume registration using phase congruency","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Robustness (evolution); Image registration; Computer science; Artificial intelligence; Computer vision; Mutual information; Iterative closest point; Phase congruency; Pattern recognition (psychology); Volume (thermodynamics); Feature extraction; Image (mathematics); Point cloud","score_opus":0.05692372190511775,"score_gpt":0.34718266132533554,"score_spread":0.2902589394202178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132927595","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004149231,0.000007983508,0.99250805,0.0015561266,0.000118225,0.0002860905,0.0000024072974,0.0004551323,0.00091673346],"genre_scores_gemma":[0.07404503,0.0000014298466,0.91868645,0.0041146665,0.000038470323,0.000013367797,0.0000072635316,0.000006671556,0.0030866712],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988994,0.000055855213,0.00018817655,0.0003244128,0.00034503255,0.00018710323],"domain_scores_gemma":[0.9991312,0.00002777272,0.0000732279,0.00041387923,0.00015813054,0.00019579835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001656423,0.00010855822,0.000105980456,0.000102413804,0.00014501982,0.000086478,0.0004374285,0.00005981237,0.00013893844],"category_scores_gemma":[0.00013301238,0.00010042413,0.000034924913,0.00040727423,0.00004971598,0.0005731053,0.00005672971,0.00010628778,0.00005798619],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003219753,0.0010178654,0.0010859325,0.000047582686,0.000019938148,0.0003441527,0.0021839663,0.00032881577,0.5905938,0.001540552,0.24139482,0.16141035],"study_design_scores_gemma":[0.0012441087,0.00020894152,0.00062237727,0.000029540914,0.0000047724575,0.00005584066,0.000034286546,0.6937096,0.30121437,0.00002761822,0.002553439,0.00029506694],"about_ca_topic_score_codex":0.0001804422,"about_ca_topic_score_gemma":0.000018746185,"teacher_disagreement_score":0.69338083,"about_ca_system_score_codex":0.000064487474,"about_ca_system_score_gemma":0.00013653476,"threshold_uncertainty_score":0.4095177},"labels":[],"label_agreement":null},{"id":"W2133127651","doi":"10.1109/crv.2010.46","title":"A Bayesian Information Flow Approach to Image Segmentation","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Maximum a posteriori estimation; Bayesian probability; Artificial intelligence; Segmentation; Image segmentation; Computer science; Contrast (vision); Scale-space segmentation; Smoothness; Pattern recognition (psychology); Noise (video); Computer vision; Segmentation-based object categorization; Pixel; A priori and a posteriori; Bayes estimator; Flow (mathematics); Image (mathematics); Mathematics; Statistics; Maximum likelihood","score_opus":0.007132757700250087,"score_gpt":0.25707292039067786,"score_spread":0.24994016269042776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133127651","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020571626,3.0220818e-7,0.9568417,0.0006531023,0.0001717781,0.00034387567,0.0000011723954,0.00051837164,0.041263964],"genre_scores_gemma":[0.014163931,5.081837e-7,0.98191905,0.003603976,0.000031193165,0.00008358054,0.000021298156,0.0000033792403,0.00017308103],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991268,0.000022909513,0.00021767948,0.00015566933,0.00032467072,0.00015225483],"domain_scores_gemma":[0.99932265,0.000019864363,0.000048349233,0.00034593616,0.00009748642,0.00016571615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002949681,0.000081839346,0.000067365254,0.00015649515,0.00006153491,0.00031894693,0.000474469,0.00004424496,0.0001474477],"category_scores_gemma":[0.00008816031,0.00007090231,0.000024190775,0.00033571792,0.000022983057,0.0027219185,0.000121960875,0.00012875457,0.00031856706],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000223796,0.00006580109,0.000022430328,0.0000175364,0.000004299493,6.7361105e-7,0.0021035688,0.000011740624,0.068119526,0.010612714,0.030208427,0.888831],"study_design_scores_gemma":[0.00043098204,0.00007725818,0.0005354354,0.000005643552,0.0000036453894,0.000019743453,0.0002115963,0.42982846,0.5624513,0.0019181535,0.0041981013,0.00031966437],"about_ca_topic_score_codex":0.000021108888,"about_ca_topic_score_gemma":0.0000030676142,"teacher_disagreement_score":0.88851136,"about_ca_system_score_codex":0.000019563224,"about_ca_system_score_gemma":0.00003291213,"threshold_uncertainty_score":0.40946412},"labels":[],"label_agreement":null},{"id":"W2133358550","doi":"10.1007/s10278-009-9234-4","title":"Interactive Modeling and Evaluation of Tumor Growth","year":2009,"lang":"en","type":"article","venue":"Journal of Digital Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; McMaster University","funders":"","keywords":"Initialization; Computer science; Segmentation; Parametric statistics; Simple (philosophy); Artificial intelligence; Convergence (economics); Machine learning; Mathematics; Statistics","score_opus":0.02476807327143979,"score_gpt":0.32825749946584737,"score_spread":0.30348942619440755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133358550","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10543107,0.00014733724,0.89249134,0.0006380083,0.0000534278,0.00004706176,3.6991455e-7,0.00001408606,0.0011772999],"genre_scores_gemma":[0.94834304,0.000005611748,0.05138037,0.00024094856,0.000025837428,2.8406004e-7,1.985667e-7,0.000002043768,0.0000016660183],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989856,0.0000317829,0.00032997192,0.00007761805,0.0005027812,0.000072269286],"domain_scores_gemma":[0.9988556,0.00004560868,0.00028814873,0.00007027736,0.00068017474,0.000060197755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005937099,0.000053841046,0.00011331299,0.00015662238,0.000016079197,0.00018402628,0.00019750893,0.000006364872,0.0000023628484],"category_scores_gemma":[0.000388106,0.00004496985,0.000038223654,0.00010985082,0.000021986061,0.0034609481,0.00004172988,0.000097035874,4.0121068e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012446054,0.00010128215,0.0007124817,0.000009685108,0.00001684214,0.00002562489,0.0011782119,0.00018656843,0.01189058,0.00052503124,0.0001772196,0.98516405],"study_design_scores_gemma":[0.0005691659,0.00014381306,0.00072128273,0.00018989095,0.000023443403,0.00033270178,0.00020875088,0.8955624,0.04867565,0.05347989,0.0000025769152,0.00009045088],"about_ca_topic_score_codex":8.991534e-7,"about_ca_topic_score_gemma":2.502702e-8,"teacher_disagreement_score":0.98507357,"about_ca_system_score_codex":0.00004157182,"about_ca_system_score_gemma":0.00006157626,"threshold_uncertainty_score":0.25091037},"labels":[],"label_agreement":null},{"id":"W2133460059","doi":"10.1109/iccv.2011.6126484","title":"Convex multi-region probabilistic segmentation with shape prior in the isometric log-ratio transformation space","year":2011,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Probabilistic logic; Segmentation; Image segmentation; Prior probability; Artificial intelligence; Transformation (genetics); Computer science; Function (biology); Pattern recognition (psychology); Mathematics; Regular polygon; Domain (mathematical analysis); Scale-space segmentation; Energy minimization; Bayesian probability; Mathematical analysis; Geometry; Physics","score_opus":0.056261651776078475,"score_gpt":0.27757203091914256,"score_spread":0.2213103791430641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133460059","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009295064,0.000011854558,0.9869664,0.0008143406,0.000038832313,0.0011483661,3.4485984e-7,0.00020116987,0.0015236676],"genre_scores_gemma":[0.6662451,0.0000127136955,0.33260787,0.0008794638,0.0000076256574,0.00015941852,0.0000056240187,0.000005595504,0.00007661659],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871176,0.00016095032,0.000296602,0.00024911732,0.00039685005,0.0001846928],"domain_scores_gemma":[0.9993474,0.0001045868,0.000109682194,0.00029439983,0.00009140435,0.000052519783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004794684,0.00012455795,0.00011225982,0.00028397192,0.00007334646,0.00010631974,0.00051218626,0.000049243165,0.00008077723],"category_scores_gemma":[0.000064151354,0.00007553868,0.000023448398,0.0011731512,0.00007963519,0.0012300847,0.000024069172,0.00012979216,0.000030878364],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011323335,0.001988759,0.008118436,0.00033815502,0.0000461382,0.00009276196,0.1506714,0.00006647536,0.005078144,0.107029475,0.0013293284,0.7251277],"study_design_scores_gemma":[0.0060881125,0.0022518025,0.11619714,0.0001838756,0.00005924463,0.00019236805,0.0073285066,0.6601937,0.20091018,0.0053661,0.00010903981,0.0011199616],"about_ca_topic_score_codex":0.00010619803,"about_ca_topic_score_gemma":0.0000770006,"teacher_disagreement_score":0.7240077,"about_ca_system_score_codex":0.000066246954,"about_ca_system_score_gemma":0.00005700911,"threshold_uncertainty_score":0.30803782},"labels":[],"label_agreement":null},{"id":"W2133594842","doi":"10.1109/tbme.2011.2168394","title":"Semiautomatic Detection of Scoliotic Rib Borders From Posteroanterior Chest Radiographs","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; Polytechnique Montréal","funders":"Centre hospitalier universitaire Sainte-Justine","keywords":"Rib cage; Radiography; Scoliosis; Computer science; Artificial intelligence; Medicine; Computer vision; Radiology; Nuclear medicine; Anatomy; Surgery","score_opus":0.012071048823126975,"score_gpt":0.2256973314783492,"score_spread":0.2136262826552222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133594842","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021663627,0.000033053126,0.9766736,0.000043661537,0.00075198786,0.00018665213,0.000010033147,0.0006162076,0.000021184014],"genre_scores_gemma":[0.84979725,0.00003471265,0.15001702,0.00007566148,0.000016588823,0.00003632175,0.0000013871033,0.000015499136,0.00000557622],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986292,0.00003415711,0.00041279235,0.00028564825,0.00040198205,0.00023622565],"domain_scores_gemma":[0.99919564,0.00009813636,0.00007980247,0.0003689657,0.000037047248,0.00022040593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015761536,0.00017071527,0.00022198561,0.00054017134,0.00004709858,0.000026180238,0.00045203473,0.00012695546,0.00008732017],"category_scores_gemma":[0.000018036802,0.00016532923,0.000119540106,0.00082042994,0.00011356006,0.00030968574,0.0000043056743,0.00024950606,0.000016187869],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019662206,0.00041155683,0.000006474409,0.0001368908,0.00014180758,0.000029430586,0.0016404544,0.00016999728,0.6429798,0.00002006893,0.000023613822,0.3544202],"study_design_scores_gemma":[0.00043159665,0.00028163727,0.0007437645,0.00020765554,0.000028212178,0.000015535785,0.00002855204,0.12802702,0.86995965,0.000050792143,0.000032591965,0.00019298769],"about_ca_topic_score_codex":0.00014684658,"about_ca_topic_score_gemma":0.0000037919588,"teacher_disagreement_score":0.8281336,"about_ca_system_score_codex":0.000053967542,"about_ca_system_score_gemma":0.000032438642,"threshold_uncertainty_score":0.674193},"labels":[],"label_agreement":null},{"id":"W2133732451","doi":"10.1109/icip.2004.1421861","title":"Image segmentation as regularized clustering: a fully global curve evolution method","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Image segmentation; Cluster analysis; Partition (number theory); Segmentation; Segmentation-based object categorization; Energy functional; Mathematics; Scale-space segmentation; Artificial intelligence; Image (mathematics); Minification; Computer science; Algorithm; Pattern recognition (psychology); Mathematical optimization; Mathematical analysis; Combinatorics","score_opus":0.013154895721781423,"score_gpt":0.33655869417953876,"score_spread":0.32340379845775735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133732451","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021552278,0.00002389834,0.98214203,0.0026599718,0.0001379552,0.00036201515,0.000002006636,0.00092027243,0.01353635],"genre_scores_gemma":[0.0070324214,0.000005465747,0.98988724,0.0018803723,0.0000851766,0.000049470425,0.000009918531,0.00000726793,0.0010426692],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981174,0.00024788437,0.00036687666,0.00044291993,0.0005462906,0.00027863405],"domain_scores_gemma":[0.99904156,0.00005778995,0.00013256594,0.000454595,0.00014611571,0.00016737802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007453172,0.00015599595,0.0001554159,0.000082463856,0.00009747932,0.00023387017,0.00058490847,0.000078989564,0.00039271513],"category_scores_gemma":[0.0001361084,0.00014548229,0.00007320834,0.00046039588,0.000050146697,0.0015487043,0.00027557474,0.00009896461,0.0003512483],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003445016,0.00018825554,0.00009196559,0.000029083692,0.000036291083,0.000023486846,0.0004833064,0.000044374407,0.19838405,0.03682342,0.012810293,0.751051],"study_design_scores_gemma":[0.0019302036,0.00026963913,0.001162316,0.000034385965,0.000024424455,0.00026708108,0.00017665635,0.7065028,0.26637572,0.021597436,0.0011145384,0.0005448379],"about_ca_topic_score_codex":0.00016011753,"about_ca_topic_score_gemma":0.000030485007,"teacher_disagreement_score":0.75050616,"about_ca_system_score_codex":0.00043519598,"about_ca_system_score_gemma":0.00010424911,"threshold_uncertainty_score":0.5932596},"labels":[],"label_agreement":null},{"id":"W2134012543","doi":"10.1109/tmi.2014.2375207","title":"Three-Dimensional Nonrigid MR-TRUS Registration Using Dual Optimization","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Canadian Institutes of Health Research","keywords":"Image registration; Similarity (geometry); Fiducial marker; Artificial intelligence; Magnetic resonance imaging; Ultrasound; Prostate biopsy; Computer science; Feature (linguistics); Prostate; Computer vision; Mathematics; Pattern recognition (psychology); Nuclear medicine; Medicine; Image (mathematics); Radiology","score_opus":0.018931771642169408,"score_gpt":0.28929809744678703,"score_spread":0.2703663258046176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134012543","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005258613,0.000015445692,0.99521244,0.0023019721,0.00091265,0.00018887602,0.0000021086464,0.00055391126,0.00028674956],"genre_scores_gemma":[0.25902492,0.000011017537,0.7369688,0.0036835892,0.00019062824,0.000030520794,0.000007811221,0.0000277157,0.000054998694],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971139,0.00015396254,0.00048677213,0.00051883305,0.0013920746,0.00033447385],"domain_scores_gemma":[0.9986163,0.00026065813,0.00014344223,0.0004688137,0.00014572404,0.00036506852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008981904,0.00020442542,0.0001972991,0.00023500518,0.00032375616,0.00016817787,0.00044055088,0.00011307756,0.00049068936],"category_scores_gemma":[0.00012217491,0.00019661491,0.00009315093,0.000431123,0.00020581514,0.00086978124,0.000007496692,0.0004587799,0.000042809992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026430127,0.0004570546,0.000026177928,0.000039727154,0.000041246447,0.000094189105,0.00018835098,0.15314792,0.009822299,0.001011623,0.0022033746,0.8329416],"study_design_scores_gemma":[0.00046311386,0.000042444422,0.000013291991,0.00009978416,0.00001599911,0.00012419274,0.000006934571,0.9731548,0.025349256,0.00044527225,0.00008344591,0.00020145542],"about_ca_topic_score_codex":0.00011282489,"about_ca_topic_score_gemma":0.000022623577,"teacher_disagreement_score":0.8327401,"about_ca_system_score_codex":0.000111134956,"about_ca_system_score_gemma":0.0001943083,"threshold_uncertainty_score":0.80177236},"labels":[],"label_agreement":null},{"id":"W2134366841","doi":"10.1007/978-3-540-85988-8_89","title":"Spherical Demons: Fast Surface Registration","year":2008,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Ellison Medical Foundation; National Institute of Biomedical Imaging and Bioengineering; National Center for Research Resources; National Institute of Neurological Disorders and Stroke; National Institutes of Health; National Science Foundation","keywords":"Computer science; Artificial intelligence; Image registration; Computer vision; Surface (topology); Interpolation (computer graphics); Spline (mechanical); Convolution (computer science); Signed distance function; Algorithm; Mathematics; Geometry; Image (mathematics); Artificial neural network; Physics","score_opus":0.021221607152094157,"score_gpt":0.2804948580389244,"score_spread":0.25927325088683023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134366841","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013597061,0.000060303224,0.98411715,0.0012303743,0.0004147127,0.00018184145,3.085585e-7,0.00033139074,0.00006685995],"genre_scores_gemma":[0.43290177,0.000011113159,0.56586146,0.0011646239,0.000051218718,0.0000032606258,5.210457e-7,0.000003404761,0.0000026087064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976074,0.000086946864,0.0003232678,0.0007207307,0.0008363591,0.00042525784],"domain_scores_gemma":[0.9986628,0.0002519566,0.000104884995,0.00069902156,0.0001149336,0.00016636254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063959876,0.00016190764,0.00017160166,0.00012348633,0.00025349914,0.0002020415,0.0017263239,0.00007300635,0.000014969489],"category_scores_gemma":[0.0002523146,0.00014392253,0.00003914945,0.0020199227,0.00069603615,0.0010833354,0.00039853234,0.00027109124,0.000025451613],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043234945,0.00013800949,0.004641663,0.000011364897,0.0000028474476,0.00019301419,0.002078765,0.018298673,0.026751665,0.00038455523,0.00030705263,0.9471881],"study_design_scores_gemma":[0.00023370313,0.00014625656,0.004531471,0.000035399007,9.555179e-7,0.00021614342,7.36616e-7,0.76981723,0.21910912,0.0055812793,0.00004502692,0.0002827057],"about_ca_topic_score_codex":0.000055830897,"about_ca_topic_score_gemma":0.0000156477,"teacher_disagreement_score":0.9469054,"about_ca_system_score_codex":0.00013254186,"about_ca_system_score_gemma":0.00031135074,"threshold_uncertainty_score":0.58689904},"labels":[],"label_agreement":null},{"id":"W2134436132","doi":"10.1109/cvpr.2008.4587464","title":"A Scalable graph-cut algorithm for N-D grids","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":108,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Maxima and minima; Scalability; Speedup; Grid; Algorithm; Computer graphics; Graph; Graphics; Theoretical computer science; Parallel computing; Artificial intelligence; Computer graphics (images); Mathematics","score_opus":0.02421634503250038,"score_gpt":0.2826947544486435,"score_spread":0.25847840941614314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134436132","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008643685,0.000024002227,0.99453384,0.0003970445,0.00014233385,0.00024112016,0.000001793188,0.00060265115,0.0039707874],"genre_scores_gemma":[0.00066974235,0.000032433436,0.99262863,0.001995737,0.000053340522,0.00009052484,0.0000032927287,0.00000562088,0.0045206877],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99915797,0.000017974966,0.00015708896,0.00023706212,0.00022835305,0.0002015693],"domain_scores_gemma":[0.9994081,0.00007027456,0.000032031014,0.00028681956,0.00009084915,0.000111924135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016614681,0.00007516267,0.000095585245,0.00008136278,0.00011988623,0.000041643303,0.0004825496,0.000039748233,0.00010336908],"category_scores_gemma":[0.00003274052,0.000062371844,0.000057595276,0.00027009912,0.00007145637,0.00041351691,0.00008906462,0.00005425503,0.000051991068],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.675071e-7,0.00007797249,0.000056010966,0.0000070303176,0.000009894988,0.000016256477,0.00016352095,4.1088603e-7,0.0011109232,0.0060165473,0.2190617,0.77347887],"study_design_scores_gemma":[0.0012350383,0.0003769653,0.00042637408,0.000019787434,0.0000070994515,0.0001679095,0.000030629744,0.18178482,0.7721582,0.024564678,0.018734472,0.000494049],"about_ca_topic_score_codex":0.000026679569,"about_ca_topic_score_gemma":0.0000012582382,"teacher_disagreement_score":0.7729848,"about_ca_system_score_codex":0.00001610724,"about_ca_system_score_gemma":0.000046424535,"threshold_uncertainty_score":0.254345},"labels":[],"label_agreement":null},{"id":"W2134531380","doi":"10.1109/igarss.2007.4422809","title":"Combination of feature-based and area-based image registration technique for high resolution remote sensing image","year":2007,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Panchromatic film; Artificial intelligence; Computer vision; Image registration; Computer science; Multispectral image; Feature detection (computer vision); Homography; Feature (linguistics); Feature extraction; Image resolution; Matching (statistics); Image (mathematics); Template matching; Pattern recognition (psychology); Image processing; Mathematics","score_opus":0.01512242781178954,"score_gpt":0.28765238632404855,"score_spread":0.27252995851225903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134531380","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005169091,0.000008791932,0.9956691,0.0018930791,0.00005438458,0.00093857036,0.000003847233,0.0003277178,0.0005876006],"genre_scores_gemma":[0.14496233,0.0000015158382,0.8544663,0.00042934585,0.0000144031455,0.0000043246914,0.000043915912,0.000009184212,0.00006867471],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99870205,0.00007038152,0.00035807755,0.00032920713,0.00034012477,0.00020018472],"domain_scores_gemma":[0.9985292,0.0003077176,0.00027762976,0.00037119014,0.00043525067,0.00007903376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016481343,0.00013098137,0.00015897013,0.00025260582,0.000096416305,0.00008606016,0.00018452825,0.0001230618,0.000004578219],"category_scores_gemma":[0.00036898255,0.00012666802,0.000044324934,0.00032669326,0.000151875,0.00044755507,0.0000321384,0.000109293775,5.027248e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004585352,0.00004261375,0.0000048596576,0.000092167415,0.000003006045,0.000005948568,0.000024828823,0.0000018012734,0.93930906,0.0043724775,0.0020471627,0.054050233],"study_design_scores_gemma":[0.0006552783,0.00020803597,0.00030139508,0.00007426273,0.000006781982,0.000004824732,0.000008404144,0.11471168,0.8761165,0.007765089,0.00003014197,0.00011761258],"about_ca_topic_score_codex":0.000119908254,"about_ca_topic_score_gemma":0.000025365236,"teacher_disagreement_score":0.14444542,"about_ca_system_score_codex":0.000089109584,"about_ca_system_score_gemma":0.00008238799,"threshold_uncertainty_score":0.5165372},"labels":[],"label_agreement":null},{"id":"W2134819732","doi":"10.1109/ccece.1998.685586","title":"The starbyte algorithm for registration of noisy breast images","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Noise (video); Computer science; Artificial intelligence; Image registration; Computer vision; Transformation (genetics); Algorithm; Control point; Pattern recognition (psychology); Image (mathematics)","score_opus":0.017448718923662822,"score_gpt":0.26803574606748864,"score_spread":0.2505870271438258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134819732","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000008654721,0.000049139166,0.99336207,0.0027917123,0.00006203126,0.00019315402,0.0000056226077,0.00011860675,0.0034090292],"genre_scores_gemma":[0.0033185189,0.00005757675,0.9912015,0.00031702762,0.000036699712,0.000037275357,0.0000018414256,0.000003652982,0.0050258627],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993269,0.00002467566,0.00019264074,0.0001266017,0.00022272409,0.000106461],"domain_scores_gemma":[0.99930024,0.00016150794,0.00008976673,0.0002908173,0.00012158607,0.0000360943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003084038,0.00004864074,0.0000566812,0.000022308106,0.00007805356,0.000077922115,0.00040829944,0.000019392011,0.000064187116],"category_scores_gemma":[0.0000490392,0.000031193482,0.0000332519,0.00010641564,0.000076586635,0.00028729314,0.000042445943,0.00003308326,0.0000067569013],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.7051978e-7,0.00002047182,0.0000023440568,0.000003637218,0.0000035224996,3.9518972e-7,0.000051298604,1.5089638e-7,0.0025924705,0.003874873,0.047861647,0.9455887],"study_design_scores_gemma":[0.0005168894,0.00023409168,0.00034624006,0.000020090025,0.000007531296,0.00003238403,0.00008970844,0.34306857,0.6419196,0.008813088,0.00476297,0.00018883747],"about_ca_topic_score_codex":0.00002237795,"about_ca_topic_score_gemma":0.000003008644,"teacher_disagreement_score":0.9453999,"about_ca_system_score_codex":0.000011068747,"about_ca_system_score_gemma":0.000010184614,"threshold_uncertainty_score":0.12720332},"labels":[],"label_agreement":null},{"id":"W2134941041","doi":"10.1002/jmri.21768","title":"Evaluation of performance metrics for bias field correction in MR brain images","year":2009,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Smoothing; Voxel; Computer science; Segmentation; Rank correlation; Artificial intelligence; Statistics; Pattern recognition (psychology); Spearman's rank correlation coefficient; Mathematics; Computer vision; Machine learning","score_opus":0.03782004197619619,"score_gpt":0.336712965069359,"score_spread":0.2988929230931628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134941041","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029813966,0.006407552,0.9588732,0.0035641738,0.0004903605,0.00029555347,5.090551e-7,0.000019789342,0.0005348543],"genre_scores_gemma":[0.7939349,0.00036973887,0.2045931,0.0009213218,0.0000801389,0.000008620875,3.5023777e-7,0.000005303016,0.00008651615],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979251,0.00017195266,0.0006450935,0.00014063196,0.00095317874,0.00016404498],"domain_scores_gemma":[0.99789053,0.00052656117,0.00046898355,0.00017947146,0.00088698155,0.000047450292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051407176,0.00008720048,0.00019655027,0.00048794693,0.000031196883,0.0000591553,0.0004123941,0.000029737952,0.000021233835],"category_scores_gemma":[0.0038030674,0.00007900475,0.000067704386,0.000641367,0.000029161203,0.0008209787,0.000026230045,0.00017523943,5.6222854e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015568854,0.00004810451,0.0016694518,0.000009347108,8.949236e-7,0.000002942632,0.00016835849,0.000058858273,0.0058145295,0.000021579814,0.005521173,0.9866692],"study_design_scores_gemma":[0.0020537865,0.0014459442,0.08602446,0.00047838895,0.00003515047,0.000110561035,0.00008906222,0.5597977,0.34534255,0.0028018875,0.0016389505,0.00018153751],"about_ca_topic_score_codex":0.0000069734588,"about_ca_topic_score_gemma":0.0000010776233,"teacher_disagreement_score":0.9864876,"about_ca_system_score_codex":0.00011519701,"about_ca_system_score_gemma":0.00017187478,"threshold_uncertainty_score":0.45529017},"labels":[],"label_agreement":null},{"id":"W2134973016","doi":"10.1109/imtc.2008.4547012","title":"Mean Shift Particle-Based Texture Granularity","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Granularity; Texture (cosmology); Image texture; Computer science; Segmentation; Texture filtering; Image segmentation; Artificial intelligence; Computer vision; Layer (electronics); Pattern recognition (psychology); Mathematics; Materials science; Image (mathematics)","score_opus":0.026641341399078974,"score_gpt":0.27111046391152216,"score_spread":0.24446912251244318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134973016","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005989209,0.000022348822,0.988744,0.0023881441,0.00006030937,0.00011486786,4.8712263e-7,0.0007913746,0.0018892293],"genre_scores_gemma":[0.71349555,0.000002602602,0.2817698,0.0044581178,0.000018446834,0.000011498476,0.0000016012456,0.0000035613132,0.00023883989],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989952,0.00006468991,0.00015825314,0.00023811389,0.00035457514,0.0001891728],"domain_scores_gemma":[0.99928063,0.00005631545,0.000032599695,0.00043778145,0.00004095263,0.00015173694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019467193,0.00008217845,0.000090084606,0.00003393427,0.00011123274,0.00004565489,0.0005223155,0.0000447062,0.00026562513],"category_scores_gemma":[0.000044716762,0.000065988694,0.00004539849,0.00025382978,0.00009697296,0.00032276148,0.0000753342,0.00010990627,0.00012476426],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003730407,0.0017616452,0.043712072,0.00009536297,0.00005663,0.0011027406,0.0076316134,0.000044796183,0.033083998,0.33605877,0.14788622,0.42852885],"study_design_scores_gemma":[0.00091812265,0.00015250538,0.019762939,0.000014204708,0.0000052776827,0.00002949533,0.0000141343635,0.057525698,0.91148305,0.0075507653,0.0021700903,0.00037374193],"about_ca_topic_score_codex":0.00005387368,"about_ca_topic_score_gemma":0.000012876775,"teacher_disagreement_score":0.878399,"about_ca_system_score_codex":0.000018700573,"about_ca_system_score_gemma":0.000058610673,"threshold_uncertainty_score":0.2908409},"labels":[],"label_agreement":null},{"id":"W2135041814","doi":"10.1109/iembs.2006.259306","title":"Validation and Improved Registration of Bone Segmentation Using Contour Coherency","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"National Center for Research Resources","keywords":"Artificial intelligence; Segmentation; Similarity (geometry); Computer vision; Computer science; Image segmentation; Pattern recognition (psychology); Parameterized complexity; Image registration; Mathematics; Image (mathematics); Algorithm","score_opus":0.01948783095434662,"score_gpt":0.2854413271048916,"score_spread":0.265953496150545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135041814","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08723634,0.000024444145,0.9112124,0.00009933535,0.000035143923,0.00020017834,7.117388e-7,0.00009425269,0.0010972143],"genre_scores_gemma":[0.49417794,0.0000042946494,0.5055166,0.000052044525,0.000016860477,0.0000060388065,0.000010113289,0.0000024345065,0.00021369963],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99925184,0.000041331674,0.00030548262,0.00015211107,0.00017750691,0.00007172384],"domain_scores_gemma":[0.9995046,0.00003065368,0.00020610432,0.00013638454,0.00009651919,0.000025717223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025359925,0.000057464527,0.00008406286,0.00006374181,0.000034416356,0.00006294994,0.00007328525,0.00003265925,0.000024659912],"category_scores_gemma":[0.000028174505,0.000053912725,0.000014374779,0.00012637007,0.00003675046,0.0006116464,0.000028694385,0.000028431987,7.1060396e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018703824,0.000028117583,0.0003728104,0.000017257507,0.000002134605,4.7211907e-7,0.00005847153,0.0000051082866,0.9691981,0.00557688,0.00031989,0.02441886],"study_design_scores_gemma":[0.00031353298,0.000056833916,0.0013698861,0.000012467319,0.0000054712173,0.000005776604,0.000034225734,0.046884704,0.9474292,0.0038114765,0.000005550863,0.00007092424],"about_ca_topic_score_codex":0.0006658935,"about_ca_topic_score_gemma":0.000016073813,"teacher_disagreement_score":0.4069416,"about_ca_system_score_codex":0.000024910201,"about_ca_system_score_gemma":0.0000289298,"threshold_uncertainty_score":0.2198497},"labels":[],"label_agreement":null},{"id":"W2135418695","doi":"10.1109/wiamis.2007.59","title":"Motion-based Object Segmentation using Sprites and Anisotropic Diffusion","year":2007,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Sprite (computer graphics); Segmentation; Coding (social sciences); Image segmentation; Background subtraction; Anisotropic diffusion; Pixel; Image (mathematics); Mathematics","score_opus":0.0180466126727154,"score_gpt":0.29529991883071327,"score_spread":0.27725330615799787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135418695","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13231948,0.00002162077,0.8668344,0.000112666945,0.000069050315,0.00013666169,2.3527265e-7,0.00024263286,0.0002632486],"genre_scores_gemma":[0.36610827,0.0000042837814,0.6331064,0.000711289,0.000018930159,0.0000017148498,0.000002304261,0.0000035347316,0.00004326303],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99917156,0.00003466461,0.0001806323,0.00021581743,0.00024900655,0.00014830078],"domain_scores_gemma":[0.99953574,0.00010403334,0.000057511043,0.00016594004,0.000048347967,0.00008845161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029885612,0.00007786875,0.000070212656,0.00012884144,0.00009841692,0.0000976757,0.00014545854,0.000036851223,0.0000575557],"category_scores_gemma":[0.00004179624,0.000068119865,0.000018244684,0.00021500386,0.000042589367,0.00036725448,0.00006567677,0.00005151587,0.0000055408686],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000044477215,0.000087012704,0.017774774,0.000026550286,0.0000045633647,0.000014660675,0.00024819362,0.000013516119,0.5740525,0.0021878302,0.00015300604,0.4054329],"study_design_scores_gemma":[0.0005102879,0.00008148543,0.026003351,0.000022705111,0.0000052542546,0.000007916036,0.00008100744,0.14115664,0.83115315,0.0008004276,0.000023563542,0.00015421488],"about_ca_topic_score_codex":0.00007846332,"about_ca_topic_score_gemma":0.000007942667,"teacher_disagreement_score":0.4052787,"about_ca_system_score_codex":0.00005142325,"about_ca_system_score_gemma":0.000021996915,"threshold_uncertainty_score":0.27778473},"labels":[],"label_agreement":null},{"id":"W2135430220","doi":"10.1109/iembs.2005.1616741","title":"High Quality Appearance Models of Heart Sub-Components Based on MR Images","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials","funders":"","keywords":"Computer science; Artificial intelligence; Quality (philosophy); Computer vision; Pattern recognition (psychology); Physics","score_opus":0.037956657234341426,"score_gpt":0.31542919149653065,"score_spread":0.2774725342621892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135430220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015092021,0.000021649284,0.97963107,0.0033720108,0.000055660643,0.0001646912,0.0000031680531,0.0003203612,0.0013393806],"genre_scores_gemma":[0.59121853,0.0000035514163,0.40604636,0.0026096348,0.000016726199,0.000008944439,0.0000017143993,0.0000038828903,0.000090665155],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998429,0.00014249011,0.0003591456,0.00030597698,0.0005792284,0.00018417707],"domain_scores_gemma":[0.9990455,0.000091542104,0.00009777448,0.00057975994,0.000085581545,0.00009987476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054973364,0.000109110515,0.00019486836,0.00008924301,0.000036342284,0.000038794424,0.00054168416,0.00004095188,0.000060307684],"category_scores_gemma":[0.000039264854,0.0000938533,0.000061894076,0.00018890457,0.00007661958,0.0005309149,0.000079354475,0.00009773464,0.000057228746],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006239197,0.0012895952,0.0004342222,0.0001277626,0.000018329536,0.0000054928255,0.00023032985,0.008863756,0.8147075,0.031901624,0.02431527,0.11804372],"study_design_scores_gemma":[0.00032815873,0.00005383666,0.0019946226,0.000035838882,0.0000010431457,4.859279e-7,0.000002095637,0.12943715,0.86633456,0.0016533029,0.000052781834,0.00010615239],"about_ca_topic_score_codex":0.000121744175,"about_ca_topic_score_gemma":0.000002397435,"teacher_disagreement_score":0.5761265,"about_ca_system_score_codex":0.000034326516,"about_ca_system_score_gemma":0.00003184,"threshold_uncertainty_score":0.38272265},"labels":[],"label_agreement":null},{"id":"W2135471427","doi":"10.1007/978-3-642-04271-3_58","title":"A Fuzzy Region-Based Hidden Markov Model for Partial-Volume Classification in Brain MRI","year":2009,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"","keywords":"Partial volume; Voxel; Computer science; Pattern recognition (psychology); Artificial intelligence; Fuzzy logic; Computation; Contextual image classification; Image (mathematics); Algorithm","score_opus":0.02816207240631424,"score_gpt":0.30161384798780283,"score_spread":0.2734517755814886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135471427","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009927542,0.000025750185,0.9730914,0.024806164,0.00020156548,0.0006247946,0.0000010018089,0.00023385655,0.000022687436],"genre_scores_gemma":[0.45260528,0.0000017238835,0.5404938,0.0068033417,0.000045185378,0.00004117241,0.0000022037968,0.0000042166394,0.0000030901115],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99734634,0.00010476946,0.00045112753,0.0009508254,0.00058982306,0.00055709266],"domain_scores_gemma":[0.99827933,0.00044723862,0.00015146776,0.00082068273,0.0001496046,0.00015169165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013264968,0.00019999966,0.00022604645,0.0005504732,0.00013468825,0.0003231231,0.0018611468,0.00011288452,0.0000019172198],"category_scores_gemma":[0.00046699616,0.0001899236,0.000060665378,0.0018921543,0.00026076994,0.0008258578,0.00013619184,0.00024228249,0.000004211579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010989563,0.00010387325,0.00042057506,0.000011166078,6.8456563e-7,0.000010397086,0.0006918951,0.026737427,0.0034162172,0.0006812778,0.00043336087,0.96748215],"study_design_scores_gemma":[0.00042164593,0.00013955298,0.0020780936,0.00006064873,0.0000010383873,0.0000050272756,4.5569087e-7,0.94442785,0.012691732,0.039951857,0.000011496913,0.0002105927],"about_ca_topic_score_codex":0.000015923917,"about_ca_topic_score_gemma":0.000024777153,"teacher_disagreement_score":0.96727157,"about_ca_system_score_codex":0.00025409806,"about_ca_system_score_gemma":0.00045398422,"threshold_uncertainty_score":0.77448595},"labels":[],"label_agreement":null},{"id":"W2135921972","doi":"10.5430/jbgc.v3n1p6","title":"Non-uniform illumination correction in infrared images based on a modified fuzzy c-means algorithm","year":2012,"lang":"en","type":"article","venue":"Journal of Biomedical Graphics and Computing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Normalization (sociology); Brightness; Shading; Artificial intelligence; Pixel; Algorithm; Computer science; Color constancy; Computer vision; Multiplicative function; Mathematics; Image (mathematics); Optics; Computer graphics (images); Physics","score_opus":0.011864403863628631,"score_gpt":0.2698766518422326,"score_spread":0.25801224797860395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135921972","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008841213,0.00004939923,0.9893824,0.0005224356,0.00083983585,0.00008908031,7.938894e-7,0.00003403365,0.00024084425],"genre_scores_gemma":[0.7870954,0.000042512398,0.21183279,0.0007386989,0.00027047904,0.000001494145,0.0000029563666,0.0000065674417,0.000009080475],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982759,0.00008617558,0.0005653072,0.00014106608,0.00067316473,0.00025839702],"domain_scores_gemma":[0.9988386,0.0002533471,0.00036993335,0.00012530122,0.00014033618,0.00027249425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018685499,0.0001199699,0.0002150143,0.00078669144,0.00009217122,0.00008434556,0.0002847374,0.000108071225,0.000003241041],"category_scores_gemma":[0.00017943124,0.000098378405,0.00007153711,0.00080663984,0.00013517827,0.0004487173,0.00007720627,0.00044149222,5.176574e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014016937,0.00036280975,0.0010919619,0.000030149225,0.000012996193,0.00003391338,0.0008616779,0.00010661234,0.0007738863,0.00049872097,0.0011818607,0.9950314],"study_design_scores_gemma":[0.0007812252,0.00037727514,0.00707383,0.00023459921,0.000006544652,0.00006097713,0.00008780788,0.9883254,0.0018823091,0.00093115127,0.000117273616,0.00012157366],"about_ca_topic_score_codex":0.0000115050225,"about_ca_topic_score_gemma":4.035123e-7,"teacher_disagreement_score":0.9949098,"about_ca_system_score_codex":0.000053243682,"about_ca_system_score_gemma":0.000065349916,"threshold_uncertainty_score":0.4011755},"labels":[],"label_agreement":null},{"id":"W2135944657","doi":"10.1016/j.compmedimag.2008.07.004","title":"Efficient interactive 3D Livewire segmentation of complex objects with arbitrary topology","year":2008,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Robustness (evolution); Segmentation; Computer science; Network topology; Computer vision; Artificial intelligence; Scale-space segmentation; Image segmentation; Synthetic data; Pattern recognition (psychology); Algorithm","score_opus":0.015839153051179018,"score_gpt":0.2792759575551961,"score_spread":0.2634368045040171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135944657","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07450177,0.00016173314,0.9235183,0.0009899166,0.00018547973,0.0002004622,0.0000022462839,0.00022544219,0.00021466393],"genre_scores_gemma":[0.60580504,0.00017373775,0.39085373,0.0030715226,0.000046744015,0.000016126933,0.000014854984,0.000010543412,0.000007674357],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99811554,0.00018550757,0.00037977088,0.00041151576,0.00065018976,0.0002574954],"domain_scores_gemma":[0.9987889,0.00028743705,0.00019091561,0.00028667707,0.0001586915,0.00028738097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002921404,0.00018275187,0.00032955172,0.000241704,0.00015318512,0.000033908553,0.00044327762,0.00006565582,0.00003194278],"category_scores_gemma":[0.00008748138,0.00014732913,0.000049874747,0.00042027238,0.0010123791,0.00017051645,0.00028015388,0.00031462265,0.0000017277431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030562465,0.0017473344,0.008339809,0.00052224094,0.00035311255,0.0023285078,0.022410985,0.000055227996,0.029591957,0.009958409,0.0037115486,0.9206752],"study_design_scores_gemma":[0.00385077,0.00051964493,0.019077731,0.0004892239,0.000033974193,0.0019259765,0.00034202242,0.95799994,0.013976596,0.0010670272,0.00017600789,0.0005410728],"about_ca_topic_score_codex":0.000056158817,"about_ca_topic_score_gemma":0.0000016743049,"teacher_disagreement_score":0.95794475,"about_ca_system_score_codex":0.000018798171,"about_ca_system_score_gemma":0.00014757273,"threshold_uncertainty_score":0.6007908},"labels":[],"label_agreement":null},{"id":"W2136056439","doi":"10.1109/isbi.2010.5490209","title":"Atlas-based organ &amp;#x00026; bone approximation for ex-vivo &amp;#x03BC;MRI mouse data: A pilot study","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"SickKids Foundation; Hospital for Sick Children","funders":"","keywords":"Atlas (anatomy); Magnetic resonance imaging; Data set; Geodesic; Computer science; Artificial intelligence; Anatomy; Pattern recognition (psychology); Computer vision; Mathematics; Medicine; Radiology; Geometry","score_opus":0.08199429340110981,"score_gpt":0.34726363508334684,"score_spread":0.26526934168223704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136056439","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024335928,0.0000056182953,0.97029734,0.0010499796,0.0003068077,0.0021778133,0.000036180958,0.0013243977,0.00046594843],"genre_scores_gemma":[0.05355519,0.0000023523721,0.94076204,0.0009885537,0.00015392462,0.00027914796,0.00025443596,0.000038149556,0.0039662067],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99712354,0.00012725232,0.00061429024,0.0009758039,0.0007281232,0.00043098364],"domain_scores_gemma":[0.996092,0.00033095788,0.00022629014,0.0027912753,0.00028662066,0.00027286314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015447532,0.00028415132,0.00031743004,0.00023869547,0.00020593175,0.0004625543,0.0022173994,0.000080834645,0.00050692476],"category_scores_gemma":[0.00067705044,0.00024244338,0.00004588236,0.0005152579,0.00010998972,0.0014020791,0.00061231613,0.00031933375,0.00022404072],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000106273175,0.0061242986,0.0005389215,0.0001254714,0.00006794237,0.000005390107,0.0012093536,0.00001617623,0.78643477,0.0039530685,0.17462867,0.026789658],"study_design_scores_gemma":[0.010654739,0.004910294,0.0005928531,0.00006294198,0.00013282974,0.000041949832,0.00039928107,0.14503488,0.75830674,0.0050500636,0.07232083,0.002492607],"about_ca_topic_score_codex":0.00026343687,"about_ca_topic_score_gemma":0.0010581798,"teacher_disagreement_score":0.1450187,"about_ca_system_score_codex":0.00004468116,"about_ca_system_score_gemma":0.00016189962,"threshold_uncertainty_score":0.9886554},"labels":[],"label_agreement":null},{"id":"W2136918979","doi":"10.1093/nar/gkq548","title":"FACADE : a fast and sensitive algorithm for the segmentation and calling of high resolution array CGH data","year":2010,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Occupational Cancer Research Centre; Canadian Centre for Applied Research in Cancer Control; BC Cancer Agency","funders":"Canadian Institutes of Health Research","keywords":"Biology; Segmentation; High resolution; Facade; Resolution (logic); Algorithm; Computational biology; Computer vision; Artificial intelligence; Remote sensing; Computer science; Archaeology","score_opus":0.07159836907197428,"score_gpt":0.3847882709494875,"score_spread":0.31318990187751317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136918979","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014749571,0.00004692513,0.98280686,0.0016213969,0.0000672332,0.000571083,0.000048861915,0.00004325663,0.00004479493],"genre_scores_gemma":[0.17039284,0.0001534829,0.8291156,0.00010089257,0.00008664166,0.000040061677,0.000027111799,0.0000091272395,0.00007420777],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984687,0.00015425011,0.00018598148,0.0003755722,0.0005770047,0.0002385087],"domain_scores_gemma":[0.9982307,0.0008100356,0.000059092024,0.0005377444,0.00026933744,0.00009311436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024462661,0.00007308134,0.000104075094,0.00011361681,0.0002791607,0.00013841853,0.00062171166,0.00007879684,0.000008878569],"category_scores_gemma":[0.00043260754,0.000054203218,0.000011596359,0.0002518689,0.0004744126,0.0005191969,0.00055091246,0.00042038388,0.0000018838722],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000072193934,0.000015696249,0.000029099234,0.000017525055,0.000011078604,0.0000012230594,0.00088526896,4.8812774e-7,0.18748365,0.00094614713,0.0007140228,0.8098886],"study_design_scores_gemma":[0.0006759705,0.00023471897,0.003402715,0.000042196643,0.00001120896,0.000022655397,0.0011145711,0.57759196,0.413176,0.0029399458,0.000653597,0.00013445028],"about_ca_topic_score_codex":0.00023493862,"about_ca_topic_score_gemma":0.00002366101,"teacher_disagreement_score":0.80975413,"about_ca_system_score_codex":0.00001964672,"about_ca_system_score_gemma":0.000058888054,"threshold_uncertainty_score":0.22103432},"labels":[],"label_agreement":null},{"id":"W2136997771","doi":"10.1109/crv.2010.56","title":"Fast FEM-Based Non-Rigid Registration","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"University of Alberta","keywords":"Finite element method; Image registration; Computer science; Discretization; Grid; Algorithm; Displacement field; Displacement (psychology); Noise (video); Computer vision; Regular grid; Stability (learning theory); Finite difference; Image (mathematics); Mathematics; Geometry; Machine learning","score_opus":0.009512384172239919,"score_gpt":0.2778152577283217,"score_spread":0.2683028735560818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136997771","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015686697,5.0088005e-7,0.9664941,0.0017608802,0.00022023494,0.000109360066,2.9238015e-7,0.00042446677,0.029421497],"genre_scores_gemma":[0.27973598,3.9421622e-7,0.7167352,0.0019987586,0.000045628087,0.000015905376,0.0000037794869,0.0000033590663,0.0014609897],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99928224,0.000014353099,0.00014248464,0.0001962322,0.00024940562,0.00011531261],"domain_scores_gemma":[0.9993292,0.000037935013,0.00005134954,0.0004287803,0.00006185285,0.0000908616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002204373,0.00006235749,0.000055355307,0.000053020616,0.000048585363,0.0001375487,0.00046278283,0.000047389953,0.00031352683],"category_scores_gemma":[0.000058575046,0.00005209986,0.000024997667,0.00015645925,0.000049923612,0.00036174784,0.00003920959,0.00013600923,0.00012853985],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025526224,0.00012567668,0.00050959614,0.000015562855,0.0000039601464,0.00001629699,0.00014108796,0.000003986224,0.55325985,0.03744063,0.06579181,0.34268898],"study_design_scores_gemma":[0.00021371427,0.00005780385,0.001718972,0.0000054978973,0.0000012672195,0.00000438088,0.0000064462556,0.052535277,0.94216025,0.0012423993,0.0019299604,0.00012402424],"about_ca_topic_score_codex":0.000037539106,"about_ca_topic_score_gemma":0.000031426523,"teacher_disagreement_score":0.3889004,"about_ca_system_score_codex":0.000008768819,"about_ca_system_score_gemma":0.00007345194,"threshold_uncertainty_score":0.34328988},"labels":[],"label_agreement":null},{"id":"W2137186887","doi":"10.1109/tbme.2009.2012423","title":"Fluid Vector Flow and Applications in Brain Tumor Segmentation","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":142,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Massachusetts General Hospital","keywords":"Computer science; Flow (mathematics); Segmentation; Artificial intelligence; Physics; Mechanics","score_opus":0.006624408759743347,"score_gpt":0.2418567824867283,"score_spread":0.23523237372698494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137186887","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013200332,0.000027963762,0.99645746,0.0013635296,0.00013280945,0.00028256667,0.000005411879,0.00039295765,0.0000172659],"genre_scores_gemma":[0.60667014,0.000050949133,0.39137974,0.0015251156,0.00007231103,0.00022374462,0.000009894212,0.000016153652,0.000051925173],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894714,0.000022088167,0.0002550517,0.00028386537,0.00028637165,0.00020545152],"domain_scores_gemma":[0.9994261,0.00013586882,0.000025197385,0.00020294983,0.000016937662,0.00019289744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017680226,0.0001293931,0.0001276222,0.0003370736,0.00005521284,0.000052866628,0.0002202176,0.000060133858,0.000023008835],"category_scores_gemma":[0.000015696449,0.00012720985,0.000032581935,0.00063294236,0.000039024908,0.00028140724,0.0000018799249,0.0002082242,0.00000972078],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005836192,0.00034142908,0.0000022453362,0.00003563846,0.00001226161,0.000021057975,0.0004918945,0.0038944127,0.22961617,0.0006515056,0.00029616704,0.7646314],"study_design_scores_gemma":[0.0009915275,0.00035960178,0.0008555558,0.0001030541,0.000009170623,0.000036475252,0.000048224454,0.7093187,0.286467,0.00029187914,0.0011214621,0.00039737028],"about_ca_topic_score_codex":0.000007767659,"about_ca_topic_score_gemma":0.0000013868263,"teacher_disagreement_score":0.764234,"about_ca_system_score_codex":0.000080177364,"about_ca_system_score_gemma":0.000026391896,"threshold_uncertainty_score":0.5187467},"labels":[],"label_agreement":null},{"id":"W2137651406","doi":"10.1016/j.cviu.2012.05.002","title":"MDS-based segmentation model for the fusion of contour and texture cues in natural images","year":2012,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Artificial intelligence; Computer science; Segmentation; Computer vision; Image segmentation; Pattern recognition (psychology); Scale-space segmentation; Image texture","score_opus":0.040397928621731714,"score_gpt":0.32368772310928534,"score_spread":0.28328979448755365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137651406","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024507267,0.000666298,0.9954542,0.00082807126,0.00014344235,0.00037904765,0.0000028612976,0.000053508356,0.000021855709],"genre_scores_gemma":[0.6430338,0.00006582017,0.35637665,0.00046779617,0.000027033038,0.000007893643,0.000003360295,0.000005029433,0.000012596444],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913245,0.000058183028,0.00022783282,0.00020135652,0.00019172375,0.00018845928],"domain_scores_gemma":[0.999241,0.00038520628,0.00010427118,0.0001525784,0.0000477276,0.00006921807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051745516,0.00011920821,0.00014790006,0.0001373424,0.00013763731,0.00015930117,0.00018223826,0.00004098359,0.0000030346869],"category_scores_gemma":[0.00002891928,0.000080278754,0.000034086945,0.00012998178,0.00012636007,0.00091011595,0.00015239595,0.00010281369,2.1863445e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017189764,0.0003237559,0.002430261,0.0005082352,0.00004346865,0.000006074409,0.010659504,0.00025030572,0.2826638,0.021789094,0.013771289,0.6673823],"study_design_scores_gemma":[0.00085248856,0.00008356324,0.0009543672,0.00009769222,0.000006959641,0.0000038677226,0.00030026157,0.9780804,0.017499251,0.002006243,0.000009973671,0.00010491538],"about_ca_topic_score_codex":0.0000059204895,"about_ca_topic_score_gemma":0.0000026318987,"teacher_disagreement_score":0.9778301,"about_ca_system_score_codex":0.000059602204,"about_ca_system_score_gemma":0.00001706694,"threshold_uncertainty_score":0.32736728},"labels":[],"label_agreement":null},{"id":"W2137993841","doi":"10.1109/tpami.2005.106","title":"Multiregion level-set partitioning of synthetic aperture radar images","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":198,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; National Aeronautics and Space Administration","keywords":"Synthetic aperture radar; Speckle noise; Artificial intelligence; Multiplicative noise; Speckle pattern; Segmentation; Computer vision; Image segmentation; Radar imaging; Computer science; Scale-space segmentation; Regularization (linguistics); Inverse synthetic aperture radar; Multiplicative function; Segmentation-based object categorization; Pattern recognition (psychology); Algorithm; Mathematics; Radar; Transmission (telecommunications)","score_opus":0.03177248076099596,"score_gpt":0.2973464705193594,"score_spread":0.26557398975836344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137993841","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00061129173,0.000095908785,0.99790347,0.001080967,0.00004435842,0.0000983854,0.00004078577,0.000088557,0.000036268913],"genre_scores_gemma":[0.9471628,0.00039241527,0.05155757,0.00069160195,0.0000123052,0.00001844675,0.000006199681,0.000007235457,0.00015139073],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99868786,0.000094448544,0.00038572671,0.00037140594,0.0003027016,0.00015783038],"domain_scores_gemma":[0.9991107,0.00018222253,0.00012711792,0.0004017628,0.00007407104,0.00010412286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022922116,0.00015603243,0.0002454061,0.00037232463,0.00011649129,0.000064183594,0.0003349224,0.000053083782,0.00020274709],"category_scores_gemma":[0.000013927833,0.00013423072,0.00017389329,0.0006058231,0.00010810697,0.0002454781,0.0000055594865,0.00017613515,0.000017434542],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043232303,0.000121675126,0.00013145673,0.000015178388,0.00015220749,0.000002869941,0.00033669372,0.0020762691,0.003971551,0.000023342334,0.00004226434,0.99312216],"study_design_scores_gemma":[0.00007164044,0.00008157866,0.00038995963,0.000042001215,0.0001676369,0.0000095376345,0.000040852578,0.108267084,0.8906118,0.00006856988,0.000083337756,0.00016599418],"about_ca_topic_score_codex":0.00035879295,"about_ca_topic_score_gemma":0.0002879432,"teacher_disagreement_score":0.99295616,"about_ca_system_score_codex":0.000023712568,"about_ca_system_score_gemma":0.000013509576,"threshold_uncertainty_score":0.547377},"labels":[],"label_agreement":null},{"id":"W2138181193","doi":"10.1109/isbi.2004.1398479","title":"Anatomical guided segmentation with non-stationary tissue class distributions in an expectation-maximization framework","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Center for Research Resources; National Cancer Institute; National Institute of Mental Health","keywords":"Class (philosophy); Segmentation; Computer science; Artificial intelligence; Pattern recognition (psychology); Expectation–maximization algorithm; Maximization; Image segmentation; Mathematics; Mathematical optimization; Maximum likelihood; Statistics","score_opus":0.013647105522496287,"score_gpt":0.32189115125778756,"score_spread":0.3082440457352913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138181193","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029815406,0.0000065419554,0.96651894,0.0022580577,0.000049874252,0.0004471643,0.0000056682707,0.000353098,0.0005452463],"genre_scores_gemma":[0.38429523,0.00000584841,0.61473286,0.0005675217,0.000041443047,0.00010389933,0.00018737014,0.000008213812,0.00005761575],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983385,0.000112793074,0.00043027897,0.0004288611,0.0004567991,0.00023277671],"domain_scores_gemma":[0.99900395,0.00014229791,0.00012331623,0.0003779378,0.000197522,0.00015499556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017298266,0.0001511676,0.00013942082,0.00022840803,0.00011280068,0.00016519426,0.00040557238,0.00009969294,0.00028261007],"category_scores_gemma":[0.00009126163,0.00013807055,0.0000184482,0.0008532309,0.00007093669,0.0022584104,0.000055777444,0.0001658575,0.000047023124],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000090025074,0.002245122,0.021602597,0.00005893533,0.000054479457,0.00008866578,0.013651894,0.012764147,0.04536512,0.16787536,0.016227918,0.7199757],"study_design_scores_gemma":[0.001797308,0.0004759055,0.028363084,0.00010910582,0.000016359027,0.000035091343,0.0012500507,0.56714976,0.39096627,0.008724939,0.00033818427,0.0007739246],"about_ca_topic_score_codex":0.000031513995,"about_ca_topic_score_gemma":0.00009718922,"teacher_disagreement_score":0.7192018,"about_ca_system_score_codex":0.00024868728,"about_ca_system_score_gemma":0.00011573851,"threshold_uncertainty_score":0.56303537},"labels":[],"label_agreement":null},{"id":"W2138265313","doi":"10.1109/icip.2004.1421665","title":"Sar image segmentation with active contours and level sets","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Synthetic aperture radar; Artificial intelligence; Computer vision; Computer science; Image segmentation; Scale-space segmentation; Speckle noise; Regularization (linguistics); Speckle pattern; Segmentation; Radar imaging; Inverse synthetic aperture radar; Multiplicative noise; Segmentation-based object categorization; Algorithm; Pattern recognition (psychology); Radar; Transmission (telecommunications)","score_opus":0.021764597599489913,"score_gpt":0.2986161679703871,"score_spread":0.2768515703708972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138265313","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007813159,0.000010509464,0.9854752,0.0019767,0.000015896027,0.00019003416,0.0000025478562,0.00022533155,0.004290622],"genre_scores_gemma":[0.078939125,0.000011170276,0.9185553,0.0019007084,0.000017989576,0.000010455475,0.000003752189,0.0000047304266,0.00055675575],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99930114,0.00003242169,0.000104020044,0.00021468004,0.00022306012,0.00012466956],"domain_scores_gemma":[0.9995955,0.00004554021,0.000050043604,0.00015370794,0.00006282343,0.00009238849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000107801636,0.00008037513,0.00007559422,0.000051487463,0.000051021285,0.00010368624,0.00016627449,0.000023154673,0.00010551247],"category_scores_gemma":[0.000014596629,0.00006021512,0.000009619633,0.000102043916,0.00007193538,0.001186892,0.00006138492,0.000060599024,0.000025310213],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007854675,0.0000366277,0.000085727355,0.000005086551,0.000011626093,0.0000066672396,0.0010280414,8.592343e-7,0.019322084,0.0013203863,0.004504479,0.97367054],"study_design_scores_gemma":[0.00084611325,0.00015843351,0.005375453,0.00001667903,0.0000065429663,0.000031517913,0.00033191763,0.0065022036,0.9857376,0.0004758693,0.00032781519,0.0001898173],"about_ca_topic_score_codex":0.00003292257,"about_ca_topic_score_gemma":0.000025023706,"teacher_disagreement_score":0.97348076,"about_ca_system_score_codex":0.000040142007,"about_ca_system_score_gemma":0.000028856073,"threshold_uncertainty_score":0.24555013},"labels":[],"label_agreement":null},{"id":"W2138785714","doi":"10.1109/icpr.1994.577045","title":"A sequential method of extracting contour chains from an image","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Image (mathematics); Computer science; Tracing; Artificial intelligence; Degree (music); Computer vision; Ray tracing (physics); Pattern recognition (psychology); Algorithm; Topology (electrical circuits); Mathematics; Combinatorics","score_opus":0.05412227702897334,"score_gpt":0.36862589770802023,"score_spread":0.3145036206790469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138785714","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009001313,0.000013423337,0.98994344,0.00044236882,0.00008357229,0.00009722057,0.0000029041794,0.00030506897,0.008211895],"genre_scores_gemma":[0.06322381,0.0000052400624,0.93542826,0.00068946363,0.0000558677,0.000006284405,0.0000021307626,0.000005225402,0.000583715],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989148,0.00016225666,0.00025412647,0.00025130613,0.0002765987,0.00014092641],"domain_scores_gemma":[0.9991638,0.00016176845,0.00011987103,0.00037361417,0.00007336561,0.00010754584],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003718417,0.000076814285,0.00013410988,0.000060185277,0.000034492045,0.00007610869,0.00051123044,0.00004117254,0.0019426605],"category_scores_gemma":[0.00011810811,0.000068159825,0.000041640254,0.0001347111,0.000037675203,0.00089856127,0.00010403032,0.000095580726,0.000026797685],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012233794,0.00011504086,0.000031821746,0.0000048254233,0.000012330797,0.00002187621,0.0014671095,6.7848885e-7,0.45459023,0.0033954582,0.0028617135,0.5374977],"study_design_scores_gemma":[0.000255175,0.00006393663,0.00013790454,0.000010588918,0.0000051558086,0.0000075820294,0.000115519644,0.2829265,0.71507066,0.0011608923,0.00013623966,0.00010986914],"about_ca_topic_score_codex":0.0005164681,"about_ca_topic_score_gemma":0.000015006751,"teacher_disagreement_score":0.5373878,"about_ca_system_score_codex":0.000016672953,"about_ca_system_score_gemma":0.000011360136,"threshold_uncertainty_score":0.9989697},"labels":[],"label_agreement":null},{"id":"W2138942340","doi":"10.1007/978-3-540-75757-3_49","title":"Real-Time Synthesis of Image Slices in Deformed Tissue from Nominal Volume Images","year":2007,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"","keywords":"Pixel; Interpolation (computer graphics); Voxel; Intersection (aeronautics); Computer vision; Volume (thermodynamics); Artificial intelligence; Deformation (meteorology); Computer science; Imaging phantom; Image (mathematics); Algorithm; Geometry; Mathematics; Materials science; Optics; Physics; Engineering","score_opus":0.007265543374456992,"score_gpt":0.2771822098489601,"score_spread":0.2699166664745031,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138942340","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08691119,0.000031899963,0.9119953,0.00038744952,0.00020599029,0.00021471514,0.0000039522783,0.00013438005,0.00011514905],"genre_scores_gemma":[0.40404248,0.000007447863,0.59571564,0.00016692345,0.000052463278,0.0000066709504,0.0000010099149,0.000005653393,0.0000016762019],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99712694,0.00009786882,0.0006215801,0.0007843109,0.00077990047,0.0005893744],"domain_scores_gemma":[0.9974485,0.0012790228,0.00022276297,0.0007405579,0.00014890468,0.00016023009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020743778,0.000216473,0.00036147123,0.0006998565,0.00007672972,0.00020069249,0.0023772393,0.00009864189,0.00005976998],"category_scores_gemma":[0.00063064776,0.00019260135,0.00004581369,0.0020340697,0.00071975274,0.0012768882,0.00060779374,0.00023715186,0.00003436599],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007242044,0.00006816724,0.0017438793,0.00001345067,0.0000019525773,0.00006179415,0.0008043471,0.0001361492,0.33225444,0.000012998754,0.000022626564,0.66487294],"study_design_scores_gemma":[0.00016312412,0.00008177454,0.033297412,0.00011331639,0.000002301748,0.000010350459,0.0000022037545,0.09320382,0.87059784,0.0023214447,0.0000051406396,0.00020128756],"about_ca_topic_score_codex":0.00147429,"about_ca_topic_score_gemma":0.00014787556,"teacher_disagreement_score":0.66467166,"about_ca_system_score_codex":0.00019842468,"about_ca_system_score_gemma":0.00021070756,"threshold_uncertainty_score":0.7854055},"labels":[],"label_agreement":null},{"id":"W2139271254","doi":"10.1109/iembs.2009.5333519","title":"Unsupervised segmentation of the prostate using MR images based on level set with a shape prior","year":2009,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto General Hospital; Princess Margaret Cancer Centre; University of Toronto; University Health Network; Mount Sinai Hospital","funders":"","keywords":"Ellipse; Segmentation; Computer science; Prostate cancer; Artificial intelligence; Level set (data structures); Computer vision; Image segmentation; Prostate; Prostate biopsy; Data set; Pattern recognition (psychology); Set (abstract data type); Medicine; Cancer; Mathematics","score_opus":0.04211432996070158,"score_gpt":0.3017251683994823,"score_spread":0.25961083843878074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139271254","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039229684,0.0000026638854,0.9580589,0.0017068583,0.000020139385,0.000517243,0.000005671122,0.00011775808,0.00034106473],"genre_scores_gemma":[0.45871708,9.026284e-7,0.5383162,0.0028620143,0.000006113509,0.000007518256,0.000002729357,0.00000447301,0.00008296577],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99884707,0.000089082285,0.00020460949,0.00022987524,0.00048827814,0.000141071],"domain_scores_gemma":[0.999304,0.00004486641,0.00011410435,0.00038334806,0.00010328718,0.000050386192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019395346,0.000107364656,0.000100415215,0.00007614045,0.000075078366,0.00007209169,0.0004218646,0.000024457559,0.000049622122],"category_scores_gemma":[0.000030900166,0.00006289582,0.00003296492,0.0003841001,0.00006223865,0.00026884297,0.000041973628,0.00007325377,0.0000021173403],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010667977,0.00041645346,0.0020903742,0.000067368346,0.000019527331,0.0000122250385,0.001536094,0.0018648139,0.47108775,0.0009124536,0.0015549978,0.52033126],"study_design_scores_gemma":[0.0004982129,0.00031717037,0.006186782,0.00007699367,0.00000621123,0.0000029835558,0.000073370196,0.25646675,0.7360326,0.00023752521,0.0000028120942,0.0000986214],"about_ca_topic_score_codex":0.000021317057,"about_ca_topic_score_gemma":0.000002114788,"teacher_disagreement_score":0.5202326,"about_ca_system_score_codex":0.000038372462,"about_ca_system_score_gemma":0.00012029246,"threshold_uncertainty_score":0.25648174},"labels":[],"label_agreement":null},{"id":"W2140441019","doi":"10.1109/tmi.2009.2021652","title":"A Framework for Geometric Analysis of Vascular Structures: Application to Cerebral Aneurysms","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":334,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Robustness (evolution); Computer science; Artificial intelligence; Segmentation; Geometric modeling; Geometry; Mathematics; Biology; Gene","score_opus":0.01207030249323512,"score_gpt":0.315923835142984,"score_spread":0.3038535326497489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140441019","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005804263,0.00003168161,0.9960843,0.0024140277,0.00017205525,0.00043450424,0.000010485393,0.00024922818,0.000023299002],"genre_scores_gemma":[0.55929726,0.000011793619,0.43687335,0.0037023532,0.00002879424,0.00006905208,0.0000041671733,0.0000068794557,0.000006359343],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977389,0.000064402186,0.00044420254,0.00047815658,0.000992168,0.00028220162],"domain_scores_gemma":[0.9983776,0.00039881215,0.000105938816,0.0006108668,0.00014352611,0.00036324156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005046701,0.0001517186,0.00031969804,0.0013787545,0.00011104819,0.00006029275,0.0007898663,0.00009720054,0.0001266204],"category_scores_gemma":[0.00020512479,0.00014113766,0.00026572598,0.0042248415,0.00006484333,0.00024234116,0.0000035595117,0.00028632165,0.000006089682],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009901961,0.00018172922,0.000024161118,0.000016927499,0.00013500376,0.0000036026252,0.00026486415,0.0022701009,0.00167038,0.0023710856,0.0002012148,0.992851],"study_design_scores_gemma":[0.00090976135,0.00036272654,0.0030292256,0.0001065109,0.00075675687,0.000014485172,0.00010046601,0.75165075,0.21928655,0.022917392,0.00028643524,0.00057894166],"about_ca_topic_score_codex":0.000025287229,"about_ca_topic_score_gemma":0.0000029233436,"teacher_disagreement_score":0.9922721,"about_ca_system_score_codex":0.000060642386,"about_ca_system_score_gemma":0.00006366241,"threshold_uncertainty_score":0.5755427},"labels":[],"label_agreement":null},{"id":"W2140509667","doi":"10.1007/978-3-642-02611-9_18","title":"Implicit Active-Contouring with MRF","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Markov random field; Artificial intelligence; Active contour model; Cut; Segmentation; Image segmentation; Contouring; Computer vision; Algorithm; Computer graphics (images)","score_opus":0.01258313461399542,"score_gpt":0.26081993714436585,"score_spread":0.24823680253037045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140509667","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000022353932,0.00010644742,0.9887498,0.0007568415,0.0003243798,0.00044685972,0.0000020012585,0.0004329099,0.00915843],"genre_scores_gemma":[0.011917078,0.00003158551,0.9830588,0.003999823,0.00034095175,0.000013472017,0.0000035012922,0.00003199424,0.00060281286],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9961238,0.000029759727,0.00043242826,0.0014861469,0.0012709036,0.0006569642],"domain_scores_gemma":[0.99758965,0.00027942169,0.00030744635,0.00133094,0.00023650443,0.0002560601],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00054356526,0.00050732837,0.0004950772,0.0007356996,0.0001969479,0.0005329358,0.0032772932,0.00024293062,0.000040171974],"category_scores_gemma":[0.000061754625,0.00041365443,0.000084136045,0.0005364543,0.0006007626,0.0009879248,0.00073085167,0.00088444795,0.000030715],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000051465045,0.000016677324,0.000006843938,0.000012612552,0.000006659418,0.00012835089,0.0003120249,0.0004959668,0.00048648924,0.002972382,0.000027055354,0.9955298],"study_design_scores_gemma":[0.0020875335,0.0026020282,0.0014468536,0.0033613567,0.000049176902,0.000878544,9.403595e-7,0.20969868,0.2604312,0.51104075,0.003858147,0.0045448043],"about_ca_topic_score_codex":0.000023308534,"about_ca_topic_score_gemma":0.00003976965,"teacher_disagreement_score":0.990985,"about_ca_system_score_codex":0.0003638275,"about_ca_system_score_gemma":0.00048078594,"threshold_uncertainty_score":0.99983156},"labels":[],"label_agreement":null},{"id":"W2140584173","doi":"10.1109/tbme.2010.2048752","title":"Tracking Endocardial Motion Via Multiple Model Filtering","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; St Joseph's Health Care; CARE Canada; London Health Sciences Centre; General Electric (Canada)","funders":"","keywords":"Segmentation; Tracking (education); Artificial intelligence; Computer science; Computer vision; Motion estimation; Motion (physics); Pattern recognition (psychology); Mathematics","score_opus":0.012280752224501154,"score_gpt":0.23903162644727635,"score_spread":0.2267508742227752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140584173","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027672693,0.0000031235556,0.9940344,0.00018257005,0.0017126404,0.0001611928,0.000007642791,0.0010963315,0.00003484451],"genre_scores_gemma":[0.6706995,0.0000060593497,0.32901713,0.00010639934,0.000086643595,0.00004953146,0.0000023035332,0.00001700692,0.000015429978],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845105,0.000016854143,0.0003106625,0.00036714386,0.0005109496,0.00034333096],"domain_scores_gemma":[0.99908143,0.0001377105,0.000039161496,0.00039003242,0.000042062893,0.00030959406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025280812,0.00018783922,0.0001694379,0.00030997652,0.000110975,0.00009125753,0.00047159987,0.00015791283,0.00004951032],"category_scores_gemma":[0.000043713248,0.00018974244,0.00011919696,0.00037424284,0.00006360833,0.0005305591,0.0000057474267,0.00069314806,0.000032014737],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002320482,0.00008455377,5.3656584e-7,0.00001419466,0.000013154749,0.000010473612,0.00011777701,0.026064971,0.62864935,0.000054312448,0.000029813991,0.3449585],"study_design_scores_gemma":[0.00022183995,0.000027852164,0.0000125146,0.000015297688,0.000004426237,0.000022076534,0.0000016127503,0.657762,0.34165823,0.000037382477,0.00010821409,0.00012852196],"about_ca_topic_score_codex":0.000012547141,"about_ca_topic_score_gemma":0.0000024878734,"teacher_disagreement_score":0.6679322,"about_ca_system_score_codex":0.000051343777,"about_ca_system_score_gemma":0.000031190182,"threshold_uncertainty_score":0.7737472},"labels":[],"label_agreement":null},{"id":"W2140915205","doi":"10.1109/isbi.2006.1624955","title":"Fast fluid registration using inverse filtering for non-rigid image registration","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Image registration; Computer science; Inverse; Computer vision; Artificial intelligence; Image (mathematics); Algorithm; Mathematics; Geometry","score_opus":0.02825921398671261,"score_gpt":0.3007084608214809,"score_spread":0.2724492468347683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140915205","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00319903,0.0000023623043,0.98815423,0.00035812677,0.00013390288,0.00039320506,0.0000025929482,0.00034285366,0.007413689],"genre_scores_gemma":[0.037845388,0.0000020703292,0.9600422,0.0003731768,0.00014565644,0.00003778883,0.000033636425,0.000009835332,0.0015102807],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988286,0.00002487858,0.00035545608,0.00032562693,0.0002642074,0.00020126492],"domain_scores_gemma":[0.9992345,0.000043386168,0.00016154043,0.00037055704,0.00013090472,0.000059110433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003172709,0.0001179741,0.000106135434,0.0000895778,0.00012854818,0.00030881894,0.00031925802,0.00005768302,0.000035589645],"category_scores_gemma":[0.00005958963,0.00011663999,0.000053602893,0.00019076123,0.00005858364,0.0013776296,0.00005585219,0.00005572716,0.000011366302],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004642776,0.00003630414,0.00002192873,0.000036946723,0.0000031153418,0.0000061595288,0.00007499844,0.00006808258,0.9548883,0.005750265,0.031032203,0.008077076],"study_design_scores_gemma":[0.00027165082,0.000058524285,0.00010866421,0.000024121906,0.0000045485444,0.000012106511,0.000026905847,0.35781896,0.639051,0.002098166,0.00037273648,0.0001525753],"about_ca_topic_score_codex":0.00030908803,"about_ca_topic_score_gemma":0.0000612726,"teacher_disagreement_score":0.3577509,"about_ca_system_score_codex":0.000082731516,"about_ca_system_score_gemma":0.00007028201,"threshold_uncertainty_score":0.47564408},"labels":[],"label_agreement":null},{"id":"W2141197099","doi":"10.1109/cvpr.2006.333","title":"Weakly Supervised Top-down Image Segmentation","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Segmentation; Computer science; Artificial intelligence; Scale-space segmentation; Segmentation-based object categorization; Image segmentation; Set (abstract data type); Pattern recognition (psychology); Minimum spanning tree-based segmentation; Image (mathematics); Training set; Top-down and bottom-up design; Computer vision","score_opus":0.00828248159199732,"score_gpt":0.2605544460249735,"score_spread":0.25227196443297617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141197099","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032364514,0.000015346528,0.96410733,0.0011865898,0.000111520254,0.00018985718,9.706922e-7,0.00075978984,0.030392151],"genre_scores_gemma":[0.03768655,0.0000055675378,0.95657206,0.0016407286,0.000074021234,0.000036438367,0.00001908329,0.0000075837575,0.0039579994],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988927,0.00005154515,0.00024209148,0.00027204197,0.00034624262,0.00019533324],"domain_scores_gemma":[0.99942595,0.000045576122,0.00004720531,0.0003357627,0.000075779666,0.00006973763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019521457,0.00009924112,0.00009016803,0.00009168216,0.000066251916,0.00021065073,0.00045963074,0.00003676248,0.0007312545],"category_scores_gemma":[0.00001999243,0.000086302636,0.0000416433,0.00027395083,0.00004518341,0.0009288143,0.00010786051,0.000066177185,0.00026988026],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024577403,0.00013419754,0.0005338173,0.000016370675,0.0000060906173,0.000024689733,0.00017769403,0.000002648214,0.6873722,0.021334479,0.09909204,0.19130333],"study_design_scores_gemma":[0.00030749367,0.00004539538,0.0018340609,0.000006036197,0.0000029155435,0.000007337437,0.00003870914,0.007139837,0.9848249,0.0049393293,0.0006913675,0.00016262839],"about_ca_topic_score_codex":0.00021825635,"about_ca_topic_score_gemma":0.000009565483,"teacher_disagreement_score":0.2974527,"about_ca_system_score_codex":0.00004336607,"about_ca_system_score_gemma":0.000031415144,"threshold_uncertainty_score":0.8006724},"labels":[],"label_agreement":null},{"id":"W2141389304","doi":"10.1109/iembs.2001.1017334","title":"Appearance-based modelling and segmentation of the hippocampus from MR images","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; A priori and a posteriori; Image segmentation; Computer vision; Pattern recognition (psychology); Scale-space segmentation; Shape analysis (program analysis); Computation; Algorithm","score_opus":0.01534419571605504,"score_gpt":0.25348523294738834,"score_spread":0.23814103723133329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141389304","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031187462,0.00015107669,0.9668195,0.001289374,0.000039628005,0.00013227339,0.0000015968267,0.000096201366,0.00028284077],"genre_scores_gemma":[0.49214602,0.000013640219,0.5070042,0.00078137295,0.000014236966,0.000005546268,7.458874e-7,0.0000021711937,0.000032064036],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99930733,0.000050401868,0.00016691764,0.0001641276,0.00022939245,0.00008180149],"domain_scores_gemma":[0.99955523,0.000052603365,0.000075925775,0.00024245048,0.000037830803,0.00003595792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013646999,0.00006036124,0.00007136497,0.000029986997,0.000044047476,0.000044842618,0.00029279373,0.000023934028,0.000032250904],"category_scores_gemma":[0.00001168487,0.000041159794,0.000024518551,0.000113623835,0.0000707189,0.00031789232,0.000068147594,0.00005485913,0.000004580503],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000062782397,0.000078077195,0.0010493525,0.000025180116,0.000012517653,7.459116e-7,0.0007460273,0.0075233774,0.13171895,0.00076946465,0.0012327329,0.8568373],"study_design_scores_gemma":[0.00019510865,0.000010141896,0.00028338225,0.00002590607,0.0000027957276,3.0982014e-7,0.000017248503,0.31232744,0.6834577,0.0036211906,0.000013315838,0.000045513305],"about_ca_topic_score_codex":0.000082087674,"about_ca_topic_score_gemma":0.0000040685204,"teacher_disagreement_score":0.8567918,"about_ca_system_score_codex":0.000015866803,"about_ca_system_score_gemma":0.000025237192,"threshold_uncertainty_score":0.16784477},"labels":[],"label_agreement":null},{"id":"W2141502150","doi":"10.1109/icassp.2008.4517764","title":"Fast automated stopping-time and edge-strength estimation for anisotropic diffusion","year":2008,"lang":"en","type":"article","venue":"Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Smoothing; Anisotropic diffusion; Enhanced Data Rates for GSM Evolution; Estimator; Noise (video); Computer science; Algorithm; Iterative and incremental development; Edge detection; Diffusion; Scale space; Stopping time; Iterative method; Process (computing); Mathematical optimization; Computer vision; Mathematics; Image processing; Image (mathematics); Statistics; Physics","score_opus":0.03129967091420923,"score_gpt":0.2915892096156246,"score_spread":0.2602895387014154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141502150","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20375247,0.000025765172,0.7936955,0.0008637382,0.00017200926,0.00035852077,0.000014671723,0.0003141815,0.0008031374],"genre_scores_gemma":[0.84314376,0.00006388116,0.15617946,0.00019746512,0.00007396559,0.000015233505,0.00000397911,0.000010408141,0.0003118333],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986276,0.00000838615,0.0003469662,0.00034819284,0.0004946087,0.00017422008],"domain_scores_gemma":[0.99865454,0.00007390855,0.00036026794,0.00006885606,0.0007583333,0.000084076564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019820686,0.00017482444,0.00019485281,0.00014374891,0.000285958,0.0002374144,0.00056523696,0.00008049088,0.000013685082],"category_scores_gemma":[0.00018881445,0.0001366724,0.00003835492,0.00012854784,0.0003076629,0.0006228373,0.00014676241,0.0001537643,0.0000015617278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006798187,0.00018686897,0.00040475512,0.00028888477,0.000033264092,0.0000025604147,0.0014533655,0.0000763492,0.74199015,0.004754871,0.0015712103,0.24916975],"study_design_scores_gemma":[0.00041810158,0.00014267187,0.0006968167,0.0003571998,0.000014880874,0.000042103715,0.00009347723,0.90770453,0.086582355,0.0037893932,0.000008364996,0.00015011623],"about_ca_topic_score_codex":0.0000063340594,"about_ca_topic_score_gemma":1.7849925e-7,"teacher_disagreement_score":0.9076282,"about_ca_system_score_codex":0.000047852394,"about_ca_system_score_gemma":0.000087114706,"threshold_uncertainty_score":0.5573339},"labels":[],"label_agreement":null},{"id":"W2141548717","doi":"10.1109/tip.2008.2006425","title":"A Region Merging Prior for Variational Level Set Image Segmentation","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; General Electric (Canada)","funders":"","keywords":"Image segmentation; Segmentation; Artificial intelligence; Scale-space segmentation; Computer science; Pattern recognition (psychology); Set (abstract data type); Constant (computer programming); Image (mathematics); Segmentation-based object categorization; Level set (data structures); Mathematics; Computer vision; Algorithm","score_opus":0.06183671043305122,"score_gpt":0.32369753782884847,"score_spread":0.26186082739579725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141548717","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041051192,0.000023921253,0.9972423,0.00086281396,0.00023686657,0.00056139746,0.000019243927,0.0005062308,0.00013674541],"genre_scores_gemma":[0.1228836,0.000026823793,0.8754757,0.0006847718,0.000067850335,0.0003287535,0.000012963678,0.000028341943,0.0004912047],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982005,0.00007062823,0.00040666177,0.00051761145,0.0004961029,0.00030853722],"domain_scores_gemma":[0.998913,0.00013389344,0.0002022139,0.00028192985,0.0003460941,0.00012288067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028457405,0.00020830502,0.00017345736,0.0002895641,0.0008579389,0.00023599713,0.00039260247,0.000077122306,0.000027609085],"category_scores_gemma":[0.00003697709,0.00021706341,0.000106819745,0.00048362196,0.00012836169,0.0019871255,0.000004114698,0.00020078747,0.00002086043],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008534519,0.00039603846,0.000006834805,0.00024226197,0.00004598699,0.000041441715,0.0051629227,0.00045310092,0.22103804,0.000093744,0.0031095226,0.7693248],"study_design_scores_gemma":[0.0014103422,0.0001613821,0.00011678227,0.00013052723,0.00003374226,0.00020964685,0.00015468505,0.16433181,0.83211243,0.00080739154,0.000110977926,0.00042026202],"about_ca_topic_score_codex":0.000011441085,"about_ca_topic_score_gemma":0.0000021768788,"teacher_disagreement_score":0.7689045,"about_ca_system_score_codex":0.00014650406,"about_ca_system_score_gemma":0.00027301032,"threshold_uncertainty_score":0.8851589},"labels":[],"label_agreement":null},{"id":"W2141834896","doi":"10.1109/iccv.2013.222","title":"GrabCut in One Cut","year":2013,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":228,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Cut; Segmentation; Image segmentation; Regularization (linguistics); Computer science; Simple (philosophy); Artificial intelligence; Algorithm; Mathematics; Graph; Pattern recognition (psychology); Theoretical computer science","score_opus":0.025029307924084147,"score_gpt":0.2696420757773301,"score_spread":0.24461276785324598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141834896","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023468316,0.000006363886,0.9734543,0.0021102207,0.000036654128,0.00013319825,3.1373308e-8,0.00025002597,0.02166239],"genre_scores_gemma":[0.16555506,0.000009325432,0.828045,0.0037787315,0.00001387208,0.000056833716,5.403648e-7,0.0000027837611,0.0025378072],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994768,0.00002262281,0.0001143168,0.00012637982,0.00014892056,0.0001109444],"domain_scores_gemma":[0.99966604,0.000028328823,0.000015607957,0.00020794442,0.000025251717,0.000056834524],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010019446,0.000036013098,0.000051279847,0.00006600495,0.000010144045,0.00006629301,0.00035806364,0.000019805966,0.0009586845],"category_scores_gemma":[0.000030235742,0.00003041705,0.0000105284125,0.00018422319,0.00001905434,0.0005027452,0.00009825861,0.00005502618,0.00070447184],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.5455727e-7,0.00015963378,0.0014563511,0.000009430323,0.0000036900908,0.000008930203,0.0004166084,5.351085e-7,0.021464549,0.04842456,0.06764536,0.86041],"study_design_scores_gemma":[0.00068948173,0.00014533901,0.035662666,0.000048790673,0.0000014470802,0.0000089056975,0.00007572951,0.034575373,0.82353604,0.103489585,0.0013486993,0.0004179692],"about_ca_topic_score_codex":0.0002962077,"about_ca_topic_score_gemma":0.00001700081,"teacher_disagreement_score":0.859992,"about_ca_system_score_codex":0.000015141297,"about_ca_system_score_gemma":0.000012294035,"threshold_uncertainty_score":0.9999546},"labels":[],"label_agreement":null},{"id":"W2142060887","doi":"10.5565/rev/elcvia.81","title":"SKCS-A Separable Kernel Family with Compact Support to improve visual segmentation of handwritten data","year":2005,"lang":"en","type":"article","venue":"ELCVIA Electronic Letters on Computer Vision and Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Hôpital Notre-Dame","funders":"","keywords":"Computer science; Kernel (algebra); Segmentation; Artificial intelligence; Separable space; Pattern recognition (psychology); Kernel method; Support vector machine; Mathematics","score_opus":0.007595402802328166,"score_gpt":0.3000651067748277,"score_spread":0.2924697039724995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142060887","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051583935,0.0000428088,0.943393,0.0043965597,0.0000308338,0.00032191523,0.000010940125,0.0001436997,0.00007633257],"genre_scores_gemma":[0.6205974,0.00007587433,0.35556525,0.023188274,0.00014621315,0.000014557778,0.00024011535,0.000027956325,0.00014436044],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970609,0.00015604262,0.00052896346,0.00097505737,0.0007493509,0.00052966306],"domain_scores_gemma":[0.9982082,0.00011070137,0.00024707502,0.0010759796,0.00012090942,0.0002371005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006384052,0.00030282236,0.00052689895,0.0006532588,0.00012409959,0.00033050164,0.0010667603,0.000050299903,0.00006874927],"category_scores_gemma":[0.000010495177,0.00024018524,0.000110842266,0.0012630725,0.00008987298,0.0013215172,0.00035745953,0.00024895265,0.000041122476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019534129,0.00070205174,0.0006452761,0.00003900245,0.0014508975,0.000046049936,0.0007345854,0.0009902641,0.3767562,0.00015288708,0.07993451,0.53835297],"study_design_scores_gemma":[0.0021960794,0.0036242197,0.0061610793,0.00006320801,0.0005460808,0.000021598593,0.00003919821,0.79189694,0.19186747,0.00003170826,0.0027780063,0.0007743878],"about_ca_topic_score_codex":0.00009036387,"about_ca_topic_score_gemma":0.000030912284,"teacher_disagreement_score":0.7909067,"about_ca_system_score_codex":0.00013736574,"about_ca_system_score_gemma":0.00009159479,"threshold_uncertainty_score":0.979447},"labels":[],"label_agreement":null},{"id":"W2142097781","doi":"10.1109/camp.1997.631905","title":"VLSI architecture for the embedded extraction of dominant points on object contours","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"CMC Microsystems","keywords":"Computer science; Computer vision; Very-large-scale integration; Artificial intelligence; Sketch; Piecewise; Scale space; Noise (video); Enhanced Data Rates for GSM Evolution; Set (abstract data type); Edge detection; Algorithm; Image processing; Mathematics; Image (mathematics)","score_opus":0.0268593609360607,"score_gpt":0.30749452006618827,"score_spread":0.2806351591301276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142097781","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00045854558,0.00003287357,0.99296796,0.0031002867,0.00015997216,0.00043430453,0.000001445352,0.00009640715,0.0027482207],"genre_scores_gemma":[0.47499362,0.000038838793,0.5195172,0.0029671234,0.00008265732,0.00008333283,0.0000013517663,0.000010452248,0.0023054637],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99921966,0.000049561448,0.00017977209,0.00017299004,0.00024794496,0.00013007832],"domain_scores_gemma":[0.9988418,0.00061383104,0.00009733188,0.0003463774,0.00005864119,0.0000420227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030060386,0.000075751384,0.00009945336,0.000057798043,0.0000615554,0.000035447007,0.00040369367,0.00003563911,0.00020121869],"category_scores_gemma":[0.00019759516,0.000043021453,0.00006629393,0.00011763522,0.000054078515,0.0001228209,0.00003399187,0.000091615024,0.000014218421],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025983265,0.00017072145,0.0000075619723,0.000022260203,0.000025083942,0.0000035964292,0.0014600101,0.00003378996,0.049516257,0.00861535,0.048953045,0.8911663],"study_design_scores_gemma":[0.00058264675,0.00033864524,0.0002291964,0.000029870813,0.0000090036765,0.000014260546,0.000103687526,0.0434828,0.9502008,0.00336959,0.0015332927,0.00010623616],"about_ca_topic_score_codex":0.000017554039,"about_ca_topic_score_gemma":0.000008577754,"teacher_disagreement_score":0.90068454,"about_ca_system_score_codex":0.000017927965,"about_ca_system_score_gemma":0.000008912608,"threshold_uncertainty_score":0.22032034},"labels":[],"label_agreement":null},{"id":"W2142196641","doi":"10.1109/icsmc.1993.384944","title":"Textured image segmentation using autoregressive model and artificial neural network","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Autoregressive model; Artificial intelligence; Segmentation; Artificial neural network; Image segmentation; Computer science; Pattern recognition (psychology); Image (mathematics); Image texture; Texture (cosmology); Computer vision; Identification (biology); Scale-space segmentation; Mathematics; Statistics","score_opus":0.04706500682278777,"score_gpt":0.29917182009263205,"score_spread":0.2521068132698443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142196641","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008800835,0.00006655061,0.98936635,0.00045257082,0.00010685877,0.00017632867,8.128388e-7,0.00031596387,0.0007136979],"genre_scores_gemma":[0.11940699,0.000009681449,0.87901205,0.0011890176,0.000090936715,0.000009740352,0.0000020125935,0.000007470646,0.00027209488],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990114,0.000053658467,0.00020505473,0.00027309117,0.00024458262,0.00021221236],"domain_scores_gemma":[0.9995292,0.000035474808,0.000084627114,0.00019589071,0.000051269366,0.000103560786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011964827,0.00010674304,0.00010144142,0.000051116305,0.0001346051,0.00022710352,0.00021077339,0.000044942077,0.00012438276],"category_scores_gemma":[0.000024728453,0.00009218248,0.000024651126,0.00015112838,0.00007811211,0.000841792,0.00012645249,0.000091537855,0.00001083614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008663644,0.00013236512,0.0001904628,0.000027778551,0.000026913169,0.0000660724,0.0028571987,0.014823714,0.23997755,0.0095943,0.037775695,0.6945193],"study_design_scores_gemma":[0.000103796534,0.000020626358,0.000054132,0.0000076218307,0.0000046955747,0.0000120538,0.000017669292,0.97922075,0.017162954,0.0032853733,0.000004067776,0.00010626467],"about_ca_topic_score_codex":0.0000106232055,"about_ca_topic_score_gemma":0.000002731882,"teacher_disagreement_score":0.964397,"about_ca_system_score_codex":0.0000305423,"about_ca_system_score_gemma":0.000010106741,"threshold_uncertainty_score":0.37590927},"labels":[],"label_agreement":null},{"id":"W2142284881","doi":"10.1117/12.654301","title":"Multi-angle deformation analysis of Hoffa's fat pad","year":2006,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Fat pad; Infrapatellar fat pad; Deformation (meteorology); Biomedical engineering; Knee Joint; Filter (signal processing); Computer science; Anatomy; Materials science; Mathematics; Computer vision; Medicine; Adipose tissue; Surgery; Osteoarthritis","score_opus":0.011603838822199123,"score_gpt":0.2452957738220148,"score_spread":0.2336919349998157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142284881","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9249786,0.000050889423,0.07286595,0.000813487,0.00011667332,0.00039525947,0.000026783808,0.00015429733,0.0005980317],"genre_scores_gemma":[0.31929922,0.000032473028,0.6802236,0.0000965622,0.000114845345,0.000091842296,0.00001701594,0.000022091246,0.00010234748],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99743825,2.6778846e-8,0.000943439,0.00035899863,0.0009469327,0.00031236725],"domain_scores_gemma":[0.9971713,0.00013421009,0.00066053023,0.00009614055,0.0018432436,0.000094593845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006987489,0.0002477369,0.000466709,0.00031398243,0.000063624844,0.00012477547,0.001479486,0.00015471538,0.000011738695],"category_scores_gemma":[0.00039347724,0.0002055261,0.00075390755,0.0011556356,0.00019954446,0.0011453696,0.00024706798,0.00020228387,0.000001038819],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016985348,0.00021973743,0.0010091907,0.0002587945,0.00070896436,7.5431174e-8,0.00029572874,0.00027281416,0.70282865,0.29061988,0.002531803,0.0012373964],"study_design_scores_gemma":[0.0008420471,0.00019927643,0.0069288383,0.00014556227,0.0004209998,0.000004423236,0.00049613515,0.33943862,0.649153,0.0017361757,0.0003033868,0.00033151486],"about_ca_topic_score_codex":0.000040443512,"about_ca_topic_score_gemma":5.2999087e-7,"teacher_disagreement_score":0.6073576,"about_ca_system_score_codex":0.00014409615,"about_ca_system_score_gemma":0.000024567078,"threshold_uncertainty_score":0.8381111},"labels":[],"label_agreement":null},{"id":"W2142366297","doi":"10.1371/journal.pone.0070059","title":"Using Manifold Learning for Atlas Selection in Multi-Atlas Segmentation","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; Medical Research Council; National Institute of Biomedical Imaging and Bioengineering; University of California, Los Angeles; Canadian Institutes of Health Research; National Institutes of Health; Servier; Eisai; Eli Lilly and Company; Alzheimer's Research Trust; National Institute on Aging; National Institute for Health and Care Research; Northern California Institute for Research and Education; University of California, San Diego; Biogen; BioClinica; University College London Hospitals NHS Foundation Trust; Engineering and Physical Sciences Research Council; Dana Foundation; Bayer HealthCare; Alzheimer's Disease Neuroimaging Initiative; F. Hoffmann-La Roche; Versus Arthritis; AstraZeneca; Bristol-Myers Squibb; Synarc; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Atlas (anatomy); Isomap; Nonlinear dimensionality reduction; Segmentation; Artificial intelligence; Manifold alignment; Computer science; Embedding; Pattern recognition (psychology); Manifold (fluid mechanics); Selection (genetic algorithm); Machine learning; Dimensionality reduction; Biology; Anatomy","score_opus":0.11205856008702321,"score_gpt":0.3153095464240938,"score_spread":0.20325098633707062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142366297","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1686166,0.000012763865,0.8303226,0.000114468065,0.000020617528,0.00066268083,1.9961047e-7,0.00020664383,0.00004341121],"genre_scores_gemma":[0.20779552,0.000009409276,0.7914564,0.0001440205,0.000032051186,0.00019673664,0.000005774524,0.000010281106,0.0003498363],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989793,0.00007324645,0.00023159214,0.00025195334,0.0002616866,0.00020223505],"domain_scores_gemma":[0.999547,0.00008105555,0.00010159245,0.00008957426,0.00012150576,0.000059264123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002042587,0.00008753403,0.00012024005,0.0001531406,0.00008323835,0.00012971426,0.00018832028,0.000052871612,0.00006821757],"category_scores_gemma":[0.00013938555,0.00009264567,0.000023507013,0.00027977035,0.000012271596,0.00084146106,0.000056433022,0.00013430708,0.000053973934],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024466633,0.0004178454,0.0047728694,0.00006393189,0.000020183408,8.5248666e-7,0.0004374415,0.00010178446,0.98383343,0.00012468905,0.00012723115,0.010097314],"study_design_scores_gemma":[0.00035087435,0.000070612456,0.0017233328,0.00006448019,0.0000074495338,9.3328293e-7,0.000038913,0.47787514,0.5195763,0.00020043494,0.000003425236,0.000088075125],"about_ca_topic_score_codex":0.00019046657,"about_ca_topic_score_gemma":0.000017393002,"teacher_disagreement_score":0.47777337,"about_ca_system_score_codex":0.00012715925,"about_ca_system_score_gemma":0.000026169764,"threshold_uncertainty_score":0.37779808},"labels":[],"label_agreement":null},{"id":"W2142507690","doi":"10.1109/ccece.2007.51","title":"Statistical Deformation Model For Intensity Based Image Registration","year":2007,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Affine transformation; Principal component analysis; Wavelet; Subspace topology; Artificial intelligence; Deformation (meteorology); Image registration; Computer science; Computer vision; Transformation (genetics); Pattern recognition (psychology); Wavelet transform; Image (mathematics); Mathematics; Algorithm; Geometry","score_opus":0.03137369457745342,"score_gpt":0.3273444424187352,"score_spread":0.2959707478412818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142507690","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015959272,6.482236e-7,0.9972122,0.0005797337,0.00004898906,0.00025950372,0.000003753095,0.00032501767,0.0014106181],"genre_scores_gemma":[0.1156115,2.8041484e-7,0.88236785,0.0018150046,0.000013476522,0.0000127565645,0.000033261455,0.0000029656526,0.00014289646],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920684,0.000011312883,0.0002470788,0.00015654415,0.00022518696,0.00015303763],"domain_scores_gemma":[0.99929434,0.00013129771,0.0000652456,0.00020415019,0.00021821134,0.00008674998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086578715,0.000060566093,0.00006870527,0.00006284042,0.000058897593,0.00008093303,0.00019425116,0.00003793161,0.000018356572],"category_scores_gemma":[0.0002590128,0.00005270582,0.000023280676,0.00008954294,0.000042163418,0.0006399449,0.000027440012,0.00005166839,0.000011385014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013424044,0.00027504103,0.000103233244,0.00013941679,0.000011546293,0.0000135588625,0.0007751746,0.00021851469,0.074873224,0.4772811,0.084134236,0.36204073],"study_design_scores_gemma":[0.00016175948,0.000038172144,0.00021886129,0.0000031152213,0.0000016937955,0.0000016979141,0.000010832758,0.83759594,0.15207359,0.00981259,0.000023277453,0.000058472455],"about_ca_topic_score_codex":0.000011183326,"about_ca_topic_score_gemma":0.000012963561,"teacher_disagreement_score":0.8373774,"about_ca_system_score_codex":0.000053612257,"about_ca_system_score_gemma":0.00005395156,"threshold_uncertainty_score":0.21492809},"labels":[],"label_agreement":null},{"id":"W2142761958","doi":"10.1109/cvpr.2010.5539903","title":"A study on continuous max-flow and min-cut approaches","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":238,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Maximum flow problem; Flow (mathematics); Minimum cut; Mathematics; Maximum cut; A priori and a posteriori; Mathematical optimization; Cut; Continuous optimization; Algorithm; Minimum-cost flow problem; Image segmentation; Computer science; Segmentation; Optimization problem; Artificial intelligence; Graph; Flow network; Discrete mathematics","score_opus":0.04405937074025668,"score_gpt":0.2846806397403958,"score_spread":0.24062126900013914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142761958","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14248693,0.000004379128,0.8433232,0.00074471196,0.00015372243,0.00045151988,4.4672728e-7,0.00039861162,0.012436485],"genre_scores_gemma":[0.63292843,7.117832e-7,0.36506626,0.00072116294,0.000029473495,0.000039101556,4.8612765e-7,0.0000045702973,0.001209767],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99919397,0.000040044444,0.00012581146,0.00028496367,0.00022983507,0.0001253909],"domain_scores_gemma":[0.99940187,0.00007503247,0.000031911266,0.00036834422,0.000022390575,0.00010047294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030563097,0.00008326247,0.00010282857,0.00005860959,0.000046370835,0.00014301218,0.00035799626,0.00003516331,0.000056048524],"category_scores_gemma":[0.00006766922,0.0000621928,0.000015057946,0.00009647391,0.000060234714,0.00018367378,0.00015329265,0.00015706876,0.0000350692],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000060598377,0.0008708227,0.0050266595,0.00000825295,0.000022960723,0.00006878789,0.003855967,2.050979e-7,0.0063937698,0.010882696,0.008803316,0.9640605],"study_design_scores_gemma":[0.006505326,0.00619371,0.101013474,0.000056530396,0.000060743092,0.00028251944,0.0073779337,0.063741855,0.79536515,0.012139158,0.005126646,0.0021369464],"about_ca_topic_score_codex":0.000015387583,"about_ca_topic_score_gemma":0.00002055177,"teacher_disagreement_score":0.96192354,"about_ca_system_score_codex":0.000004199098,"about_ca_system_score_gemma":0.000013125293,"threshold_uncertainty_score":0.25361484},"labels":[],"label_agreement":null},{"id":"W2142806209","doi":"10.1007/978-3-540-30125-7_30","title":"Parameterized Hierarchical Annealing for Scientific Models","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Parameterized complexity; Computer science; Simulated annealing; Computational complexity theory; Binary number; Theoretical computer science; Mathematical optimization; Algorithm; Mathematics","score_opus":0.04154809855304583,"score_gpt":0.29514569779073985,"score_spread":0.25359759923769404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142806209","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000027159813,0.00017869657,0.99507135,0.00086169416,0.0018230861,0.0010457928,0.0000138167925,0.0004335914,0.0005448151],"genre_scores_gemma":[0.014366683,0.000017347103,0.98335654,0.0016588039,0.00024683872,0.000059232407,0.000019483683,0.000038410348,0.00023668348],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99481493,0.000039258233,0.00074017537,0.00213143,0.0014254265,0.0008487705],"domain_scores_gemma":[0.99672925,0.00069547235,0.00030836658,0.0015452089,0.00039225322,0.00032944532],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0018114842,0.0005156434,0.0005965175,0.0011294209,0.00047170342,0.0015396717,0.0040571243,0.00035248234,0.0000246491],"category_scores_gemma":[0.00024136262,0.0004759465,0.00021396666,0.00068643765,0.0018220093,0.0011257608,0.0011473204,0.0007594409,0.000015956699],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015926884,0.000040468924,8.410636e-7,0.00009215191,0.000011125742,0.00004990063,0.0010125338,0.029940112,0.0013797272,0.080921605,0.000048095964,0.8864875],"study_design_scores_gemma":[0.0002938034,0.00014159555,8.408578e-7,0.000265348,0.000004384405,0.00001629191,5.7750462e-8,0.4508721,0.009244588,0.53868747,0.00012295258,0.00035058518],"about_ca_topic_score_codex":0.000009416731,"about_ca_topic_score_gemma":0.000006953526,"teacher_disagreement_score":0.88613695,"about_ca_system_score_codex":0.00044653332,"about_ca_system_score_gemma":0.0012196444,"threshold_uncertainty_score":0.9997692},"labels":[],"label_agreement":null},{"id":"W2142916759","doi":"10.1109/tip.2010.2066982","title":"Multiregion Image Segmentation by Parametric Kernel Graph Cuts","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":213,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Electric (Canada); Institut National de la Recherche Scientifique","funders":"","keywords":"Piecewise; Synthetic aperture radar; Cut; Image segmentation; Kernel (algebra); Mathematics; Algorithm; Graph; Artificial intelligence; Computer science; Segmentation; Pattern recognition (psychology); Discrete mathematics","score_opus":0.011990999046263775,"score_gpt":0.28872948647771907,"score_spread":0.2767384874314553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142916759","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004099212,0.000042217835,0.99308354,0.000512337,0.00050638546,0.00038113722,0.000010531776,0.00097439095,0.00039024532],"genre_scores_gemma":[0.36455506,0.000040492137,0.63426626,0.0005834053,0.000032161617,0.00013598235,0.000006959135,0.000030440508,0.00034923232],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99781424,0.00008366338,0.00043361116,0.00064416806,0.00063130213,0.0003930294],"domain_scores_gemma":[0.99870956,0.00012505944,0.00021481975,0.00048345598,0.000238503,0.00022860107],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034782654,0.00027521944,0.00020181999,0.0005201803,0.00044764736,0.00066392083,0.0006597695,0.00013903386,0.00009820223],"category_scores_gemma":[0.000037381076,0.00027172398,0.000111599635,0.0013692207,0.00023801542,0.0028299622,0.0000053149424,0.0006850855,0.000104832165],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006695616,0.0002245774,0.0000038981375,0.000039363913,0.000007211813,0.000007481672,0.00028996103,0.000008811054,0.52965635,0.000005503203,0.0012332383,0.46851695],"study_design_scores_gemma":[0.0005780043,0.00007847014,0.000029868319,0.000045401655,0.000021670283,0.000032469274,0.000070506234,0.03442559,0.9638802,0.00041683324,0.00010395531,0.00031705652],"about_ca_topic_score_codex":0.000047251833,"about_ca_topic_score_gemma":0.000008277012,"teacher_disagreement_score":0.46819988,"about_ca_system_score_codex":0.000066228364,"about_ca_system_score_gemma":0.00009498875,"threshold_uncertainty_score":0.9999735},"labels":[],"label_agreement":null},{"id":"W2143090675","doi":"10.1016/j.cmpb.2006.07.001","title":"Prostate boundary segmentation from ultrasound images using 2D active shape models: Optimisation and extension to 3D","year":2006,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":83,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"Canadian Institutes of Health Research","keywords":"Segmentation; Computer science; Artificial intelligence; Boundary (topology); Active shape model; 3D ultrasound; Image segmentation; Computer vision; Landmark; Slicing; Gold standard (test); Ultrasound; Pattern recognition (psychology); Mathematics; Medicine; Statistics","score_opus":0.05907135633123749,"score_gpt":0.3701108132026969,"score_spread":0.3110394568714594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143090675","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.079385094,0.000617128,0.9184602,0.00041002227,0.0002124417,0.00073848566,0.000004089823,0.00015513139,0.000017414848],"genre_scores_gemma":[0.013184964,0.00013517021,0.98567665,0.0007019764,0.00015579227,0.000045494446,0.00007943228,0.000013488581,0.0000070157294],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980529,0.00033372943,0.00042518837,0.0006434069,0.00028825205,0.00025653676],"domain_scores_gemma":[0.999062,0.00029245205,0.00013955934,0.00025373782,0.00010514334,0.00014710131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010198944,0.00020736782,0.0002844124,0.0002844754,0.000118198506,0.00027290324,0.000191324,0.00010247246,0.0000056953763],"category_scores_gemma":[0.000028989589,0.00017661239,0.000020543595,0.0005379865,0.00021497344,0.00073924696,0.00023099531,0.00018237052,4.819228e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019668272,0.000064740394,0.00032785212,0.000021291848,0.000008352181,0.000011435758,0.0015161586,0.0000427858,0.08747878,0.00004807797,0.00007470831,0.91038615],"study_design_scores_gemma":[0.0017024664,0.00081564754,0.013846643,0.00049337273,0.000039405455,0.000076897806,0.00024794583,0.8930027,0.057044167,0.031893965,0.00031716426,0.000519613],"about_ca_topic_score_codex":0.00053602434,"about_ca_topic_score_gemma":0.0000060501975,"teacher_disagreement_score":0.9098665,"about_ca_system_score_codex":0.00007104614,"about_ca_system_score_gemma":0.000032716718,"threshold_uncertainty_score":0.7202044},"labels":[],"label_agreement":null},{"id":"W2143272559","doi":"10.1109/tip.2010.2048965","title":"A Novel Rotationally Invariant Region-Based Hidden Markov Model for Efficient 3-D Image Segmentation","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Image segmentation; Artificial intelligence; Invariant (physics); Segmentation; Pattern recognition (psychology); Hidden Markov model; Image processing; Computer science; Computer vision; Scale-space segmentation; Markov chain; Markov process; Mathematics; Image (mathematics); Statistics; Machine learning","score_opus":0.02213765973395871,"score_gpt":0.2904218959343003,"score_spread":0.2682842362003416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143272559","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00057209085,0.0000061934143,0.9958239,0.0016207698,0.0002857018,0.000940035,0.000031660027,0.0005763802,0.00014322043],"genre_scores_gemma":[0.20595999,0.0000012852394,0.79212356,0.0010789741,0.000036734036,0.00058371207,0.000011729018,0.00003270132,0.00017129336],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978596,0.000039483562,0.00049163145,0.0006454952,0.00059816573,0.0003656014],"domain_scores_gemma":[0.9983524,0.00023234132,0.00024740375,0.00043583752,0.00054393354,0.00018804689],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047919192,0.0002649802,0.00019821255,0.00035765127,0.000531808,0.0006166697,0.0006061571,0.000116964606,0.000032832235],"category_scores_gemma":[0.00006307336,0.00026521925,0.00013423257,0.00048588702,0.00017250386,0.0012005647,0.0000047309923,0.0004023252,0.00001231787],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044785,0.000442662,4.4049247e-7,0.000116258205,0.000010744492,0.000004420864,0.0005889885,0.0045811892,0.7447186,0.00006209109,0.00022545933,0.24920434],"study_design_scores_gemma":[0.00081292714,0.00004306059,0.0000069855687,0.000053636435,0.000019392884,0.000014856076,0.000026204832,0.6674196,0.33110797,0.00029270648,0.0000031646093,0.00019946096],"about_ca_topic_score_codex":0.0000134664915,"about_ca_topic_score_gemma":0.000015434502,"teacher_disagreement_score":0.66283846,"about_ca_system_score_codex":0.00011127762,"about_ca_system_score_gemma":0.0005727409,"threshold_uncertainty_score":0.99998},"labels":[],"label_agreement":null},{"id":"W2143493257","doi":"10.1145/1836845.1836903","title":"A work-efficient GPU algorithm for level set segmentation","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Algorithm; Computer science; Set (abstract data type); Segmentation; Computational complexity theory; Domain (mathematical analysis); Field (mathematics); Image segmentation; Level set (data structures); Reduction (mathematics); Artificial intelligence; Mathematics","score_opus":0.041394047225461555,"score_gpt":0.3241311243055851,"score_spread":0.2827370770801235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143493257","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00083897874,0.0000035596772,0.9967503,0.0005868667,0.00048666148,0.0004908152,0.000007952232,0.00034947533,0.0004853756],"genre_scores_gemma":[0.0056687556,0.0000011172003,0.99156445,0.0012176925,0.00007311465,0.00016258004,0.000018964794,0.000007451352,0.0012858844],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99902105,0.000020868243,0.00019277193,0.0002835223,0.0002849303,0.00019684053],"domain_scores_gemma":[0.9993041,0.00008961461,0.00006114913,0.0003224913,0.00010997685,0.00011267458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039449288,0.0000918185,0.00008031479,0.000082479986,0.0000811599,0.00013234808,0.00045600257,0.00005449032,0.00012725739],"category_scores_gemma":[0.000060095535,0.00007886782,0.000045378587,0.00024254103,0.000042542717,0.00015805489,0.00010669569,0.00010558904,0.00004531685],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010310539,0.00004841008,0.000016165928,0.0000033886322,0.0000044214166,9.834187e-7,0.00023446155,0.0000035147095,0.013406328,0.0019088805,0.015182487,0.96918994],"study_design_scores_gemma":[0.0009077995,0.00013698117,0.00063527114,0.000012712264,0.000007443274,0.00001259268,0.00009100048,0.29190543,0.70119506,0.0015379534,0.0032236367,0.00033412894],"about_ca_topic_score_codex":0.000010099571,"about_ca_topic_score_gemma":0.0000040036903,"teacher_disagreement_score":0.9688558,"about_ca_system_score_codex":0.000019627383,"about_ca_system_score_gemma":0.000046469006,"threshold_uncertainty_score":0.32161367},"labels":[],"label_agreement":null},{"id":"W2143503452","doi":"10.1109/iembs.2005.1616447","title":"Adaboost and Support Vector Machines for White Matter Lesion Segmentation in MR Images","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"College of Science and Health","keywords":"AdaBoost; Artificial intelligence; Support vector machine; Segmentation; Computer science; Image segmentation; Pattern recognition (psychology); Computer vision","score_opus":0.015419509150374402,"score_gpt":0.3036841557888406,"score_spread":0.28826464663846624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143503452","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036062393,0.00001900429,0.98855114,0.0056379777,0.00005339622,0.00036865633,0.0000043826726,0.00013408126,0.0016251414],"genre_scores_gemma":[0.073698886,0.00002264454,0.91776574,0.004823629,0.000056152716,0.000093631024,0.000019833684,0.000009625498,0.0035098356],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992087,0.000029766055,0.00021489021,0.0002457007,0.00015188054,0.00014909814],"domain_scores_gemma":[0.99965674,0.000054982196,0.00004848362,0.00014889815,0.000030255736,0.000060657687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022555048,0.00008960951,0.00009466172,0.000110842906,0.000037609836,0.00010498546,0.0001782264,0.0000323781,0.00036561815],"category_scores_gemma":[0.000016439906,0.00007433856,0.000019109753,0.000102359925,0.000028645914,0.000794664,0.00008298183,0.00004806904,0.000039409104],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028495935,0.0001485699,0.029589,0.00009689052,0.000007727482,0.000007131981,0.0013154285,0.0000063581215,0.13249908,0.0008553601,0.11042802,0.72501796],"study_design_scores_gemma":[0.0015719256,0.00022532212,0.14565225,0.00004302018,0.000007847815,0.000026314312,0.000083209976,0.019108942,0.83015,0.0012441792,0.0014782693,0.00040869243],"about_ca_topic_score_codex":0.000017356846,"about_ca_topic_score_gemma":0.000027770899,"teacher_disagreement_score":0.72460926,"about_ca_system_score_codex":0.000031141084,"about_ca_system_score_gemma":0.000016619255,"threshold_uncertainty_score":0.40032625},"labels":[],"label_agreement":null},{"id":"W2143830140","doi":"10.1109/tmi.2004.826954","title":"An Interacting Multiple Model Probabilistic Data Association Filter for Cavity Boundary Extraction From Ultrasound Images","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Queen's University","funders":"University of British Columbia","keywords":"Artificial intelligence; Computer vision; Robustness (evolution); Computer science; Image segmentation; Active contour model; Boundary (topology); Level set (data structures); Segmentation; Mathematics; Pattern recognition (psychology)","score_opus":0.03316280661924547,"score_gpt":0.3441154613224595,"score_spread":0.31095265470321404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143830140","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029896684,0.00002012442,0.9921553,0.0023052131,0.0010678178,0.0005049605,0.00025083218,0.00067057234,0.000035511744],"genre_scores_gemma":[0.5983294,0.000023217051,0.39991695,0.0012598672,0.00014525771,0.000116691706,0.00014365013,0.000026179703,0.00003876407],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969029,0.00016432497,0.00055586285,0.0008894615,0.001062461,0.00042502698],"domain_scores_gemma":[0.9962099,0.0019748183,0.00024067941,0.001014503,0.00019956051,0.00036049742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012927481,0.00024028424,0.00024628683,0.00015607639,0.0004522636,0.0005041524,0.0012777785,0.00014212007,0.00010368562],"category_scores_gemma":[0.0013780292,0.00024121522,0.000093319046,0.0002231411,0.000117507225,0.0038598427,0.000014260239,0.0007341426,0.000021875954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009997846,0.0022366226,0.00020357453,0.00009339027,0.00013927867,0.000052375268,0.0021666994,0.02398749,0.17908812,0.00005053391,0.0037077332,0.7881742],"study_design_scores_gemma":[0.0011648786,0.000050344395,0.000087971406,0.00015353576,0.00005214565,0.000023835553,0.00010945023,0.8755831,0.11913429,0.0032678337,0.00007819547,0.00029441415],"about_ca_topic_score_codex":0.00051207485,"about_ca_topic_score_gemma":0.00018016841,"teacher_disagreement_score":0.8515956,"about_ca_system_score_codex":0.0005869197,"about_ca_system_score_gemma":0.00036636676,"threshold_uncertainty_score":0.9836471},"labels":[],"label_agreement":null},{"id":"W2144585946","doi":"10.1109/tmi.2010.2052065","title":"A Coupled Global Registration and Segmentation Framework With Application to Magnetic Resonance Prostate Imagery","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"National Center for Research Resources; National Institute of Biomedical Imaging and Bioengineering","keywords":"Artificial intelligence; Computer science; Computer vision; Segmentation; Image registration; Active shape model; Image segmentation; Task (project management); Medical imaging; Set (abstract data type); Pattern recognition (psychology); Image (mathematics)","score_opus":0.0050882966430884965,"score_gpt":0.2742740159563468,"score_spread":0.2691857193132583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144585946","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065219724,0.00004929207,0.98622084,0.005774518,0.00025808482,0.0006559906,0.0000060094617,0.00041472714,0.00009854401],"genre_scores_gemma":[0.49498007,0.000045437,0.5011166,0.0033951118,0.000051939038,0.00034612181,0.0000035588766,0.000014208635,0.00004699818],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99782914,0.000062792635,0.00033283944,0.0005689136,0.0009263212,0.00027999235],"domain_scores_gemma":[0.9987419,0.00016176589,0.00008700524,0.00045741865,0.00012970832,0.0004221508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048390936,0.00018292411,0.00015062164,0.00011011436,0.00020280914,0.00022150333,0.00036734287,0.000088370965,0.00007407208],"category_scores_gemma":[0.00008646318,0.00016366216,0.000027809401,0.0006353945,0.0002318561,0.0005995045,0.000006368434,0.00052293786,0.000029421246],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003814763,0.00010602522,0.00020483823,0.000018988096,0.0000039289343,0.000025598338,0.0003448328,0.000023156848,0.015511542,0.0005140535,0.00018197359,0.9830269],"study_design_scores_gemma":[0.0037483824,0.00093284674,0.008843974,0.0008405819,0.00009606227,0.0008369635,0.00046778144,0.7832529,0.18832497,0.00961429,0.001472757,0.0015684745],"about_ca_topic_score_codex":0.0000857853,"about_ca_topic_score_gemma":0.00007528933,"teacher_disagreement_score":0.9814584,"about_ca_system_score_codex":0.000064225074,"about_ca_system_score_gemma":0.0001426503,"threshold_uncertainty_score":0.66739494},"labels":[],"label_agreement":null},{"id":"W2144887288","doi":"10.1016/j.cmpb.2006.04.009","title":"Anatomical structure modeling from medical images","year":2006,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"U.S. National Library of Medicine; National Center for Research Resources; National Institute of Mental Health; Natural Sciences and Engineering Research Council of Canada; National Science Foundation; Institut national de recherche en informatique et en automatique (INRIA); Consortia for Improving Medicine with Innovation and Technology","keywords":"Delaunay triangulation; Tetrahedron; Computer science; Artificial intelligence; Computer vision; Set (abstract data type); Marching cubes; Triangulation; Medical imaging; 3D modeling; Algorithm; Mathematics; Visualization; Geometry","score_opus":0.031599991599225816,"score_gpt":0.368739441009692,"score_spread":0.3371394494104662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144887288","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011729635,0.001141608,0.9841024,0.0020496321,0.0004256655,0.00022261532,0.0000024910648,0.00028408592,0.000041858555],"genre_scores_gemma":[0.04488202,0.00006513763,0.95336306,0.0011085557,0.0005019552,0.000013874095,0.0000478955,0.000010627493,0.000006860019],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976747,0.0003666095,0.00053490995,0.00058464333,0.00051746715,0.00032168996],"domain_scores_gemma":[0.99900055,0.0002637056,0.00007570791,0.0003705255,0.000057450172,0.00023206774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012142401,0.0002018363,0.0003667066,0.00022378986,0.000051830775,0.00012642285,0.0006847765,0.00026082137,0.000056381934],"category_scores_gemma":[0.000060685383,0.00015264579,0.000037702684,0.0005527801,0.00025890383,0.00022329694,0.00043511123,0.00047822692,0.0000011196996],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003418194,0.000060375252,0.0009665313,0.00001587658,0.0000078117255,0.000061380066,0.00012581352,0.0000041409367,0.0017360976,0.0007572299,0.00052044063,0.9957409],"study_design_scores_gemma":[0.0010361811,0.00015888971,0.0015486176,0.00018461284,0.000008520717,0.000048353148,0.000014976366,0.93885696,0.0042628148,0.052478395,0.0011464743,0.000255207],"about_ca_topic_score_codex":0.0005346607,"about_ca_topic_score_gemma":0.000013262951,"teacher_disagreement_score":0.99548566,"about_ca_system_score_codex":0.000029309665,"about_ca_system_score_gemma":0.00004510022,"threshold_uncertainty_score":0.62247145},"labels":[],"label_agreement":null},{"id":"W2144937751","doi":"10.1109/isspit.2006.270795","title":"An Elliptical Level Set Method for Automatic TRUS Prostate Image Segmentation","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Council","keywords":"Initialization; Artificial intelligence; Computer science; Image segmentation; Computer vision; Segmentation; Level set (data structures); Prostate; Speckle noise; Histogram; Speckle pattern; Level set method; Pattern recognition (psychology); Medicine; Image (mathematics)","score_opus":0.03305218314415599,"score_gpt":0.3719127787637671,"score_spread":0.3388605956196111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144937751","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010186777,0.000004410193,0.99604905,0.0006230053,0.00008261488,0.0007999178,0.000015692714,0.0007512501,0.00065535697],"genre_scores_gemma":[0.004035704,0.0000011613818,0.9938917,0.0008418156,0.00006263035,0.00022136932,0.000076768316,0.000013813295,0.0008550421],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985113,0.00013967359,0.00036364465,0.00038094027,0.0003380404,0.00026640279],"domain_scores_gemma":[0.99913675,0.00016525046,0.00009041679,0.00037746032,0.00011532016,0.00011479245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000730253,0.00013399216,0.00014438122,0.000099856334,0.00009130078,0.0002756547,0.0004396193,0.000050342558,0.00009693614],"category_scores_gemma":[0.000056261928,0.00011505018,0.00005102542,0.00020492067,0.000047682457,0.0008111238,0.000054407777,0.00006251882,0.000034331773],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009534862,0.00026301766,0.00006342072,0.00009438484,0.000014101955,0.000009295216,0.00058139185,0.00002880104,0.31875533,0.025687298,0.030018747,0.6244747],"study_design_scores_gemma":[0.00046820127,0.00019656365,0.0005393974,0.000007069025,0.000008241933,0.000010062282,0.000050788414,0.5012068,0.48170403,0.015530637,0.00011777904,0.00016045918],"about_ca_topic_score_codex":0.000075667376,"about_ca_topic_score_gemma":0.000010641515,"teacher_disagreement_score":0.62431425,"about_ca_system_score_codex":0.00005193936,"about_ca_system_score_gemma":0.00005897393,"threshold_uncertainty_score":0.469161},"labels":[],"label_agreement":null},{"id":"W2145570744","doi":"10.1109/icip.1997.647741","title":"Multiscale segmentation and approximation for significant description of 2D contours","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Inscribed figure; Curvature; Image segmentation; Line segment; Computer science; Level set (data structures); Artificial intelligence; Constant (computer programming); Scale-space segmentation; Set (abstract data type); Geometry; Approximation algorithm; Algorithm; Computer vision; Pattern recognition (psychology); Mathematics","score_opus":0.04688936098986827,"score_gpt":0.2722644795132443,"score_spread":0.22537511852337602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145570744","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008453891,0.00002873898,0.9901296,0.00027849767,0.000049185972,0.00051713386,0.0000021077403,0.000113522765,0.00042733224],"genre_scores_gemma":[0.34276488,0.000019106268,0.656738,0.00013068426,0.000009093815,0.0000650401,0.0000047685394,0.0000027874528,0.00026566835],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99937147,0.000030378382,0.00020410787,0.00015824063,0.00015171058,0.000084104635],"domain_scores_gemma":[0.9995903,0.00007026314,0.00009640959,0.00011775558,0.0000793276,0.000045992023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019435296,0.00005502578,0.000081973645,0.000063702886,0.00003514092,0.000046926478,0.000110377376,0.0000296566,0.00005179218],"category_scores_gemma":[0.000053689608,0.000048635615,0.000019792686,0.00008702555,0.00004422613,0.0005607853,0.000023705004,0.000023241117,0.0000025184652],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000041869084,0.00010343095,0.0003145783,0.00006897588,0.00000782943,3.2119405e-7,0.0013579816,0.0000038597773,0.37724409,0.0058819237,0.005551844,0.60946095],"study_design_scores_gemma":[0.00062025816,0.00015795985,0.0007639252,0.000015156847,0.0000065748977,0.0000018104536,0.00026984356,0.35429156,0.6421605,0.0015930588,0.000030267283,0.00008911855],"about_ca_topic_score_codex":0.00001705429,"about_ca_topic_score_gemma":0.0000017784945,"teacher_disagreement_score":0.60937184,"about_ca_system_score_codex":0.000018765164,"about_ca_system_score_gemma":0.0000036912513,"threshold_uncertainty_score":0.19833027},"labels":[],"label_agreement":null},{"id":"W2145649715","doi":"10.1109/iembs.2008.4650554","title":"Measuring and evaluating ground truth for boundary detection in medical images","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Ground truth; Boundary (topology); Computer science; Artificial intelligence; Process (computing); Computer vision; Interface (matter); Mathematics","score_opus":0.08407120490181105,"score_gpt":0.3466606757328352,"score_spread":0.26258947083102413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145649715","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05901828,0.00008013656,0.9397224,0.0002610989,0.00010733592,0.000195051,1.9608827e-7,0.00018494036,0.00043056085],"genre_scores_gemma":[0.6345167,0.000036676265,0.36489213,0.00037825454,0.000043318945,0.000063842526,6.263271e-7,0.0000052670566,0.00006319461],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987966,0.000074809286,0.00022303207,0.0002513032,0.00049413746,0.00016014682],"domain_scores_gemma":[0.9993607,0.00031275625,0.000040735795,0.00013044696,0.000055614928,0.000099763165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001160282,0.00006958494,0.000100125646,0.0001069103,0.00013728913,0.000068762754,0.00021042103,0.00005040204,0.000029699033],"category_scores_gemma":[0.0009812088,0.00006178715,0.000020226908,0.00015716224,0.00009696974,0.00052339013,0.00010035294,0.000104412844,0.0000021831352],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000070619935,0.0000322079,0.0002962243,0.000027988148,0.000004073925,0.000019765885,0.00046858957,5.5469616e-7,0.013152614,0.00036096026,0.00014062133,0.9854893],"study_design_scores_gemma":[0.0027508857,0.00056747167,0.030670965,0.00013176678,0.000007374554,0.0005054039,0.00017771125,0.21852304,0.73110527,0.014989014,0.00011463831,0.00045647245],"about_ca_topic_score_codex":0.00005982578,"about_ca_topic_score_gemma":0.000021385009,"teacher_disagreement_score":0.98503286,"about_ca_system_score_codex":0.000046333702,"about_ca_system_score_gemma":0.000089994035,"threshold_uncertainty_score":0.2519607},"labels":[],"label_agreement":null},{"id":"W2145846051","doi":"10.1007/978-3-642-33415-3_81","title":"A Fast Convex Optimization Approach to Segmenting 3D Scar Tissue from Delayed-Enhancement Cardiac MR Images","year":2012,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Robarts Clinical Trials","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Segmentation; Solver; Regular polygon; Relaxation (psychology); Algorithm; Artificial intelligence; Operator (biology); Pattern recognition (psychology); Mathematical optimization; Mathematics; Medicine","score_opus":0.012357015133411586,"score_gpt":0.271404154268908,"score_spread":0.25904713913549643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145846051","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024449644,0.0002246582,0.9948858,0.00037416205,0.0011132049,0.000592133,0.00000505577,0.0002607974,0.00009923495],"genre_scores_gemma":[0.24050896,0.0000087025055,0.7576086,0.0015411869,0.0002517458,0.000056815534,0.00001021489,0.000010581732,0.0000032192756],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99675614,0.00015193947,0.00041608434,0.00090220006,0.0009820809,0.0007915726],"domain_scores_gemma":[0.99832755,0.00020992898,0.00014341414,0.00079611735,0.00017506404,0.00034789418],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001253686,0.0002648484,0.00030202174,0.00034728303,0.00024366214,0.0005444802,0.0016686324,0.000084233034,0.000027692378],"category_scores_gemma":[0.00015129035,0.0002456945,0.000045101726,0.0018313276,0.00022479956,0.0016882615,0.0008456342,0.00024025726,0.000030978837],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029608116,0.00018678416,0.0011875757,0.000015607799,0.000012208708,0.000002542839,0.0046007214,0.16172916,0.04881058,0.000049507435,0.00017951432,0.78322285],"study_design_scores_gemma":[0.00012688506,0.00006353661,0.00030997998,0.000037500467,0.000004645213,0.000003278838,0.000003639036,0.6033198,0.39576674,0.00008343465,0.000027035892,0.0002534874],"about_ca_topic_score_codex":0.00012273216,"about_ca_topic_score_gemma":0.0000017200559,"teacher_disagreement_score":0.78296936,"about_ca_system_score_codex":0.00022955875,"about_ca_system_score_gemma":0.00011481183,"threshold_uncertainty_score":0.9999995},"labels":[],"label_agreement":null},{"id":"W2146045281","doi":"10.1109/tpami.2004.1262180","title":"Nonparametric multiscale energy-based model and its application in some imagery problems","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Nonparametric statistics; Computer science; Artificial intelligence; Regularization (linguistics); Energy minimization; Pattern recognition (psychology); Rendering (computer graphics); Segmentation; Minification; Multiresolution analysis; Mathematical optimization; Computer vision; Algorithm; Mathematics; Wavelet; Wavelet transform","score_opus":0.018357711292542118,"score_gpt":0.2761142035637797,"score_spread":0.2577564922712376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146045281","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029048917,0.00016886127,0.9962015,0.00040459036,0.000022984992,0.00017582931,0.000013192998,0.00009995456,0.000008158183],"genre_scores_gemma":[0.97531265,0.0004282755,0.023257015,0.00082845055,0.0000046436876,0.00012570253,0.0000052123555,0.000008105158,0.000029942568],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986283,0.000046203666,0.00036727008,0.0005141167,0.00026148936,0.00018258166],"domain_scores_gemma":[0.99934703,0.00008327621,0.000092559785,0.00029932373,0.000050377985,0.00012745787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021886862,0.0001676125,0.0002322456,0.0009629309,0.000086090105,0.000084207095,0.00026800347,0.00006372112,0.000009771817],"category_scores_gemma":[0.000005434829,0.00015420736,0.000087605076,0.0014240246,0.000054966553,0.00039701807,0.0000056459025,0.00018582797,0.000005401092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003342656,0.00024646198,0.00011065495,0.000022194377,0.00004044088,0.0000037323766,0.0001259702,0.34687337,0.003728823,0.00015821772,6.788118e-7,0.6486861],"study_design_scores_gemma":[0.000115071765,0.000037665068,0.00013487425,0.000016343438,0.000044448832,0.0000016162758,0.0000036083961,0.7113036,0.28705356,0.0011671749,8.272326e-7,0.00012121497],"about_ca_topic_score_codex":0.0012442028,"about_ca_topic_score_gemma":0.0012505336,"teacher_disagreement_score":0.9729445,"about_ca_system_score_codex":0.000054714892,"about_ca_system_score_gemma":0.0000277766,"threshold_uncertainty_score":0.6288394},"labels":[],"label_agreement":null},{"id":"W2146149474","doi":"10.1007/11566489_106","title":"Spectral Clustering Algorithms for Ultrasound Image Segmentation","year":2005,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Center for Research Resources; U.S. National Library of Medicine; National Institute of Mental Health","keywords":"Segmentation-based object categorization; Artificial intelligence; Image segmentation; Scale-space segmentation; Computer science; Segmentation; Pattern recognition (psychology); Cluster analysis; Spectral clustering; Computer vision; Minimum spanning tree-based segmentation; Region growing","score_opus":0.017301420795834768,"score_gpt":0.30902466448877397,"score_spread":0.2917232436929392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146149474","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023751764,0.000034350567,0.9950803,0.0011908234,0.0005376769,0.0004779059,0.0000019185563,0.00028461104,0.000017221853],"genre_scores_gemma":[0.16603765,0.000006388708,0.83172196,0.0018751194,0.00030834367,0.000037898822,0.0000028506604,0.000007949803,0.0000018235835],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99782664,0.00004410277,0.00033690906,0.00072064827,0.0005445132,0.0005272206],"domain_scores_gemma":[0.9987064,0.00044671414,0.00010281984,0.000478661,0.00012806637,0.00013731713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092952285,0.00018073783,0.00016558147,0.00031063013,0.00020044051,0.00057362375,0.001422102,0.00005538646,0.00001640189],"category_scores_gemma":[0.00021230006,0.00016822678,0.00005433757,0.0010280542,0.00028362536,0.0017861222,0.00027526883,0.00017252626,0.000013809512],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025075701,0.00004213015,0.00008774158,0.00001151049,0.0000019754389,0.000004096678,0.0009911068,0.006765484,0.0773877,0.00006558466,0.00003932732,0.91460085],"study_design_scores_gemma":[0.00025035447,0.00007503026,0.00031488194,0.000017730927,0.0000011614023,0.00002844058,8.1274595e-7,0.5902978,0.40634456,0.0025146774,0.000012005208,0.00014253032],"about_ca_topic_score_codex":0.00001607451,"about_ca_topic_score_gemma":0.000031656175,"teacher_disagreement_score":0.91445833,"about_ca_system_score_codex":0.0002605401,"about_ca_system_score_gemma":0.00012808465,"threshold_uncertainty_score":0.6860089},"labels":[],"label_agreement":null},{"id":"W2146330379","doi":"10.1109/iembs.2005.1616449","title":"Intensity Robust Viscous Fluid Deformation Based Morphometry Using Regionally Adapted Mutual Information","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Deformation (meteorology); Mutual information; Intensity (physics); Geology; Computer science; Artificial intelligence; Physics; Optics","score_opus":0.030725807379947786,"score_gpt":0.2588863381075773,"score_spread":0.22816053072762948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146330379","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008672401,0.0000064394253,0.9877024,0.0011259781,0.000109927125,0.0002079362,0.0000016509393,0.00062168407,0.0015515637],"genre_scores_gemma":[0.28390026,0.0000025724116,0.7097016,0.0062721646,0.000042514108,0.0000060804095,0.00003680184,0.00000406345,0.000033945376],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985518,0.000045197605,0.0004470418,0.00015808905,0.0005901137,0.00020775257],"domain_scores_gemma":[0.99893785,0.00004814317,0.0001731844,0.00035197928,0.0003647039,0.00012416442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043674014,0.00012930568,0.00013323213,0.00036190427,0.000118977194,0.00019388631,0.00043435182,0.00008027402,0.00014802227],"category_scores_gemma":[0.00015717329,0.00011574667,0.000050883773,0.00054809166,0.000050004088,0.004882254,0.0001313425,0.00013109388,0.00015802146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015356211,0.00046560355,0.0005343701,0.00014682513,0.00007284602,0.000021068927,0.0028273305,0.053110473,0.04381015,0.014872696,0.091148816,0.79283625],"study_design_scores_gemma":[0.0003137033,0.000041535168,0.00042877172,0.000021190683,0.0000041123044,0.00003098763,0.000059610702,0.9443985,0.053805344,0.00007713849,0.00067089644,0.00014821546],"about_ca_topic_score_codex":0.00005263243,"about_ca_topic_score_gemma":0.000005757778,"teacher_disagreement_score":0.89128804,"about_ca_system_score_codex":0.00020958573,"about_ca_system_score_gemma":0.00012722662,"threshold_uncertainty_score":0.4720012},"labels":[],"label_agreement":null},{"id":"W2146549011","doi":"10.1109/cvpr.2013.287","title":"Efficient 3D Endfiring TRUS Prostate Segmentation with Globally Optimized Rotational Symmetry","year":2013,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Prostate biopsy; Computer science; Segmentation; Relaxation (psychology); Artificial intelligence; Image segmentation; Prostate; Computation; Algorithm; Computer vision","score_opus":0.007375486814900313,"score_gpt":0.2437098592447222,"score_spread":0.2363343724298219,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146549011","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014700009,0.000018605977,0.97737074,0.0006966953,0.000084883424,0.00087234826,0.0000014460609,0.0005644974,0.0056907474],"genre_scores_gemma":[0.08388504,0.000003616282,0.91413903,0.0012338393,0.000019107776,0.0002530691,0.0000118404905,0.000009552769,0.00044489384],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981924,0.00007168984,0.0003074147,0.00038449303,0.0007684386,0.00027556357],"domain_scores_gemma":[0.9991143,0.00009054493,0.00012863221,0.00028475522,0.00021830386,0.00016350311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026132405,0.00015517522,0.00013662237,0.00012217269,0.00010458567,0.00037723826,0.00045539127,0.00003647782,0.0005497749],"category_scores_gemma":[0.000042287673,0.000115808805,0.000031465195,0.00042254257,0.000065917,0.0005255038,0.0001337965,0.000096361866,0.00020428312],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006762281,0.000578898,0.001468839,0.00008778391,0.00013170022,0.000046083565,0.0024227642,0.019682461,0.028614962,0.01653533,0.00981501,0.92054856],"study_design_scores_gemma":[0.0029608142,0.00036276114,0.005908266,0.000081316415,0.000016455377,0.000053740023,0.0003465299,0.8624323,0.12602285,0.001201627,0.000054675882,0.00055867626],"about_ca_topic_score_codex":0.0001284891,"about_ca_topic_score_gemma":0.000001481716,"teacher_disagreement_score":0.9199899,"about_ca_system_score_codex":0.00010373892,"about_ca_system_score_gemma":0.00008929727,"threshold_uncertainty_score":0.601965},"labels":[],"label_agreement":null},{"id":"W2146865307","doi":"10.1109/tpami.2004.1273940","title":"A statistical model for contours in images","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal; Université de Montréal","funders":"","keywords":"Artificial intelligence; Statistical model; Computer science; Image segmentation; Segmentation; Pattern recognition (psychology); Expectation–maximization algorithm; Markov random field; Iterated function; Simulated annealing; Algorithm; Mathematics; Maximum likelihood; Statistics","score_opus":0.02626716470366291,"score_gpt":0.3198691011978763,"score_spread":0.2936019364942134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146865307","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023784973,0.000024591938,0.99877083,0.00057563494,0.000037921694,0.00018852086,0.00007442606,0.00007481636,0.000015418842],"genre_scores_gemma":[0.8408649,0.00009831518,0.15824768,0.0006498634,0.00000392895,0.00007034188,0.000004372977,0.0000054711672,0.000055134675],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988925,0.000033147437,0.0003153383,0.00037760902,0.00019443859,0.00018695973],"domain_scores_gemma":[0.9993944,0.0001476175,0.000051700445,0.00024536616,0.000049614664,0.00011131152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022960032,0.00013155921,0.00022546432,0.00039007174,0.000069848946,0.00008492645,0.00027275563,0.000041545678,0.000037176582],"category_scores_gemma":[0.0000111113795,0.00011684633,0.00010149607,0.00046144018,0.00007207699,0.00020679396,0.0000030160104,0.00015311438,0.0000049000914],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010211692,0.00021147562,0.00007796481,0.000015432288,0.0000951597,0.0000094181005,0.00043205338,0.079106495,0.0005225613,0.00055852276,0.000011978233,0.9189487],"study_design_scores_gemma":[0.0001922141,0.00009622348,0.00019144686,0.000016715254,0.00009477989,0.0000027801495,0.000022626034,0.7776835,0.21634248,0.005207237,0.0000013345445,0.00014865339],"about_ca_topic_score_codex":0.00078370556,"about_ca_topic_score_gemma":0.0012435345,"teacher_disagreement_score":0.91880006,"about_ca_system_score_codex":0.00004723961,"about_ca_system_score_gemma":0.00003071066,"threshold_uncertainty_score":0.4764855},"labels":[],"label_agreement":null},{"id":"W2146917638","doi":"10.1109/tmi.2013.2251421","title":"Groupwise Conditional Random Forests for Automatic Shape Classification and Contour Quality Assessment in Radiotherapy Planning","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Princess Margaret Cancer Centre; University Health Network","funders":"Canadian Institutes of Health Research","keywords":"Conditional random field; Random forest; Segmentation; Computer science; Artificial intelligence; Quality assurance; Quality (philosophy); Radiation treatment planning; Set (abstract data type); Decision tree; Plan (archaeology); Stability (learning theory); Pattern recognition (psychology); Data mining; Radiation therapy; Machine learning; Medicine","score_opus":0.03933759797099011,"score_gpt":0.37526504559800145,"score_spread":0.3359274476270113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146917638","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011367011,0.000064960375,0.98127174,0.0059296205,0.00021147353,0.00084261113,0.0000056359954,0.00026190816,0.000045027165],"genre_scores_gemma":[0.8461192,0.00003248576,0.15067823,0.0022391465,0.000038684928,0.00084882707,0.0000100945035,0.000012674755,0.000020605952],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978903,0.00022889448,0.0005573502,0.00038705676,0.00066526985,0.0002711158],"domain_scores_gemma":[0.99815065,0.0011564285,0.00012616733,0.00022401204,0.00008476858,0.00025799952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011306856,0.00015609147,0.0002582342,0.00022357218,0.0001548029,0.00017760236,0.00031973168,0.00007786738,0.00037900946],"category_scores_gemma":[0.00008963951,0.00014253918,0.000066595654,0.00018666263,0.00017640184,0.00083406304,0.0000030248345,0.00034404825,0.0000053776425],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019523515,0.0003057709,0.0014546638,0.00009012082,0.00003284941,0.000011369456,0.0006639517,0.00010876964,0.004707018,0.0009688489,0.0011737938,0.9904633],"study_design_scores_gemma":[0.003135631,0.000042854972,0.032577973,0.00013443625,0.0000064833876,0.000017594508,0.00013141721,0.9595644,0.0010688864,0.0031273058,0.00003017412,0.00016283312],"about_ca_topic_score_codex":0.00006848725,"about_ca_topic_score_gemma":0.0000121535595,"teacher_disagreement_score":0.9903005,"about_ca_system_score_codex":0.00014000114,"about_ca_system_score_gemma":0.00013628982,"threshold_uncertainty_score":0.5812579},"labels":[],"label_agreement":null},{"id":"W2147364566","doi":"10.1016/j.engappai.2006.01.011","title":"Automatic clinical image segmentation using pathological modeling, PCA and SVM","year":2006,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Computer science; Support vector machine; Segmentation; Pattern recognition (psychology); Image segmentation; Principal component analysis; Feature extraction; Classifier (UML); Image processing; Computer vision; Image (mathematics)","score_opus":0.04819741666510396,"score_gpt":0.3513870900051519,"score_spread":0.3031896733400479,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147364566","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055526853,0.00006520413,0.9436411,0.00006624401,0.00005009025,0.00031248922,0.000002318022,0.00030502008,0.0000306566],"genre_scores_gemma":[0.38017753,0.000014392475,0.6196766,0.00001712477,0.000047603062,0.000053974156,0.0000036435413,0.0000061750657,0.000002937655],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985564,0.00003736005,0.00072421867,0.00031305113,0.00021034139,0.00015867894],"domain_scores_gemma":[0.9991979,0.00014527855,0.00013192461,0.00034337444,0.0001119919,0.00006951327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005085915,0.00011563928,0.00016892623,0.0001297828,0.00006736097,0.00007746398,0.00036205412,0.00007312869,0.000016614276],"category_scores_gemma":[0.0001034896,0.00011779023,0.000048086735,0.00036643297,0.000108169574,0.0002787028,0.00011453601,0.00013522597,0.000012648956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027148633,0.00033882394,0.0001773768,0.000083472536,0.000013911953,0.000005038277,0.0002039186,0.040744297,0.15112266,0.08213652,0.000048431055,0.7251228],"study_design_scores_gemma":[0.00001461268,0.000027651402,0.000093481736,0.000016848917,0.0000071200116,0.0000064026394,0.000024954417,0.91941917,0.071305856,0.00897133,0.000007667921,0.00010492233],"about_ca_topic_score_codex":0.000046695644,"about_ca_topic_score_gemma":0.0000011791032,"teacher_disagreement_score":0.87867486,"about_ca_system_score_codex":0.000027569164,"about_ca_system_score_gemma":0.000026726064,"threshold_uncertainty_score":0.48033464},"labels":[],"label_agreement":null},{"id":"W2147469139","doi":"10.1016/j.ultras.2006.07.003","title":"Needle and seed segmentation in intra-operative 3D ultrasound-guided prostate brachytherapy","year":2006,"lang":"en","type":"article","venue":"Ultrasonics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Robarts Clinical Trials","funders":"","keywords":"Brachytherapy; Prostate brachytherapy; Prostate; 3D ultrasound; Ultrasound; Medicine; Segmentation; Radiology; Computer science; Radiation therapy; Artificial intelligence; Internal medicine; Cancer","score_opus":0.00820559523059305,"score_gpt":0.2713475718095558,"score_spread":0.26314197657896277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147469139","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24207352,0.00026528118,0.7550285,0.0005568454,0.00012495116,0.0006957847,0.000007509295,0.00031624243,0.00093136285],"genre_scores_gemma":[0.5025805,0.00035628182,0.49481124,0.0015915089,0.000056657525,0.00010708689,0.000055493652,0.000021937176,0.0004192646],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984692,0.0001335829,0.00039304554,0.00039822585,0.00030815482,0.00029782407],"domain_scores_gemma":[0.99922174,0.00024969433,0.00011428195,0.00025464545,0.00008050532,0.000079134334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039972182,0.000177309,0.00018080718,0.00013843707,0.00009006102,0.00025917724,0.00026895318,0.00007667464,0.000028232333],"category_scores_gemma":[0.000097105105,0.00016731856,0.000025066118,0.0004691419,0.00010212325,0.00086203305,0.000036870275,0.00019249532,0.000013682718],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029383114,0.0005285178,0.04919407,0.00006822767,0.000034373898,0.00005969851,0.012828114,0.00040007394,0.8156868,0.01122412,0.005668119,0.10427847],"study_design_scores_gemma":[0.0039203553,0.00046193233,0.09050733,0.00012890754,0.000014335433,0.000264511,0.0008385486,0.024150992,0.8677482,0.010306799,0.0006809431,0.0009771236],"about_ca_topic_score_codex":0.00025074393,"about_ca_topic_score_gemma":0.00009200871,"teacher_disagreement_score":0.26050702,"about_ca_system_score_codex":0.0001120024,"about_ca_system_score_gemma":0.000074908916,"threshold_uncertainty_score":0.68230534},"labels":[],"label_agreement":null},{"id":"W2147868149","doi":"10.1109/42.993131","title":"Computation of the mid-sagittal plane in 3-D brain images","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":151,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Sagittal plane; Computation; Algorithm; Image plane; Plane (geometry); Iterated function; Similarity (geometry); Computer science; Mathematics; Artificial intelligence; Computer vision; Geometry; Mathematical analysis; Image (mathematics)","score_opus":0.015404633935397509,"score_gpt":0.27917851478097283,"score_spread":0.26377388084557535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147868149","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016931155,0.000058541817,0.9836208,0.013326551,0.00046541673,0.00017118134,0.000003563137,0.00015141683,0.000509373],"genre_scores_gemma":[0.9788568,0.000030458603,0.017980918,0.0029382557,0.000023397859,0.000020886968,6.296068e-7,0.000009925094,0.00013870774],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99800575,0.00020042772,0.00040828466,0.0002730164,0.0008878589,0.00022469113],"domain_scores_gemma":[0.9990801,0.00035574974,0.000091949616,0.00029246107,0.000045767778,0.00013394826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044854463,0.00012197343,0.00016030816,0.00019744538,0.00008820292,0.000045300894,0.0006729804,0.00005614215,0.00030225152],"category_scores_gemma":[0.00010331819,0.00009472353,0.00007974725,0.00058881677,0.00026726414,0.00037279722,0.000007734811,0.0004496626,0.000026452353],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000038670746,0.00035795063,0.0001283269,0.000033850298,0.000010347576,0.000049882616,0.0008476058,0.00045530693,0.008301529,0.00004656568,0.0051465095,0.98461825],"study_design_scores_gemma":[0.0015986831,0.00006468073,0.0011690103,0.0005098086,0.0000123301215,0.00013166126,0.00013668946,0.48704055,0.5080427,0.00081098446,0.00017689278,0.0003060033],"about_ca_topic_score_codex":0.00006909678,"about_ca_topic_score_gemma":0.000014581683,"teacher_disagreement_score":0.98431224,"about_ca_system_score_codex":0.00005438177,"about_ca_system_score_gemma":0.000041063835,"threshold_uncertainty_score":0.38627136},"labels":[],"label_agreement":null},{"id":"W2148023989","doi":"","title":"COMPUTATIONAL PLACENTAL PATHOLOGY: USING PLACENTAL GEOMETRY TO ASSESS PLACENTAL FUNCTION","year":2009,"lang":"en","type":"dissertation","venue":"Summit (Simon Fraser University)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Mitacs","keywords":"Placenta; Obstetrics; Fetus; Chorionic villi; Placenta Diseases; Medicine; Andrology; Biology; Pregnancy","score_opus":0.020811034363562807,"score_gpt":0.26576464386722576,"score_spread":0.24495360950366296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148023989","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30730942,0.000066390465,0.6855631,0.00008622347,0.0018491684,0.00084505905,0.000114446644,0.0007387517,0.0034274554],"genre_scores_gemma":[0.69963276,0.00009521367,0.2701829,0.0034044052,0.0006415497,0.000014001133,0.010394946,0.00021165807,0.015422582],"study_design_codex":"not_applicable","study_design_gemma":"qualitative","domain_scores_codex":[0.99632734,0.00027821475,0.00047668538,0.0012117402,0.0010828824,0.00062314153],"domain_scores_gemma":[0.9981309,0.00014719684,0.00041404643,0.0005264302,0.00028324325,0.0004981444],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028053386,0.00058340316,0.0005315343,0.0017651188,0.0004396775,0.00024565376,0.0013934697,0.00058053574,0.00024275538],"category_scores_gemma":[0.00006214327,0.00073105044,0.0002444762,0.0018236265,0.000086015076,0.0012825619,0.00028472455,0.0007635561,0.00016983085],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0070896456,0.006553598,0.18500806,0.0013174472,0.0017563205,0.012944845,0.0016869724,0.02068573,0.006694692,0.020652423,0.4025831,0.33302718],"study_design_scores_gemma":[0.040035248,0.010111752,0.11761693,0.005768916,0.0033043874,0.000015476,0.24326615,0.18830357,0.23695283,0.008412057,0.12160135,0.024611348],"about_ca_topic_score_codex":0.00008763019,"about_ca_topic_score_gemma":0.0024504894,"teacher_disagreement_score":0.41538018,"about_ca_system_score_codex":0.0010863028,"about_ca_system_score_gemma":0.00043150832,"threshold_uncertainty_score":0.99951404},"labels":[],"label_agreement":null},{"id":"W2148310803","doi":"10.1109/lsp.2009.2036654","title":"KPAC: A Kernel-Based Parametric Active Contour Method for Fast Image Segmentation","year":2009,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Active contour model; Artificial intelligence; Kernel (algebra); Computer vision; Computer science; Image segmentation; Parametric statistics; Segmentation; Scale-space segmentation; Pattern recognition (psychology); Mathematics; Statistics","score_opus":0.020273126132299764,"score_gpt":0.32887448648951567,"score_spread":0.3086013603572159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148310803","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014198887,0.000027571565,0.99267346,0.004579705,0.000102874685,0.0006408784,0.000006777292,0.00047485784,0.0000740051],"genre_scores_gemma":[0.15834253,7.173872e-7,0.820832,0.020549592,0.00011484095,0.000104610495,0.000012689598,0.000016011818,0.00002701109],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979291,0.00016252528,0.0003762296,0.00057756994,0.000540132,0.0004144386],"domain_scores_gemma":[0.998728,0.0003432946,0.00032857817,0.0002295171,0.00021274992,0.0001578679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058638887,0.00024033425,0.00025622293,0.0003492523,0.00020834224,0.00046435444,0.0006079637,0.00007600451,0.000016013539],"category_scores_gemma":[0.000099370845,0.0002297045,0.00011184913,0.000790408,0.000083741696,0.0012180046,0.000018538045,0.00020743642,0.000010514388],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026391112,0.000056198853,0.000002968935,0.000034462202,0.000007228024,0.000009349203,0.00026950237,0.00016716054,0.47938025,0.0000074425607,0.0027999931,0.51723903],"study_design_scores_gemma":[0.0008839215,0.0002120818,0.00012062285,0.00007357721,0.000021764354,0.000006334281,0.000046169636,0.18523069,0.8125022,0.00060259306,0.0000334598,0.00026658096],"about_ca_topic_score_codex":0.000012470533,"about_ca_topic_score_gemma":4.285083e-7,"teacher_disagreement_score":0.5169725,"about_ca_system_score_codex":0.00017273238,"about_ca_system_score_gemma":0.00014421718,"threshold_uncertainty_score":0.9367078},"labels":[],"label_agreement":null},{"id":"W2148826257","doi":"10.1109/iadcc.2009.4809012","title":"LV Contour Extraction from Cardiac MR Images Using Random Walks Approach","year":2009,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Hospital for Sick Children","keywords":"Image segmentation; Segmentation; Random walk; Artificial intelligence; Computer science; Image (mathematics); Computer vision; Range (aeronautics); Selection (genetic algorithm); Random walker algorithm; Variable (mathematics); Image processing; Pattern recognition (psychology); Scale-space segmentation; Mathematics; Statistics; Engineering","score_opus":0.024091254436704235,"score_gpt":0.3056271260639887,"score_spread":0.28153587162728444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148826257","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021407793,0.00014571962,0.98589575,0.00031880662,0.00021953127,0.0002531707,0.0000024457863,0.000555732,0.010468047],"genre_scores_gemma":[0.1570853,0.00002937096,0.8413794,0.0009369928,0.00015145286,0.000008727612,0.000010164899,0.0000054215157,0.0003931502],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99870515,0.00013296942,0.00024671206,0.00035406195,0.00036401517,0.00019708832],"domain_scores_gemma":[0.9992307,0.00010797412,0.000089622816,0.00038353133,0.00007224525,0.000115924966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035417543,0.00012419127,0.0002097343,0.00007366421,0.000085562155,0.00022418966,0.00037897853,0.000070604714,0.00009331043],"category_scores_gemma":[0.0000648412,0.000104978295,0.00008681819,0.0001694421,0.000034166205,0.0009661003,0.000038390983,0.00014239342,0.000021659851],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019900814,0.00014178532,0.00008431328,0.000004842185,0.000028924936,0.0000136866765,0.00037589477,0.000024563296,0.5262682,0.0010384149,0.013461522,0.45853797],"study_design_scores_gemma":[0.00152607,0.000071081464,0.002543906,0.000023612642,0.00003008188,0.000010878863,0.00012933929,0.10370956,0.88546175,0.005570414,0.00053769356,0.00038563836],"about_ca_topic_score_codex":0.00030518646,"about_ca_topic_score_gemma":5.4170016e-7,"teacher_disagreement_score":0.45815232,"about_ca_system_score_codex":0.000055779397,"about_ca_system_score_gemma":0.000035961042,"threshold_uncertainty_score":0.42808905},"labels":[],"label_agreement":null},{"id":"W2149473206","doi":"10.1109/icip.2009.5414213","title":"Geometric registration of images with arbitrarily-shaped local intensity variations from shadows","year":2009,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer vision; Artificial intelligence; Pixel; Computer science; Intensity (physics); Image registration; Focus (optics); Set (abstract data type); Image (mathematics); Optics; Physics","score_opus":0.014379825072992961,"score_gpt":0.25460203986400304,"score_spread":0.24022221479101008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149473206","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012040911,0.000017728365,0.9928174,0.0013961481,0.00003599914,0.00016313633,0.0000039705164,0.00028545293,0.004076061],"genre_scores_gemma":[0.5438897,0.0000048559514,0.45499852,0.0009651441,0.000014118354,0.0000031895127,0.000011994407,0.000001976352,0.000110521694],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99883133,0.000036671627,0.00030176816,0.0002808604,0.00041771372,0.00013163485],"domain_scores_gemma":[0.9989728,0.00010738576,0.00016564089,0.0004163441,0.0002504902,0.000087378685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020630355,0.000098926546,0.00016498886,0.0002061952,0.000045276174,0.000078131656,0.00041323339,0.00005232264,0.00012362386],"category_scores_gemma":[0.00013093838,0.00007756104,0.00003303942,0.00089672406,0.000083926185,0.00064053055,0.00004337216,0.00011676428,0.000011555205],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011092932,0.0012992244,0.0021861794,0.00003054551,0.00012672546,0.00008308791,0.0012356704,0.00008190196,0.11011163,0.08883328,0.02959386,0.76630694],"study_design_scores_gemma":[0.001214689,0.0011486625,0.1712808,0.00007384826,0.00004721796,0.000025268775,0.00015948096,0.08807299,0.7020738,0.03531051,0.00008132185,0.0005113762],"about_ca_topic_score_codex":0.0004886037,"about_ca_topic_score_gemma":0.000018905903,"teacher_disagreement_score":0.7657956,"about_ca_system_score_codex":0.000034669167,"about_ca_system_score_gemma":0.0000767636,"threshold_uncertainty_score":0.31628472},"labels":[],"label_agreement":null},{"id":"W2149663006","doi":"10.1109/icassp.2004.1326596","title":"A novel segmentation technique for carotid ultrasound images","year":2004,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Segmentation; Image segmentation; Ultrasound; Radiology; Medicine","score_opus":0.0180374563035573,"score_gpt":0.29884274616407525,"score_spread":0.28080528986051795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149663006","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000015659583,0.0000071175477,0.9965183,0.0006926007,0.00008742927,0.0011296604,0.0000072176504,0.0004810943,0.0010608807],"genre_scores_gemma":[0.009109409,0.000007792884,0.98857397,0.0012330888,0.000036149282,0.0007272938,0.000013452159,0.000010124647,0.00028873023],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990198,0.000013604007,0.00022135263,0.0003031319,0.00023914153,0.00020294516],"domain_scores_gemma":[0.9993079,0.00011713132,0.000071338465,0.00030173914,0.00011351345,0.00008840014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030195384,0.00011328502,0.00010364514,0.00010025256,0.00008642749,0.0001263112,0.00046152854,0.000052361964,0.000032170126],"category_scores_gemma":[0.00012893334,0.00010063483,0.00005654611,0.00022775766,0.00005352924,0.00065722247,0.00006201269,0.00006781468,0.000015055208],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010533096,0.00006073673,0.000014880431,0.000012945779,0.000004806776,5.590226e-7,0.00011412479,0.000008131872,0.9904396,0.0075328043,0.0012393901,0.0005709444],"study_design_scores_gemma":[0.0005090958,0.000099759825,0.00013737247,0.000015260432,0.0000034094955,0.00003826246,0.000036071717,0.000004991635,0.9915338,0.007478114,0.000014689461,0.00012917536],"about_ca_topic_score_codex":0.000074921714,"about_ca_topic_score_gemma":0.0000051268935,"teacher_disagreement_score":0.009093749,"about_ca_system_score_codex":0.00010459547,"about_ca_system_score_gemma":0.00008001515,"threshold_uncertainty_score":0.41037694},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low"}],"label_agreement":"agree"},{"id":"W2150227353","doi":"10.1109/bmei.2008.352","title":"Texture Feature based Automated Seeded Region Growing in Abdominal MRI Segmentation","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Image texture; Feature (linguistics); Image segmentation; Region growing; Computer vision; Seeding; Computer science; Segmentation; Texture (cosmology); Pattern recognition (psychology); Scale-space segmentation; Variogram; Image (mathematics); Physics","score_opus":0.017077118347123933,"score_gpt":0.2747531864917025,"score_spread":0.25767606814457855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150227353","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044797543,0.000021324793,0.9890346,0.0034155615,0.00009978782,0.00029412817,3.7278107e-7,0.0016933492,0.0009611167],"genre_scores_gemma":[0.27684155,0.00001376125,0.7169217,0.005487203,0.00003400121,0.000043279295,0.00002014557,0.00001124184,0.00062711793],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865437,0.000108174216,0.0002388408,0.00036034756,0.00040507148,0.0002332233],"domain_scores_gemma":[0.9993651,0.000076243974,0.00008620305,0.00030819804,0.00006472562,0.00009955404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021515947,0.00014252405,0.00014819678,0.00023953468,0.00010180342,0.00006393961,0.00041712553,0.00011590412,0.000047184345],"category_scores_gemma":[0.00004939971,0.00012493021,0.0000465633,0.0006848671,0.000055079458,0.0010728964,0.00006428462,0.00020081835,0.000024577012],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010523403,0.0006624177,0.009707912,0.00015742959,0.000037373065,0.0026585117,0.0044887625,0.00073259097,0.29778767,0.0024173264,0.4884435,0.1928013],"study_design_scores_gemma":[0.001449307,0.00010000554,0.008837469,0.00008739852,0.0000042301094,0.00016431359,0.00011449428,0.6210831,0.36744395,0.00021715506,0.00018717535,0.00031141745],"about_ca_topic_score_codex":0.00005561971,"about_ca_topic_score_gemma":0.000008787171,"teacher_disagreement_score":0.6203505,"about_ca_system_score_codex":0.000117619515,"about_ca_system_score_gemma":0.00009110655,"threshold_uncertainty_score":0.5094507},"labels":[],"label_agreement":null},{"id":"W2150708286","doi":"10.1109/dspws.2006.265472","title":"Towards Medical Ultrasound Image Segmentation with Limited Prior Knowledge","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Computer vision; Segmentation; Computer science; Image segmentation; Ultrasound; Initialization; Active contour model; Noise (video); Scale-space segmentation; Ellipse; Medical imaging; Image (mathematics); Pattern recognition (psychology); Mathematics; Medicine; Radiology","score_opus":0.009467913540469568,"score_gpt":0.2825149950663048,"score_spread":0.27304708152583523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150708286","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036785905,0.00003671506,0.9582076,0.00097048824,0.00008847956,0.00026305713,0.0000010596287,0.0008625776,0.03589141],"genre_scores_gemma":[0.06508962,0.000018448867,0.93145573,0.0011551788,0.00012171995,0.00006373354,0.000030590465,0.000015723306,0.002049255],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99814934,0.00009506276,0.00030882127,0.00037430626,0.0008084889,0.00026399165],"domain_scores_gemma":[0.999037,0.00016820288,0.000079640224,0.000356175,0.00016937248,0.00018957819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041325588,0.00015397825,0.00014541551,0.0001249168,0.000093198774,0.00021069271,0.00064755126,0.00008181854,0.0008621467],"category_scores_gemma":[0.00013096348,0.00011132023,0.00003686238,0.0005184358,0.00015523256,0.0007449641,0.00011447857,0.00014563193,0.00015498354],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002769313,0.001145611,0.0012742509,0.000095585565,0.000053370804,0.00019837328,0.0011666963,0.0000022195518,0.095790915,0.03379215,0.11949208,0.74696106],"study_design_scores_gemma":[0.0018108508,0.00035130585,0.009260889,0.00008112946,0.000018556948,0.00019981686,0.00011910964,0.0071236887,0.9761326,0.0026216127,0.0017427529,0.0005377031],"about_ca_topic_score_codex":0.0001539737,"about_ca_topic_score_gemma":0.00008246447,"teacher_disagreement_score":0.88034165,"about_ca_system_score_codex":0.00007687285,"about_ca_system_score_gemma":0.00022756327,"threshold_uncertainty_score":0.9439902},"labels":[],"label_agreement":null},{"id":"W2151059949","doi":"10.1109/ccece.2011.6030435","title":"A novel Accelerated Greedy Snake Algorithm for active contours","year":2011,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Convergence (economics); Algorithm; Greedy algorithm; Computer science; Pixel; Similarity (geometry); Object (grammar); Relaxation (psychology); Artificial intelligence; Mathematical optimization; Image (mathematics); Mathematics","score_opus":0.10284117520899189,"score_gpt":0.3178101030545348,"score_spread":0.2149689278455429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151059949","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000073994874,0.0000039634415,0.9911418,0.00015062247,0.00015271634,0.00042842093,0.000008226708,0.0004922177,0.0075480193],"genre_scores_gemma":[0.0033492518,0.0000021269886,0.9937947,0.0015877861,0.00003324802,0.000126412,0.000005723373,0.000007756284,0.0010929959],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991476,0.000016108464,0.00017065961,0.00027642734,0.00017703029,0.0002121725],"domain_scores_gemma":[0.9992837,0.0000655515,0.00007033855,0.00026047867,0.00019878658,0.000121170255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016363207,0.00010016953,0.00011795131,0.00007045884,0.000056897636,0.00005959058,0.0005927176,0.00005390652,0.0002935651],"category_scores_gemma":[0.000047197103,0.00008225766,0.000045343422,0.00018957726,0.00005110266,0.0006065252,0.000106149346,0.0000693486,0.000024707353],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004505544,0.000120381555,0.0000044375947,0.0000026620867,0.000018975568,0.0000028808506,0.0008024147,1.9614516e-8,0.0076455236,0.0044343565,0.004663479,0.98230034],"study_design_scores_gemma":[0.0010989577,0.00029073865,0.0009976506,0.0000107555,0.000007937779,0.000012683794,0.00015980979,0.05914136,0.9345334,0.0029076904,0.00059529947,0.00024368484],"about_ca_topic_score_codex":0.00014701198,"about_ca_topic_score_gemma":0.0000127326075,"teacher_disagreement_score":0.9820567,"about_ca_system_score_codex":0.000028564762,"about_ca_system_score_gemma":0.000055251985,"threshold_uncertainty_score":0.335437},"labels":[],"label_agreement":null},{"id":"W2151096815","doi":"10.1007/pl00010988","title":"Endocardial Boundary E timation and Tracking in Echocardiographic Images using Deformable Template and Markov Random Fields","year":2001,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Heart Institute; Computer Research Institute of Montréal","funders":"Institut national de recherche en informatique et en automatique (INRIA)","keywords":"Artificial intelligence; Markov random field; Segmentation; Image segmentation; Pattern recognition (psychology); Active contour model; Hidden Markov model; Prior probability; Computer science; Computer vision; Tracking (education); Point distribution model; Level set (data structures); Boundary (topology); Mathematics; Bayesian probability","score_opus":0.013309599970584101,"score_gpt":0.28822004179571764,"score_spread":0.2749104418251335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151096815","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08100115,0.00036333705,0.9180935,0.00015453038,0.0000061451888,0.00019407719,0.0000040184796,0.000042815955,0.00014041898],"genre_scores_gemma":[0.9730347,0.0011928962,0.025442401,0.00020406673,0.000022896344,0.00007713143,0.000015317102,0.0000037888537,0.000006794769],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991832,0.000056142773,0.00023228172,0.00027080273,0.0001268941,0.00013067412],"domain_scores_gemma":[0.99956083,0.00007225628,0.00007637218,0.00019045277,0.000030762676,0.00006933863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033666057,0.00008864965,0.00019987891,0.00037422526,0.00016753616,0.00033136804,0.00010143076,0.00003975085,0.000008950024],"category_scores_gemma":[0.0000074910254,0.000083186096,0.000055329616,0.00078261964,0.00006782107,0.00050519226,0.00007636919,0.000086977314,7.393505e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034839827,0.000022851948,0.1830606,0.000022009028,0.00012387603,0.0000047318986,0.00021221326,0.00006948624,0.0011407206,0.00003653605,0.000021665624,0.8152818],"study_design_scores_gemma":[0.0020905784,0.000040613573,0.42314714,0.000059911803,0.0006845383,0.00010415706,0.00019167636,0.5629509,0.0052943616,0.0042521483,0.00062034494,0.0005636056],"about_ca_topic_score_codex":0.00031884073,"about_ca_topic_score_gemma":0.00006398409,"teacher_disagreement_score":0.8926511,"about_ca_system_score_codex":0.000011238607,"about_ca_system_score_gemma":0.0000086656255,"threshold_uncertainty_score":0.33922303},"labels":[],"label_agreement":null},{"id":"W2151670649","doi":"10.1007/978-3-642-23944-1_11","title":"A PET/CT Directed, 3D Ultrasound-Guided Biopsy System for Prostate Cancer","year":2011,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials","funders":"National Center for Research Resources; National Cancer Institute","keywords":"Prostate cancer; Biopsy; Prostate biopsy; Ultrasound; Medicine; Segmentation; Radiology; Prostate; Imaging phantom; 3D ultrasound; Image registration; Computer science; Cancer; Artificial intelligence; Image (mathematics)","score_opus":0.02483587470806197,"score_gpt":0.2926995473445426,"score_spread":0.26786367263648064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151670649","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007863241,0.000107740976,0.9886905,0.00018702041,0.0013550178,0.00081493537,0.0000069116936,0.00092115067,0.0000534622],"genre_scores_gemma":[0.4268817,0.0000066691023,0.5723964,0.0004928536,0.00006255589,0.00014678508,0.0000011506187,0.000009191659,0.0000027279946],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9971262,0.000088484296,0.00045140253,0.0010210945,0.00062583096,0.00068698835],"domain_scores_gemma":[0.9980932,0.00038679596,0.00019514146,0.0008041334,0.0003145369,0.00020620834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010428842,0.00026030804,0.00028279156,0.000382976,0.00024597367,0.00033720332,0.0022270193,0.000021077436,0.000016170938],"category_scores_gemma":[0.00029127573,0.00021530344,0.00006777318,0.0019687025,0.00043701203,0.00086822396,0.00037494898,0.00020389946,0.000010518201],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025091464,0.00023487948,0.00545945,0.0003286923,0.000026111546,0.00054717436,0.006601331,0.001273561,0.040165737,0.0010105124,0.00033650245,0.94399095],"study_design_scores_gemma":[0.0005886821,0.00016517467,0.0014797538,0.0002954661,0.000007732432,0.0014628936,0.0000033059687,0.4461989,0.5460431,0.00322438,0.000029240084,0.0005013626],"about_ca_topic_score_codex":0.00043647576,"about_ca_topic_score_gemma":0.0000427456,"teacher_disagreement_score":0.9434896,"about_ca_system_score_codex":0.00028798444,"about_ca_system_score_gemma":0.00036015938,"threshold_uncertainty_score":0.87798196},"labels":[],"label_agreement":null},{"id":"W2151740248","doi":"10.1109/icpr.2000.905412","title":"Guiding ziplock snakes with a priori information","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Initialization; A priori and a posteriori; Computer science; Artificial intelligence; Computation; Computer vision; Process (computing); Noise (video); Active contour model; Pattern recognition (psychology); Image segmentation; Algorithm; Image (mathematics)","score_opus":0.027555602014607924,"score_gpt":0.22613578711148116,"score_spread":0.19858018509687325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151740248","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003860998,0.000008506107,0.96417314,0.0008912473,0.00003376706,0.00010441132,1.17567865e-7,0.00054681743,0.033855885],"genre_scores_gemma":[0.080489986,0.00001384949,0.9160287,0.0025164369,0.000018093164,0.000018175622,0.0000011390389,0.0000027592675,0.0009108996],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999382,0.0000136873805,0.00014326429,0.000085021115,0.00026386735,0.00011211293],"domain_scores_gemma":[0.99959195,0.00002332378,0.000048944115,0.00021420325,0.00006124581,0.000060312326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009935321,0.00005529857,0.00005162486,0.00007324946,0.000048806658,0.00015939488,0.00029650406,0.00002012432,0.00023524242],"category_scores_gemma":[0.00003808977,0.00003870294,0.000011366801,0.0002291907,0.00002022819,0.0020077901,0.000058065132,0.00003285945,0.00022659898],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017804118,0.00004632054,0.00041163302,0.000023876786,0.000012102683,0.000007933724,0.0029805503,0.00000808602,0.0013044731,0.028047998,0.08438887,0.88276637],"study_design_scores_gemma":[0.0017354927,0.0007995984,0.0020654537,0.00015378343,0.000014417679,0.00015704313,0.00049113,0.4082309,0.5000411,0.0022914219,0.08301558,0.0010040742],"about_ca_topic_score_codex":0.000008560817,"about_ca_topic_score_gemma":0.0000012940707,"teacher_disagreement_score":0.8817623,"about_ca_system_score_codex":0.000020369844,"about_ca_system_score_gemma":0.000008292479,"threshold_uncertainty_score":0.2912547},"labels":[],"label_agreement":null},{"id":"W2152125436","doi":"10.1007/11559573_122","title":"A Narrow-Band Level-Set Method with Dynamic Velocity for Neural Stem Cell Cluster Segmentation","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Set (abstract data type); Cluster (spacecraft); Segmentation; Neural stem cell; Computer science; Level set (data structures); Artificial intelligence; Pattern recognition (psychology); Stem cell; Biology; Cell biology","score_opus":0.028489496141003032,"score_gpt":0.30564687059436246,"score_spread":0.2771573744533594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152125436","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009624788,0.00016232856,0.9962733,0.00093893515,0.00055393646,0.0014121347,0.000028477963,0.0002390647,0.00029562015],"genre_scores_gemma":[0.010119238,0.000010225188,0.98490965,0.003692867,0.00018955301,0.00007863892,0.000028489794,0.000047091373,0.00092425715],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957809,0.0000962598,0.0006197866,0.0016419595,0.0012107377,0.0006503743],"domain_scores_gemma":[0.99731064,0.00065396127,0.00046736182,0.0009976579,0.00034437305,0.0002260116],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013244476,0.0005832266,0.0005251784,0.00063705683,0.00027121275,0.0005660611,0.002334219,0.00029088292,0.00001898128],"category_scores_gemma":[0.000026095939,0.00049045106,0.00012491478,0.0004631549,0.00047518034,0.0008043943,0.00046987628,0.00062043575,0.000009624183],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026171827,0.000028694767,0.0000068005047,0.00009679704,0.000009929763,0.000017548304,0.0012764244,0.0164117,0.001213068,0.00017086948,0.00012485535,0.98061717],"study_design_scores_gemma":[0.0009979822,0.0005903641,0.000036288693,0.00020221478,0.000022390048,0.00009021813,0.0000012238046,0.92662865,0.06364312,0.0067325733,0.00024977778,0.0008052006],"about_ca_topic_score_codex":0.0000122838155,"about_ca_topic_score_gemma":0.00009948643,"teacher_disagreement_score":0.97981197,"about_ca_system_score_codex":0.00049538375,"about_ca_system_score_gemma":0.00048536007,"threshold_uncertainty_score":0.9997547},"labels":[],"label_agreement":null},{"id":"W2152144623","doi":"10.1109/robio.2009.5420762","title":"Adaptive local threshold with shape information and its application to object segmentation","year":2009,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Segmentation; Artificial intelligence; Image segmentation; Scale-space segmentation; Segmentation-based object categorization; Classifier (UML); Pattern recognition (psychology); Computer science; Computer vision; Ground truth","score_opus":0.010288472424930015,"score_gpt":0.2599049468543696,"score_spread":0.24961647442943957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152144623","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027432921,0.00001139553,0.99327314,0.0010334067,0.000011193102,0.00058324356,7.0861313e-7,0.00029197824,0.0020516305],"genre_scores_gemma":[0.72450083,0.000007171937,0.27019003,0.0051951227,0.000009693612,0.000057622114,0.000008234912,0.000001970871,0.00002929287],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992318,0.000014278704,0.00017121823,0.00017088142,0.00029454776,0.00011724398],"domain_scores_gemma":[0.99955344,0.00001686128,0.00006097554,0.00014766389,0.000109115565,0.00011194188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014382912,0.00008548399,0.00007120039,0.00011065853,0.000056879875,0.000102324455,0.00018646382,0.00003134982,0.000016309532],"category_scores_gemma":[0.000009151609,0.00006730256,0.000008361564,0.00032125288,0.00001604844,0.0018693629,0.000041926916,0.00005581226,0.00005691273],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015819252,0.000018987916,0.000020915038,0.000004122814,0.0000030718556,6.309026e-7,0.0006825486,0.00008072257,0.0038168046,0.016095044,0.00055765064,0.9787037],"study_design_scores_gemma":[0.00063020043,0.0012578366,0.0050696586,0.000031232237,0.000007439317,0.000019742149,0.0004210555,0.64190334,0.3480084,0.0021566618,0.00018852182,0.0003059207],"about_ca_topic_score_codex":0.000008786167,"about_ca_topic_score_gemma":0.0000043423333,"teacher_disagreement_score":0.9783978,"about_ca_system_score_codex":0.000046478588,"about_ca_system_score_gemma":0.000028989116,"threshold_uncertainty_score":0.27445185},"labels":[],"label_agreement":null},{"id":"W2152249876","doi":"10.1109/iccv.2001.937511","title":"Flux maximizing geometric flows","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Vector flow; Image segmentation; Surface (topology); Level set (data structures); Computer science; Segmentation; Computer vision; Image (mathematics); Flux (metallurgy); Set (abstract data type); Artificial intelligence; Geometric modeling; Flow (mathematics); Simple (philosophy); Interpretation (philosophy); Geometric flow; Contrast (vision); Geometry; Mathematics","score_opus":0.03538211640945541,"score_gpt":0.25777193947439564,"score_spread":0.22238982306494023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152249876","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023579202,0.00008269948,0.9451935,0.0006478136,0.00011484594,0.00007010825,1.202101e-7,0.0006631057,0.05299201],"genre_scores_gemma":[0.03950516,0.000026641303,0.9496502,0.0016178354,0.000034918252,0.000011804733,6.295233e-7,0.0000047120184,0.009148099],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991886,0.000023895134,0.00014314512,0.00019583624,0.00028049477,0.00016800339],"domain_scores_gemma":[0.9994534,0.000059233866,0.00002750224,0.00032912183,0.00003329149,0.00009743438],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001553669,0.00006191044,0.00007054371,0.0002305313,0.000047231686,0.00010865096,0.00055452314,0.000028340195,0.003831342],"category_scores_gemma":[0.00010313633,0.000052075167,0.000029981744,0.0008900378,0.000015078654,0.0004405775,0.00014631978,0.00007192381,0.001107788],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.878333e-8,0.00003675149,0.000026369691,0.0000039078623,0.000003204177,0.000025675363,0.00009061827,0.0000015385056,0.0017802309,0.0021481116,0.07810919,0.9177743],"study_design_scores_gemma":[0.0007527006,0.00019221463,0.0006018339,0.00002555024,0.0000070467595,0.00015577137,0.000042087468,0.5224589,0.44655916,0.0036907098,0.024882568,0.00063147006],"about_ca_topic_score_codex":0.000007814328,"about_ca_topic_score_gemma":3.7646967e-7,"teacher_disagreement_score":0.9171428,"about_ca_system_score_codex":0.00002270231,"about_ca_system_score_gemma":0.000004120012,"threshold_uncertainty_score":0.99966997},"labels":[],"label_agreement":null},{"id":"W2153101259","doi":"10.1109/ccece.2002.1013055","title":"A fast moving object edge detection approach","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Object detection; Edge detection; Segmentation; Image segmentation; Enhanced Data Rates for GSM Evolution; Object (grammar); Object-class detection; Tracking (education); Scale-space segmentation; Viola–Jones object detection framework; Motion detection; Video tracking; Image processing; Segmentation-based object categorization; Pattern recognition (psychology); Image (mathematics); Motion (physics); Face detection; Facial recognition system","score_opus":0.024620132599071372,"score_gpt":0.24611202417973418,"score_spread":0.22149189158066282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153101259","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002483056,0.000024039227,0.93916297,0.00009103644,0.00008449022,0.000108221124,9.276399e-8,0.0007560004,0.059524834],"genre_scores_gemma":[0.3481198,0.000008888021,0.6483595,0.0007195403,0.000049130405,0.000031992262,4.0783738e-7,0.0000053071753,0.0027054858],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992269,0.000042319014,0.00012910043,0.00023187252,0.00022126343,0.00014853924],"domain_scores_gemma":[0.99955803,0.000027340278,0.000034026216,0.00027698735,0.000030176503,0.00007345833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015107372,0.00006833619,0.00006630141,0.000085169304,0.00006981431,0.00011366164,0.00035576153,0.000036066565,0.00021966777],"category_scores_gemma":[0.00004602671,0.000058291265,0.000031000433,0.00030076,0.000025669975,0.00047208546,0.00010774179,0.00009159309,0.00013733316],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.6471616e-7,0.00005437209,0.000013691398,0.0000077524155,0.000003497184,0.0000026864773,0.0004549113,0.000004167899,0.010218802,0.0007559033,0.0026216335,0.9858623],"study_design_scores_gemma":[0.00018235059,0.000068642796,0.00018526372,0.0000064149976,0.0000023834023,0.000035586367,0.000073206465,0.71119696,0.28725502,0.0003929055,0.00042966648,0.00017161432],"about_ca_topic_score_codex":0.000020494837,"about_ca_topic_score_gemma":0.0000018280068,"teacher_disagreement_score":0.9856907,"about_ca_system_score_codex":0.000033180895,"about_ca_system_score_gemma":0.0000047627846,"threshold_uncertainty_score":0.2405208},"labels":[],"label_agreement":null},{"id":"W2153480371","doi":"10.1016/j.neuroimage.2004.05.007","title":"Fast and robust parameter estimation for statistical partial volume models in brain MRI","year":2004,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":694,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Partial volume; Voxel; Estimator; Covariance; Expectation–maximization algorithm; Computer science; Computation; Estimation theory; Mathematics; Artificial intelligence; Volume (thermodynamics); Segmentation; Pattern recognition (psychology); Algorithm; Statistics; Maximum likelihood","score_opus":0.03321427581360456,"score_gpt":0.2983870262631428,"score_spread":0.26517275044953825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153480371","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024811653,0.0000064236456,0.99405485,0.002782496,0.00006580627,0.00038436538,0.000013448422,0.00014537029,0.000066070075],"genre_scores_gemma":[0.06821063,0.0000038219237,0.93005234,0.001573172,0.000018359757,0.000074733776,0.000011862431,0.000010304575,0.0000448014],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890566,0.000062492494,0.00023984932,0.00036919408,0.00020079203,0.00022200895],"domain_scores_gemma":[0.9993459,0.000248168,0.00004815451,0.00022436373,0.000030946892,0.00010243873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024709405,0.00010194534,0.00012647128,0.00008270745,0.000044789296,0.00016073692,0.00021744502,0.000042077518,0.000010565372],"category_scores_gemma":[0.00037245592,0.0001022955,0.000019689496,0.00012712806,0.00009052101,0.00074911275,0.00009146075,0.00011730975,0.000008830616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000690796,0.0005007837,0.0002813416,0.00018185811,0.0000115339035,0.00025723613,0.0021386568,0.10836341,0.009927583,0.10380016,0.014778586,0.75968975],"study_design_scores_gemma":[0.00065144844,0.00014418816,0.0010350383,0.0000136854205,0.000002547921,0.000011518798,0.0000045268366,0.9501883,0.0040702955,0.043739468,0.000032015254,0.000107000415],"about_ca_topic_score_codex":0.000029720411,"about_ca_topic_score_gemma":0.0000063524312,"teacher_disagreement_score":0.8418248,"about_ca_system_score_codex":0.00003158688,"about_ca_system_score_gemma":0.000040129038,"threshold_uncertainty_score":0.41714895},"labels":[],"label_agreement":null},{"id":"W2153569953","doi":"10.1109/crv.2008.15","title":"Prostate Segmentation from 2-D Ultrasound Images Using Graph Cuts and Domain Knowledge","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Initialization; Computer science; Robustness (evolution); Cut; Domain knowledge; Graph; Prior probability; Artificial intelligence; Segmentation; Inference; Image segmentation; Pattern recognition (psychology); Computer vision; Algorithm; Theoretical computer science; Bayesian probability","score_opus":0.022711032508569713,"score_gpt":0.287771797159327,"score_spread":0.2650607646507573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153569953","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14363258,0.00018719608,0.85450405,0.00009991336,0.00008445694,0.00021688455,0.0000038865273,0.0002923757,0.0009786609],"genre_scores_gemma":[0.09342836,0.00018879819,0.90559,0.00036617828,0.000033173335,0.000015700532,0.000012228391,0.00000873016,0.0003568432],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99894685,0.000094025316,0.0002195063,0.00034112725,0.00021989328,0.00017859954],"domain_scores_gemma":[0.9993583,0.00014959146,0.000072466835,0.00023244659,0.00006578942,0.00012141911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016294024,0.00012236885,0.00011950556,0.00010818482,0.00017909377,0.00011740381,0.00023087703,0.000038253587,0.00006567345],"category_scores_gemma":[0.000031628824,0.00010724651,0.00002650059,0.00026573052,0.00016906261,0.0008886514,0.00011003618,0.00007854156,0.000018861534],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006589353,0.0001314957,0.008198478,0.000016292817,0.000028739261,0.000052366147,0.007452135,0.0000016477688,0.9411638,0.00091956387,0.008447883,0.03358104],"study_design_scores_gemma":[0.0007503991,0.000074038275,0.008702282,0.000033754815,0.000008910691,0.000117612384,0.00023471721,0.0010902481,0.9715324,0.01699099,0.00014623436,0.00031838697],"about_ca_topic_score_codex":0.00017942385,"about_ca_topic_score_gemma":0.000009816638,"teacher_disagreement_score":0.051085938,"about_ca_system_score_codex":0.000034525277,"about_ca_system_score_gemma":0.000046777084,"threshold_uncertainty_score":0.43733856},"labels":[],"label_agreement":null},{"id":"W2154082737","doi":"10.1046/j.1528-1157.2002.043s1019.x","title":"Structural Image Analysis in Epilepsy","year":2002,"lang":"en","type":"article","venue":"Epilepsia","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Epilepsy; Medicine; Neuroscience; Psychology","score_opus":0.01935967755329636,"score_gpt":0.2745053814978077,"score_spread":0.2551457039445113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154082737","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030927176,0.00008526,0.96182626,0.0009949931,0.00012604836,0.000140553,0.000002310027,0.0003374442,0.0055599385],"genre_scores_gemma":[0.70141804,0.000024062978,0.29728812,0.0007434677,0.00003405729,0.000018343335,0.0000050360827,0.0000062041336,0.00046266834],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986388,0.00010665507,0.00031128112,0.00035620187,0.00031778635,0.0002692465],"domain_scores_gemma":[0.99915504,0.00007645772,0.00007782725,0.00053969835,0.000037492504,0.0001134514],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022524728,0.00011711708,0.00021359956,0.00040541092,0.000045417826,0.00011766502,0.0007181632,0.000051151863,0.001973822],"category_scores_gemma":[0.00009205463,0.00010763935,0.00010071239,0.0016849304,0.00006603363,0.00064479525,0.00013606086,0.00016776213,0.00024389902],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008432968,0.0002839087,0.18888523,0.000045423712,0.00033999354,0.00070885126,0.006096732,0.00014522491,0.011000751,0.0223843,0.071393184,0.698708],"study_design_scores_gemma":[0.0006603432,0.000088196786,0.33947176,0.000017599503,0.00005398987,0.000016453572,0.000044813503,0.6400215,0.014570544,0.0040334514,0.00051238015,0.0005089712],"about_ca_topic_score_codex":0.000047600268,"about_ca_topic_score_gemma":0.000013125525,"teacher_disagreement_score":0.69819903,"about_ca_system_score_codex":0.00005359853,"about_ca_system_score_gemma":0.0000072815305,"threshold_uncertainty_score":0.9989385},"labels":[],"label_agreement":null},{"id":"W2154306361","doi":"10.1109/titb.2011.2159806","title":"Nonrigid Image Registration Using an Entropic Similarity","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Image registration; Similarity (geometry); Artificial intelligence; Similarity measure; Computer science; Image (mathematics); Computer vision; Volume (thermodynamics); Measure (data warehouse); Pattern recognition (psychology); Mathematics; Data mining","score_opus":0.03244974579841401,"score_gpt":0.2899415480893299,"score_spread":0.2574918022909159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154306361","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067634443,0.00000490234,0.9899335,0.0012521051,0.0003206044,0.00035259002,0.000006679663,0.0008269318,0.00053923414],"genre_scores_gemma":[0.6278703,0.00003309595,0.37113172,0.00086052535,0.000013250501,0.000060169194,0.000010609231,0.000006164918,0.000014168287],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984872,0.000049761504,0.00065842405,0.00022141973,0.0003264608,0.00025675137],"domain_scores_gemma":[0.9988986,0.000023199498,0.0002268677,0.00061264826,0.00014376693,0.00009488908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036729846,0.00016435777,0.00018537244,0.0017640238,0.0001258943,0.000040059134,0.00061314955,0.00034640467,0.0001298953],"category_scores_gemma":[0.000025975289,0.0001562804,0.000032639906,0.0014705698,0.00032305528,0.0032519428,0.0000051028824,0.00052853645,0.00005013673],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011941926,0.0018701574,0.00030030924,0.00016605757,0.00007225249,0.00008152811,0.008436694,0.0001725817,0.102775745,0.015538628,0.0009661847,0.86950046],"study_design_scores_gemma":[0.0017710254,0.0010361401,0.0005266332,0.000107805376,0.000021051932,0.0001223643,0.0007175539,0.08434164,0.9013804,0.009340375,0.0002593774,0.00037563642],"about_ca_topic_score_codex":0.00012487287,"about_ca_topic_score_gemma":0.000015626822,"teacher_disagreement_score":0.8691248,"about_ca_system_score_codex":0.00016014703,"about_ca_system_score_gemma":0.00007787548,"threshold_uncertainty_score":0.6372929},"labels":[],"label_agreement":null},{"id":"W2154537621","doi":"10.1016/j.media.2009.10.002","title":"CPOL: Complex phase order likelihood as a similarity measure for MR–CT registration","year":2009,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Similarity measure; Similarity (geometry); Artificial intelligence; Measure (data warehouse); Image registration; Pattern recognition (psychology); Fiducial marker; Computer science; Computer vision; Mutual information; Noise (video); Phase (matter); Mathematics; Image (mathematics); Data mining; Physics","score_opus":0.028935580101750315,"score_gpt":0.3702821879122302,"score_spread":0.34134660781047993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154537621","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007147395,0.00006259866,0.9775931,0.019237095,0.000050009756,0.00037129823,0.00001179661,0.00047667156,0.0014826823],"genre_scores_gemma":[0.29283005,0.000051157334,0.6854076,0.02069677,0.0002454433,0.00009588463,0.0003424498,0.000017990777,0.0003126862],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996102,0.00023023676,0.0006987868,0.000708857,0.0017824254,0.00047772317],"domain_scores_gemma":[0.99745756,0.0002355706,0.00024216689,0.0008661849,0.0005545357,0.0006440072],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0018338125,0.000245766,0.0005245142,0.0003631519,0.0002116661,0.00030831393,0.0011995493,0.00011629812,0.0012633242],"category_scores_gemma":[0.0034454092,0.0002151734,0.00037799572,0.002320952,0.00018130186,0.00071047986,0.00009609289,0.00030878084,0.000047121546],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038050563,0.0014652204,0.00008633489,0.00003201018,0.0005520648,0.00031696807,0.0003225931,0.0000035406977,0.013588559,0.0017007675,0.08084557,0.9010483],"study_design_scores_gemma":[0.0070159175,0.0016773706,0.0014828931,0.00009717685,0.0021657315,0.00009880111,0.00014462833,0.8651804,0.077000186,0.028897913,0.014880775,0.0013582542],"about_ca_topic_score_codex":0.00012734203,"about_ca_topic_score_gemma":0.00009179829,"teacher_disagreement_score":0.8996901,"about_ca_system_score_codex":0.00007252604,"about_ca_system_score_gemma":0.00029600805,"threshold_uncertainty_score":0.99964964},"labels":[],"label_agreement":null},{"id":"W2154870965","doi":"10.1109/cbms.2004.35","title":"Contrast enhancement of microcalcifications in mammograms using morphological enhancement and non-flat structuring elements","year":2004,"lang":"en","type":"article","venue":"Computer-Based Medical Systems","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Structuring; Contrast enhancement; Contrast (vision); Computer science; Computer vision; Image enhancement; Artificial intelligence; Image (mathematics); Radiology; Medicine; Magnetic resonance imaging","score_opus":0.023830463349649184,"score_gpt":0.29436228926913016,"score_spread":0.270531825919481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154870965","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28177026,0.00017419722,0.71688664,0.00017347123,0.00034217647,0.00057179766,0.0000019534732,0.00006184901,0.000017665132],"genre_scores_gemma":[0.7172743,0.000022442688,0.28228053,0.00027811638,0.00006172434,0.000063148815,0.000009355869,0.000007561429,0.0000028173295],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9967687,0.00014517382,0.0010946456,0.0005813616,0.00097589224,0.00043420304],"domain_scores_gemma":[0.9986332,0.00013839106,0.00031670567,0.0004441753,0.00009790755,0.00036963754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008993501,0.00023016216,0.00045125445,0.00020393493,0.00008028818,0.00009394519,0.00074896944,0.00015511857,0.000033978366],"category_scores_gemma":[0.000055738146,0.00020102513,0.00005876124,0.0003859095,0.00024126146,0.000170666,0.0002744788,0.0002444127,0.000004963228],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000080396116,0.0029059858,0.011926404,0.0019498364,0.00019823438,0.0008678335,0.0012269632,0.00235082,0.6513812,0.0076666665,0.000322767,0.3191229],"study_design_scores_gemma":[0.005817121,0.0009293587,0.0022436525,0.0029026666,0.000019314524,0.00010141909,0.00007986092,0.41879115,0.5678594,0.0004963213,0.00014035922,0.00061934313],"about_ca_topic_score_codex":0.00025121207,"about_ca_topic_score_gemma":0.000008080344,"teacher_disagreement_score":0.43550405,"about_ca_system_score_codex":0.00022943823,"about_ca_system_score_gemma":0.00033651813,"threshold_uncertainty_score":0.8197567},"labels":[],"label_agreement":null},{"id":"W2155045386","doi":"10.1109/iembs.1995.575154","title":"Displaying EEG data for neurosurgical guidance","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"Medical Research Council","keywords":"Electroencephalography; Neurosurgery; Computer science; Scalp; Computer vision; Epilepsy; Artificial intelligence; Medical physics; Radiology; Medicine; Neuroscience; Surgery; Psychology","score_opus":0.10473951509636324,"score_gpt":0.33775945649983136,"score_spread":0.2330199414034681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155045386","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000053956966,0.000051524486,0.99279207,0.0021580472,0.00013739489,0.00015927032,0.000006220335,0.00037158688,0.0042699114],"genre_scores_gemma":[0.02237623,0.000024585268,0.9711252,0.003790524,0.00004699844,0.000031292184,0.000009945051,0.000006432457,0.0025887906],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908084,0.000026180473,0.00017232493,0.0003553468,0.00019942997,0.00016589944],"domain_scores_gemma":[0.99871296,0.00016914308,0.000036547,0.000967596,0.000029275734,0.00008447509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024284999,0.00006283762,0.000075794946,0.000027209875,0.000065142856,0.0001212766,0.0015706661,0.000023402534,0.00023914521],"category_scores_gemma":[0.00024244399,0.000050983035,0.000020634194,0.00013304068,0.000033456636,0.0007005833,0.00047978235,0.000051546504,0.0000587647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015839611,0.00009588802,0.00013433037,0.00002094227,0.000005038017,0.000018815166,0.000069346475,0.0000015130026,0.0041899374,0.022925228,0.5309937,0.4415437],"study_design_scores_gemma":[0.0003740873,0.00006799429,0.000337812,0.000013978653,0.000003208664,0.000018984452,0.000004415849,0.9192673,0.011003532,0.0009898832,0.067733414,0.00018541256],"about_ca_topic_score_codex":0.000003115124,"about_ca_topic_score_gemma":5.9614115e-7,"teacher_disagreement_score":0.91926575,"about_ca_system_score_codex":0.000007525365,"about_ca_system_score_gemma":0.000005197498,"threshold_uncertainty_score":0.29187146},"labels":[],"label_agreement":null},{"id":"W2155231246","doi":"10.1109/iciap.2003.1234087","title":"Dense 3D interpretation of image sequences: a variational approach using anisotropic diffusion","year":2004,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Anisotropic diffusion; Interpretation (philosophy); Anisotropy; Diffusion; Image (mathematics); Computer science; Artificial intelligence; Computer vision; Physics; Optics; Quantum mechanics","score_opus":0.01906998446361338,"score_gpt":0.2833990458616345,"score_spread":0.26432906139802115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155231246","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009880025,0.000009054083,0.98841166,0.00008224017,0.00005982126,0.00015678153,0.0000010399414,0.00014873064,0.0012506326],"genre_scores_gemma":[0.29516166,0.000002986361,0.70459217,0.00020074597,0.000012149072,0.000005865349,0.000004390593,0.0000030866263,0.000016961432],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998948,0.00007031725,0.0002741827,0.00022660334,0.00036676895,0.00011416979],"domain_scores_gemma":[0.9994234,0.000044869448,0.0001312928,0.00021745222,0.00012805435,0.000054974174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022619819,0.00008166451,0.000109012406,0.00011071081,0.000048711507,0.00006277259,0.00033906868,0.000042019263,0.00004374285],"category_scores_gemma":[0.00009153866,0.00006931502,0.00003628808,0.00027628551,0.00007684147,0.000566836,0.000119153614,0.0000640366,0.000005627992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019662455,0.00060650986,0.0003274573,0.00013671526,0.000038009766,0.000018764182,0.006106726,0.0030976422,0.7615851,0.13448118,0.000057639932,0.0935246],"study_design_scores_gemma":[0.00048015683,0.00008229889,0.00056772295,0.000049401027,0.000008205446,0.000029926685,0.00008671345,0.88499594,0.09761687,0.015946345,0.0000024345525,0.00013400127],"about_ca_topic_score_codex":0.00015652375,"about_ca_topic_score_gemma":0.0000018707325,"teacher_disagreement_score":0.8818983,"about_ca_system_score_codex":0.0000992847,"about_ca_system_score_gemma":0.00012335579,"threshold_uncertainty_score":0.28265846},"labels":[],"label_agreement":null},{"id":"W2156010062","doi":"10.1109/tbme.2008.921158","title":"A Morphology-Based Approach for Interslice Interpolation of Anatomical Slices From Volumetric Images","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; University of Victoria; Simon Fraser University","funders":"","keywords":"Interpolation (computer graphics); Computer science; Algorithm; Process (computing); Mathematical morphology; Medial axis; Topology (electrical circuits); Artificial intelligence; Mathematics; Image processing; Image (mathematics)","score_opus":0.01703927751684491,"score_gpt":0.24896217566278545,"score_spread":0.23192289814594053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156010062","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0060615516,0.000027467686,0.99281543,0.00013097205,0.00031596795,0.00023605171,0.000046816167,0.00035352923,0.00001220133],"genre_scores_gemma":[0.51240087,0.000007746653,0.48738202,0.000096554075,0.000021618293,0.00006374892,0.000011840823,0.000009019011,0.000006581835],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870723,0.000030671486,0.00037710578,0.00033899574,0.0003382535,0.0002077269],"domain_scores_gemma":[0.99901724,0.00038768144,0.000079840764,0.0002810931,0.00006576633,0.0001684073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017274269,0.00015013934,0.00023388934,0.00063546456,0.000056312045,0.000017964121,0.00047926643,0.00013705932,0.000033233373],"category_scores_gemma":[0.000057851266,0.00014419747,0.00013152188,0.00075974874,0.000152648,0.00023225558,0.000004146821,0.00022970363,0.0000037147931],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014386725,0.0018101645,0.000035465462,0.00024575173,0.00024140901,0.000027265576,0.0009893036,0.013248389,0.76765364,0.00011641744,0.0024718563,0.2130165],"study_design_scores_gemma":[0.0005130741,0.00016385411,0.00007664923,0.000028147171,0.000011196978,0.000007263773,0.000011483711,0.73366964,0.26533753,0.000009049007,0.00005895262,0.00011313978],"about_ca_topic_score_codex":0.00005785576,"about_ca_topic_score_gemma":2.7071815e-7,"teacher_disagreement_score":0.72042125,"about_ca_system_score_codex":0.000059097776,"about_ca_system_score_gemma":0.000051231367,"threshold_uncertainty_score":0.5880202},"labels":[],"label_agreement":null},{"id":"W2156024736","doi":"10.1109/isbi.2004.1398493","title":"A new image segmentation and smoothing model","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Smoothing; Level set (data structures); Image segmentation; Initialization; Partial differential equation; Segmentation; Computer science; Scale-space segmentation; Artificial intelligence; Computer vision; Decoupling (probability); Level set method; Active contour model; Image denoising; Noise reduction; Algorithm; Mathematics; Mathematical analysis","score_opus":0.013549096038934517,"score_gpt":0.28826086048129873,"score_spread":0.2747117644423642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156024736","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002972508,0.000023843406,0.9894648,0.0022991737,0.00001585794,0.000088376735,1.4137389e-7,0.00034576023,0.007464798],"genre_scores_gemma":[0.008610786,0.0000155055,0.98699504,0.0023188696,0.000029044995,0.0000049870973,7.553968e-7,0.0000034205195,0.0020215954],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994302,0.000013323041,0.00011848967,0.00017016317,0.00016858513,0.00009927816],"domain_scores_gemma":[0.9996774,0.000020396557,0.00002884707,0.00014506983,0.000020299722,0.000107943924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012598828,0.000055694894,0.00005001074,0.000049622067,0.00003880785,0.00015049656,0.00018323916,0.00002049332,0.00008903659],"category_scores_gemma":[0.000016822682,0.00004921222,0.000011732214,0.000080994185,0.00001546813,0.0012270352,0.00009883489,0.000049472186,0.000029154706],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.8795575e-7,0.00000930318,0.0000090501835,0.0000033813333,0.000002056155,0.0000010360642,0.00091171364,0.000031961772,0.05528002,0.007838376,0.01391992,0.9219926],"study_design_scores_gemma":[0.0002238017,0.000015642801,0.000032862972,0.0000055897394,0.000001859051,0.000005737002,0.000034656834,0.70895547,0.28553295,0.00498598,0.00011762769,0.00008779979],"about_ca_topic_score_codex":0.000025678482,"about_ca_topic_score_gemma":0.0000042373026,"teacher_disagreement_score":0.9219048,"about_ca_system_score_codex":0.000023267361,"about_ca_system_score_gemma":0.000033933033,"threshold_uncertainty_score":0.20068161},"labels":[],"label_agreement":null},{"id":"W2156040653","doi":"10.82308/37532","title":"Object detection and analysis using coherency filtering","year":2006,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Object (grammar); Computer vision; Artificial intelligence; Object detection; Pattern recognition (psychology)","score_opus":0.018023963649401637,"score_gpt":0.2701737063199789,"score_spread":0.25214974267057727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156040653","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93265146,0.00039806298,0.040838007,0.0000047797635,0.0014170673,0.0011653653,0.0002657452,0.0014522556,0.02180727],"genre_scores_gemma":[0.8845172,0.000111854584,0.112279624,0.00015698308,0.00005071287,0.00012946449,0.00045565685,0.000109067776,0.00218944],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9959689,0.00035460334,0.0009077208,0.0012958965,0.0009201379,0.00055277895],"domain_scores_gemma":[0.9976845,0.00016142806,0.0006946069,0.0008577992,0.0003237141,0.0002779795],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00076803507,0.0006052883,0.00073297153,0.0012143197,0.00085951143,0.00035837505,0.0008680661,0.00056394254,0.000096991505],"category_scores_gemma":[0.0003020048,0.00066733395,0.00033290038,0.0019973621,0.000053400803,0.0015027451,0.00023700549,0.0009404899,0.000024797311],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019408155,0.000079818084,0.000043089185,0.00021305018,0.0004028468,0.000062675936,0.000013791882,0.000045209053,0.4437819,0.0014477939,0.00000191243,0.5538885],"study_design_scores_gemma":[0.00046928896,0.0001440097,0.0039294055,0.00026982513,0.0008828084,0.00003794672,0.00006724429,0.00681008,0.9777437,0.0078102956,0.0005874649,0.0012479671],"about_ca_topic_score_codex":0.0009794222,"about_ca_topic_score_gemma":0.0012993092,"teacher_disagreement_score":0.5526405,"about_ca_system_score_codex":0.00051857077,"about_ca_system_score_gemma":0.000043529377,"threshold_uncertainty_score":0.99957776},"labels":[],"label_agreement":null},{"id":"W2156163819","doi":"10.1109/icip.2008.4711863","title":"Graph-cut optimization of the ratio of functions and its application to image segmentation","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Image segmentation; Cut; Computer science; Maximum cut; Graph; Artificial intelligence; Image (mathematics); Computer vision; Theoretical computer science","score_opus":0.013311281558650561,"score_gpt":0.26297064601009784,"score_spread":0.2496593644514473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156163819","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006668858,0.000010880221,0.9916134,0.0005035353,0.000037937567,0.00046758127,0.0000023606303,0.000062730156,0.0006327074],"genre_scores_gemma":[0.4963591,0.000038933253,0.50279284,0.00033659436,0.0000097225175,0.000078562814,0.0000070108867,0.000004506986,0.0003727066],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992931,0.00004395684,0.00022987922,0.00014677162,0.00022850919,0.0000578173],"domain_scores_gemma":[0.99938893,0.000034579803,0.00012171076,0.00022443944,0.00018945099,0.000040866074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011956118,0.00005249181,0.000071638,0.00008694113,0.00007838942,0.000010224123,0.00020446003,0.000022956629,0.000027163078],"category_scores_gemma":[0.000050279137,0.000039489852,0.000022841048,0.0005185767,0.00005532853,0.00038696715,0.00008287539,0.000031035965,0.0000031785594],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008293397,0.0001464946,0.0007841427,0.000048758942,0.000017785746,3.053632e-7,0.0017808148,0.0058400785,0.9526092,0.009842896,0.003644264,0.025276968],"study_design_scores_gemma":[0.00014423116,0.000050098082,0.0018562323,0.000007660358,0.000005167445,0.0000046729747,0.000058504902,0.07299573,0.92463917,0.00017590846,0.0000106126145,0.000051989675],"about_ca_topic_score_codex":0.000020205684,"about_ca_topic_score_gemma":0.000003551105,"teacher_disagreement_score":0.48969024,"about_ca_system_score_codex":0.000012393908,"about_ca_system_score_gemma":0.000028781224,"threshold_uncertainty_score":0.16103494},"labels":[],"label_agreement":null},{"id":"W2156494846","doi":"10.1109/icalip.2010.5685096","title":"Colour and texture based pyramidal image segmentation","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Artificial intelligence; Image segmentation; Image texture; Segmentation; Computer science; Pattern recognition (psychology); Histogram; Computer vision; Pyramid (geometry); Range segmentation; Region growing; Segmentation-based object categorization; Scale-space segmentation; Minimum spanning tree-based segmentation; Image (mathematics); Mathematics","score_opus":0.004716820669382093,"score_gpt":0.2612543655896091,"score_spread":0.256537544920227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156494846","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010965679,0.0000030742465,0.98397696,0.0018836387,0.00013161312,0.00016866741,9.98595e-7,0.00037282135,0.0024965412],"genre_scores_gemma":[0.17427218,0.0000014294699,0.82251453,0.0027882277,0.000026230156,0.000018571493,0.0000038720705,0.000004242871,0.0003707099],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99933463,0.00002558671,0.00011606178,0.00020749589,0.00019970852,0.00011648518],"domain_scores_gemma":[0.99952054,0.000059916973,0.00003961723,0.00021932613,0.00005561289,0.000104966144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020159996,0.00007233112,0.00006273624,0.0000586384,0.000058869155,0.00018054167,0.0002481126,0.000052892206,0.0003759907],"category_scores_gemma":[0.00005511705,0.000059535727,0.0000157337,0.000126774,0.000088759334,0.00048253828,0.00007427783,0.00015156306,0.000031721393],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023054583,0.00005154484,0.00046468625,0.000012462299,0.000003058215,0.000010489227,0.00014392004,2.1485086e-7,0.8207454,0.0061427676,0.010716843,0.16170627],"study_design_scores_gemma":[0.00051631866,0.00007353166,0.0050590364,0.0000047978438,0.0000034693644,0.000013018858,0.000035925874,0.051204253,0.9405075,0.0019006482,0.00052011217,0.00016142297],"about_ca_topic_score_codex":0.000017442302,"about_ca_topic_score_gemma":0.000014329374,"teacher_disagreement_score":0.1633065,"about_ca_system_score_codex":0.000008239587,"about_ca_system_score_gemma":0.000035947243,"threshold_uncertainty_score":0.41168344},"labels":[],"label_agreement":null},{"id":"W2156773621","doi":"10.1109/ccece.2005.1557359","title":"An improved algorithm for image registration using robust feature extraction","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Artificial intelligence; Robustness (evolution); Feature extraction; Singular value decomposition; Image registration; Pattern recognition (psychology); Feature (linguistics); Computer science; Computer vision; Algorithm; Mathematics; Image (mathematics)","score_opus":0.021290745165385892,"score_gpt":0.32047671139735845,"score_spread":0.2991859662319726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156773621","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001430203,0.000008802294,0.99795943,0.00034343012,0.00014504613,0.00035083582,0.0000029134592,0.0004763909,0.0005701081],"genre_scores_gemma":[0.0016984786,0.0000012415844,0.9969064,0.00022741818,0.00019827612,0.000027039223,0.00003881498,0.000008105696,0.0008942183],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991854,0.000031152063,0.00016950884,0.00028916934,0.00016932294,0.00015548711],"domain_scores_gemma":[0.9993597,0.000030366691,0.00011385483,0.00029450565,0.00014603412,0.00005551548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025537767,0.000089735084,0.0000755207,0.00006312393,0.00011038863,0.00029417564,0.00024477855,0.000076405006,0.000018376064],"category_scores_gemma":[0.000018654262,0.00008222577,0.00003643169,0.0001487482,0.000028456427,0.0017711265,0.000017915378,0.00007798004,0.0000017841239],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001838086,0.00007953084,0.0000029683326,0.000007866754,0.0000022639674,0.0000034446914,0.000019076795,0.000028354658,0.6280767,0.0009946541,0.008062742,0.3627206],"study_design_scores_gemma":[0.00015479415,0.000059508988,0.00007282249,0.0000035426167,0.0000033993854,0.000015472619,0.0000141411865,0.7169684,0.2814052,0.0010627976,0.00015373837,0.00008614443],"about_ca_topic_score_codex":0.00022269094,"about_ca_topic_score_gemma":0.000023579873,"teacher_disagreement_score":0.71694005,"about_ca_system_score_codex":0.000062149484,"about_ca_system_score_gemma":0.000043802516,"threshold_uncertainty_score":0.33530697},"labels":[],"label_agreement":null},{"id":"W2156909660","doi":"10.1120/jacmp.v11i3.3175","title":"Assessment of a commercially available automatic deformable registration system","year":2010,"lang":"en","type":"article","venue":"Journal of Applied Clinical Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Cancer Foundation; University of Alberta","funders":"","keywords":"Image registration; Computer science; Protocol (science); Artificial intelligence; Imaging phantom; Computer vision; Modality (human–computer interaction); Transformation (genetics); Range (aeronautics); Image (mathematics); Nuclear medicine; Medicine","score_opus":0.03393541882435368,"score_gpt":0.3748182151467843,"score_spread":0.3408827963224306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156909660","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010123028,0.0000064347973,0.9740407,0.0004487608,0.00092609314,0.0001952836,9.4179484e-7,0.00008835334,0.014170426],"genre_scores_gemma":[0.6068429,0.000021541853,0.3919432,0.000665734,0.000490529,0.000006911655,0.0000014807766,0.000009115665,0.000018627197],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995244,0.0001275419,0.0022527252,0.00020529826,0.0019494237,0.00022097],"domain_scores_gemma":[0.9957925,0.00096610514,0.001849237,0.00052981457,0.000371569,0.00049078657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056289216,0.00014258566,0.0006569283,0.00006135089,0.00006219221,0.000060626397,0.0013468468,0.00023459077,0.00013609031],"category_scores_gemma":[0.00071381364,0.00010796571,0.00021738972,0.00031579338,0.0003049393,0.0003977245,0.00018089701,0.0013538424,0.000025487823],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003396944,0.0016745295,0.0008463826,0.00046706037,0.00017318847,0.00006964622,0.00015255106,0.00001643981,0.006174091,0.20940572,0.023040762,0.75794566],"study_design_scores_gemma":[0.013968902,0.004860658,0.015129901,0.0025542346,0.00050992705,0.0004925863,0.0004302419,0.7376172,0.11039684,0.10809732,0.004390818,0.0015513661],"about_ca_topic_score_codex":0.0000060649686,"about_ca_topic_score_gemma":0.0000020661964,"teacher_disagreement_score":0.75639427,"about_ca_system_score_codex":0.00005340256,"about_ca_system_score_gemma":0.0011777371,"threshold_uncertainty_score":0.5881847},"labels":[],"label_agreement":null},{"id":"W2156928155","doi":"10.1109/tpami.2006.97","title":"Joint multiregion segmentation and parametric estimation of image motion by basis function representation and level set evolution","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Institut National de la Recherche Scientifique; Communications Research Centre Canada","funders":"","keywords":"Artificial intelligence; Segmentation; Scale-space segmentation; Motion estimation; Image segmentation; Mathematics; Segmentation-based object categorization; Computer vision; Pattern recognition (psychology); Motion field; Basis function; Range segmentation; Parametric statistics; Computer science; Algorithm; Mathematical analysis","score_opus":0.032228623451944785,"score_gpt":0.29368333733191987,"score_spread":0.2614547138799751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156928155","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02922074,0.00012015213,0.9700413,0.00016391273,0.00006395614,0.0002610708,0.00004699155,0.00007551563,0.000006380558],"genre_scores_gemma":[0.9508938,0.00027339233,0.04859693,0.00006603353,0.0000076001506,0.000036615813,0.000072538096,0.000007859354,0.00004520254],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983473,0.00015799771,0.0005141157,0.000496417,0.00035342842,0.00013076933],"domain_scores_gemma":[0.99917656,0.000112141846,0.00027431373,0.00024631596,0.00011531424,0.000075360374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034080548,0.00017539681,0.00023757687,0.000804209,0.00015362001,0.00011802961,0.00008923973,0.00007439856,0.000019554596],"category_scores_gemma":[0.000018276005,0.00017068161,0.00008233756,0.0011377026,0.00011330348,0.00063147966,0.0000066110147,0.00012355263,0.0000021148385],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001491061,0.00013253507,0.001007383,0.000042779135,0.00009480914,8.0447535e-7,0.00016425965,0.0058864607,0.019268801,0.000041747844,0.000044915312,0.9733006],"study_design_scores_gemma":[0.00016067826,0.00013476152,0.015468397,0.00001874469,0.0002465448,0.000007006967,0.000074167685,0.5108977,0.47228009,0.0005798236,5.3055106e-7,0.00013156986],"about_ca_topic_score_codex":0.0041586366,"about_ca_topic_score_gemma":0.00024775724,"teacher_disagreement_score":0.973169,"about_ca_system_score_codex":0.00005931822,"about_ca_system_score_gemma":0.000008832735,"threshold_uncertainty_score":0.6960194},"labels":[],"label_agreement":null},{"id":"W2157368450","doi":"10.1109/iembs.2000.900416","title":"Vascular tree extraction from MRA and power Doppler US image volumes","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Medical Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Magnetic resonance imaging; Ultrasound; Tree (set theory); Computer science; Artificial intelligence; Computer vision; Image registration; Doppler ultrasound; Radiology; Image (mathematics); Medicine; Mathematics","score_opus":0.012475368311081361,"score_gpt":0.2519714461976552,"score_spread":0.23949607788657384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157368450","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009564862,0.00019577293,0.9753464,0.0006866773,0.00011890364,0.00011029767,0.0000012033631,0.0004107937,0.013565088],"genre_scores_gemma":[0.104602955,0.00018604577,0.8901579,0.0017580568,0.000049702783,0.000018925542,0.0000036661572,0.000010176397,0.0032126093],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99906653,0.000046443296,0.00015792235,0.0003031222,0.0002837266,0.00014227703],"domain_scores_gemma":[0.99939203,0.000060758975,0.00004299213,0.0003523199,0.000041949817,0.000109955035],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00011681972,0.000093069575,0.000098231845,0.00005843659,0.000054775297,0.00019663427,0.00025193518,0.000050232647,0.005277224],"category_scores_gemma":[0.000042085845,0.00007917836,0.00003786639,0.000118139695,0.000059078106,0.0009801384,0.00010042467,0.00009361844,0.0002747569],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020948012,0.00020560023,0.0012008144,0.000008124685,0.00004913052,0.00006434737,0.0009503485,2.4599188e-7,0.09935025,0.000949553,0.28211945,0.6151],"study_design_scores_gemma":[0.0028523053,0.00042823364,0.112380244,0.00007286527,0.00006594838,0.00011236308,0.00027716177,0.19292665,0.62163883,0.010997993,0.05671513,0.0015323072],"about_ca_topic_score_codex":0.00017393996,"about_ca_topic_score_gemma":0.000011839055,"teacher_disagreement_score":0.6135677,"about_ca_system_score_codex":0.000018892615,"about_ca_system_score_gemma":0.0000046132263,"threshold_uncertainty_score":0.9956321},"labels":[],"label_agreement":null},{"id":"W2157481672","doi":"10.1109/icip.2008.4711823","title":"Image segmentation using histogram specification","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Scale-space segmentation; Histogram; Image segmentation; Artificial intelligence; Computer science; Segmentation-based object categorization; Segmentation; Computer vision; Region growing; Image histogram; Histogram matching; Pattern recognition (psychology); Image texture; Image (mathematics)","score_opus":0.057874860137579254,"score_gpt":0.313251656833109,"score_spread":0.25537679669552976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157481672","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004827312,0.00001550221,0.98960555,0.0002024344,0.00011120288,0.00015665138,2.635988e-7,0.00051326805,0.0045678266],"genre_scores_gemma":[0.02341876,0.000026032114,0.97558224,0.00047152294,0.00003616557,0.000010808194,0.00000533773,0.000005240033,0.00044388426],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999149,0.000041052255,0.00019258737,0.00021468579,0.00028040935,0.00012227053],"domain_scores_gemma":[0.999461,0.000022786544,0.00007323157,0.00029286122,0.000079183475,0.000070961214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012645636,0.00006897236,0.00006543097,0.00009381961,0.00011891757,0.000050214225,0.0003020348,0.00002662509,0.00020398606],"category_scores_gemma":[0.000024965795,0.000065133754,0.000028776898,0.00027092797,0.00007160416,0.00085041695,0.000056764275,0.000053652064,0.00010267533],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000022963382,0.00012792512,0.0005104606,0.000010264073,0.000007048343,0.000032475735,0.0010488852,0.000003819583,0.8404531,0.0037957458,0.016978513,0.13702948],"study_design_scores_gemma":[0.0004308499,0.000072028044,0.0033789168,0.00001052332,0.0000053854305,0.00015840265,0.00009393266,0.09397026,0.89900756,0.0012911914,0.0012603459,0.00032063248],"about_ca_topic_score_codex":0.000041034004,"about_ca_topic_score_gemma":6.924019e-7,"teacher_disagreement_score":0.13670884,"about_ca_system_score_codex":0.00009905175,"about_ca_system_score_gemma":0.0000386905,"threshold_uncertainty_score":0.26560774},"labels":[],"label_agreement":null},{"id":"W2158035368","doi":"10.1016/j.neuroimage.2006.04.225","title":"Segmentation of focal cortical dysplasia lesions on MRI using level set evolution","year":2006,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"Canadian Institutes of Health Research; Scottish Rite Charitable Foundation of Canada","keywords":"Cortical dysplasia; Segmentation; Computer science; Cortex (anatomy); Dysplasia; Epilepsy; Level set (data structures); Magnetic resonance imaging; Artificial intelligence; Medicine; Pattern recognition (psychology); Neuroscience; Radiology; Pathology; Psychology","score_opus":0.061345727273617735,"score_gpt":0.32488249107249073,"score_spread":0.263536763798873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158035368","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.081190884,0.0000064858777,0.91751134,0.00019316145,0.00015162323,0.00020009211,0.000012691773,0.00014420398,0.0005895341],"genre_scores_gemma":[0.73601705,0.000002267389,0.26353154,0.00028256653,0.000048835882,0.0000075167886,0.000013606342,0.000010680459,0.00008594522],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984754,0.00017272813,0.0003366225,0.0003229613,0.0004967201,0.00019559255],"domain_scores_gemma":[0.99925387,0.0001220763,0.00012829709,0.00034588948,0.000079237594,0.00007061333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002019848,0.00011620764,0.0001271488,0.00014293643,0.000101193924,0.00005024049,0.0003028167,0.00005287376,0.000025358673],"category_scores_gemma":[0.00009726507,0.00011480816,0.000051630785,0.00030302478,0.00010798841,0.00030858454,0.00010470316,0.00016276135,0.000022891418],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016942427,0.00023834873,0.0011020147,0.00002374451,0.0000046332502,0.00003892511,0.000078267774,0.00037992635,0.97120965,0.011145849,0.006265816,0.009495866],"study_design_scores_gemma":[0.0007844804,0.00047320107,0.095729925,0.000062213294,0.000021821324,0.000053905074,0.000033355936,0.13664618,0.76323074,0.002624855,0.00007307232,0.0002662281],"about_ca_topic_score_codex":0.00008440739,"about_ca_topic_score_gemma":0.0000050533968,"teacher_disagreement_score":0.65482616,"about_ca_system_score_codex":0.00007951133,"about_ca_system_score_gemma":0.000065974884,"threshold_uncertainty_score":0.46817407},"labels":[],"label_agreement":null},{"id":"W2158139302","doi":"10.1109/crv.2005.14","title":"An Automatic Segmentation Combining Mixture Analysis and Adaptive Region Information: A Level Set Approach","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Image segmentation; Scale-space segmentation; Segmentation; Artificial intelligence; Mixture model; Segmentation-based object categorization; Computer science; Region growing; Pattern recognition (psychology); Boundary (topology); Computer vision; Minification; Gaussian; Range segmentation; Minimum spanning tree-based segmentation; Mathematics","score_opus":0.036311114596188174,"score_gpt":0.2920036996340614,"score_spread":0.25569258503787323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158139302","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026123342,0.000009330713,0.9951922,0.0003810052,0.000014470663,0.00023243776,0.0000024475607,0.00038310152,0.0011726364],"genre_scores_gemma":[0.3352959,0.000004668475,0.66330624,0.0012406452,0.000012873897,0.000028286493,0.000062981155,0.000002240899,0.0000461764],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989425,0.00010906784,0.00028529696,0.00020386481,0.000327164,0.00013213618],"domain_scores_gemma":[0.9993179,0.000036899626,0.00014212888,0.00030569086,0.000075939184,0.000121451856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031479963,0.00010962213,0.00014821846,0.00031687168,0.00010363459,0.00026498316,0.00028877042,0.000056842677,0.000029158315],"category_scores_gemma":[0.00001742185,0.00009541623,0.000036458743,0.00066007505,0.00004373773,0.002973277,0.00007245328,0.00008581652,0.000010389803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000049404666,0.00009229997,0.00062674633,0.00002733755,0.00015795826,0.000001649359,0.012129238,0.000366529,0.00032310572,0.0049544633,0.0037831836,0.97753257],"study_design_scores_gemma":[0.00024489596,0.00011258815,0.0028464193,0.0000059305908,0.00005207241,0.000016810855,0.0008917362,0.9903605,0.005090903,0.00019847957,0.000033997072,0.00014565518],"about_ca_topic_score_codex":0.000030766343,"about_ca_topic_score_gemma":0.000008672517,"teacher_disagreement_score":0.989994,"about_ca_system_score_codex":0.000046253066,"about_ca_system_score_gemma":0.000028611576,"threshold_uncertainty_score":0.38909608},"labels":[],"label_agreement":null},{"id":"W2158481811","doi":"10.1007/978-3-642-40811-3_26","title":"Left-Invariant Metrics for Diffeomorphic Image Registration with Spatially-Varying Regularisation","year":2013,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Baycrest Hospital","funders":"","keywords":"Diffeomorphism; Invariant (physics); Computer science; Artificial intelligence; Computer vision; Metric (unit); Image registration; Image (mathematics); Pattern recognition (psychology); Algorithm; Mathematics; Pure mathematics","score_opus":0.01618200912438223,"score_gpt":0.2599516482335468,"score_spread":0.24376963910916455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158481811","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036325483,0.000019707953,0.99215704,0.0026885583,0.0002824045,0.00096194417,9.231491e-7,0.0002376342,0.00001924791],"genre_scores_gemma":[0.40219295,0.0000025285974,0.59681386,0.0008653801,0.00006765613,0.00004533281,0.0000040483574,0.0000067618153,0.000001489092],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974785,0.00007918906,0.00038043942,0.0007849309,0.0008296957,0.0004472582],"domain_scores_gemma":[0.9979444,0.00051995297,0.00023255292,0.0007195606,0.00043975702,0.00014373493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010712352,0.0002009287,0.00020514098,0.00049971015,0.0002465796,0.00095602253,0.00131448,0.00008370623,0.00001575592],"category_scores_gemma":[0.00060823926,0.00015966706,0.000037598842,0.001701911,0.0003696663,0.002152727,0.00023496585,0.00020711143,0.000008460109],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008548944,0.00010209089,0.0007991503,0.000055799203,0.0000069127104,0.000015572003,0.0012171546,0.005245003,0.08481598,0.0011006499,0.00008942916,0.90654373],"study_design_scores_gemma":[0.00034296192,0.0002188316,0.0016915414,0.000050759496,0.0000030821018,0.000027145761,4.6851213e-7,0.77130216,0.21030335,0.015854044,0.0000045064016,0.00020117016],"about_ca_topic_score_codex":0.00017205748,"about_ca_topic_score_gemma":0.000040522245,"teacher_disagreement_score":0.90634257,"about_ca_system_score_codex":0.0001736652,"about_ca_system_score_gemma":0.0002722042,"threshold_uncertainty_score":0.9218947},"labels":[],"label_agreement":null},{"id":"W2158649156","doi":"10.1016/j.media.2004.06.009","title":"Tuning and comparing spatial normalization methods","year":2004,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":221,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Normalization (sociology); Spatial normalization; Computer science; Artificial intelligence; Image registration; Algorithm; Range (aeronautics); Process (computing); Computer vision; Pattern recognition (psychology); Image (mathematics); Voxel","score_opus":0.017013804513317972,"score_gpt":0.3596544167055799,"score_spread":0.34264061219226194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158649156","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001901331,0.00007964429,0.995826,0.0011643918,0.000049851787,0.00006240285,2.4286882e-7,0.00026134972,0.0006547512],"genre_scores_gemma":[0.20088038,0.000055221848,0.79793775,0.0010231526,0.000046174715,0.000009834174,0.00001403267,0.0000056828035,0.000027786687],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981279,0.00021266403,0.00036875982,0.0003603374,0.00070985535,0.00022048496],"domain_scores_gemma":[0.9990189,0.000106570675,0.000104456165,0.00033518692,0.00009442331,0.00034048196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013208109,0.00011845558,0.00030965975,0.00035772388,0.00011895704,0.00019005917,0.0005123242,0.00007624339,0.0002561587],"category_scores_gemma":[0.0008187642,0.00010480159,0.00009455473,0.001344722,0.00016753946,0.00058275135,0.00033323202,0.00018850622,0.000013764151],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048649567,0.00017552999,0.010412726,0.0000478784,0.000632988,0.00021891815,0.0019508282,0.00019266893,0.00890336,0.00369737,0.00028697465,0.9734759],"study_design_scores_gemma":[0.0007235616,0.000047302034,0.0058726906,0.000043372118,0.00035853023,0.000023454082,0.000056391953,0.9461278,0.043861266,0.0024556175,0.00014532967,0.00028470924],"about_ca_topic_score_codex":0.0006681653,"about_ca_topic_score_gemma":0.00009688716,"teacher_disagreement_score":0.9731912,"about_ca_system_score_codex":0.000044778793,"about_ca_system_score_gemma":0.0000643148,"threshold_uncertainty_score":0.4273685},"labels":[],"label_agreement":null},{"id":"W2158842276","doi":"10.1109/tmi.2012.2186976","title":"Simultaneous Nonrigid Registration, Segmentation, and Tumor Detection in MRI Guided Cervical Cancer Radiation Therapy","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Princess Margaret Cancer Centre; Ontario Institute for Cancer Research","funders":"National Institute of Biomedical Imaging and Bioengineering","keywords":"Segmentation; Artificial intelligence; Computer science; Magnetic resonance imaging; Image registration; Image segmentation; Cervical cancer; Radiation therapy; Computer vision; Medical imaging; Data set; Pattern recognition (psychology); Cancer; Radiology; Medicine; Image (mathematics)","score_opus":0.014759845497226533,"score_gpt":0.3118477798881443,"score_spread":0.2970879343909178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158842276","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009528849,0.00034356007,0.985722,0.0029807906,0.0006946787,0.0003755266,0.0000034292584,0.00028449134,0.00006666111],"genre_scores_gemma":[0.97520894,0.00076229224,0.01948377,0.0041301604,0.00014668284,0.00020173138,0.0000036594495,0.000018792514,0.000043976015],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977051,0.00021764227,0.00051403296,0.0003801998,0.0008116596,0.00037138886],"domain_scores_gemma":[0.9988007,0.0003496824,0.000125965,0.0002608262,0.0000927982,0.00037003463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007658282,0.00018964324,0.00018059109,0.00026910283,0.00019677824,0.00011643559,0.00027637117,0.00008262676,0.00026489398],"category_scores_gemma":[0.0000926853,0.00017958856,0.000046472516,0.0005179582,0.00014418889,0.0011414378,0.000003918092,0.00039902903,0.000016532293],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017534077,0.0002297072,0.0005813132,0.000018423476,0.000013525202,0.00002307879,0.0011069773,0.00031835356,0.011490697,0.000032786025,0.00029588307,0.98587173],"study_design_scores_gemma":[0.0025092268,0.00010269217,0.0019477359,0.00013940467,0.00002111573,0.00022545975,0.00023334226,0.4525357,0.540378,0.00030615242,0.0010653796,0.00053580536],"about_ca_topic_score_codex":0.00037296864,"about_ca_topic_score_gemma":0.000106553205,"teacher_disagreement_score":0.9853359,"about_ca_system_score_codex":0.0002063924,"about_ca_system_score_gemma":0.00011735526,"threshold_uncertainty_score":0.7323409},"labels":[],"label_agreement":null},{"id":"W2158914083","doi":"10.1109/iembs.2005.1616166","title":"Evaluation of Segmentation algorithms for Medical Imaging","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":137,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Segmentation; Computer science; Weighting; Image segmentation; Artificial intelligence; Market segmentation; Task (project management); Process (computing); Metric (unit); Matching (statistics); Scale-space segmentation; Medical imaging; Segmentation-based object categorization; Machine learning; Object (grammar); Algorithm; Computer vision; Pattern recognition (psychology); Data mining; Mathematics; Medicine","score_opus":0.042274673966622336,"score_gpt":0.3925639867987006,"score_spread":0.35028931283207826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158914083","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00027606983,0.000065512766,0.99499565,0.002539657,0.00009273034,0.00037421688,7.7466257e-7,0.00013646772,0.0015188967],"genre_scores_gemma":[0.05439662,0.000008324005,0.944239,0.0010905891,0.00008091243,0.00010897064,0.000007386711,0.0000040757154,0.00006408638],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978863,0.000088605404,0.00027309713,0.00016761772,0.0014798893,0.00010450094],"domain_scores_gemma":[0.9991014,0.000105023006,0.00008309555,0.0001709696,0.0004661512,0.000073382274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032789265,0.00005282014,0.00007276962,0.00007786841,0.000029054137,0.000026827083,0.00031578582,0.000026125581,0.00045772252],"category_scores_gemma":[0.00044384814,0.000046084548,0.00003307713,0.00012703206,0.000034299435,0.0005355946,0.000052872707,0.000034749937,0.000009217342],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.126761e-7,0.000032870317,0.000019136669,0.000004145566,0.000004519307,1.0653982e-7,0.00012858097,0.000010046588,0.0029434965,0.0013162715,0.0031267253,0.9924136],"study_design_scores_gemma":[0.0004955538,0.000014824816,0.00007539031,0.000008868391,0.000010903812,0.0000026775017,0.000031738586,0.7302472,0.2669884,0.0019274772,0.00015278552,0.000044171167],"about_ca_topic_score_codex":0.000009534873,"about_ca_topic_score_gemma":0.0000043289797,"teacher_disagreement_score":0.9923694,"about_ca_system_score_codex":0.00007291448,"about_ca_system_score_gemma":0.000162306,"threshold_uncertainty_score":0.5011741},"labels":[],"label_agreement":null},{"id":"W2158966889","doi":"10.1088/0031-9155/50/6/017","title":"A lesion stabilization method for coronary angiography","year":2005,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Coronary angiography; Coronary artery disease; Lesion; Medicine; Artery; Contrast (vision); Radiology; Angiography; Matching (statistics); Computer science; Artificial intelligence; Computer vision; Cardiology; Myocardial infarction; Surgery","score_opus":0.24187058737439496,"score_gpt":0.46731065556757023,"score_spread":0.22544006819317527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158966889","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011918356,0.00038318193,0.99539727,0.0026191643,0.000056085333,0.00018295895,0.0000010792852,0.000050909937,0.00011753164],"genre_scores_gemma":[0.10961761,0.00023684383,0.88648087,0.0033471459,0.00022661062,0.00005051941,0.00002734874,0.000003837196,0.000009199381],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99943525,0.00007187705,0.00014654359,0.00019197159,0.000048131427,0.000106211766],"domain_scores_gemma":[0.99951875,0.00024950114,0.00004490023,0.00011260701,0.00004025628,0.0000339574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039701365,0.000059226142,0.0001304987,0.00008311641,0.000026344595,0.0000040511122,0.00013360054,0.000038965198,0.0000061404153],"category_scores_gemma":[0.000060525257,0.00004322383,0.000023692472,0.00022689126,0.000088341825,0.00011468891,0.000047424033,0.00004991335,5.149619e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005143802,0.00003317394,0.0014925818,0.000012414613,0.000004733061,2.9138147e-7,0.00036829928,0.0000037495017,0.016515922,0.03193527,0.0010193866,0.94860905],"study_design_scores_gemma":[0.0051592356,0.002958493,0.01092937,0.0002155718,0.000052076324,0.000023658507,0.000383469,0.2457971,0.06937165,0.64419675,0.020295106,0.0006175562],"about_ca_topic_score_codex":0.000016233515,"about_ca_topic_score_gemma":0.0000051262123,"teacher_disagreement_score":0.9479915,"about_ca_system_score_codex":0.000009695939,"about_ca_system_score_gemma":0.000010319913,"threshold_uncertainty_score":0.17626168},"labels":[],"label_agreement":null},{"id":"W2159595075","doi":"10.1109/tmi.2009.2016561","title":"<i>B</i>-Mode Ultrasound Image Simulation in Deformable 3-D Medium","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Imaging phantom; Pixel; Interpolation (computer graphics); Computer vision; Computer science; Artificial intelligence; Voxel; Deformation (meteorology); Image (mathematics); Optics; Physics","score_opus":0.009900441601966601,"score_gpt":0.31100879147259586,"score_spread":0.30110834987062923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159595075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008390737,0.000025725863,0.9926583,0.0041724974,0.00042943974,0.00022717896,0.0000024268293,0.0005627593,0.0010825975],"genre_scores_gemma":[0.9168578,0.00006869597,0.07565811,0.0072255917,0.00006039689,0.000026803707,0.0000035919934,0.000013134197,0.000085886466],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972606,0.00013087262,0.00052977324,0.0004530349,0.0011577422,0.00046797897],"domain_scores_gemma":[0.998579,0.0004452585,0.00007952804,0.00044387,0.00008151991,0.00037080038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070663856,0.00020566682,0.00022364449,0.00036938253,0.00014971965,0.00015582272,0.0007306538,0.00010047029,0.00037462308],"category_scores_gemma":[0.00013869604,0.00019288615,0.00008855257,0.00070614816,0.00013138095,0.0015387163,0.0000033839435,0.0006593396,0.00007709095],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025372678,0.00066221977,0.00003574285,0.000023267366,0.000011548534,0.00022441993,0.0010706652,0.01134543,0.025711473,0.00018226195,0.0012854242,0.9594222],"study_design_scores_gemma":[0.0009614422,0.00006756378,0.0002634181,0.00014656562,0.000008604672,0.00006664544,0.00005642888,0.8484655,0.14761807,0.0018201132,0.00022688504,0.00029873737],"about_ca_topic_score_codex":0.00007521477,"about_ca_topic_score_gemma":0.000023636056,"teacher_disagreement_score":0.95912343,"about_ca_system_score_codex":0.00015678695,"about_ca_system_score_gemma":0.00015246955,"threshold_uncertainty_score":0.78656685},"labels":[],"label_agreement":null},{"id":"W2159605714","doi":"10.1007/978-3-642-04268-3_70","title":"Asymmetric Image-Template Registration","year":2009,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Center for Research Resources; National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke; National Institutes of Health; National Science Foundation","keywords":"Pairwise comparison; Image registration; Computer science; Artificial intelligence; Image (mathematics); Computer vision; Constraint (computer-aided design); Function (biology); Domain (mathematical analysis); Pattern recognition (psychology); Mathematics; Geometry","score_opus":0.01433333251436369,"score_gpt":0.29520530832694236,"score_spread":0.28087197581257867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159605714","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009133333,0.00006400019,0.9948146,0.003029119,0.00038878774,0.0001954009,2.6369713e-7,0.0003501255,0.00024441117],"genre_scores_gemma":[0.4112666,0.0000073098677,0.58608544,0.0025694459,0.0000640346,0.0000029840742,6.823577e-7,0.000002281419,0.0000012401174],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99767137,0.00006740755,0.00033473823,0.00071202806,0.0007817155,0.00043275792],"domain_scores_gemma":[0.9986393,0.00019548103,0.000124773,0.0007376036,0.0001545828,0.00014825643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001071964,0.00016094946,0.00016076831,0.0006459303,0.00017220306,0.00059843546,0.0018688912,0.00006531218,0.00000776378],"category_scores_gemma":[0.00040211147,0.00014372777,0.00003949204,0.003956788,0.00026663247,0.001658835,0.00020653494,0.00025343284,0.00002620729],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001186461,0.000040679664,0.00005810193,0.0000029740695,5.9485706e-7,0.000029843884,0.00018561335,0.00043564677,0.012326854,0.00052307383,0.00011494184,0.9862805],"study_design_scores_gemma":[0.00029988444,0.0003181669,0.0068593966,0.000047282792,0.0000015722344,0.00007259845,2.3247462e-7,0.4790499,0.44399402,0.06898229,0.000053998116,0.0003206888],"about_ca_topic_score_codex":0.00001929508,"about_ca_topic_score_gemma":0.0000045204188,"teacher_disagreement_score":0.9859598,"about_ca_system_score_codex":0.00012495392,"about_ca_system_score_gemma":0.00017050606,"threshold_uncertainty_score":0.5861048},"labels":[],"label_agreement":null},{"id":"W2159798519","doi":"10.1109/icassp.2012.6287990","title":"Left Ventricle mass extraction utilizing a multi-step probabilistic approach","year":2012,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Ellipse; Artificial intelligence; Segmentation; Computer science; Preprocessor; Computer vision; Active contour model; Speckle noise; Image segmentation; Pattern recognition (psychology); Speckle pattern; Mathematics","score_opus":0.058733503782472794,"score_gpt":0.32937394022481137,"score_spread":0.2706404364423386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159798519","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000662201,0.000061866966,0.99037606,0.000071502705,0.00019676141,0.00029803772,2.8893183e-7,0.0005934302,0.0077398764],"genre_scores_gemma":[0.24345724,0.0000035891085,0.7555771,0.00020641662,0.000043022126,0.000019577772,0.0000021040007,0.00000599169,0.0006849676],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988696,0.00008347571,0.00020820535,0.00023208512,0.00029677677,0.0003098618],"domain_scores_gemma":[0.9993074,0.00006961098,0.00007000757,0.00033000027,0.000048064594,0.0001749347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050443975,0.000096554715,0.000094500145,0.000077186305,0.00006686307,0.0000922942,0.00035460986,0.00005215553,0.00014181921],"category_scores_gemma":[0.00014177499,0.00008256202,0.000038883376,0.0002009585,0.00003631696,0.0011273434,0.000094977186,0.00012393783,0.00012471048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012740462,0.0028708265,0.008954434,0.00029503176,0.00005395858,0.000014431813,0.003613049,0.00005734532,0.12192512,0.06505335,0.007894684,0.789255],"study_design_scores_gemma":[0.0009842647,0.000098576325,0.0098335175,0.00003507975,0.000024251853,0.00014897245,0.0006666018,0.823279,0.15957405,0.0009894258,0.0037123687,0.0006538691],"about_ca_topic_score_codex":0.000024277984,"about_ca_topic_score_gemma":8.159496e-7,"teacher_disagreement_score":0.8232217,"about_ca_system_score_codex":0.000076972516,"about_ca_system_score_gemma":0.000022140426,"threshold_uncertainty_score":0.33667815},"labels":[],"label_agreement":null},{"id":"W2159825818","doi":"10.1109/iembs.2009.5333702","title":"Fast and automatic LV mass calculation from echocardiographic images via B-spline snake model and markov random fields","year":2009,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Boundary (topology); B-spline; Spline (mechanical); Markov chain; Artificial intelligence; Computer science; Image (mathematics); Markov process; Algorithm; Computer vision; Pattern recognition (psychology); Mathematics; Mathematical analysis; Engineering; Structural engineering; Statistics; Machine learning","score_opus":0.006983780002825501,"score_gpt":0.2463616509811928,"score_spread":0.2393778709783673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159825818","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010530547,0.00015685665,0.9866147,0.0013878369,0.000034287885,0.00024465838,0.0000030811352,0.00041272357,0.0006153316],"genre_scores_gemma":[0.4917844,0.00009416412,0.50694567,0.001037562,0.000018303834,0.00000856011,0.000007511469,0.000003613085,0.00010023706],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989005,0.00006682758,0.00026442102,0.00034040285,0.0002687037,0.00015917562],"domain_scores_gemma":[0.9993403,0.00011589274,0.0000632795,0.00030344745,0.00004544063,0.00013166522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027572826,0.00013862175,0.00021167619,0.0001367882,0.00007658869,0.00018000056,0.00020312991,0.000087648536,0.00002754409],"category_scores_gemma":[0.000032559652,0.00011434365,0.000047850255,0.00019523072,0.00006139117,0.0005342646,0.00006941444,0.00011262361,0.0000021556957],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011667665,0.000038983984,0.00065085676,0.000019422874,0.00003166222,0.000013827721,0.00029919494,0.0002888018,0.016865423,0.00036182266,0.0026147852,0.9788036],"study_design_scores_gemma":[0.00088432676,0.00004842392,0.007385421,0.000020901736,0.000016092956,0.0000053832464,0.000007179266,0.9652125,0.007572898,0.018701542,0.000002978764,0.00014234109],"about_ca_topic_score_codex":0.000044944984,"about_ca_topic_score_gemma":0.0000041895787,"teacher_disagreement_score":0.97866124,"about_ca_system_score_codex":0.000009892378,"about_ca_system_score_gemma":0.000012728432,"threshold_uncertainty_score":0.46627986},"labels":[],"label_agreement":null},{"id":"W2159989926","doi":"10.1109/isbi.2008.4541310","title":"Acoustic shadows detection, application to accurate reconstruction of 3D intraoperative ultrasound","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Acoustic shadow; Segmentation; Ultrasound; Iterative reconstruction; Acoustic impedance; 3D reconstruction; Image segmentation; Acoustics; Ultrasonic sensor; Physics","score_opus":0.014434825395708695,"score_gpt":0.27025746912669946,"score_spread":0.2558226437309908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159989926","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01963792,0.0000046806936,0.9785131,0.00010402012,0.00011191898,0.00036480438,0.0000012169195,0.00026054677,0.0010017664],"genre_scores_gemma":[0.6688594,0.000018835792,0.33058748,0.00031803636,0.000025612428,0.0000696579,0.0000013402647,0.000003632575,0.00011596432],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990637,0.000050482355,0.0002911473,0.00026750908,0.00021042945,0.000116740564],"domain_scores_gemma":[0.99918395,0.000100491874,0.00009629265,0.00029451906,0.00023393633,0.00009083453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015855281,0.000084737985,0.000111876965,0.00012140878,0.00010031122,0.000026947759,0.00029078798,0.00004558273,0.000089030844],"category_scores_gemma":[0.00018686563,0.000075823664,0.000020896601,0.00051121315,0.000081945036,0.0005198024,0.000047285135,0.00007824268,0.00005743106],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000044622625,0.000032576427,0.00011811629,0.0000072266353,0.000007824872,9.826944e-7,0.00080517645,0.00018183909,0.6814714,0.00032903545,0.0004627965,0.31657854],"study_design_scores_gemma":[0.00011515932,0.00010780453,0.0012976889,0.000008305319,0.0000031941977,0.00013522035,0.0000750764,0.015577136,0.98212904,0.0003948126,0.00004661029,0.00010993025],"about_ca_topic_score_codex":0.00006457665,"about_ca_topic_score_gemma":0.000032562526,"teacher_disagreement_score":0.64922154,"about_ca_system_score_codex":0.000054570875,"about_ca_system_score_gemma":0.000048683723,"threshold_uncertainty_score":0.30919993},"labels":[],"label_agreement":null},{"id":"W2160367117","doi":"10.1007/978-3-642-02256-2_51","title":"A Scale-Space Approach to Landmark Constrained Image Registration","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Landmark; Computer science; Discretization; Maxima and minima; Constraint (computer-aided design); Image registration; TRACE (psycholinguistics); Grid; Mathematical optimization; Transformation (genetics); Image (mathematics); Scale (ratio); Artificial intelligence; Algorithm; Mathematics","score_opus":0.015403675333586114,"score_gpt":0.26467109133929173,"score_spread":0.24926741600570562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160367117","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000048518323,0.000044029166,0.9563362,0.0024130938,0.0003624613,0.00076391525,0.000004093799,0.00042510786,0.03964622],"genre_scores_gemma":[0.0033408394,0.000015001617,0.99087554,0.004245265,0.00026419168,0.000019496463,0.000012566287,0.00002170276,0.0012053702],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99577016,0.000054677246,0.0005830591,0.0016361849,0.0013518219,0.00060412154],"domain_scores_gemma":[0.99742866,0.00022909389,0.0002888438,0.0013852783,0.00029668582,0.00037142794],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012109505,0.00049302314,0.0005044889,0.00079992664,0.00018197164,0.0008371688,0.0029311897,0.00029536855,0.000018980694],"category_scores_gemma":[0.00020152364,0.00040952303,0.00010851257,0.00077375805,0.00075414276,0.0007520953,0.00060041226,0.0007013562,0.00004748854],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005177942,0.00004539958,0.000004155073,0.000028547904,0.000004114407,0.000042370877,0.0006200752,0.00045618703,0.0013879145,0.00522457,0.0004477361,0.99173373],"study_design_scores_gemma":[0.0016167171,0.001505459,0.00036839762,0.0017300544,0.00003878348,0.00067458785,0.0000019749605,0.56747293,0.0816818,0.33507207,0.0058323652,0.0040048514],"about_ca_topic_score_codex":0.000017834745,"about_ca_topic_score_gemma":0.000018653076,"teacher_disagreement_score":0.9877289,"about_ca_system_score_codex":0.00029541113,"about_ca_system_score_gemma":0.00052952196,"threshold_uncertainty_score":0.99983567},"labels":[],"label_agreement":null},{"id":"W2160744602","doi":"10.1016/j.media.2004.06.026","title":"Flux driven automatic centerline extraction","year":2004,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":185,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Institute of Biomedical Imaging and Bioengineering; Natural Sciences and Engineering Research Council of Canada","keywords":"Skeletonization; Distance transform; Computer science; Boundary (topology); Artificial intelligence; Key (lock); Function (biology); Algorithm; Computer vision; Mathematics; Image (mathematics); Mathematical analysis","score_opus":0.00872080128720197,"score_gpt":0.3128668158849142,"score_spread":0.30414601459771223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160744602","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035766917,0.000032701584,0.9895444,0.0051335213,0.000108809436,0.000107116386,0.00000135913,0.0006942457,0.00080116757],"genre_scores_gemma":[0.23456116,0.00005263226,0.76117563,0.0034680986,0.00013399169,0.000041084564,0.00007015403,0.000013784647,0.00048346174],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99720466,0.00012217353,0.0005223609,0.00044883895,0.0013899686,0.00031201332],"domain_scores_gemma":[0.9985215,0.00010573284,0.00015074162,0.00063897646,0.0001220994,0.0004609622],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00062333,0.00015905507,0.00033435653,0.00045808815,0.000101106154,0.00017342833,0.0009765759,0.000110567795,0.0031551637],"category_scores_gemma":[0.0006876292,0.00013468775,0.0002658493,0.0019414317,0.00015369324,0.000820782,0.00019851656,0.0002812977,0.0004118337],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000457058,0.0012216201,0.0010025082,0.00007566772,0.0014471479,0.001478068,0.001145029,0.0001579171,0.018690133,0.0019021695,0.010532832,0.9623423],"study_design_scores_gemma":[0.0023030778,0.0002264338,0.008465043,0.00016197994,0.001192748,0.000119585085,0.00014113256,0.860925,0.1193229,0.0049775555,0.0013325564,0.0008319687],"about_ca_topic_score_codex":0.00011579062,"about_ca_topic_score_gemma":0.000042890624,"teacher_disagreement_score":0.96151036,"about_ca_system_score_codex":0.00012451458,"about_ca_system_score_gemma":0.00012522428,"threshold_uncertainty_score":0.99775606},"labels":[],"label_agreement":null},{"id":"W2160871494","doi":"10.1109/isbi.2006.1624936","title":"Automatic Mri Brain Tissue Segmentation Using a Hybrid Statistical and Geometric Model","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Segmentation; Artificial intelligence; Voxel; Computer science; Image segmentation; White matter; Computer vision; Magnetic resonance imaging; Pattern recognition (psychology); Brain tissue; Scale-space segmentation; Biomedical engineering; Radiology; Medicine","score_opus":0.017973014726918996,"score_gpt":0.31764140220379905,"score_spread":0.29966838747688007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160871494","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008665266,0.000029654027,0.9898213,0.00040644605,0.00003292671,0.00023480838,0.0000063210155,0.0003904308,0.00041280373],"genre_scores_gemma":[0.09591013,0.0000031567622,0.9029967,0.00072527805,0.000017252969,0.000012083881,0.0000132856485,0.0000072213793,0.00031487644],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988331,0.00005869445,0.00028063147,0.0002872654,0.00034233887,0.00019794592],"domain_scores_gemma":[0.9994016,0.00019629646,0.00006853186,0.00019404659,0.00004331973,0.00009620477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002878367,0.00010580167,0.00012463039,0.00026212496,0.00007751963,0.00019466267,0.00019759197,0.0000265501,0.00012592104],"category_scores_gemma":[0.00008063571,0.0000971958,0.000011860791,0.0003977709,0.000062801424,0.00054287724,0.0001187478,0.000064504806,0.000015535254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021950666,0.00019156066,0.00025544886,0.00011670788,0.000014770623,0.00007421353,0.0001828324,0.0013290101,0.06029151,0.028034171,0.05741848,0.8520891],"study_design_scores_gemma":[0.00019444454,0.000036492016,0.00024116012,0.000007739408,0.000005104021,0.00003299826,0.0000068998593,0.93152505,0.05712187,0.010703874,0.0000114161685,0.000112924914],"about_ca_topic_score_codex":0.00015470451,"about_ca_topic_score_gemma":0.0000029944854,"teacher_disagreement_score":0.93019605,"about_ca_system_score_codex":0.000063802574,"about_ca_system_score_gemma":0.00004734751,"threshold_uncertainty_score":0.39635298},"labels":[],"label_agreement":null},{"id":"W2161258151","doi":"10.1109/isspit.2007.4458120","title":"Initial Contour for Ultrasound Carotid Artery Snakes","year":2007,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Active contour model; Contouring; Artificial intelligence; Computer vision; Robustness (evolution); Computer science; Contour line; Image segmentation; Segmentation; Computer graphics (images)","score_opus":0.02615765379663579,"score_gpt":0.3270460452775928,"score_spread":0.300888391480957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161258151","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010909766,0.0000067383676,0.982026,0.00025014294,0.00026131773,0.00029410262,0.0000018820231,0.0004017495,0.015667088],"genre_scores_gemma":[0.24999969,0.000002953734,0.7451828,0.0036454778,0.00019406289,0.000032578817,0.00000597367,0.0000066249395,0.0009298331],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99913067,0.00001735722,0.00021413513,0.00020156291,0.0002043212,0.0002319846],"domain_scores_gemma":[0.99903095,0.00048853917,0.000044745146,0.00023106317,0.00008949107,0.00011519452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055892364,0.000076957724,0.000092554896,0.000067571105,0.000057149013,0.00009687545,0.00038009917,0.00004395157,0.00010934303],"category_scores_gemma":[0.00014125799,0.00006522757,0.000046374756,0.000105952924,0.000050176524,0.00036657677,0.000026171294,0.00005664683,0.00002556947],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036422512,0.00031888162,0.0019147139,0.000069065485,0.000071388225,0.00006297461,0.0023529106,0.0000010175373,0.23837541,0.16248061,0.18125351,0.41306308],"study_design_scores_gemma":[0.00052685436,0.00016413846,0.0014307542,0.000010107158,0.0000039447746,0.000039091177,0.00009732785,0.00026148575,0.98844796,0.0051299413,0.0037046394,0.00018372654],"about_ca_topic_score_codex":0.000018785802,"about_ca_topic_score_gemma":0.000022365915,"teacher_disagreement_score":0.7500726,"about_ca_system_score_codex":0.00002395209,"about_ca_system_score_gemma":0.000037916227,"threshold_uncertainty_score":0.26599032},"labels":[],"label_agreement":null},{"id":"W2161886419","doi":"10.1109/icip.2009.5413950","title":"SRAD and optical flow based external energy for echocardiograms with primitive shape priors","year":2009,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Vector flow; Artificial intelligence; Speckle noise; Computer vision; Computer science; Image segmentation; Speckle pattern; Segmentation; Noise (video); Process (computing); Anisotropic diffusion; Active contour model; Prior probability; A priori and a posteriori; Optical flow; Image (mathematics); Sensitivity (control systems); Engineering","score_opus":0.009737415035915523,"score_gpt":0.25204395114654427,"score_spread":0.24230653611062875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161886419","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007347433,0.000023975585,0.9965284,0.00089151907,0.000025450883,0.00020928818,9.355545e-7,0.00029020218,0.0012954889],"genre_scores_gemma":[0.13796058,0.0000070295,0.8579083,0.003967365,0.0000312762,0.000033154793,0.0000024026033,0.000004897562,0.00008501166],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900115,0.000026870997,0.00014356292,0.0003303986,0.0002867191,0.00021132699],"domain_scores_gemma":[0.99938136,0.00011166354,0.00003551915,0.00021551961,0.00008035033,0.00017558293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016536599,0.00011556944,0.00013992478,0.0000689903,0.000054739136,0.00014988461,0.00027618356,0.00004598972,0.000019432062],"category_scores_gemma":[0.000033152366,0.00008355875,0.00004363369,0.00014342014,0.00007652181,0.00029848077,0.000037075944,0.000059492173,0.0000010988152],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002059333,0.000051131465,0.000044408607,0.000004020142,0.0000073274114,0.000014889914,0.00004362669,0.0000085666725,0.0018691963,0.01141198,0.00038974985,0.9861345],"study_design_scores_gemma":[0.0015017056,0.0017718266,0.00426454,0.00007252051,0.000019551742,0.00003799361,0.00002269727,0.6560382,0.33193,0.0034895674,0.0005040162,0.00034735483],"about_ca_topic_score_codex":0.0000057636616,"about_ca_topic_score_gemma":0.0000013651286,"teacher_disagreement_score":0.98578715,"about_ca_system_score_codex":0.000022129716,"about_ca_system_score_gemma":0.00005141937,"threshold_uncertainty_score":0.34074268},"labels":[],"label_agreement":null},{"id":"W2162028167","doi":"10.1109/cgiv.2006.16","title":"Active Contour Model with Shape Constraints for Bone Fracture Detection","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Active contour model; Artificial intelligence; Segmentation; Constraint (computer-aided design); Computer vision; Geodesic; Computer science; Image segmentation; Contrast (vision); Process (computing); Image (mathematics); Noise (video); Casting; Function (biology); Pattern recognition (psychology); Mathematics; Materials science; Geometry","score_opus":0.01121654472628905,"score_gpt":0.25988168192014605,"score_spread":0.248665137193857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162028167","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009405922,0.0000048798147,0.99471647,0.00056684966,0.000023533426,0.00038294957,0.0000035284354,0.0003725009,0.002988715],"genre_scores_gemma":[0.51299244,5.19904e-7,0.4853035,0.0011795992,0.000026166255,0.00005881115,0.0000033728595,0.0000046612295,0.00043090488],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992747,0.000013717567,0.00012966683,0.00023485797,0.0001938419,0.00015323008],"domain_scores_gemma":[0.9995246,0.00006311561,0.0000719774,0.0001599627,0.00012605324,0.000054290216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009558476,0.00009030658,0.000099483135,0.000051407766,0.0000662167,0.00006865388,0.00017160327,0.000056576813,0.000063820386],"category_scores_gemma":[0.000020511206,0.00006670672,0.000028526036,0.000093346694,0.000079694946,0.00044177615,0.000023851417,0.00007943624,0.0000045918437],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066961635,0.00015040788,0.00001600166,0.000021259217,0.00002433767,0.0000111880545,0.00024607597,0.00064190157,0.06983324,0.010205498,0.010976458,0.9078067],"study_design_scores_gemma":[0.0005238799,0.000110431974,0.000119466786,0.000007912671,0.000004781916,0.000012808724,0.00002832995,0.4910133,0.501069,0.0068611936,0.00013506894,0.00011383024],"about_ca_topic_score_codex":0.00003923024,"about_ca_topic_score_gemma":0.000039882627,"teacher_disagreement_score":0.90769285,"about_ca_system_score_codex":0.000038404083,"about_ca_system_score_gemma":0.000049917933,"threshold_uncertainty_score":0.2720221},"labels":[],"label_agreement":null},{"id":"W2162628091","doi":"10.1109/isbi.2006.1624937","title":"Sketch Initialized Snakes for Rapid, Accurate, and Repeatable Interactive Medical Image Segmentation","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Sketch; Computer science; Artificial intelligence; Computer vision; Image segmentation; Segmentation; Image (mathematics); Computer graphics (images); Algorithm","score_opus":0.013715786552200272,"score_gpt":0.32060723467524316,"score_spread":0.30689144812304286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162628091","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011530202,0.00004298711,0.98798877,0.0016974467,0.00014555422,0.00049882545,0.0000058626856,0.00036117705,0.008106385],"genre_scores_gemma":[0.03341575,0.000064174405,0.96275413,0.0021782438,0.00012557973,0.00026552382,0.00008125341,0.000014078778,0.0011012594],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99856794,0.00009601522,0.00035754114,0.0003691973,0.00039679278,0.00021250466],"domain_scores_gemma":[0.9989404,0.00044347503,0.0001159088,0.00022648883,0.00015406884,0.0001196278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054068124,0.00012598444,0.0001647869,0.00010186428,0.00009912155,0.00025642323,0.0003324552,0.00007005922,0.00043096102],"category_scores_gemma":[0.00034511133,0.00010475196,0.000039171206,0.00017688234,0.00010261666,0.0014934093,0.00015132298,0.00009716998,0.000011772308],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000118609394,0.00042068562,0.00015252832,0.00014489797,0.000066098604,0.00007326607,0.0010790685,0.0000010261864,0.11328247,0.031131333,0.19051619,0.6630138],"study_design_scores_gemma":[0.0026849105,0.000259901,0.00040691055,0.00006398656,0.000016213251,0.000059261834,0.0003068418,0.02451722,0.94931126,0.019664893,0.0023531415,0.00035545236],"about_ca_topic_score_codex":0.0001471146,"about_ca_topic_score_gemma":0.000023235209,"teacher_disagreement_score":0.8360288,"about_ca_system_score_codex":0.00003844523,"about_ca_system_score_gemma":0.00007608557,"threshold_uncertainty_score":0.4718721},"labels":[],"label_agreement":null},{"id":"W2162930331","doi":"10.1109/iembs.2006.260614","title":"Towards an Automatic Coronary Artery Segmentation Algorithm","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Filter (signal processing); Computer vision; Computer science; Algorithm; Artificial intelligence; Fluoroscopy; Frame (networking); Segmentation; Image segmentation; Image (mathematics); Image processing; Feature extraction; Pattern recognition (psychology); Medicine; Radiology","score_opus":0.012757556249755412,"score_gpt":0.28047926198200884,"score_spread":0.26772170573225346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162930331","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035267607,0.000012890626,0.9903213,0.00028797597,0.0001569124,0.00020241395,0.0000014025268,0.0012775192,0.004212865],"genre_scores_gemma":[0.023301294,0.0000025457516,0.9747383,0.0012192647,0.00006616502,0.000040308696,0.00003443447,0.000007247929,0.00059046375],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988279,0.00007719105,0.00026055903,0.0002650219,0.00039055359,0.00017880155],"domain_scores_gemma":[0.9994303,0.000026114627,0.00006193426,0.00034068435,0.000051308038,0.000089673216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021508508,0.00010346452,0.000096625074,0.00009237732,0.00006489825,0.00015884348,0.00042398507,0.000040810235,0.00038063887],"category_scores_gemma":[0.0000066429707,0.00009111615,0.000029444702,0.00020148199,0.000041858868,0.0012409715,0.00008015163,0.0000607812,0.000097916694],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.480319e-7,0.000092083494,0.000060063518,0.0000048430684,0.0000033974463,0.000027705633,0.00008705621,0.0000018999363,0.004500276,0.002168534,0.004686517,0.9883674],"study_design_scores_gemma":[0.0006492794,0.00032925754,0.021988664,0.000018778548,0.000009941137,0.00016351267,0.00011996162,0.6768338,0.27299565,0.026303103,0.00018181832,0.00040620958],"about_ca_topic_score_codex":0.00010052734,"about_ca_topic_score_gemma":0.0000064318115,"teacher_disagreement_score":0.9879612,"about_ca_system_score_codex":0.00005684191,"about_ca_system_score_gemma":0.000049448667,"threshold_uncertainty_score":0.41677284},"labels":[],"label_agreement":null},{"id":"W2162965383","doi":"10.1016/j.compmedimag.2014.10.009","title":"3D multimodal MRI brain glioma tumor and edema segmentation: A graph cut distribution matching approach","year":2014,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CARE Canada","funders":"","keywords":"Glioma; Segmentation; Matching (statistics); Artificial intelligence; Computer science; Graph; Distribution (mathematics); Brain tumor; Pattern recognition (psychology); Medicine; Pathology; Mathematics; Cancer research; Theoretical computer science","score_opus":0.008233496131512096,"score_gpt":0.2656703201993193,"score_spread":0.2574368240678072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162965383","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011955258,0.00020272512,0.9814336,0.005194437,0.00030056457,0.00027363483,0.000006567627,0.00055504654,0.000078194105],"genre_scores_gemma":[0.18340637,0.0002204939,0.80614746,0.009668633,0.00029259323,0.00007318087,0.00015176376,0.000028505274,0.000011009823],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99720216,0.00036532275,0.00048599788,0.0007167555,0.0008201076,0.0004096626],"domain_scores_gemma":[0.99828917,0.00046749564,0.00016852235,0.00037518668,0.00009576224,0.00060385204],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014357031,0.0002808376,0.00035944337,0.00021216391,0.0003415415,0.00042687685,0.0005422611,0.000096873744,0.000008350399],"category_scores_gemma":[0.00027079997,0.00025263062,0.0000727593,0.0005314006,0.0005519006,0.00058012677,0.0005147823,0.00040422598,0.0000019604236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028562183,0.0003054636,0.0011385435,0.00037218657,0.00006812802,0.00015211945,0.00156032,0.000008171051,0.0035917806,0.027068874,0.006987528,0.9587183],"study_design_scores_gemma":[0.0023787795,0.000083344,0.0016711042,0.00020775947,0.000022363552,0.0007118652,0.00007683422,0.98036885,0.0010512394,0.011800307,0.0011695101,0.00045805666],"about_ca_topic_score_codex":0.000045827008,"about_ca_topic_score_gemma":0.0000012988266,"teacher_disagreement_score":0.9803607,"about_ca_system_score_codex":0.000020261361,"about_ca_system_score_gemma":0.000053673717,"threshold_uncertainty_score":0.9999926},"labels":[],"label_agreement":null},{"id":"W2163068451","doi":"10.1109/cvpr.2004.330","title":"Efficient Computation of Closed Contours using Modified Baum-Welch Updating","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computation; Computer science; Image (mathematics); Artificial intelligence; Tangent; Algorithm; Bayesian probability; Pattern recognition (psychology); Hidden Markov model; Computer vision; Mathematical optimization; Mathematics","score_opus":0.034486604280659236,"score_gpt":0.32440985772697667,"score_spread":0.2899232534463174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163068451","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.075630575,0.000015800968,0.9205784,0.0002652299,0.000068014626,0.00017650591,5.5665663e-7,0.00019045912,0.003074452],"genre_scores_gemma":[0.5434594,4.1566622e-7,0.4562447,0.0002520674,0.000019394813,0.000001845609,0.0000012330803,0.000003129566,0.000017763872],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877864,0.000073642004,0.0003772546,0.00022008154,0.00038460092,0.00016579914],"domain_scores_gemma":[0.9993326,0.000079431804,0.0001720809,0.00020315353,0.00013737263,0.00007537749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039922516,0.00008733707,0.00014067274,0.000110386165,0.00005664169,0.000048779024,0.00032006236,0.000039716313,0.00004484847],"category_scores_gemma":[0.00006170795,0.00007938471,0.00004005564,0.00025759192,0.00004969342,0.000175249,0.00010761603,0.00007023235,0.000009177534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009595374,0.00033590934,0.00010590402,0.00005174843,0.000026741325,0.000005085885,0.0019870787,0.14748397,0.26332068,0.027248185,0.0013879697,0.5580371],"study_design_scores_gemma":[0.00026046974,0.000021294783,0.00009907636,0.00002006479,0.000002757114,0.0000023336117,0.00006393231,0.7781462,0.22114897,0.0001605877,0.0000045815796,0.0000697459],"about_ca_topic_score_codex":0.000059756283,"about_ca_topic_score_gemma":0.0000022894496,"teacher_disagreement_score":0.6306622,"about_ca_system_score_codex":0.00005162712,"about_ca_system_score_gemma":0.00004800396,"threshold_uncertainty_score":0.32372144},"labels":[],"label_agreement":null},{"id":"W2163202132","doi":"10.1109/crv.2008.13","title":"Accurate Boundary Localization using Dynamic Programming on Snakes","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Boundary (topology); Computer vision; Object (grammar); Active contour model; Hidden Markov model; Energy (signal processing); Noise (video); Dynamic programming; Pattern recognition (psychology); Reduction (mathematics); Algorithm; Image segmentation; Image (mathematics); Mathematics","score_opus":0.03839064558370086,"score_gpt":0.3221941933348564,"score_spread":0.2838035477511555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163202132","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00410499,0.00002037413,0.9940751,0.00014017074,0.000114448696,0.00017746011,2.210199e-7,0.00063921156,0.00072801835],"genre_scores_gemma":[0.29953772,0.000019613013,0.69862103,0.0014751062,0.000018957699,0.0000118008675,0.0000058374603,0.000008876966,0.0003010792],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990424,0.000048038415,0.00018452475,0.00024058625,0.0003088299,0.00017562191],"domain_scores_gemma":[0.99950784,0.000034416487,0.00006456732,0.00025685012,0.000062261126,0.00007405896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013240942,0.00008917092,0.00008116085,0.00009844035,0.00022139004,0.00012302784,0.0003134554,0.0000382059,0.00004440572],"category_scores_gemma":[0.000050839593,0.00007594515,0.00002690574,0.00034020506,0.00009166969,0.0005906856,0.00007565141,0.00007589135,0.000033010903],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010399138,0.00031259423,0.0009071555,0.000046192406,0.000025774407,0.00019491388,0.0017833482,0.0012046995,0.007126787,0.006461961,0.0038026273,0.97812355],"study_design_scores_gemma":[0.00021137939,0.00012755848,0.00038556333,0.000039838098,0.0000029348912,0.00007210812,0.000035614496,0.9566429,0.0397713,0.0007697099,0.0017235331,0.00021752474],"about_ca_topic_score_codex":0.00002338675,"about_ca_topic_score_gemma":0.0000031845789,"teacher_disagreement_score":0.97790605,"about_ca_system_score_codex":0.000070493,"about_ca_system_score_gemma":0.000078132965,"threshold_uncertainty_score":0.30969533},"labels":[],"label_agreement":null},{"id":"W2163354684","doi":"10.1259/dmfr.20120208","title":"An optimized process flow for rapid segmentation of cortical bones of the craniofacial skeleton using the level-set method","year":2013,"lang":"en","type":"article","venue":"Dentomaxillofacial Radiology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sunnybrook Health Science Centre; University of Toronto; Sunnybrook Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Segmentation; Cortical bone; Craniofacial; Artificial intelligence; Computer science; Process (computing); Cadaveric spasm; Computer vision; Data set; Skeleton (computer programming); Image processing; Biomedical engineering; Pattern recognition (psychology); Anatomy; Image (mathematics); Medicine","score_opus":0.048467589945944224,"score_gpt":0.3734578106858594,"score_spread":0.3249902207399152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163354684","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12503819,0.000026142587,0.8729945,0.0002883123,0.00040886106,0.0011580539,0.000024499195,0.000049211863,0.000012262659],"genre_scores_gemma":[0.22782417,0.000014302414,0.7713375,0.00048451909,0.000077401295,0.00021608718,0.00001757063,0.000014805353,0.0000136297285],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978337,0.00058531895,0.0005915671,0.00032966735,0.00037820052,0.00028153512],"domain_scores_gemma":[0.9985079,0.00021423514,0.0003743111,0.00047132987,0.00034998235,0.00008221405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088208704,0.0001613213,0.00035136178,0.00009425808,0.000179333,0.00004836081,0.0011056436,0.00013946382,0.00007177771],"category_scores_gemma":[0.00045228595,0.00010080788,0.00014451204,0.00034461418,0.00038338557,0.00034610415,0.00011253275,0.00013609261,0.00000132479],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001727164,0.0002710157,0.002727504,0.00012951386,0.00015864577,0.0000022123088,0.0080885645,0.0026526651,0.69256073,0.003920755,0.0005179439,0.2887977],"study_design_scores_gemma":[0.0017667966,0.000542375,0.010301154,0.00002088973,0.0000752015,0.0000765823,0.00062484975,0.5424535,0.43932483,0.0045006494,0.000051771833,0.00026137743],"about_ca_topic_score_codex":0.0001187262,"about_ca_topic_score_gemma":0.0000041041876,"teacher_disagreement_score":0.5398009,"about_ca_system_score_codex":0.00003890181,"about_ca_system_score_gemma":0.00014307135,"threshold_uncertainty_score":0.41108263},"labels":[],"label_agreement":null},{"id":"W2163431839","doi":"10.1109/tpami.2008.15","title":"IRGS: Image Segmentation Using Edge Penalties and Region Growing","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":227,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Memorial University of Newfoundland; Tsinghua University; Canadian Space Agency; University of Calgary","keywords":"Markov random field; Artificial intelligence; Image segmentation; Context (archaeology); Segmentation; Pattern recognition (psychology); Computer vision; Computer science; Synthetic aperture radar; Enhanced Data Rates for GSM Evolution; Process (computing); Representation (politics)","score_opus":0.03939144759579165,"score_gpt":0.30149675215729327,"score_spread":0.2621053045615016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163431839","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020312406,0.000102593665,0.9790253,0.00021523068,0.00007888063,0.00012185813,0.0000048280435,0.0001182855,0.000020633459],"genre_scores_gemma":[0.9441246,0.00095852325,0.05416563,0.000632626,0.000014186167,0.000014109291,0.0000029676871,0.000008582578,0.00007878338],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986768,0.000102946986,0.00033839885,0.00043093343,0.0002777897,0.00017314566],"domain_scores_gemma":[0.99932426,0.00009570181,0.00010952499,0.00027592163,0.00006940003,0.00012516913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017866012,0.00017404542,0.00023012125,0.0004932535,0.00034048368,0.00012064715,0.00021246872,0.000046352427,0.00005158084],"category_scores_gemma":[0.0000057607717,0.0001580351,0.00011002433,0.00066657166,0.00015720495,0.00089191645,0.000007945352,0.00018069563,0.0000052829346],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000085494075,0.00011202647,0.0011434557,0.000035465815,0.00024353123,0.000060426613,0.0017663161,0.0010778205,0.015446362,0.00002443397,0.000020540525,0.98006105],"study_design_scores_gemma":[0.00011569582,0.000099658944,0.00078916014,0.000036787234,0.00023499531,0.00013654372,0.0001870934,0.2248928,0.77308744,0.00015133388,0.0000052447917,0.00026324036],"about_ca_topic_score_codex":0.0008416704,"about_ca_topic_score_gemma":0.0000752557,"teacher_disagreement_score":0.97979784,"about_ca_system_score_codex":0.000039187587,"about_ca_system_score_gemma":0.00001709833,"threshold_uncertainty_score":0.6444484},"labels":[],"label_agreement":null},{"id":"W2163537464","doi":"10.1109/bmei.2008.257","title":"Wavelet-Based Medical Image Registration for Retrieval Applications","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Wavelet; Computer science; Artificial intelligence; Image registration; Mutual information; Domain (mathematical analysis); Computer vision; Pattern recognition (psychology); Wavelet transform; Computation; Matching (statistics); Rigid transformation; Image retrieval; Noise (video); Image (mathematics); Algorithm; Mathematics","score_opus":0.026835104732436863,"score_gpt":0.31448917887096645,"score_spread":0.28765407413852956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163537464","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004898016,0.000008480247,0.989422,0.0059676534,0.000042824584,0.0005412295,0.0000022624722,0.0005700201,0.0033965262],"genre_scores_gemma":[0.008346967,0.000010776831,0.9872643,0.003173747,0.000088370325,0.0001944605,0.000029862977,0.0000068080562,0.0008847073],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99865586,0.0000320011,0.00027105282,0.0002872044,0.00059161586,0.00016224287],"domain_scores_gemma":[0.99894685,0.0002083155,0.00007533382,0.00042193703,0.00016921683,0.00017835396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042940423,0.00007915478,0.00009357606,0.000065157365,0.0001536317,0.00004406116,0.0005886327,0.000078923214,0.00019897836],"category_scores_gemma":[0.0003793961,0.00007007402,0.000053582236,0.00027660863,0.00014637712,0.0002985468,0.00004278678,0.00008798114,0.000045433826],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004505866,0.00065432966,0.00005700031,0.000121955076,0.000023974955,0.00006248104,0.00026760716,9.775388e-7,0.044024166,0.2126776,0.33777246,0.40429237],"study_design_scores_gemma":[0.0015992222,0.00028073485,0.00039073624,0.000022464523,0.000007889865,0.000085344735,0.000016010401,0.17469026,0.7898051,0.010298458,0.022383533,0.00042022878],"about_ca_topic_score_codex":0.000007619985,"about_ca_topic_score_gemma":0.0000023567738,"teacher_disagreement_score":0.74578094,"about_ca_system_score_codex":0.000036574325,"about_ca_system_score_gemma":0.00038944013,"threshold_uncertainty_score":0.28575358},"labels":[],"label_agreement":null},{"id":"W2163640832","doi":"10.1117/1.1579017","title":"Segmentation of breast tumors in mammograms using fuzzy sets","year":2003,"lang":"en","type":"article","venue":"Journal of Electronic Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Universidade de São Paulo; Conselho Nacional de Desenvolvimento Científico e Tecnológico; University of Calgary","keywords":"Computer science; Cover (algebra); Medical imaging; Segmentation; Image segmentation; Breast imaging; Multimedia; Computer vision; Artificial intelligence; Medical physics; Data science; Mammography; Breast cancer; Medicine; Engineering","score_opus":0.009355169760470638,"score_gpt":0.2853766857514503,"score_spread":0.2760215159909797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163640832","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11842064,0.00045034077,0.8804642,0.00022684675,0.00009775106,0.000093128125,2.8472903e-7,0.000018093277,0.00022874623],"genre_scores_gemma":[0.84486634,0.000028978742,0.15492706,0.00014688559,0.000015446141,0.000001011793,3.404828e-7,0.00000759417,0.000006327509],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99835724,0.00016768285,0.0006106127,0.00012479114,0.00041276673,0.00032689498],"domain_scores_gemma":[0.999036,0.00004971155,0.0005463722,0.00014345757,0.00015629231,0.000068143025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011734285,0.0000973302,0.00020363458,0.00035698366,0.000029686884,0.000053211046,0.0003397339,0.000019722702,0.000018312607],"category_scores_gemma":[0.00006449775,0.000090847716,0.000071164424,0.0005199898,0.000042691543,0.00091173063,0.00003034643,0.00028770254,7.879657e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040752308,0.00046479152,0.02843079,0.00009634496,0.00007378472,0.0002756032,0.001980144,0.00093911163,0.5345672,0.010363665,0.00038506126,0.42238277],"study_design_scores_gemma":[0.003774433,0.0004439519,0.006464371,0.0008194415,0.000057231864,0.009423421,0.0010361361,0.045628525,0.89228183,0.039342005,0.00012663593,0.00060199853],"about_ca_topic_score_codex":0.000026335263,"about_ca_topic_score_gemma":0.000003219374,"teacher_disagreement_score":0.72644573,"about_ca_system_score_codex":0.00031367922,"about_ca_system_score_gemma":0.00039081313,"threshold_uncertainty_score":0.37046626},"labels":[],"label_agreement":null},{"id":"W2163695678","doi":"10.1016/j.media.2007.04.002","title":"Validation of vessel-based registration for correction of brain shift","year":2007,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":94,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Imaging phantom; Image registration; Iterative closest point; Computer science; Artificial intelligence; Computer vision; Point set registration; Displacement (psychology); Outlier; Deformation (meteorology); Matching (statistics); Point (geometry); Mathematics; Nuclear medicine; Geology; Image (mathematics); Point cloud; Medicine; Geometry; Statistics","score_opus":0.0460120816706241,"score_gpt":0.40532354460114456,"score_spread":0.35931146293052046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163695678","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[6.213336e-7,0.13365261,0.8652651,0.00018090893,0.00017406174,0.0004791346,0.0000153719,0.00008862661,0.0001435666],"genre_scores_gemma":[0.00009950364,0.6697749,0.3269977,0.00041318836,0.00021851338,0.00025851536,0.0018686951,0.000044879613,0.00032412083],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.995792,0.00038567072,0.0016832403,0.0005255163,0.0013964829,0.00021713984],"domain_scores_gemma":[0.99517256,0.0019074571,0.0015661838,0.0007676268,0.00039258093,0.00019359692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035270755,0.00025848183,0.0015871704,0.0012286863,0.000044798813,0.00005254226,0.0009864505,0.00040406332,0.0002229727],"category_scores_gemma":[0.003943198,0.00022029497,0.0010536518,0.00316065,0.00023831714,0.00028949272,0.00007381865,0.00026903595,0.000004638981],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000028549027,0.000113589704,0.0000019821628,0.003385831,0.00034970653,0.000004771956,0.000032902626,0.0000031420261,0.00002005675,0.000080174716,0.0037508723,0.99225414],"study_design_scores_gemma":[0.0021539186,0.0012552427,0.000040037907,0.020605374,0.026163053,0.000018268336,0.000058174875,0.20273994,0.0986974,0.0010885014,0.6449225,0.0022575846],"about_ca_topic_score_codex":0.000070273134,"about_ca_topic_score_gemma":0.000010869538,"teacher_disagreement_score":0.98999655,"about_ca_system_score_codex":0.00008870281,"about_ca_system_score_gemma":0.0005895625,"threshold_uncertainty_score":0.8983368},"labels":[],"label_agreement":null},{"id":"W2164213525","doi":"10.1109/iccv.1995.466850","title":"Topologically adaptable snakes","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":337,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Grid; Subdivision; Segmentation; Python (programming language); Merge (version control); Artificial intelligence; Representation (politics); Topology (electrical circuits); Image segmentation; Parametric statistics; Computer vision; Theoretical computer science; Mathematics; Geography; Geometry","score_opus":0.04028086866436647,"score_gpt":0.24266044814518095,"score_spread":0.20237957948081448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164213525","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008575465,0.000049549777,0.8637194,0.0021417362,0.00005729749,0.000048912294,8.85323e-8,0.00057306275,0.1333242],"genre_scores_gemma":[0.06307022,0.000038686543,0.91443217,0.0047075534,0.000024026423,0.000012269142,2.6445298e-7,0.0000022863198,0.01771253],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993994,0.000024849984,0.00010692441,0.00015992319,0.00017225501,0.00013664099],"domain_scores_gemma":[0.9995877,0.000042295913,0.000020179732,0.00024848332,0.000027188284,0.00007415653],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009715339,0.00004845586,0.00005815202,0.000030028017,0.000041015952,0.00007139673,0.0004748933,0.000027981494,0.005559643],"category_scores_gemma":[0.000056091263,0.000035005538,0.000021068612,0.00014566447,0.000037070302,0.0003128079,0.00010143655,0.00005205855,0.00047361673],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.5124242e-7,0.000104889004,0.00010875002,0.0000033291606,0.000004678259,0.000028112481,0.00019526483,0.0000011329515,0.0044671954,0.13199432,0.22716247,0.6359295],"study_design_scores_gemma":[0.0010023447,0.00083235634,0.0016858625,0.000028980658,0.000008106945,0.00011177996,0.00015635719,0.22924921,0.5769594,0.05490894,0.13401774,0.0010389538],"about_ca_topic_score_codex":0.0000106418465,"about_ca_topic_score_gemma":0.000001105715,"teacher_disagreement_score":0.63489056,"about_ca_system_score_codex":0.000009983876,"about_ca_system_score_gemma":0.0000034887414,"threshold_uncertainty_score":0.9953494},"labels":[],"label_agreement":null},{"id":"W2164229935","doi":"10.1109/tmi.2005.853639","title":"Real-time fusion of endoscopic views with dynamic 3-D cardiac images: a phantom study","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lawson Health Research Institute; Robarts Clinical Trials; Western University; Sunnybrook Health Science Centre","funders":"","keywords":"Imaging phantom; Computer vision; Artificial intelligence; Computer science; Image registration; Magnetic resonance imaging; Image warping; Image quality; Image fusion; Medical imaging; Medicine; Nuclear medicine; Radiology; Image (mathematics)","score_opus":0.010012551490030457,"score_gpt":0.3005406801381858,"score_spread":0.29052812864815536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164229935","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0140594775,0.000054312288,0.9827239,0.0012369064,0.00022732998,0.0006252315,0.0000068863187,0.00048449752,0.0005814771],"genre_scores_gemma":[0.81732476,0.0003227391,0.18102562,0.000625105,0.000058797214,0.00016002991,0.000003015653,0.000035716446,0.0004442228],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9965289,0.00030911466,0.0005753065,0.0005451821,0.0016839136,0.00035758465],"domain_scores_gemma":[0.99845356,0.00025426922,0.0001518376,0.0006782307,0.00011949688,0.00034258998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009410773,0.00024382418,0.00042018216,0.00031319057,0.00015016981,0.00008148441,0.0009104727,0.00005918028,0.0005223342],"category_scores_gemma":[0.000027318665,0.00019422866,0.000114695555,0.00060062815,0.00023587512,0.00066544773,0.00001282402,0.00042386525,0.00009059672],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021185888,0.0012604516,0.00010625689,0.00003817761,0.000069597074,0.00007695583,0.001521955,0.000068916546,0.055888027,0.000006998129,0.0006655706,0.9402759],"study_design_scores_gemma":[0.0059328536,0.001483657,0.0012953916,0.0011931721,0.00029200304,0.00010109631,0.0011334528,0.29264414,0.69414425,0.000116034695,0.00058737304,0.0010765955],"about_ca_topic_score_codex":0.00012166278,"about_ca_topic_score_gemma":0.000021776483,"teacher_disagreement_score":0.9391993,"about_ca_system_score_codex":0.0001217645,"about_ca_system_score_gemma":0.0001931768,"threshold_uncertainty_score":0.7920415},"labels":[],"label_agreement":null},{"id":"W2164488868","doi":"10.1109/icbbe.2008.906","title":"A Hybrid Fuzzy Based Algorithm for 3D Human Airway Segmentation","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Segmentation; Computer science; Voxel; Artificial intelligence; Image segmentation; Fuzzy logic; Region growing; Computer vision; Scale-space segmentation; Segmentation-based object categorization; Process (computing); Pattern recognition (psychology); Data mining; Algorithm","score_opus":0.02713210790174349,"score_gpt":0.2986057235987285,"score_spread":0.271473615696985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164488868","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021531514,0.00001201097,0.9964634,0.00030460983,0.00014730968,0.0004803673,0.0000048616257,0.00062793243,0.001744169],"genre_scores_gemma":[0.0027265127,0.0000046853625,0.992634,0.0026961742,0.00005734403,0.00018095576,0.00003732259,0.000009500317,0.0016535313],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99887866,0.000042193584,0.00024129105,0.0003076953,0.0003257483,0.0002044078],"domain_scores_gemma":[0.9993216,0.00008341962,0.000074902026,0.00030267087,0.000107782784,0.000109648725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021929978,0.00010805147,0.00011403758,0.00011069036,0.00021323755,0.000054998956,0.00039798013,0.000027699258,0.00015662819],"category_scores_gemma":[0.00002091828,0.0000983274,0.000059831284,0.00013366256,0.00006242446,0.0004615699,0.000055831766,0.0000581535,0.000037471145],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010752035,0.0000913479,0.00001921091,0.000009606827,0.000006257002,0.00001473115,0.0001089602,0.000001248287,0.006360789,0.00059338985,0.03846901,0.95432436],"study_design_scores_gemma":[0.0010956463,0.00030041087,0.00010522185,0.000011491261,0.000005384904,0.000022806375,0.000012828032,0.29777437,0.69743025,0.0016583371,0.0013403328,0.00024290325],"about_ca_topic_score_codex":0.000025720981,"about_ca_topic_score_gemma":0.0000010539407,"teacher_disagreement_score":0.9540815,"about_ca_system_score_codex":0.000053008273,"about_ca_system_score_gemma":0.000063387575,"threshold_uncertainty_score":0.4009675},"labels":[],"label_agreement":null},{"id":"W2164950955","doi":"10.1017/s1431927612014316","title":"Measurement of Palladium Crust Thickness on Catalysts by Optical Microscopy and Image Analysis","year":2013,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Palladium; Materials science; Microscopy; Catalysis; Optical microscope; Crust; Optics; Geology; Chemistry; Composite material; Scanning electron microscope; Physics; Geochemistry; Organic chemistry","score_opus":0.010448550639241194,"score_gpt":0.2723406979228116,"score_spread":0.2618921472835704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164950955","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1302759,0.0023419682,0.86534923,0.0011469176,0.00005356556,0.00039041607,0.000033478944,0.0001133474,0.00029517696],"genre_scores_gemma":[0.77506447,0.0006377347,0.22276059,0.001112095,0.000020315136,0.00006624488,0.000047165915,0.000023459032,0.00026792186],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99734247,0.00015718701,0.0006687662,0.0008394717,0.00057718146,0.00041490849],"domain_scores_gemma":[0.998153,0.0000922797,0.00026860848,0.00077448867,0.00040009472,0.0003115378],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00078211946,0.00033081774,0.0007330497,0.0005448945,0.00017829992,0.00040788788,0.0006130594,0.00014759529,0.000113124064],"category_scores_gemma":[0.00009866417,0.0002859645,0.00021965137,0.0012430606,0.00044089148,0.00045781475,0.0002891184,0.0002202401,0.00003102],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009715439,0.00014829413,0.0011589502,0.00007081055,0.0006939879,0.000003379309,0.0002897307,9.4599864e-7,0.98420465,0.000037326005,0.0063691367,0.007013091],"study_design_scores_gemma":[0.00040166194,0.00010346944,0.002140034,0.000048434176,0.00077914214,0.0000059877107,0.00009741005,0.00085755123,0.9950222,0.00007368633,0.00016788808,0.00030255705],"about_ca_topic_score_codex":0.00082495314,"about_ca_topic_score_gemma":0.000044010125,"teacher_disagreement_score":0.64478856,"about_ca_system_score_codex":0.00008099059,"about_ca_system_score_gemma":0.000056424393,"threshold_uncertainty_score":0.99995923},"labels":[],"label_agreement":null},{"id":"W2165918841","doi":"10.1109/titb.2011.2163724","title":"Fisher–Tippett Region-Merging Approach to Transrectal Ultrasound Prostate Lesion Segmentation","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Segmentation; Prostate; Ultrasound; Artificial intelligence; Computer science; Image segmentation; Contrast (vision); Medicine; Pattern recognition (psychology); Radiology","score_opus":0.02190050953933941,"score_gpt":0.2556427577277109,"score_spread":0.23374224818837147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165918841","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058159227,0.000008069094,0.9879403,0.0020683417,0.00040130314,0.0009863181,0.000007476313,0.0010692772,0.0017029528],"genre_scores_gemma":[0.71315,0.00010534087,0.28455374,0.0015932067,0.000013702354,0.00048225085,0.000022556627,0.000013234361,0.000065966975],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980156,0.000055643923,0.0007804425,0.0003370865,0.00043788928,0.00037337508],"domain_scores_gemma":[0.9989884,0.000059741724,0.00018541835,0.00050387107,0.00012366768,0.00013893549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038552837,0.00024148104,0.00024705176,0.0028046155,0.00016618485,0.00004532795,0.0006661527,0.00032317822,0.000036591417],"category_scores_gemma":[0.00002977113,0.0002249212,0.000052115833,0.0028609599,0.00021125416,0.001976783,0.000006171269,0.0005671808,0.00008805848],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000119768585,0.00060170965,0.00012983293,0.00011693734,0.000042787935,0.000016434044,0.017210709,0.00023800482,0.02158549,0.0017333084,0.0018850457,0.95632],"study_design_scores_gemma":[0.0029453896,0.0014353733,0.00076003216,0.00030442784,0.000029444027,0.0003528547,0.0041182465,0.00873116,0.97707105,0.0029330621,0.00062827126,0.00069071853],"about_ca_topic_score_codex":0.00006119357,"about_ca_topic_score_gemma":0.0000048538914,"teacher_disagreement_score":0.9556292,"about_ca_system_score_codex":0.00020300632,"about_ca_system_score_gemma":0.00005608324,"threshold_uncertainty_score":0.91720206},"labels":[],"label_agreement":null},{"id":"W2166102477","doi":"10.1007/978-1-4939-2441-7_16","title":"Diffeomorphic Image Matching with Left-Invariant Metrics","year":2015,"lang":"en","type":"book-chapter","venue":"Fields Institute communications","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Baycrest Hospital; Canada Research Chairs; University of Toronto; Fleming College","funders":"","keywords":"Invariant (physics); Diffeomorphism; Matching (statistics); Artificial intelligence; Image matching; Computer vision; Mathematics; Computer science; Image (mathematics); Pattern recognition (psychology); Pure mathematics; Statistics; Mathematical physics","score_opus":0.092536796660689,"score_gpt":0.31742957339712075,"score_spread":0.22489277673643177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166102477","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[5.860914e-7,0.0005958918,0.67356753,0.0029044407,0.00018595632,0.00032263438,0.000023344885,0.00042982027,0.32196978],"genre_scores_gemma":[0.0012590012,0.0016627209,0.8826672,0.0014720004,0.00009424387,0.00005582526,0.0002434861,0.00005174188,0.11249382],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980415,0.00007069454,0.0005185276,0.00044352282,0.0006778955,0.00024787724],"domain_scores_gemma":[0.9938299,0.0002514615,0.00037787494,0.0048438897,0.00044806898,0.00024879657],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004883751,0.0003529168,0.00040589846,0.00039950426,0.0003489217,0.0003027191,0.004962882,0.0003746631,0.00016714008],"category_scores_gemma":[0.0001546598,0.00031464582,0.00009858915,0.00017790293,0.0005224904,0.00092228176,0.002079132,0.0012990001,0.00020583592],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004004082,0.000065015694,0.000001864849,0.000042718668,0.00009622496,0.00006216043,0.000685747,0.0000074335326,0.000040330156,0.94758403,0.027346259,0.024064235],"study_design_scores_gemma":[0.0009465067,0.0004341741,0.000011904079,0.0010551361,0.0002144042,0.00029551587,0.000051505536,0.0040839356,0.00058929675,0.2535958,0.73706037,0.0016614671],"about_ca_topic_score_codex":0.00011085242,"about_ca_topic_score_gemma":0.0003886818,"teacher_disagreement_score":0.7097141,"about_ca_system_score_codex":0.00017510679,"about_ca_system_score_gemma":0.00048260836,"threshold_uncertainty_score":0.99993056},"labels":[],"label_agreement":null},{"id":"W2166105980","doi":"10.1109/83.826782","title":"Multiscale methods for the segmentation and reconstruction of signals and images","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Segmentation; Computer science; Image segmentation; Algorithm; Artificial intelligence; Scale-space segmentation; Gaussian; Pattern recognition (psychology); Mathematics","score_opus":0.02198357390391216,"score_gpt":0.3529141955225916,"score_spread":0.3309306216186795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166105980","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011205124,0.0003286244,0.9975694,0.00040920993,0.000060377093,0.0003539571,0.0000054531565,0.00009631436,0.000056151664],"genre_scores_gemma":[0.05807032,0.00024789813,0.9412571,0.00013660343,0.000011034426,0.000106316285,3.832722e-7,0.000007985153,0.00016233385],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99922496,0.00008751677,0.00023502306,0.00023389555,0.00010839119,0.00011021118],"domain_scores_gemma":[0.9993393,0.00030957762,0.00008857149,0.0001280269,0.00008913318,0.00004540776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044310786,0.000094641924,0.00011516271,0.00008103623,0.00027441685,0.00015344568,0.0001256801,0.00003666891,0.00004845567],"category_scores_gemma":[0.0000135323,0.00007294683,0.000031036463,0.00017307879,0.00022450669,0.0009012032,0.0000015123397,0.00009364939,7.062055e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009856556,0.000019479106,0.0000011378223,0.000049952912,0.000006944838,1.1746544e-7,0.00040842523,0.00002916095,0.16520296,0.0000016903706,0.000014722838,0.8342555],"study_design_scores_gemma":[0.00031593075,0.00006749417,0.00005474316,0.000060382336,0.000027994916,0.00003037832,0.00014251005,0.10294579,0.895698,0.0005583284,0.000018081617,0.00008038389],"about_ca_topic_score_codex":0.000012635325,"about_ca_topic_score_gemma":0.0000010036479,"teacher_disagreement_score":0.83417517,"about_ca_system_score_codex":0.000013498768,"about_ca_system_score_gemma":0.00002427994,"threshold_uncertainty_score":0.29746854},"labels":[],"label_agreement":null},{"id":"W2166183883","doi":"10.1109/iscas.2008.4542228","title":"A feature-based image registration technique for images of different scale","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Affine transformation; Image registration; Computer vision; Feature (linguistics); Pattern recognition (psychology); Computer science; Feature extraction; Wavelet; Robustness (evolution); Zernike polynomials; Mathematics; Image (mathematics); Geometry","score_opus":0.01697505044178111,"score_gpt":0.28331399415946884,"score_spread":0.26633894371768774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166183883","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00033114123,0.000010374792,0.99557495,0.0016584299,0.000035918496,0.0007306723,0.000005283521,0.00030253793,0.0013507016],"genre_scores_gemma":[0.06113478,0.000007864143,0.937165,0.00034744115,0.000018367242,0.00028259383,0.000011681333,0.0000070885367,0.0010251502],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99909586,0.000040767805,0.00020097382,0.00024537556,0.0002729513,0.00014406942],"domain_scores_gemma":[0.99911475,0.00010820091,0.00012931382,0.00040771748,0.00017154963,0.0000684643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018984641,0.00010604977,0.00015606199,0.00009991887,0.00006559127,0.000028900185,0.00042310287,0.00006127409,0.000021496158],"category_scores_gemma":[0.000085026935,0.00008345675,0.000083151484,0.00016097233,0.00014317497,0.0003009527,0.000046305555,0.00007048814,0.0000018428261],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009001006,0.00012416001,0.000114753595,0.000053923486,0.000003399757,0.0000035136368,0.00004465152,2.494164e-7,0.9580575,0.0014508707,0.036182433,0.0039554955],"study_design_scores_gemma":[0.00027421326,0.00016326664,0.000568459,0.00002132569,0.000002813371,0.000009502173,0.0000039524216,0.0012836729,0.996068,0.0014065811,0.00010489571,0.000093297735],"about_ca_topic_score_codex":0.000011372493,"about_ca_topic_score_gemma":0.0000026810922,"teacher_disagreement_score":0.06080364,"about_ca_system_score_codex":0.000028310573,"about_ca_system_score_gemma":0.00006450338,"threshold_uncertainty_score":0.34032676},"labels":[],"label_agreement":null},{"id":"W2166219471","doi":"10.1016/j.nicl.2014.08.008","title":"Statistical normalization techniques for magnetic resonance imaging","year":2014,"lang":"en","type":"article","venue":"NeuroImage Clinical","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":413,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Commonwealth Scientific and Industrial Research Organisation; National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; University of California, Los Angeles; Canadian Institutes of Health Research; National Institutes of Health; Servier; Eisai; National Institute of Neurological Disorders and Stroke; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; Synarc; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Medpace; Bristol-Myers Squibb; Eli Lilly and Company; National Institute of Mental Health; Novartis Pharmaceuticals Corporation; F. Hoffmann-La Roche; Alzheimer's Drug Discovery Foundation; Foundation for the National Institutes of Health","keywords":"Normalization (sociology); Spatial normalization; Artificial intelligence; Histogram; Computer science; Magnetic resonance imaging; Pattern recognition (psychology); Functional magnetic resonance imaging; Histogram matching; Neuroimaging; Image processing; Computer vision; Medicine; Image (mathematics); Psychology; Radiology; Voxel; Neuroscience","score_opus":0.03384082117144467,"score_gpt":0.37314872526136283,"score_spread":0.3393079040899182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166219471","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000106853375,0.00007860784,0.99543124,0.0012743765,0.00036134874,0.0004620455,0.000009141232,0.0008793835,0.0013970053],"genre_scores_gemma":[0.02033227,0.000058613503,0.9698284,0.009063881,0.00031769305,0.00012174913,0.000018580418,0.000029464267,0.00022930086],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974393,0.0004029017,0.0007698629,0.00069838465,0.00035100454,0.0003385002],"domain_scores_gemma":[0.9970471,0.0016996043,0.00014534991,0.00071289414,0.00019485291,0.00020017727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016925596,0.00016256621,0.00025987558,0.00008562063,0.000111173154,0.0001916332,0.0008166413,0.00007657187,0.000051339946],"category_scores_gemma":[0.004042193,0.00015736712,0.00009451832,0.00020208891,0.00025553064,0.00048613467,0.000235886,0.0002506822,0.00004144459],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011467798,0.00008839595,0.002610919,0.000015659965,6.737216e-7,0.0000107470505,0.000015461128,1.2898309e-7,0.0006771243,0.01400651,0.024135914,0.958427],"study_design_scores_gemma":[0.0025847557,0.0025828858,0.09665108,0.00012945008,0.000046517467,0.00008961092,0.000009121157,0.44852686,0.03347382,0.03844412,0.3763523,0.0011094614],"about_ca_topic_score_codex":0.000003988996,"about_ca_topic_score_gemma":9.0193447e-7,"teacher_disagreement_score":0.95731753,"about_ca_system_score_codex":0.00001732954,"about_ca_system_score_gemma":0.00005252663,"threshold_uncertainty_score":0.64172447},"labels":[],"label_agreement":null},{"id":"W2166663328","doi":"10.1109/iembs.1995.575177","title":"Model-based multiple active contours matching for radiographic images","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Polytechnique Montréal","funders":"","keywords":"Artificial intelligence; Computer science; Smoothing; Computer vision; Matching (statistics); Object (grammar); Pattern recognition (psychology); Segmentation; Mathematics","score_opus":0.035441189778509595,"score_gpt":0.2821380919734704,"score_spread":0.2466969021949608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166663328","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004145431,0.000035242716,0.99553,0.0015454481,0.000063655534,0.00041002568,0.000006861562,0.0006209898,0.0013732396],"genre_scores_gemma":[0.31712723,0.0000070655824,0.6804657,0.0019409601,0.000013984337,0.00010263615,0.0000025829968,0.000007574487,0.00033229135],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906987,0.000033864188,0.00016654405,0.0002901545,0.00021465118,0.00022490397],"domain_scores_gemma":[0.9991511,0.00028021264,0.000064746884,0.00030627617,0.00008462821,0.000113045586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016461714,0.00010960189,0.00012594038,0.00015018515,0.00010135534,0.000106950494,0.00049785717,0.000043262837,0.000075646545],"category_scores_gemma":[0.000079381636,0.000095606825,0.00009692992,0.00020010467,0.00006126323,0.0005505876,0.000044937467,0.00008103801,0.000009926106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003124846,0.0005505777,0.00031940086,0.00008230173,0.00007444695,0.000016132573,0.002099682,0.0025849638,0.10372541,0.010647729,0.12081479,0.7590533],"study_design_scores_gemma":[0.000553922,0.000044158256,0.000040490613,0.0000098253695,0.0000035103183,9.476772e-7,0.00002395764,0.8030467,0.19246535,0.0036295184,0.00006130937,0.000120308025],"about_ca_topic_score_codex":0.000025820831,"about_ca_topic_score_gemma":0.0000054420443,"teacher_disagreement_score":0.8004617,"about_ca_system_score_codex":0.00002632565,"about_ca_system_score_gemma":0.000014785965,"threshold_uncertainty_score":0.38987333},"labels":[],"label_agreement":null},{"id":"W2166964210","doi":"10.1016/j.ultrasmedbio.2014.08.013","title":"Near Real-Time Robust Non-rigid Registration of Volumetric Ultrasound Images for Neurosurgery","year":2014,"lang":"en","type":"article","venue":"Ultrasound in Medicine & Biology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Image registration; Artificial intelligence; Ultrasound; Gradient descent; Similarity (geometry); Metric (unit); Residual; Distortion (music); Stochastic gradient descent; Computer vision; Image (mathematics); Pattern recognition (psychology); Radiology; Algorithm; Medicine","score_opus":0.018137932762585268,"score_gpt":0.29389456212487247,"score_spread":0.2757566293622872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166964210","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03412661,0.00006472824,0.9610029,0.0008909263,0.00047732197,0.0005068336,0.000010088173,0.00016615607,0.0027544203],"genre_scores_gemma":[0.59212244,0.0008450544,0.40324584,0.0018806198,0.00053644524,0.00014948021,0.00028061293,0.000040726714,0.0008987783],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99748695,0.0003053341,0.00092418253,0.00058328686,0.00028645125,0.0004137669],"domain_scores_gemma":[0.9925162,0.00601226,0.00046368287,0.0006548231,0.00022035734,0.00013262875],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0024175108,0.00022396298,0.0005823384,0.0004314399,0.000084326595,0.000039804952,0.000746262,0.00016186187,0.0001042728],"category_scores_gemma":[0.010465326,0.00018588148,0.00007782617,0.0009362694,0.00074194884,0.0002928223,0.000048336635,0.00021659416,0.00001405081],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033483626,0.00014603259,0.009734911,0.00010180367,0.000018460409,0.0000035028227,0.00038206798,0.000037866474,0.8818669,0.0019334962,0.06754077,0.038200743],"study_design_scores_gemma":[0.014958265,0.017320326,0.3129853,0.0012786835,0.00028081168,0.0006507091,0.0004289911,0.062291045,0.4514101,0.07171781,0.06287434,0.003803595],"about_ca_topic_score_codex":0.00039078333,"about_ca_topic_score_gemma":0.000010893402,"teacher_disagreement_score":0.5579958,"about_ca_system_score_codex":0.000050490664,"about_ca_system_score_gemma":0.00010012489,"threshold_uncertainty_score":0.99786997},"labels":[],"label_agreement":null},{"id":"W2167679119","doi":"10.1016/j.neuroimage.2009.10.032","title":"Feature-based morphometry: Discovering group-related anatomical patterns","year":2009,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":99,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"National Center for Research Resources","keywords":"Pattern recognition (psychology); Feature (linguistics); Artificial intelligence; Voxel; Computer science; Contrast (vision); Population; Set (abstract data type); Scale (ratio); Probabilistic logic; Image (mathematics); Feature vector; Cartography; Geography; Medicine","score_opus":0.01063985209850597,"score_gpt":0.26221122401343455,"score_spread":0.25157137191492857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167679119","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04435752,0.000025779575,0.95002544,0.003725452,0.00020493231,0.00018660302,0.000005572421,0.00082935963,0.00063933484],"genre_scores_gemma":[0.90764093,0.000008291458,0.08580105,0.0063612,0.00003589703,0.000007555268,0.000017363467,0.000014355529,0.00011335401],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99840915,0.00010839885,0.00020292628,0.00050458364,0.00045078748,0.00032417945],"domain_scores_gemma":[0.99899507,0.00008377728,0.00008776824,0.00062203535,0.000029630592,0.00018174443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018454078,0.00017777219,0.0001785739,0.00019804343,0.00007426506,0.00025229307,0.00083845673,0.00007630968,0.00005871157],"category_scores_gemma":[0.00010985733,0.00016447366,0.00008918658,0.00057469966,0.00004770001,0.00063191366,0.00011388084,0.00035946173,0.000046166853],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030412166,0.0007142236,0.0051781554,0.00005389455,0.000022332504,0.0014667294,0.00024880347,0.000045707213,0.49293476,0.008206486,0.02702337,0.46407512],"study_design_scores_gemma":[0.0029983015,0.0010795044,0.269363,0.00015238505,0.00002855922,0.00012114566,0.000016949456,0.13338298,0.58556384,0.003326463,0.002748198,0.0012187232],"about_ca_topic_score_codex":0.000010753851,"about_ca_topic_score_gemma":9.550348e-7,"teacher_disagreement_score":0.8642244,"about_ca_system_score_codex":0.000046799865,"about_ca_system_score_gemma":0.000025763684,"threshold_uncertainty_score":0.6707041},"labels":[],"label_agreement":null},{"id":"W2168081935","doi":"10.1109/tip.2005.852200","title":"Image segmentation and selective smoothing by using Mumford-Shah model","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":143,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Level set (data structures); Active contour model; Mathematics; Piecewise; Smoothing; Image segmentation; Partial differential equation; Level set method; Segmentation; Artificial intelligence; Scale-space segmentation; Initialization; Algorithm; Computer vision; Mathematical optimization; Computer science; Mathematical analysis","score_opus":0.018634013562404884,"score_gpt":0.30297650808306553,"score_spread":0.2843424945206606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168081935","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037342808,0.00010060778,0.9946287,0.00037171552,0.000056327513,0.00028452795,0.000009725466,0.00053077366,0.0002833349],"genre_scores_gemma":[0.278366,0.000027631848,0.72067094,0.0007430551,0.000022620026,0.00003931808,0.0000017933626,0.000024113424,0.000104523126],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982866,0.000066520195,0.00035702705,0.0005401267,0.00041385388,0.0003358797],"domain_scores_gemma":[0.9992462,0.0000551182,0.00015661602,0.00021603703,0.00017175238,0.00015432233],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028192217,0.00023473197,0.00018213804,0.00023296197,0.0005474407,0.00053973676,0.00029228654,0.00008461929,0.000019682515],"category_scores_gemma":[0.000011730277,0.0002452386,0.000050630166,0.00044086087,0.0001755346,0.0042902837,0.0000064732894,0.00035285176,0.000009301269],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000075457706,0.00009589899,5.670395e-7,0.00004233337,0.000009001854,0.000001986883,0.0018182589,0.0012478989,0.5532523,0.0000030372191,0.00020168646,0.44331953],"study_design_scores_gemma":[0.0002466837,0.000024335755,7.600954e-7,0.00004661209,0.000014501711,0.000016140631,0.00009107604,0.5410687,0.45818615,0.00014950571,0.0000055374894,0.00015002904],"about_ca_topic_score_codex":0.000023189019,"about_ca_topic_score_gemma":0.000005605907,"teacher_disagreement_score":0.5398208,"about_ca_system_score_codex":0.00022418832,"about_ca_system_score_gemma":0.00012824223,"threshold_uncertainty_score":1},"labels":[],"label_agreement":null},{"id":"W2168260192","doi":"10.1109/crv.2009.19","title":"An Efficient and Fast Active Contour Model for Salient Object Detection","year":2009,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Active contour model; Level set (data structures); Artificial intelligence; Polarity (international relations); Salient; Computer science; Computer vision; Function (biology); Smoothing; Isotropy; Pattern recognition (psychology); Mathematics; Algorithm; Image (mathematics); Image segmentation; Physics","score_opus":0.015375921022762957,"score_gpt":0.3002376620356291,"score_spread":0.28486174101286615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168260192","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013158914,0.0000053275953,0.9855021,0.00023007522,0.000040730232,0.0003545055,0.0000016251827,0.00030580728,0.00040092005],"genre_scores_gemma":[0.7121326,0.0000018003735,0.2867568,0.0009916753,0.000013569746,0.000022078864,9.985635e-7,0.0000022515126,0.000078247394],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930483,0.000023769997,0.000116178264,0.00025905634,0.00015847747,0.00013769706],"domain_scores_gemma":[0.9995634,0.000031006177,0.000040739404,0.00018677273,0.00007135716,0.000106731415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016678384,0.00007349777,0.0000783495,0.0000614089,0.000077778226,0.00008829489,0.00017410552,0.000033820303,0.0000035953065],"category_scores_gemma":[0.000027119813,0.00006121403,0.000021993466,0.00008051707,0.000021806592,0.0002684238,0.000023513656,0.000044623204,0.0000013729559],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015708834,0.000104570754,0.0000012055482,0.0000032655207,0.0000029762477,6.349706e-7,0.0010937277,0.0010623666,0.072594136,0.0032101774,0.00017935506,0.9217319],"study_design_scores_gemma":[0.00018760479,0.0002388597,0.00015091078,0.0000027385797,0.0000018434354,0.0000015207402,0.000046634585,0.7134113,0.28402522,0.0018707883,0.0000038196786,0.000058791848],"about_ca_topic_score_codex":0.000010096016,"about_ca_topic_score_gemma":0.000008636921,"teacher_disagreement_score":0.92167306,"about_ca_system_score_codex":0.000039165203,"about_ca_system_score_gemma":0.00002115738,"threshold_uncertainty_score":0.24962355},"labels":[],"label_agreement":null},{"id":"W2168287284","doi":"10.1109/isbi.2006.1625026","title":"Shape vs. Volume: Invariant Shape Descriptors for 3D Region of Interest Characterization in MRI","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Institute on Aging; Deutsches Krebsforschungszentrum","keywords":"Caudate nucleus; Thalamus; Parkinson's disease; Invariant (physics); Region of interest; Subthalamic nucleus; Shape analysis (program analysis); Neuroscience; Nuclear medicine; Anatomy; Medicine; Psychology; Mathematics; Disease; Biology; Pathology; Radiology; Deep brain stimulation","score_opus":0.04068126510733724,"score_gpt":0.26471470867834046,"score_spread":0.22403344357100322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168287284","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022994235,0.0000055810374,0.9748394,0.001181357,0.00014181116,0.00041540485,0.0000033173633,0.00015784816,0.0002610763],"genre_scores_gemma":[0.45956624,0.000015127912,0.53806627,0.0012868654,0.00006773073,0.00009359222,0.000089001216,0.000014140649,0.000801064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891263,0.00005087553,0.00044521512,0.00027656776,0.00014115416,0.00017357548],"domain_scores_gemma":[0.99938524,0.00004593511,0.00016529055,0.00025273662,0.00010524413,0.00004557592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026395312,0.00010054845,0.00016004176,0.00019673619,0.000028663362,0.000068225134,0.00044273358,0.0000676712,0.00011583362],"category_scores_gemma":[0.00007777808,0.00009307152,0.00003782346,0.00032759234,0.000054971082,0.0006906037,0.000110114335,0.00006601721,0.0000074719806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000085009575,0.0007304715,0.017110227,0.0002566171,0.000017477081,0.000041830077,0.00097811,0.000006488861,0.31187767,0.068709604,0.03601932,0.5641672],"study_design_scores_gemma":[0.00086786965,0.0003301652,0.044167463,0.00015940945,0.000006506821,0.000014453178,0.000026046722,0.785751,0.16331606,0.002622114,0.002434748,0.00030419018],"about_ca_topic_score_codex":0.000113275186,"about_ca_topic_score_gemma":0.00004972007,"teacher_disagreement_score":0.7857445,"about_ca_system_score_codex":0.00005308108,"about_ca_system_score_gemma":0.00004079895,"threshold_uncertainty_score":0.37953463},"labels":[],"label_agreement":null},{"id":"W2168567852","doi":"10.1109/isspit.2006.270779","title":"MRI Brain Extraction with Combined Expectation Maximization and Geodesic Active Contours","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Geodesic; Robustness (evolution); Computer science; Artificial intelligence; Maximization; Statistical parametric mapping; Pattern recognition (psychology); Expectation–maximization algorithm; Parametric statistics; Computer vision; Magnetic resonance imaging; Feature extraction; Mathematics; Mathematical optimization; Maximum likelihood; Radiology","score_opus":0.005276937525245311,"score_gpt":0.24290122689908725,"score_spread":0.23762428937384195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168567852","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012351148,0.0000071908926,0.98255455,0.0018286032,0.000035649817,0.0002524054,4.2322446e-7,0.00036132179,0.0026087165],"genre_scores_gemma":[0.56768405,0.0000062037916,0.43068093,0.00060568354,0.000028213752,0.00004656244,0.000024875175,0.000008073946,0.00091541396],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992376,0.00005913619,0.00013609396,0.00023180022,0.0002251104,0.00011027621],"domain_scores_gemma":[0.9995262,0.0001045556,0.000095936484,0.00013863202,0.00008874376,0.000045928176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010887924,0.000083584695,0.00007887206,0.000090571084,0.00007233746,0.00011508172,0.00010302424,0.00003699973,0.00004017933],"category_scores_gemma":[0.00002360582,0.000069154536,0.000010204421,0.00019329751,0.00005003226,0.0010316132,0.000021951515,0.0000572857,0.0000044262742],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002986752,0.00071310427,0.005723056,0.0000660358,0.00006886827,0.00006657648,0.0033594456,0.000561591,0.14303307,0.12887229,0.04675253,0.6704847],"study_design_scores_gemma":[0.003212429,0.00079890474,0.107426465,0.000053087875,0.00002053318,0.000062224004,0.0006998507,0.12599188,0.7473232,0.013601442,0.00023450352,0.0005754824],"about_ca_topic_score_codex":0.00017741104,"about_ca_topic_score_gemma":0.000053463107,"teacher_disagreement_score":0.66990924,"about_ca_system_score_codex":0.000044089,"about_ca_system_score_gemma":0.00002459738,"threshold_uncertainty_score":0.282004},"labels":[],"label_agreement":null},{"id":"W2169123171","doi":"10.1109/aiccsa.2008.4493596","title":"Active contours initialization for ultrasound carotid artery images","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Contouring; Initialization; Active contour model; Sensitivity (control systems); Computer science; Artificial intelligence; Computer vision; Ultrasound; Point (geometry); Algorithm; Segmentation; Pattern recognition (psychology); Image segmentation; Mathematics; Radiology; Engineering; Medicine","score_opus":0.028972555418412208,"score_gpt":0.2942836788130975,"score_spread":0.2653111233946853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169123171","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00079968845,0.0000062492472,0.99286586,0.00028829512,0.00014386448,0.00038349576,0.0000062429003,0.00039435417,0.0051119626],"genre_scores_gemma":[0.29897824,0.00006328241,0.6963796,0.0028797076,0.0001247036,0.00018195076,0.0000348621,0.000012322947,0.0013453335],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992411,0.000040611583,0.00015725792,0.00021562193,0.00019295281,0.00015245104],"domain_scores_gemma":[0.9992478,0.00025413538,0.000060935665,0.00020704222,0.00015469892,0.00007541842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010902946,0.000081177626,0.00009916198,0.00006665286,0.00011139684,0.000054503213,0.00028497283,0.000037701968,0.0000741204],"category_scores_gemma":[0.00015561508,0.00007252363,0.000040998697,0.0001326683,0.00008668541,0.00075173157,0.00002377617,0.000044555567,0.000015556096],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049217517,0.00052036374,0.003011205,0.00008064935,0.0001364864,0.000065183776,0.010654658,0.000015130528,0.39864492,0.041638665,0.44756564,0.09761788],"study_design_scores_gemma":[0.00042016822,0.00010434642,0.0024581247,0.000007812844,0.0000038539265,0.00006132638,0.00008385207,0.0003837183,0.99383706,0.002134397,0.00036010367,0.00014526198],"about_ca_topic_score_codex":0.000023473383,"about_ca_topic_score_gemma":0.000003364213,"teacher_disagreement_score":0.59519213,"about_ca_system_score_codex":0.00003274845,"about_ca_system_score_gemma":0.00006780031,"threshold_uncertainty_score":0.29574278},"labels":[],"label_agreement":null},{"id":"W2169160934","doi":"10.1109/icbbe.2009.5162937","title":"Improved T-Snake Model Based Edge Detection of the Coronary Arterial Walls in Intravascular Ultrasound Images","year":2009,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children","funders":"","keywords":"Intravascular ultrasound; Speckle noise; Artificial intelligence; Computer science; Computer vision; Intersection (aeronautics); Speckle pattern; Enhanced Data Rates for GSM Evolution; Pattern recognition (psychology); Radiology; Medicine; Engineering","score_opus":0.008058789370659793,"score_gpt":0.23762782142292693,"score_spread":0.22956903205226714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169160934","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033178065,0.000009720219,0.96566147,0.00032970554,0.00012186413,0.0003063009,0.0000015168856,0.0001235945,0.00026774805],"genre_scores_gemma":[0.870278,0.0000052762516,0.1286489,0.0009735718,0.000017945458,0.0000122682495,0.0000012342823,0.000003677277,0.00005913189],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99896055,0.000096027594,0.00028591818,0.00023659825,0.00025953833,0.00016137159],"domain_scores_gemma":[0.999286,0.000063621555,0.00008250452,0.0004618694,0.000057706315,0.000048288806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032598941,0.00010128564,0.00012748713,0.0000748783,0.000037157082,0.00004946449,0.0005671728,0.00006126556,0.000027971182],"category_scores_gemma":[0.00009267039,0.00007151034,0.00006740283,0.00024753952,0.000069950656,0.00035126976,0.00004731104,0.00012101086,0.0000017111864],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007900054,0.00007603919,0.000021950895,0.000004264103,0.000002278292,0.0000010357879,0.00007505048,0.000086685126,0.93815947,0.00010437796,0.00010731644,0.061353646],"study_design_scores_gemma":[0.00039992854,0.000080864185,0.006148771,0.000010657411,0.0000029199634,0.000003641722,0.000007377347,0.16756405,0.8242595,0.0014443527,0.0000025451754,0.00007535321],"about_ca_topic_score_codex":0.000042109918,"about_ca_topic_score_gemma":0.000020341044,"teacher_disagreement_score":0.8370999,"about_ca_system_score_codex":0.000048163165,"about_ca_system_score_gemma":0.00007415765,"threshold_uncertainty_score":0.29161072},"labels":[],"label_agreement":null},{"id":"W2169279485","doi":"10.1007/978-3-642-04271-3_109","title":"Left Ventricle Segmentation via Graph Cut Distribution Matching","year":2009,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; St Joseph's Health Care; CARE Canada","funders":"","keywords":"Computer science; Cut; Bhattacharyya distance; Segmentation; Active contour model; Image segmentation; Artificial intelligence; Matching (statistics); Kernel density estimation; Algorithm; Pattern recognition (psychology); Mathematics","score_opus":0.008528817064882666,"score_gpt":0.27544496817248554,"score_spread":0.26691615110760286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169279485","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014785288,0.000053796786,0.9830047,0.0011885106,0.00039387878,0.00024173231,0.0000012069485,0.00032165024,0.000009267627],"genre_scores_gemma":[0.57316786,0.0000056184213,0.4253874,0.0013781362,0.000049837818,0.0000027430995,0.000005745871,0.0000023605292,2.6418465e-7],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99778765,0.00007982952,0.00031613815,0.00063136045,0.0007392251,0.0004457628],"domain_scores_gemma":[0.99901503,0.00013319099,0.000118822,0.00048548132,0.0001059926,0.00014149798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074895564,0.00016504836,0.00014936575,0.0002724482,0.00023606859,0.00040290155,0.0012785365,0.000059768674,0.000010866777],"category_scores_gemma":[0.000083298764,0.00015284104,0.000046596197,0.0017289594,0.0001689347,0.0014240353,0.00021370688,0.00023258502,0.000015257729],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018531937,0.0000619069,0.00030774827,0.0000037254765,0.0000010876329,0.000014317192,0.00078211055,0.0032455956,0.037433192,0.00034212068,0.000019280867,0.95778704],"study_design_scores_gemma":[0.00024793323,0.00016260936,0.006515243,0.000042887277,0.0000020603818,0.00005405788,0.0000010948438,0.32866806,0.5425734,0.12148617,0.000008527279,0.00023798374],"about_ca_topic_score_codex":0.000027563161,"about_ca_topic_score_gemma":0.0000073114948,"teacher_disagreement_score":0.9575491,"about_ca_system_score_codex":0.00020811772,"about_ca_system_score_gemma":0.00008860725,"threshold_uncertainty_score":0.62326765},"labels":[],"label_agreement":null},{"id":"W2169451412","doi":"10.1016/j.compmedimag.2007.11.004","title":"SketchSnakes: Sketch-line initialized Snakes for efficient interactive medical image segmentation","year":2008,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Sketch; Computer science; Computer vision; Artificial intelligence; Segmentation; Computer graphics (images); Initialization; Robustness (evolution); Image segmentation; Object (grammar); Algorithm","score_opus":0.021472156532947255,"score_gpt":0.32395098885215395,"score_spread":0.3024788323192067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169451412","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010941987,0.00030207305,0.9776627,0.008674508,0.0009647081,0.0006148532,0.000011778522,0.000723614,0.0001037895],"genre_scores_gemma":[0.18565217,0.0017597275,0.7911686,0.020022044,0.00072887674,0.00036042262,0.00018255037,0.000071191884,0.000054456515],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99578047,0.00031295605,0.00083492015,0.00081781944,0.0016983148,0.00055550213],"domain_scores_gemma":[0.99689656,0.0010825648,0.00025356113,0.00045818486,0.00036790548,0.0009412228],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014281278,0.0003608381,0.0005403716,0.0003737018,0.00041787262,0.00019628169,0.0010187415,0.00019087941,0.00010690981],"category_scores_gemma":[0.0016103653,0.00031357392,0.00018597576,0.0005559274,0.0010569501,0.0005370219,0.0004969912,0.00056484865,0.000008352262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026514163,0.0013701075,0.0004356208,0.00043495497,0.00023656878,0.0015077974,0.006628933,0.000005252846,0.008357711,0.010299465,0.024330778,0.94612765],"study_design_scores_gemma":[0.007210094,0.00026287884,0.00047372608,0.00051790447,0.000040338404,0.0010920024,0.0001727402,0.9691952,0.014012755,0.0038998963,0.0024213027,0.0007011702],"about_ca_topic_score_codex":0.000030798572,"about_ca_topic_score_gemma":0.0000026812882,"teacher_disagreement_score":0.96918994,"about_ca_system_score_codex":0.000046975783,"about_ca_system_score_gemma":0.00037642877,"threshold_uncertainty_score":0.99993163},"labels":[],"label_agreement":null},{"id":"W2169769956","doi":"10.1118/1.4828836","title":"Novel multimodality segmentation using level sets and Jensen‐Rényi divergence","year":2013,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; McGill University Health Centre; National Science Foundation","keywords":"Segmentation; Imaging phantom; Positron emission tomography; Image segmentation; Image registration; Artificial intelligence; Mathematics; Nuclear medicine; Histogram; Computer science; Medical imaging; Active contour model; Standardized uptake value; Pattern recognition (psychology); Medicine; Image (mathematics)","score_opus":0.07814629249687183,"score_gpt":0.3360516542940352,"score_spread":0.2579053617971634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169769956","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057039835,0.00001682969,0.94160104,0.00067893515,0.00018774721,0.0002479297,0.0000050405056,0.0001602238,0.00006241742],"genre_scores_gemma":[0.5290258,0.00001953852,0.46861038,0.0021673397,0.00010106436,0.000028326413,0.000009357163,0.000007980754,0.000030242245],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981862,0.000059937815,0.00026794497,0.0003531225,0.0008901294,0.0002426822],"domain_scores_gemma":[0.99902827,0.00013386822,0.00009971678,0.0002908351,0.00011694057,0.00033037778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030707906,0.00013635652,0.00015546982,0.00002808454,0.000119921206,0.000102149956,0.0004327485,0.000081123275,0.00015618237],"category_scores_gemma":[0.0002274555,0.0001204271,0.000033812026,0.00022353009,0.00020738595,0.0008189387,0.00036663652,0.00019255868,0.0000462018],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011430816,0.00018310377,0.0020308024,0.000042370604,0.000016108486,0.0000068311856,0.0006841321,0.000006011649,0.044117127,0.00054658815,0.0013918558,0.9509739],"study_design_scores_gemma":[0.0016050319,0.00010821015,0.033431236,0.00016775908,0.000025191075,0.00004650844,0.00010464845,0.5511563,0.38900772,0.023574045,0.000050633298,0.0007227415],"about_ca_topic_score_codex":0.0005377177,"about_ca_topic_score_gemma":0.0000030608896,"teacher_disagreement_score":0.95025116,"about_ca_system_score_codex":0.00004987018,"about_ca_system_score_gemma":0.00008549649,"threshold_uncertainty_score":0.4910875},"labels":[],"label_agreement":null},{"id":"W2169816047","doi":"10.1109/icpr.2000.903600","title":"Optimal line detector","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Detector; Infinite impulse response; Computer science; Line (geometry); Impulse (physics); Algorithm; Gaussian; Finite impulse response; Impulse response; Filter (signal processing); Scale (ratio); Computer vision; Mathematics; Digital filter; Physics; Telecommunications; Mathematical analysis","score_opus":0.030405615186511203,"score_gpt":0.2767204251535919,"score_spread":0.24631480996708072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169816047","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000615235,0.000029929422,0.9886299,0.00082887226,0.000063716085,0.000051347255,1.3484029e-7,0.0005927493,0.009188114],"genre_scores_gemma":[0.06012733,0.000011572475,0.933847,0.0016692481,0.000035354704,0.000008022967,1.9977828e-7,0.000002734543,0.004298508],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99948823,0.000015424264,0.0000956642,0.00013484283,0.00015938096,0.000106482104],"domain_scores_gemma":[0.99962664,0.000028210132,0.000017603448,0.00022785364,0.000024562427,0.00007515295],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006974652,0.000043747328,0.000045558932,0.000037496706,0.000024795792,0.000054351665,0.00037413585,0.000020423944,0.002272068],"category_scores_gemma":[0.000040371997,0.000035073397,0.00001930638,0.00014038941,0.000020562227,0.00024998764,0.00008998371,0.000049813854,0.00049494806],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.826458e-7,0.00008078151,0.000024982457,0.0000047401495,0.0000052216383,0.000025697273,0.00025323397,0.000008806314,0.01413558,0.0034248228,0.06449292,0.91754276],"study_design_scores_gemma":[0.00019726902,0.00012964076,0.00006889367,0.0000048168085,0.0000012817429,0.00001525887,0.000006461346,0.31408,0.68112284,0.00024136638,0.0039948965,0.00013729704],"about_ca_topic_score_codex":0.0000038436256,"about_ca_topic_score_gemma":4.2684957e-7,"teacher_disagreement_score":0.9174054,"about_ca_system_score_codex":0.0000099174395,"about_ca_system_score_gemma":0.0000030689937,"threshold_uncertainty_score":0.99864},"labels":[],"label_agreement":null},{"id":"W2170391517","doi":"10.1109/83.846240","title":"KCS-new kernel family with compact support in scale space: formulation and impact","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Kernel (algebra); Gaussian; Gaussian function; Scale space; Computer science; Representation (politics); Gaussian process; Mathematics; Algorithm; Kernel method; Pattern recognition (psychology); Artificial intelligence; Image processing; Support vector machine; Discrete mathematics; Image (mathematics)","score_opus":0.014080517409617656,"score_gpt":0.2946639673317581,"score_spread":0.28058344992214046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170391517","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054400634,0.000035579054,0.9436371,0.00033649214,0.000027112903,0.00023984561,0.0000029210146,0.0002776306,0.0010427039],"genre_scores_gemma":[0.8367856,0.000034307417,0.16240819,0.00025884973,0.00001631056,0.000010829875,0.000001902594,0.000017046757,0.00046695207],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986583,0.0000408402,0.00026628893,0.00037954169,0.00036028313,0.00029475082],"domain_scores_gemma":[0.9993956,0.00004126792,0.00007165015,0.00023298575,0.000056852186,0.00020164973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019054947,0.00019471404,0.00019295828,0.00024587632,0.00015888536,0.00034878682,0.00021903361,0.00006409562,0.00018476206],"category_scores_gemma":[0.0000025529507,0.00016168653,0.000040970805,0.00059437,0.00008080024,0.0022021553,0.0000013169105,0.00026470047,0.000025252573],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007397577,0.00015990343,0.0002512429,0.00003876376,0.000008986421,0.000019766183,0.0015417759,0.000980974,0.01613254,0.000002005705,0.00028268047,0.9805074],"study_design_scores_gemma":[0.005240179,0.0014935655,0.032418277,0.0008273769,0.00007028572,0.0003390232,0.0003585855,0.33475918,0.6216573,0.0012968678,0.00018154598,0.0013578028],"about_ca_topic_score_codex":0.00022816038,"about_ca_topic_score_gemma":0.000043877128,"teacher_disagreement_score":0.9791496,"about_ca_system_score_codex":0.00010640322,"about_ca_system_score_gemma":0.00022360208,"threshold_uncertainty_score":0.65933853},"labels":[],"label_agreement":null},{"id":"W2171246348","doi":"10.1109/ultsym.2009.5442062","title":"Preliminary results of an ultrasound segmentation method based on statistical unit-root test of B-scan radial intensity profiles","year":2009,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Institutes of Health","keywords":"Hausdorff distance; Segmentation; Artificial intelligence; Computer vision; Enhanced Data Rates for GSM Evolution; Ultrasound; Image segmentation; Computer science; Edge detection; Mathematics; Perimeter; Pattern recognition (psychology); Image (mathematics); Image processing; Radiology; Geometry; Medicine","score_opus":0.02356537715604586,"score_gpt":0.33565006069301756,"score_spread":0.3120846835369717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171246348","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0120698875,0.0000018084487,0.98562914,0.00035798232,0.000051950356,0.00038241834,0.000071397204,0.00015968451,0.001275744],"genre_scores_gemma":[0.3992612,8.1502566e-7,0.6002165,0.00039330695,0.000013763824,0.0000051780053,0.000077671284,0.000003569613,0.000027995373],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980697,0.0002917923,0.0005506918,0.00036304406,0.00055418443,0.00017059779],"domain_scores_gemma":[0.99732256,0.0015386931,0.00024000867,0.00048950675,0.00025832513,0.00015092887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090538926,0.00013644727,0.00024905294,0.00017740196,0.00004153786,0.000033115426,0.00043695339,0.00006528649,0.00005288737],"category_scores_gemma":[0.0013684633,0.00011721466,0.00003651666,0.00033378688,0.00012586745,0.00033589717,0.000033906064,0.00013041835,0.0000024542358],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001592584,0.0028652719,0.0008987758,0.00009197989,0.000020996938,0.000039111266,0.0013823186,0.00037936735,0.36342672,0.007226755,0.0069440147,0.6151321],"study_design_scores_gemma":[0.0008671511,0.0053404854,0.041938197,0.00004170157,0.000011850412,0.0000074579616,0.00007526612,0.08966259,0.86045563,0.001472509,0.000004485597,0.00012265742],"about_ca_topic_score_codex":0.00012875898,"about_ca_topic_score_gemma":0.000008752484,"teacher_disagreement_score":0.6150094,"about_ca_system_score_codex":0.000038693117,"about_ca_system_score_gemma":0.00012092394,"threshold_uncertainty_score":0.4779875},"labels":[],"label_agreement":null},{"id":"W2171349848","doi":"10.1016/j.media.2004.03.001","title":"Adaptive registration using local information measures","year":2004,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Princess Margaret Cancer Centre","funders":"National Cancer Institute; National Institutes of Health","keywords":"Computer science; Mutual information; Artificial intelligence; Entropy (arrow of time); Grid; Computer vision; Degrees of freedom (physics and chemistry); Operator (biology); Mathematics","score_opus":0.019746192222127413,"score_gpt":0.29483334089881746,"score_spread":0.27508714867669004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171349848","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00034505076,0.000027242872,0.9969818,0.0013763937,0.000051545005,0.000101140344,0.0000019630058,0.00026564588,0.00084923854],"genre_scores_gemma":[0.42158645,0.000024473795,0.57595885,0.0022997165,0.00005396219,0.0000143214,0.000038341273,0.000004814853,0.000019065217],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972416,0.00010911939,0.00051222084,0.00024777104,0.0016582051,0.00023108916],"domain_scores_gemma":[0.9987469,0.000051145817,0.000196612,0.0004223136,0.0002796746,0.0003034005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009281972,0.00013197775,0.00023903183,0.0004303162,0.000117527605,0.00020836444,0.0006344136,0.00011630683,0.00020250506],"category_scores_gemma":[0.0006849759,0.000114885726,0.00016647877,0.0018540192,0.00024583787,0.0024673755,0.00012507445,0.00021665977,0.00007649319],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015049743,0.00019698149,0.0001319177,0.000023080062,0.0006928174,0.00016896611,0.0015193358,0.0025654198,0.0022927895,0.0075040204,0.0014733718,0.98341626],"study_design_scores_gemma":[0.0012032962,0.00016183002,0.00078575267,0.00007789954,0.0006330034,0.000049552073,0.0005011419,0.9117711,0.07641132,0.0073669013,0.00049240974,0.00054578984],"about_ca_topic_score_codex":0.00066877436,"about_ca_topic_score_gemma":0.00007591531,"teacher_disagreement_score":0.98287046,"about_ca_system_score_codex":0.00018580003,"about_ca_system_score_gemma":0.00028541646,"threshold_uncertainty_score":0.4684904},"labels":[],"label_agreement":null},{"id":"W2172048779","doi":"10.1109/tbme.2009.2017509","title":"A Hybrid Geometric–Statistical Deformable Model for Automated 3-D Segmentation in Brain MRI","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Institute on Aging; National Institutes of Health; Pfizer; National Institute of Biomedical Imaging and Bioengineering; Eli Lilly and Company","keywords":"Segmentation; Artificial intelligence; Robustness (evolution); Pattern recognition (psychology); Voxel; Image segmentation; Computer science; Computer vision; Magnetic resonance imaging; Scale-space segmentation; Geodesic; Sørensen–Dice coefficient; Homogeneity (statistics); Mathematics; Medicine; Geometry","score_opus":0.01187243877105267,"score_gpt":0.2743295093931766,"score_spread":0.2624570706221239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2172048779","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00037844235,0.0000089329915,0.9964951,0.0011452168,0.0002262326,0.00041621202,0.000037956528,0.0012792667,0.000012614284],"genre_scores_gemma":[0.3190333,0.00001546058,0.6799425,0.0007841505,0.000017571088,0.00012602916,0.000020537844,0.000012610183,0.00004783664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983574,0.000021102967,0.00043115552,0.00034138074,0.0004472474,0.00040167433],"domain_scores_gemma":[0.99916255,0.0002877271,0.00004267081,0.00021932965,0.00003936775,0.00024836208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039156116,0.0001743432,0.00020096503,0.0008110589,0.00006629748,0.00007142655,0.00033388392,0.000089942965,0.000017444154],"category_scores_gemma":[0.000066371205,0.00017314531,0.000062641855,0.00083779707,0.000035012166,0.0004464349,0.000002233388,0.00024228972,0.000011562905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030241328,0.00058212015,3.0971128e-7,0.00007268937,0.000024806806,0.000036703907,0.0002814526,0.5077755,0.034019694,0.0007416939,0.0047917156,0.45164308],"study_design_scores_gemma":[0.00077442,0.00022169833,0.00001777184,0.000038989154,0.000005345152,0.000014849638,0.0000039274596,0.9489695,0.04942972,0.00028187624,0.00007004156,0.00017186532],"about_ca_topic_score_codex":0.00000863063,"about_ca_topic_score_gemma":8.136752e-7,"teacher_disagreement_score":0.4514712,"about_ca_system_score_codex":0.00020270303,"about_ca_system_score_gemma":0.0000707266,"threshold_uncertainty_score":0.7060661},"labels":[],"label_agreement":null},{"id":"W2172068854","doi":"10.1007/978-3-540-74260-9_18","title":"Constrained Sampling Using Simulated Annealing","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Simulated annealing; Computer science; Porous medium; Gibbs sampling; Sampling (signal processing); Image processing; Image (mathematics); Iterative reconstruction; Algorithm; Artificial intelligence; Computer vision; Porosity; Materials science","score_opus":0.06941341918312119,"score_gpt":0.34529893350136975,"score_spread":0.2758855143182486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2172068854","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007156281,0.00023268227,0.9962755,0.00011764948,0.0010468373,0.00042093953,0.0000034147497,0.0004975623,0.0013338918],"genre_scores_gemma":[0.020975227,0.000015111261,0.97631973,0.002278562,0.0003134526,6.920509e-7,0.000006060692,0.00003472143,0.00005645152],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957147,0.000034512424,0.0007904108,0.0014127365,0.0012652924,0.0007823368],"domain_scores_gemma":[0.99712855,0.00076815544,0.00038910017,0.0010615907,0.00036862455,0.0002839832],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017085806,0.0005049017,0.0005250662,0.0012282245,0.0002796192,0.00056026684,0.0025621976,0.00040382412,0.00005381953],"category_scores_gemma":[0.00024853143,0.0004959127,0.00015178595,0.00084118074,0.0010296755,0.0006731293,0.0009944135,0.0009428334,0.000016823127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003621422,0.000015366611,0.000016423763,0.00003744913,0.000013693608,0.00017481849,0.00043979136,0.090013914,0.002023916,0.0030413948,0.0000036141914,0.904216],"study_design_scores_gemma":[0.00024419205,0.00007642698,0.0000071732647,0.00058422773,0.000009215901,0.000095136726,2.0481993e-7,0.9438301,0.013768111,0.04064942,0.00011572778,0.0006201179],"about_ca_topic_score_codex":0.00003560335,"about_ca_topic_score_gemma":0.000009529021,"teacher_disagreement_score":0.90359586,"about_ca_system_score_codex":0.0004323447,"about_ca_system_score_gemma":0.00062283967,"threshold_uncertainty_score":0.99974924},"labels":[],"label_agreement":null},{"id":"W2173758409","doi":"10.1111/j.1467-8659.2011.01884.x","title":"A Survey on Shape Correspondence","year":2011,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":614,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Correspondence problem; Computer science; Representation (politics); Shape analysis (program analysis); Point (geometry); Polygon mesh; Correspondence analysis; Pipeline (software); Space (punctuation); Artificial intelligence; Algorithm; Mathematics; Machine learning; Geometry; Computer graphics (images); Static analysis","score_opus":0.06157752961123418,"score_gpt":0.28457563562308863,"score_spread":0.22299810601185444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2173758409","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026190833,0.00003016508,0.9948854,0.000153791,0.0008257257,0.00018861951,0.000005237823,0.00057642045,0.0007155789],"genre_scores_gemma":[0.5362929,0.000053669723,0.44370613,0.019484228,0.00009949671,0.000048198322,0.000024870287,0.000037363796,0.00025314162],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9981772,0.00020548173,0.00028175514,0.0004987189,0.0004524574,0.00038441207],"domain_scores_gemma":[0.99848694,0.00024135436,0.00010886444,0.0008136074,0.00014916435,0.00020006046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067673455,0.00018278246,0.00017343758,0.00030048107,0.00012091664,0.000114651746,0.0016546028,0.00008950648,0.00009717391],"category_scores_gemma":[0.00006164827,0.00016675108,0.00008413454,0.0007429147,0.0001340445,0.00041330094,0.00048647905,0.00025643452,0.00018489211],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060056715,0.0005635655,0.0307549,0.000019885625,0.000054886317,0.00017825355,0.0014671608,0.0000014392166,0.00012143271,0.3114369,0.16871278,0.48662877],"study_design_scores_gemma":[0.0009749166,0.0020984006,0.5498194,0.00016627723,0.000009228196,0.000041469262,0.000014237836,0.38604566,0.018574588,0.038753074,0.0023681887,0.0011345694],"about_ca_topic_score_codex":0.00008063948,"about_ca_topic_score_gemma":0.000025967036,"teacher_disagreement_score":0.55117923,"about_ca_system_score_codex":0.00001864518,"about_ca_system_score_gemma":0.000050029692,"threshold_uncertainty_score":0.6799912},"labels":[],"label_agreement":null},{"id":"W2174150962","doi":"10.1007/978-3-642-23623-5_58","title":"Model-Based Deformable Registration of Preoperative 3D to Intraoperative Low-Resolution 3D and 2D Sequences of MR Images","year":2011,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Image registration; Computer science; Computer vision; Artificial intelligence; Imaging phantom; Mutual information; Process (computing); Nuclear medicine; Image (mathematics); Medicine","score_opus":0.023999614694377926,"score_gpt":0.28027574685743345,"score_spread":0.2562761321630555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2174150962","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05598155,0.00004326226,0.9432732,0.00017525487,0.000084529456,0.00033218664,0.00000312536,0.000050623512,0.000056264613],"genre_scores_gemma":[0.49893644,0.000003203626,0.5007696,0.00027038858,0.00000697409,0.000010393532,5.607242e-7,0.0000019247739,5.161118e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827397,0.000091765956,0.00040288852,0.00049378583,0.00048889307,0.00024872314],"domain_scores_gemma":[0.9988419,0.00012396082,0.00017757043,0.0003842758,0.0003699958,0.00010230401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008341552,0.00014787937,0.00021165088,0.0002936206,0.00009910641,0.00008691045,0.000803956,0.000056359855,0.000006356904],"category_scores_gemma":[0.00019709075,0.00012021156,0.0000213973,0.0009804964,0.00075069617,0.0012173027,0.000238749,0.00013252533,8.9957973e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037918875,0.00020551567,0.0010172451,0.00011735972,0.0000067893498,0.000008192056,0.020614373,0.33993107,0.27491853,0.00093603507,0.000019946609,0.36218703],"study_design_scores_gemma":[0.00008126142,0.00020702262,0.00032956808,0.00006812474,0.0000013214171,0.0000021615565,0.0000018783641,0.5152734,0.48281592,0.0011496037,1.0480898e-7,0.000069651185],"about_ca_topic_score_codex":0.00016457628,"about_ca_topic_score_gemma":0.000049710103,"teacher_disagreement_score":0.4429549,"about_ca_system_score_codex":0.00007117131,"about_ca_system_score_gemma":0.0003646724,"threshold_uncertainty_score":0.4902085},"labels":[],"label_agreement":null},{"id":"W2175858202","doi":"10.1007/s10044-015-0494-y","title":"A biologically inspired framework for contour detection","year":2015,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Computer vision","score_opus":0.04008665302426928,"score_gpt":0.3272895018711384,"score_spread":0.28720284884686914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2175858202","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00074046094,0.000038506292,0.9980201,0.00069870346,0.000011119607,0.00027458128,0.0000065386535,0.00014555003,0.000064423635],"genre_scores_gemma":[0.7428777,0.000015990234,0.25516102,0.0011090565,0.000053820724,0.0007418012,0.000015590693,0.0000026199464,0.00002238406],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99935424,0.000029034914,0.00016650639,0.00025292241,0.00010196243,0.00009535087],"domain_scores_gemma":[0.9993157,0.00009852043,0.000085115396,0.00026465527,0.00011173065,0.0001242698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022597464,0.000062615676,0.00012617563,0.000109554734,0.000077990575,0.00009529354,0.00022831725,0.000050595616,0.0000080476],"category_scores_gemma":[0.00006440814,0.000051164305,0.00006463046,0.0005632084,0.000033557295,0.00008736714,0.000056223063,0.0000489352,0.000008508363],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013048457,0.00004696953,0.003574867,0.000004291493,0.00009103711,1.7605598e-7,0.00009699698,0.0000038850944,0.0013120022,0.005481867,0.00009480273,0.9892918],"study_design_scores_gemma":[0.0019545536,0.00065866794,0.0803435,0.000035767724,0.0013672442,0.0000102327695,0.000556463,0.31828275,0.14705245,0.4203304,0.028015342,0.0013926021],"about_ca_topic_score_codex":0.000057749516,"about_ca_topic_score_gemma":0.000033179644,"teacher_disagreement_score":0.9878992,"about_ca_system_score_codex":0.000015942582,"about_ca_system_score_gemma":0.000010603062,"threshold_uncertainty_score":0.20864199},"labels":[],"label_agreement":null},{"id":"W2176723361","doi":"10.1109/tmi.2015.2443978","title":"Statistical Biomechanical Surface Registration: Application to MR-TRUS Fusion for Prostate Interventions","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Vancouver General Hospital; London Health Sciences Centre; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Image registration; Robustness (evolution); Artificial intelligence; Computer science; Computer vision; Segmentation; Medical imaging; Context (archaeology); Image segmentation; Sensor fusion; Magnetic resonance imaging; Image fusion; Pattern recognition (psychology); Image (mathematics); Medicine; Radiology","score_opus":0.03675569478904189,"score_gpt":0.3590024359514837,"score_spread":0.3222467411624418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2176723361","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017636732,0.000023814131,0.9869266,0.0103920465,0.0007261386,0.0010264539,0.000040945197,0.00058578764,0.00010182756],"genre_scores_gemma":[0.3744481,0.000017503868,0.6217806,0.0026602456,0.000094640855,0.00051522587,0.000033596614,0.00002912749,0.0004209598],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99709415,0.00014472438,0.0006474125,0.0006281458,0.0011122084,0.00037334015],"domain_scores_gemma":[0.9977768,0.00033879734,0.000111812165,0.00052275934,0.00029647604,0.0009533496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013770623,0.00019535188,0.00022462344,0.00018998187,0.00020601225,0.00017601535,0.0006726118,0.00009589976,0.000101334444],"category_scores_gemma":[0.0002976184,0.00018750387,0.0001244076,0.00057211786,0.00013259386,0.00046539403,0.000014209905,0.0003247697,0.0001345399],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066519104,0.000547488,0.0000062685685,0.00008174146,0.000021439158,0.000024139033,0.00043592486,0.0003997199,0.007911535,0.0045089624,0.01431006,0.9716862],"study_design_scores_gemma":[0.002415679,0.0007765301,0.000023895014,0.00043685705,0.00006455428,0.00011427913,0.00030944438,0.84816706,0.13212451,0.008974078,0.0059203487,0.0006727389],"about_ca_topic_score_codex":0.000070211405,"about_ca_topic_score_gemma":0.000030156685,"teacher_disagreement_score":0.9710135,"about_ca_system_score_codex":0.00018055293,"about_ca_system_score_gemma":0.00026155956,"threshold_uncertainty_score":0.76461864},"labels":[],"label_agreement":null},{"id":"W2178117930","doi":"10.1007/1-4020-7775-0_20","title":"Multi-Resolution Image Registration Using Multi-Class Hausdorff Fraction","year":2006,"lang":"en","type":"book-chapter","venue":"Kluwer Academic Publishers eBooks","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hausdorff distance; Affine transformation; Mathematics; Class (philosophy); Fraction (chemistry); Hausdorff space; Transformation (genetics); Similarity (geometry); Image registration; Image (mathematics); Artificial intelligence; Boundary (topology); Translation (biology); Algorithm; Pattern recognition (psychology); Computer vision; Computer science; Discrete mathematics; Geometry; Mathematical analysis","score_opus":0.05488168691187433,"score_gpt":0.31061682362957865,"score_spread":0.2557351367177043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2178117930","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000073163988,0.00018828498,0.81189644,0.00031397058,0.0009121656,0.0008832604,0.00001843587,0.0011502501,0.18462986],"genre_scores_gemma":[0.00027869354,0.000022399272,0.5589902,0.0012770615,0.00066047884,0.00007403109,0.00023331796,0.0001439633,0.4383199],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9948401,0.00014612649,0.0014096336,0.0014492241,0.0014381323,0.0007168087],"domain_scores_gemma":[0.99630004,0.00012068366,0.0015203741,0.0011736702,0.000523748,0.00036146393],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0012249548,0.0007695717,0.0006367547,0.0008052459,0.00032732412,0.0012425972,0.0018386056,0.0019835804,0.00010962578],"category_scores_gemma":[0.00027466085,0.00083677046,0.000301317,0.000107406406,0.0004621619,0.004810594,0.00048135544,0.0030272154,0.00009438944],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000061032057,0.00014869335,0.000025066267,0.00031293405,0.00026292395,0.00020198185,0.00059558795,0.00014790088,0.0780512,0.012865438,0.7887652,0.11856207],"study_design_scores_gemma":[0.004137836,0.00019046606,0.0001688407,0.0013779193,0.00035828326,0.00025848718,0.000065908265,0.48928863,0.02108664,0.01615132,0.4629224,0.0039933003],"about_ca_topic_score_codex":0.00031163212,"about_ca_topic_score_gemma":0.000021565882,"teacher_disagreement_score":0.48914072,"about_ca_system_score_codex":0.0011994385,"about_ca_system_score_gemma":0.00051192194,"threshold_uncertainty_score":0.9997942},"labels":[],"label_agreement":null},{"id":"W2178518042","doi":"10.1109/icspcc.2015.7338884","title":"A new brain MRI image segmentation strategy based on wavelet transform and K-means clustering","year":2015,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; McGill University","keywords":"Artificial intelligence; Cluster analysis; Pattern recognition (psychology); Computer science; Image segmentation; Segmentation; Wavelet transform; Computer vision; Segmentation-based object categorization; Scale-space segmentation; Wavelet; Magnetic resonance imaging; Noise reduction; Medicine; Radiology","score_opus":0.026297536783486328,"score_gpt":0.298792022497449,"score_spread":0.2724944857139627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2178518042","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009272248,0.0000054196726,0.98125374,0.0065675816,0.00005735031,0.00028653897,0.0000014447508,0.00034499643,0.011390199],"genre_scores_gemma":[0.0083307065,0.000004003806,0.98623264,0.0043923683,0.000033832646,0.000015494767,0.000009124942,0.000009501615,0.0009723485],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988929,0.00005798573,0.00020284283,0.00028694383,0.00037532666,0.00018398088],"domain_scores_gemma":[0.9993078,0.00007773495,0.000041103634,0.00022549779,0.00004539232,0.00030248833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003828882,0.000121899364,0.00010626221,0.00010759978,0.00004001531,0.0002296909,0.0002523681,0.000042920707,0.000107809705],"category_scores_gemma":[0.000033441334,0.00010501819,0.000023969671,0.00018026281,0.000031303312,0.00072312704,0.0000392899,0.00009189909,0.000023068229],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019924384,0.000046932186,0.00001186412,0.000025317255,0.000006029832,0.000021482769,0.0011136752,0.000189413,0.009029326,0.0010779506,0.03915009,0.949308],"study_design_scores_gemma":[0.0014627745,0.00051940867,0.00009034124,0.00002861179,0.0000037500572,0.000009835204,0.00022738146,0.88233536,0.11268096,0.0020711643,0.00036723277,0.00020319763],"about_ca_topic_score_codex":0.0001288052,"about_ca_topic_score_gemma":0.000045846697,"teacher_disagreement_score":0.9491048,"about_ca_system_score_codex":0.00006071211,"about_ca_system_score_gemma":0.00013247773,"threshold_uncertainty_score":0.42825174},"labels":[],"label_agreement":null},{"id":"W2181991948","doi":"10.1007/978-0-387-68413-0_11","title":"Physically And Statistically Based Deformable Models For Medical Image Analysis","year":2007,"lang":"en","type":"book-chapter","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Active contour model; Artificial intelligence; Segmentation; Computer vision; Computer science; Tracking (education); Image segmentation; Active shape model; Obstacle; Market segmentation; Process (computing); Image (mathematics); Pattern recognition (psychology); Geography","score_opus":0.02668326733675813,"score_gpt":0.3113334728156627,"score_spread":0.28465020547890457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2181991948","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.185214e-7,0.000025057761,0.80181575,0.00036721746,0.00003214833,0.00035933446,0.000044553184,0.0003028986,0.1970529],"genre_scores_gemma":[0.000059636164,0.000045916702,0.9410529,0.00404152,0.000059672988,0.00003623286,0.00012605751,0.000030129897,0.05454793],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969966,0.00001945342,0.000642357,0.00069847406,0.0012721366,0.00037100047],"domain_scores_gemma":[0.99758285,0.0007206567,0.00019470538,0.0005981149,0.00035478687,0.0005489027],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009255769,0.0003317739,0.00060533505,0.0004462622,0.000087049055,0.00019992399,0.00082061003,0.00032944747,0.0009445609],"category_scores_gemma":[0.00014959538,0.0002766148,0.000242788,0.00014185683,0.0002857467,0.00045483847,0.00025874225,0.00031711895,0.000023049177],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013851842,0.00004124665,4.2032687e-7,0.00009449537,0.00029033166,0.000050847095,0.000027849865,0.000018802664,0.000026365877,0.8146945,0.006663629,0.17807764],"study_design_scores_gemma":[0.00036173165,0.00012194406,0.0000024103608,0.000044808352,0.00024741233,0.0000030889555,0.0000010817641,0.8552549,0.00052777573,0.14116131,0.0019351401,0.00033841576],"about_ca_topic_score_codex":0.000021801776,"about_ca_topic_score_gemma":0.00003263637,"teacher_disagreement_score":0.85523605,"about_ca_system_score_codex":0.00006386583,"about_ca_system_score_gemma":0.000288427,"threshold_uncertainty_score":0.9999687},"labels":[],"label_agreement":null},{"id":"W2185516405","doi":"","title":"Amincissement-sans-segmentation et rehaussement des images de niveau de gris par un filtre de chocs utilisant des champs de diffusion Segmentation-Free Thinning and Enhancement of Grayscale Images by Shock Filter and Diffusion Fields","year":2006,"lang":"fr","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Grayscale; Mathematical morphology; Artificial intelligence; Segmentation; Filter (signal processing); Computer science; Computer vision; Image processing; Mathematics; Image (mathematics)","score_opus":0.013537525382501294,"score_gpt":0.2700009832190693,"score_spread":0.256463457836568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2185516405","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4410039,0.0025853806,0.5536651,0.0018118925,0.00006745704,0.0005086953,0.000055417804,0.000094471114,0.00020767243],"genre_scores_gemma":[0.35314822,0.011801379,0.6312145,0.0013998904,0.0000708447,0.00018132746,0.00012506964,0.000039968083,0.0020188226],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9957199,0.0006985569,0.0010190633,0.0008165962,0.00076542463,0.000980453],"domain_scores_gemma":[0.99777484,0.0005945832,0.00047474165,0.00053533545,0.00021868049,0.00040180923],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015869953,0.00054634816,0.00046835103,0.00021981874,0.00056949514,0.0004589693,0.0005666499,0.00025692067,0.00065306394],"category_scores_gemma":[0.00021344193,0.0005213111,0.00011081246,0.00034577356,0.0008923407,0.0013181125,0.0007427629,0.00032836746,0.0000021574137],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054550525,0.0008214691,0.0713011,0.00066382485,0.0000642256,0.000030203486,0.012607082,0.000025744965,0.77715784,0.00022339734,0.027595527,0.10945504],"study_design_scores_gemma":[0.001745905,0.0005950872,0.07467195,0.00075307157,0.00012194926,0.000054466593,0.0030849932,0.009969031,0.9001709,0.008253379,0.00009672243,0.00048251788],"about_ca_topic_score_codex":0.0070199342,"about_ca_topic_score_gemma":0.0005104284,"teacher_disagreement_score":0.1230131,"about_ca_system_score_codex":0.00065126905,"about_ca_system_score_gemma":0.00019610456,"threshold_uncertainty_score":0.99972385},"labels":[],"label_agreement":null},{"id":"W2187548265","doi":"10.1109/iemcon.2015.7344424","title":"A curvature-based edge detector for x-ray radiographs","year":2015,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Radiography; Detector; Curvature; Enhanced Data Rates for GSM Evolution; Computer vision; Computer science; Edge detection; Artificial intelligence; Object (grammar); Image (mathematics); Mathematics; Image processing; Medicine; Radiology; Geometry","score_opus":0.04143934638681803,"score_gpt":0.30288652745896055,"score_spread":0.26144718107214254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2187548265","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013948858,0.000077743,0.9963876,0.0008981392,0.0002726253,0.0003834249,0.000001838525,0.0007136667,0.0011254805],"genre_scores_gemma":[0.03602546,0.0000010838456,0.9597525,0.003649611,0.000058818914,0.00013738383,0.0000043084456,0.000007782879,0.00036303164],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990913,0.000041622803,0.00015519072,0.00024738928,0.00026934323,0.0001951542],"domain_scores_gemma":[0.9990582,0.00012627557,0.000046047062,0.0003739124,0.0001564233,0.00023914887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004260506,0.00009127075,0.00010521589,0.00013644078,0.00003575997,0.000100913334,0.0006001229,0.000053331398,0.000027783906],"category_scores_gemma":[0.00021904468,0.00007221024,0.000073079296,0.00037561927,0.000045983656,0.00027652583,0.000049902283,0.00006573932,0.000022253764],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042853335,0.00025642736,0.00051808084,0.000056893772,0.00004426786,0.000017373664,0.00055264635,0.000020417294,0.01593607,0.015608618,0.6160593,0.35088706],"study_design_scores_gemma":[0.0036589624,0.00094939955,0.00031803895,0.000035711786,0.000020769961,0.000007240668,0.000043383585,0.13025631,0.78157043,0.012589955,0.06991361,0.0006361635],"about_ca_topic_score_codex":0.000010903269,"about_ca_topic_score_gemma":0.0000031446473,"teacher_disagreement_score":0.7656344,"about_ca_system_score_codex":0.000029087052,"about_ca_system_score_gemma":0.0001154257,"threshold_uncertainty_score":0.29446483},"labels":[],"label_agreement":null},{"id":"W2191021609","doi":"10.1016/j.media.2015.08.006","title":"Probabilistic non-linear registration with spatially adaptive regularisation","year":2015,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; University of California, San Diego; Genentech; National Institutes of Health; IXICO; Servier; Eisai; National Institute on Aging; National Institute for Health and Care Research; Seventh Framework Programme; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; University College London Hospitals NHS Foundation Trust; Alzheimer's Disease Neuroimaging Initiative; Eli Lilly and Company; U.S. Department of Defense; Medical Research Council; Meso Scale Diagnostics; Synarc; University of Southern California; University College London; Medpace; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; F. Hoffmann-La Roche; Alzheimer's Drug Discovery Foundation; Foundation for the National Institutes of Health","keywords":"Computer science; Transformation (genetics); Constraint (computer-aided design); Probabilistic logic; Bayesian probability; Inference; Artificial intelligence; Bayesian inference; Image registration; Pattern recognition (psychology); Mathematics; Image (mathematics)","score_opus":0.023972822733220846,"score_gpt":0.2925563478936194,"score_spread":0.2685835251603986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2191021609","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013778708,0.000013210213,0.9936564,0.0024715587,0.000042660788,0.00024081137,0.0000014357421,0.00026078633,0.001935254],"genre_scores_gemma":[0.25425088,0.0000072116163,0.7439831,0.0009845784,0.00013188682,0.00007124676,0.00008036369,0.000012694413,0.0004780777],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965528,0.00021260507,0.00046078063,0.0005227537,0.0020059857,0.0002450528],"domain_scores_gemma":[0.997808,0.000113287526,0.00023873757,0.0006876135,0.0005992317,0.00055314676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015098181,0.00016952418,0.0003251969,0.00028734896,0.000070728725,0.0001415626,0.0007101093,0.00011626275,0.00016584614],"category_scores_gemma":[0.0014713663,0.00012827136,0.00010161723,0.0018982996,0.0002809632,0.00079436356,0.00012554844,0.00023264985,0.00006058517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006196807,0.0035157942,0.0078409,0.00026678614,0.006193644,0.0039212722,0.014614471,0.0060161063,0.0052034906,0.020205496,0.08051499,0.8510874],"study_design_scores_gemma":[0.0007243522,0.00039634554,0.0011007743,0.000036260117,0.00041636772,0.000016705539,0.00010093771,0.9886816,0.006202537,0.0019291822,0.0001238247,0.00027106877],"about_ca_topic_score_codex":0.00039205333,"about_ca_topic_score_gemma":0.0002660361,"teacher_disagreement_score":0.98266554,"about_ca_system_score_codex":0.00012090328,"about_ca_system_score_gemma":0.00050483935,"threshold_uncertainty_score":0.52307546},"labels":[],"label_agreement":null},{"id":"W2206432400","doi":"10.1007/978-3-319-19992-4_40","title":"IMaGe: Iterative Multilevel Probabilistic Graphical Model for Detection and Segmentation of Multiple Sclerosis Lesions in Brain MRI","year":2015,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Probabilistic logic; Graphical model; Segmentation; Artificial intelligence; Multiple sclerosis; Image segmentation; Magnetic resonance imaging; Pattern recognition (psychology); Computer vision; Radiology; Medicine","score_opus":0.053347355381493766,"score_gpt":0.31040283762043647,"score_spread":0.2570554822389427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2206432400","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04216602,0.00001853787,0.95624775,0.00065557926,0.00012548182,0.00071830023,0.0000045321617,0.000063059626,7.331164e-7],"genre_scores_gemma":[0.49783683,0.0000017647903,0.5018666,0.00022771007,0.000009432232,0.000053426003,9.986613e-7,0.000003071613,1.7382246e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99825376,0.00010393889,0.00036744037,0.0005886281,0.00041927418,0.0002669396],"domain_scores_gemma":[0.99842536,0.00075989054,0.00011942095,0.00027904205,0.00028299223,0.00013327812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011711196,0.00014196572,0.000189969,0.00046136059,0.00008761094,0.00014575971,0.0005188355,0.000070353555,4.1511407e-7],"category_scores_gemma":[0.0013402354,0.00012726577,0.000029157336,0.0010727093,0.00046419018,0.00096188346,0.0002617501,0.000162768,3.177741e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024513476,0.0001507951,0.00072765636,0.000049017264,0.0000022000304,0.000001930434,0.007630998,0.057064764,0.16165257,0.000129314,0.000010933916,0.7725553],"study_design_scores_gemma":[0.00069252844,0.00014509301,0.0019842773,0.00006529524,0.0000013314357,0.0000034505247,0.000003590788,0.8389356,0.14136104,0.016696619,1.3974606e-7,0.00011101603],"about_ca_topic_score_codex":0.000056613506,"about_ca_topic_score_gemma":0.00016514827,"teacher_disagreement_score":0.78187084,"about_ca_system_score_codex":0.000132096,"about_ca_system_score_gemma":0.00017491142,"threshold_uncertainty_score":0.5189747},"labels":[],"label_agreement":null},{"id":"W2208471067","doi":"10.3389/fnins.2015.00456","title":"Non-Local Means Inpainting of MS Lesions in Longitudinal Image Processing","year":2015,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"Agence Nationale de la Recherche; Canadian Institutes of Health Research; Multiple Sclerosis Society; Multiple Sclerosis Society of Canada","keywords":"Inpainting; Lesion; Magnetic resonance imaging; Hyperintensity; Artificial intelligence; Segmentation; White matter; Medicine; Computer science; Pattern recognition (psychology); Computer vision; Nuclear medicine; Radiology; Pathology; Image (mathematics)","score_opus":0.03444150010617871,"score_gpt":0.30366735926115296,"score_spread":0.26922585915497427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2208471067","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014816774,0.000039932143,0.9834897,0.00024577696,0.00051923876,0.00016627356,5.5958145e-7,0.00007146702,0.0006502974],"genre_scores_gemma":[0.5785082,0.0000069905673,0.42123082,0.00020167344,0.000009054241,0.000011856304,2.3905005e-7,0.000004809515,0.000026331483],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981006,0.000086914304,0.00039392628,0.00048825218,0.00058677216,0.00034357246],"domain_scores_gemma":[0.99928945,0.000027314582,0.00014117468,0.00030623755,0.00008499393,0.0001508111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011269274,0.000112748654,0.0001991421,0.0004112205,0.00005075554,0.000090675894,0.00117557,0.000042374446,0.0000010838568],"category_scores_gemma":[0.00060582714,0.00010895757,0.000025928148,0.001671351,0.0005215795,0.0012576712,0.00033745702,0.0002333899,0.0000011850441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027314367,0.00066016434,0.21479255,0.00016812995,0.0000014743844,0.0004605292,0.008412634,0.0014957259,0.06829541,0.0005842851,0.0127580715,0.6923437],"study_design_scores_gemma":[0.0005912864,0.00015151504,0.047607716,0.00020961968,0.0000018699934,0.00001921117,0.0005758224,0.88698494,0.061629243,0.0018813482,0.000098391094,0.0002490077],"about_ca_topic_score_codex":0.00007138048,"about_ca_topic_score_gemma":0.000010176512,"teacher_disagreement_score":0.8854892,"about_ca_system_score_codex":0.00010220487,"about_ca_system_score_gemma":0.00024569602,"threshold_uncertainty_score":0.44431606},"labels":[],"label_agreement":null},{"id":"W2211521758","doi":"10.1109/iccv.2015.333","title":"Highly-Expressive Spaces of Well-Behaved Transformations: Keeping it Simple","year":2015,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Compute Canada","funders":"","keywords":"Simple (philosophy); Image warping; Affine transformation; Computer science; Histogram; Image (mathematics); Piecewise; Parametric statistics; Monotonic function; Algorithm; Inference; Theoretical computer science; Image registration; Artificial intelligence; Computer vision; Mathematics; Pure mathematics; Mathematical analysis","score_opus":0.035176670740638155,"score_gpt":0.3096679430923541,"score_spread":0.274491272351716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2211521758","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012059469,0.000031179916,0.969953,0.0018256564,0.00006945836,0.00018705812,0.0000014773241,0.0002607345,0.0264655],"genre_scores_gemma":[0.4468793,0.0000292091,0.55068254,0.0017874014,0.000026427928,0.000036375463,0.000009130205,0.000006793715,0.0005427936],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989466,0.000059633643,0.0003076193,0.00015357447,0.00038608085,0.00014650406],"domain_scores_gemma":[0.99920416,0.00008009413,0.000105389154,0.0002938622,0.00017380624,0.0001427178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030504313,0.0000798878,0.00013173072,0.00010506371,0.00003573694,0.00006936298,0.00051340065,0.00004040189,0.00013164157],"category_scores_gemma":[0.00009014867,0.0000677036,0.000037832117,0.00024042733,0.000058448168,0.0009670448,0.00008082543,0.00007419209,0.00005376547],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032154236,0.0006222286,0.0007206874,0.00023971622,0.000103263745,0.000035288653,0.042501904,0.00028972933,0.08869924,0.12348908,0.5326618,0.2106049],"study_design_scores_gemma":[0.00054635154,0.0001233896,0.000063700034,0.000038519003,0.0000071261425,0.000004969947,0.0010829475,0.015831502,0.9675771,0.0074991994,0.0070564975,0.00016867291],"about_ca_topic_score_codex":0.000100776706,"about_ca_topic_score_gemma":0.000008179119,"teacher_disagreement_score":0.8788779,"about_ca_system_score_codex":0.000026172032,"about_ca_system_score_gemma":0.00007883999,"threshold_uncertainty_score":0.27608725},"labels":[],"label_agreement":null},{"id":"W2232187355","doi":"10.1007/978-3-642-15352-5_7","title":"Motion Based Image Segmentation","year":2010,"lang":"en","type":"book-chapter","venue":"Springer topics in signal processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Segmentation","score_opus":0.02225340115792295,"score_gpt":0.28421717141752095,"score_spread":0.261963770259598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2232187355","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000015438454,0.000117107724,0.93448395,0.000342782,0.00019343979,0.00028489824,0.0000015403729,0.0002786149,0.064282246],"genre_scores_gemma":[0.0023400735,0.000018816405,0.97172225,0.0008618312,0.0003784199,0.000036945727,0.000026557838,0.00005059545,0.024564527],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99805844,0.000026202675,0.00049754325,0.0005735576,0.0005990791,0.0002451918],"domain_scores_gemma":[0.9990092,0.000043045857,0.00032566924,0.00036456523,0.0001627477,0.000094782205],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004469647,0.000278453,0.0002541042,0.00034986503,0.00010344358,0.00032898475,0.00064838043,0.00035153795,0.00035146924],"category_scores_gemma":[0.000029255092,0.00029564736,0.00007470966,0.00009189973,0.00011800301,0.0007001414,0.0001643348,0.00089551305,0.00004205562],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000035969217,0.00003701606,0.000032026044,0.000268832,0.000005186764,0.000060421644,0.00021686335,0.000005267617,0.028053565,0.0066034826,0.00005763502,0.9646561],"study_design_scores_gemma":[0.001296004,0.0001596484,0.0002695807,0.002057825,0.000049804075,0.000019384348,0.000018632756,0.038751286,0.7955397,0.14634219,0.013696417,0.0017995525],"about_ca_topic_score_codex":0.0000068100553,"about_ca_topic_score_gemma":0.000007621016,"teacher_disagreement_score":0.96285653,"about_ca_system_score_codex":0.00016341655,"about_ca_system_score_gemma":0.00019104267,"threshold_uncertainty_score":0.9999496},"labels":[],"label_agreement":null},{"id":"W2236257122","doi":"10.1007/978-3-319-10581-9_4","title":"Stacked Multiscale Feature Learning for Domain Independent Medical Image Segmentation","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Domain (mathematical analysis); Feature (linguistics); Image segmentation; Pattern recognition (psychology); Image (mathematics); Machine learning; Scale-space segmentation","score_opus":0.011355265691218537,"score_gpt":0.2845046924221303,"score_spread":0.27314942673091175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2236257122","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000026492407,0.00010364046,0.9944959,0.0022789363,0.0010717828,0.0010349781,0.000004745009,0.0004072413,0.0005762789],"genre_scores_gemma":[0.003144662,0.000037380618,0.9925566,0.0026655016,0.00049832155,0.00007245677,0.000046134002,0.000048769736,0.00093022035],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99437296,0.00012340222,0.0006502527,0.001639988,0.0024961575,0.0007172233],"domain_scores_gemma":[0.99690884,0.000962652,0.00045829185,0.0008907031,0.00036625098,0.0004132828],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0025758639,0.0005433268,0.0005861249,0.0006965053,0.00034438996,0.00063471997,0.0030835292,0.00061882625,0.00010312565],"category_scores_gemma":[0.0006010301,0.0004920643,0.0001725533,0.0003815608,0.00080665405,0.0006194704,0.0011252866,0.0014409306,0.00003724448],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009659576,0.000027667293,0.00002032826,0.00008940711,0.0000129361015,0.000061922605,0.0007697482,0.00058833195,0.0021486115,0.0032899482,0.00039475993,0.9925867],"study_design_scores_gemma":[0.0021343771,0.00075175264,0.00008943873,0.0010376633,0.000021548098,0.00015071615,0.0000024022634,0.8393464,0.043741915,0.10693877,0.0042417673,0.0015432229],"about_ca_topic_score_codex":0.000011200729,"about_ca_topic_score_gemma":0.000044923487,"teacher_disagreement_score":0.99104345,"about_ca_system_score_codex":0.00042066682,"about_ca_system_score_gemma":0.0005613948,"threshold_uncertainty_score":0.9997531},"labels":[],"label_agreement":null},{"id":"W2241680614","doi":"10.4271/2003-01-2175","title":"Portable, Real-time Shape Capture","year":2003,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Space Agency","keywords":"Computer science; Computer graphics (images)","score_opus":0.011551717423227116,"score_gpt":0.259150140036428,"score_spread":0.2475984226132009,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2241680614","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051632512,0.0011192786,0.0014592991,0.016675306,0.0014128471,0.0054019857,0.00013098694,0.03404254,0.88812524],"genre_scores_gemma":[0.8208163,0.0006214839,0.16627347,0.007698426,0.00012414374,0.00049760647,0.000054678978,0.00015638364,0.0037575057],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9916978,0.00052082964,0.001715242,0.0022870358,0.002118746,0.0016603816],"domain_scores_gemma":[0.9941404,0.0007486708,0.00048114223,0.0033100378,0.00027291285,0.0010468208],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0016985581,0.0011452459,0.0012924748,0.0004093186,0.0005572639,0.00041641213,0.003429799,0.0012139111,0.0036632111],"category_scores_gemma":[0.0020230825,0.000994796,0.0006023543,0.0019600769,0.0012671213,0.0016751111,0.00081526913,0.001833414,0.0008254945],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004478446,0.0004189731,0.00005009856,0.000035238685,0.000036760433,0.0002043861,0.000040171897,0.0000020755197,0.89857495,0.058595963,0.032708,0.009288627],"study_design_scores_gemma":[0.0017331911,0.0022731044,0.8439147,0.00044694386,0.0001295396,0.0007390727,0.000118430566,0.0000013262088,0.0062650125,0.018265706,0.123403825,0.002709176],"about_ca_topic_score_codex":0.00007051629,"about_ca_topic_score_gemma":0.0053660846,"teacher_disagreement_score":0.8923099,"about_ca_system_score_codex":0.00043357842,"about_ca_system_score_gemma":0.00034977958,"threshold_uncertainty_score":0.9999525},"labels":[],"label_agreement":null},{"id":"W2247410525","doi":"","title":"Wavelet transform in MRI data reconstruction","year":2015,"lang":"en","type":"dissertation","venue":"ThinkTech (Texas Tech University)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Wavelet; Wavelet transform; Artificial intelligence; Computer science; Pattern recognition (psychology); Computer vision","score_opus":0.029959311634701575,"score_gpt":0.2807679212576057,"score_spread":0.25080860962290413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2247410525","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002470031,0.000224546,0.8461425,0.0008024718,0.0014969584,0.0016777866,0.000113238806,0.0025897166,0.14448279],"genre_scores_gemma":[0.008681835,0.0022196835,0.91881716,0.00036379296,0.0002222055,0.0000104370065,0.005822883,0.00013249923,0.063729495],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99735,0.00013355154,0.0004038191,0.001008146,0.00071953435,0.00038495933],"domain_scores_gemma":[0.9974852,0.000060691615,0.00027791163,0.0017379218,0.00023561131,0.00020265958],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00081766816,0.00034738603,0.0004295622,0.0014967545,0.00011105118,0.00012053198,0.0043738605,0.0006151384,0.00010808998],"category_scores_gemma":[0.00009627018,0.0003981128,0.00007230334,0.0017259947,0.00009609334,0.0022010705,0.000362207,0.00095512765,0.000088275374],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007408498,0.00014974157,0.000033297183,0.00017567596,0.000059582682,0.0004007711,0.0012790991,0.0000013013724,0.00077271456,0.0052305665,0.016388567,0.9754346],"study_design_scores_gemma":[0.014877124,0.0019751375,0.0020425061,0.0059849857,0.00081411056,0.00064970006,0.015807455,0.03820412,0.33451295,0.2344144,0.34014562,0.0105718905],"about_ca_topic_score_codex":0.00031250512,"about_ca_topic_score_gemma":0.00090555986,"teacher_disagreement_score":0.9648627,"about_ca_system_score_codex":0.0005646188,"about_ca_system_score_gemma":0.00069604826,"threshold_uncertainty_score":0.99984705},"labels":[],"label_agreement":null},{"id":"W2247445003","doi":"10.1007/978-3-642-33555-6_1","title":"Spatio-temporal Regularization for Longitudinal Registration to an Unbiased 3D Individual Template","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Computer science; Segmentation; Regularization (linguistics); Artificial intelligence; Pattern recognition (psychology); Noise (video)","score_opus":0.04918628857076704,"score_gpt":0.3082087489117299,"score_spread":0.2590224603409629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2247445003","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006927463,0.000052424675,0.9964764,0.0007278695,0.0008878595,0.0012160353,0.000020784533,0.00031167435,0.0002376797],"genre_scores_gemma":[0.038308624,0.0000059637114,0.959181,0.0014362126,0.0005776454,0.000058138412,0.00022831834,0.000034218312,0.00016986977],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959833,0.000058269703,0.00067772355,0.0013775475,0.0013124605,0.0005907034],"domain_scores_gemma":[0.9972943,0.00025715402,0.00045218447,0.0011949191,0.00042585284,0.00037556715],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018448354,0.00043449664,0.00039642752,0.0008470029,0.0002827189,0.00076686224,0.0022778807,0.00032712464,0.00003226133],"category_scores_gemma":[0.00022908665,0.00043180285,0.00007954561,0.0005858878,0.00034867044,0.0020511374,0.00050397275,0.00038909368,0.000017295795],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017845596,0.00005729534,0.00020612034,0.00005058683,0.000010694186,0.000014313315,0.0009329354,0.0025106897,0.00039372183,0.012293762,0.00018143629,0.9833306],"study_design_scores_gemma":[0.0017547706,0.0030721335,0.002825365,0.0012674625,0.00009666442,0.00016904892,9.739076e-7,0.6581168,0.08588501,0.23657376,0.0066239918,0.0036140168],"about_ca_topic_score_codex":0.00004021098,"about_ca_topic_score_gemma":0.00016620263,"teacher_disagreement_score":0.9797166,"about_ca_system_score_codex":0.00027396582,"about_ca_system_score_gemma":0.0004797763,"threshold_uncertainty_score":0.9998134},"labels":[],"label_agreement":null},{"id":"W2255967141","doi":"10.1109/isbi.2016.7493261","title":"Sub-cortical brain structure segmentation using F-CNN'S","year":2016,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":101,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Artificial intelligence; Computer science; Segmentation; Convolutional neural network; Markov random field; Prior probability; Pattern recognition (psychology); Image segmentation; Deep learning; Computer vision; Pipeline (software); Inference; Bayesian probability","score_opus":0.018264975971332507,"score_gpt":0.3015714500962057,"score_spread":0.28330647412487314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2255967141","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056384753,0.000004858656,0.94063866,0.002169558,0.00013131821,0.00012610931,0.0000015633691,0.0003327708,0.00021038511],"genre_scores_gemma":[0.37013233,0.0000023239968,0.6269614,0.0025716352,0.000051742714,0.000004240158,0.0000014339626,0.000006730241,0.0002681398],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99893683,0.00009118819,0.00019695764,0.00026348062,0.00032045896,0.00019107078],"domain_scores_gemma":[0.9993338,0.00015905112,0.00005313123,0.00028139172,0.00005496265,0.00011765612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017154063,0.000089604415,0.00008359458,0.00007065839,0.00006363633,0.00007956646,0.00033967732,0.000051563064,0.00047792215],"category_scores_gemma":[0.00015289785,0.000054893448,0.000028397553,0.00017768856,0.00006443248,0.0007135239,0.00011915979,0.00006234108,0.00004046831],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011650552,0.000009335339,0.0002159205,0.000002679013,0.0000036932208,0.0000074305085,0.000055895965,4.0054476e-7,0.833493,0.004722748,0.0024542247,0.15903349],"study_design_scores_gemma":[0.00026719787,0.00003578948,0.00092294835,0.000015834701,0.000002596442,0.000021173006,0.0000120084305,0.0032020055,0.9905126,0.004825515,0.00006524434,0.00011705771],"about_ca_topic_score_codex":0.000009545428,"about_ca_topic_score_gemma":0.0000031401523,"teacher_disagreement_score":0.31374756,"about_ca_system_score_codex":0.000071667324,"about_ca_system_score_gemma":0.00004499811,"threshold_uncertainty_score":0.52329123},"labels":[],"label_agreement":null},{"id":"W2255980159","doi":"10.1007/978-3-642-33454-2_12","title":"Self-similarity Weighted Mutual Information: A New Nonrigid Image Registration Metric","year":2012,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mutual information; Image registration; Similarity measure; Similarity (geometry); Artificial intelligence; Stochastic gradient descent; Computer science; Metric (unit); Gradient descent; Pattern recognition (psychology); Image (mathematics); Mathematics; Computer vision; Artificial neural network","score_opus":0.012906255453948141,"score_gpt":0.2776315261798159,"score_spread":0.26472527072586777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2255980159","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008349312,0.000075413904,0.99570996,0.0014482257,0.00086510903,0.00032898295,9.300851e-7,0.0005057098,0.00023073431],"genre_scores_gemma":[0.2115028,0.000009332068,0.785815,0.0024177597,0.00023640844,0.000009025996,0.000004166898,0.0000039175666,0.0000015968949],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99729544,0.00010102338,0.0004818521,0.00041506244,0.0010824034,0.00062419],"domain_scores_gemma":[0.9981605,0.0002839762,0.00021102042,0.0007543848,0.00021913114,0.0003710162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015451418,0.00021118486,0.00019746264,0.00072677043,0.00020058597,0.0007324192,0.0017428459,0.0001083056,0.000030045006],"category_scores_gemma":[0.00042900714,0.0001861521,0.000050385894,0.004300671,0.00022454048,0.008308223,0.0005098735,0.00034730317,0.00008676202],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000044212807,0.00014177241,0.0013050537,0.00003366635,0.0000061044016,0.000007851188,0.0049850023,0.00011688134,0.0010232955,0.0017748799,0.0009266227,0.98967445],"study_design_scores_gemma":[0.00069701154,0.00020122614,0.006655152,0.000045766363,0.000007854771,0.00011277134,0.0000024348872,0.804868,0.1761589,0.009130584,0.0015341754,0.0005861227],"about_ca_topic_score_codex":0.00005976118,"about_ca_topic_score_gemma":0.000008779801,"teacher_disagreement_score":0.9890883,"about_ca_system_score_codex":0.00024843935,"about_ca_system_score_gemma":0.00047327633,"threshold_uncertainty_score":0.7591062},"labels":[],"label_agreement":null},{"id":"W2258079073","doi":"10.1007/s11548-015-1331-x","title":"Endoscopic scene labelling and augmentation using intraoperative pulsatile motion and colour appearance cues with preoperative anatomical priors","year":2016,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"Intuitive Surgical; Qatar Foundation","keywords":"Artificial intelligence; Computer vision; Computer science; Segmentation; Clutter","score_opus":0.01868374097278446,"score_gpt":0.2824191310248981,"score_spread":0.2637353900521136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2258079073","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4828458,0.00021359435,0.5160001,0.00068468833,0.00019232975,0.000050145307,0.0000013677327,0.000010262922,0.0000017095199],"genre_scores_gemma":[0.8913939,0.00035824525,0.10778041,0.00035503285,0.00009712337,0.0000049855353,0.0000015283786,0.0000046126243,0.0000041664975],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99873435,0.00033418924,0.00037523772,0.00020667561,0.00023570965,0.00011380613],"domain_scores_gemma":[0.99882245,0.0004886163,0.00030027446,0.00005861909,0.0002452305,0.000084828745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050311413,0.00011067977,0.00024619355,0.00023547719,0.00009976391,0.00009695201,0.00015618876,0.000049525413,0.00001476189],"category_scores_gemma":[0.00005266993,0.0000732832,0.000025161531,0.00007900424,0.0002200724,0.0007411804,0.00008293357,0.00013624728,1.678531e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038343575,0.00019402169,0.2594951,0.000035486897,0.0006948926,0.0004127853,0.0032312556,0.00023406671,0.035394274,0.0012046889,0.00031619554,0.6984038],"study_design_scores_gemma":[0.0069529633,0.0016832142,0.7019549,0.0016177463,0.00011406636,0.013137541,0.00044516663,0.18404178,0.08749203,0.0015807172,0.00017332287,0.00080657686],"about_ca_topic_score_codex":0.000004880689,"about_ca_topic_score_gemma":0.0000015229548,"teacher_disagreement_score":0.6975972,"about_ca_system_score_codex":0.000062884916,"about_ca_system_score_gemma":0.000076376,"threshold_uncertainty_score":0.2988402},"labels":[],"label_agreement":null},{"id":"W2266768517","doi":"10.1007/978-3-319-10470-6_62","title":"Laplacian Forests: Semantic Image Segmentation by Guided Bagging","year":2014,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Random forest; Pattern recognition (psychology); Image segmentation; Tree (set theory); Segmentation; Decision tree; Embedding; Image (mathematics); Set (abstract data type); Machine learning; Mathematics","score_opus":0.009969215583919692,"score_gpt":0.2837641409203369,"score_spread":0.27379492533641725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2266768517","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059175277,0.000027637161,0.9911975,0.0016414218,0.00049898814,0.00027082083,7.6797096e-7,0.00037848286,0.00006680134],"genre_scores_gemma":[0.40729302,0.0000030309934,0.58992624,0.0026878624,0.000065164895,0.000013131264,0.0000029667444,0.0000067903156,0.0000017998492],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99728435,0.00014415613,0.00039492952,0.00081418117,0.00081981235,0.0005425437],"domain_scores_gemma":[0.9984795,0.00034497943,0.0001471279,0.00071511464,0.00013639906,0.00017691273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014706389,0.000208993,0.0002056276,0.00036309828,0.00022637029,0.0006404992,0.0018739977,0.000065258224,0.000018664326],"category_scores_gemma":[0.00041746907,0.0001894335,0.000042381776,0.0015679144,0.0003815237,0.0015262077,0.0005072506,0.00023025082,0.00003274453],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013525888,0.000049192262,0.0014831455,0.00002380528,0.0000023973296,0.000010066406,0.00089498213,0.001176006,0.07501453,0.00024568517,0.0007300668,0.9203688],"study_design_scores_gemma":[0.00025866335,0.00007632793,0.0007167122,0.000052184096,0.0000015584039,0.000018784363,6.3042387e-7,0.64297664,0.3464141,0.009240453,0.000040052568,0.00020394045],"about_ca_topic_score_codex":0.00006174157,"about_ca_topic_score_gemma":0.000040933865,"teacher_disagreement_score":0.9201648,"about_ca_system_score_codex":0.00016708084,"about_ca_system_score_gemma":0.000102560334,"threshold_uncertainty_score":0.7724874},"labels":[],"label_agreement":null},{"id":"W2269700260","doi":"10.5623/cig2011-005","title":"Detecting Water Bodies on Radarsat Imagery","year":2011,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National University of Defense Technology","keywords":"Initialization; Synthetic aperture radar; Speckle noise; Level set (data structures); Computer science; Enhanced Data Rates for GSM Evolution; Speckle pattern; Artificial intelligence; Active contour model; Edge detection; Computer vision; Operator (biology); Detector; Function (biology); Algorithm; Remote sensing; Image segmentation; Segmentation; Image (mathematics); Image processing; Geology","score_opus":0.0338612654269689,"score_gpt":0.25163090106968883,"score_spread":0.21776963564271992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2269700260","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011917729,0.000009401989,0.9683392,0.0003159949,0.00016958703,0.00012963821,3.2997465e-7,0.0006019653,0.018516138],"genre_scores_gemma":[0.28396815,0.0000036188633,0.71411973,0.0014608883,0.000037053596,0.000013063725,9.849683e-7,0.000009692884,0.00038682384],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990164,0.000056341843,0.00020725114,0.00021098802,0.00024982562,0.00025916717],"domain_scores_gemma":[0.9993438,0.00007223705,0.000040708605,0.00042814694,0.000034562385,0.00008054518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031990692,0.000097598924,0.000109470384,0.000084777734,0.00009554856,0.00007053815,0.00045939846,0.0000331263,0.0003860356],"category_scores_gemma":[0.00011085141,0.00006606988,0.00003965162,0.00005569501,0.000057384917,0.00032859668,0.00019152071,0.00009846636,0.00071257335],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014518996,0.00023504918,0.00024445925,0.00011651659,0.000050470633,0.00019773042,0.023404522,6.349968e-7,0.055384714,0.005159359,0.010704926,0.9044871],"study_design_scores_gemma":[0.00010061639,0.000085881315,0.0003886251,0.000032947908,0.0000035840774,0.000012790218,0.000055799355,0.00095314387,0.98540854,0.012567991,0.00026427943,0.00012579143],"about_ca_topic_score_codex":0.000021404465,"about_ca_topic_score_gemma":8.054936e-7,"teacher_disagreement_score":0.93002385,"about_ca_system_score_codex":0.000019010566,"about_ca_system_score_gemma":0.000010176569,"threshold_uncertainty_score":0.91589266},"labels":[],"label_agreement":null},{"id":"W2272257752","doi":"10.1007/978-3-642-36620-8_8","title":"Novel Vector-Valued Approach to Automatic Brain Tissue Classification","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Kernel Fisher discriminant analysis; Metric (unit); Maximization; Segmentation; Similarity measure; Kernel (algebra); Mathematics","score_opus":0.035947739083124534,"score_gpt":0.30034184936264885,"score_spread":0.2643941102795243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2272257752","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000007214742,0.000053195483,0.989316,0.0023760714,0.00080264936,0.0013070194,0.0000036133395,0.0006388957,0.0054953736],"genre_scores_gemma":[0.0049031046,0.0000034671298,0.9878852,0.0058083925,0.00026830594,0.00009625453,0.0000127098,0.000040126062,0.0009824509],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951811,0.000057787143,0.00072830566,0.0018018514,0.0015818297,0.0006491406],"domain_scores_gemma":[0.9966911,0.00042151829,0.00034637493,0.0018331293,0.00031456197,0.00039333076],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011992292,0.00053885527,0.00054012745,0.0010129553,0.00019428927,0.00085600925,0.004283694,0.00035015834,0.000081456026],"category_scores_gemma":[0.00036560718,0.0004922221,0.000083623614,0.0009559058,0.0005288965,0.00084240467,0.0011633986,0.00069314067,0.00033921486],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.261042e-7,0.0000656394,8.415638e-7,0.000060793438,0.000006851932,0.0000056698345,0.00083459937,0.00084323704,0.010166456,0.02436587,0.0008663007,0.9627828],"study_design_scores_gemma":[0.00023608039,0.00022591226,0.00020091314,0.00032524724,0.000006694911,0.00006117183,3.8146027e-7,0.96027523,0.012806169,0.024209343,0.000852427,0.00080045406],"about_ca_topic_score_codex":0.000038027258,"about_ca_topic_score_gemma":0.0000058330334,"teacher_disagreement_score":0.96198237,"about_ca_system_score_codex":0.00048701887,"about_ca_system_score_gemma":0.0004583799,"threshold_uncertainty_score":0.99975294},"labels":[],"label_agreement":null},{"id":"W2275448079","doi":"10.1007/978-3-319-10404-1_27","title":"Topology Preservation and Anatomical Feasibility in Random Walker Image Registration","year":2014,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Image registration; Computer science; Smoothing; Regularization (linguistics); Computer vision; Artificial intelligence; Transformation (genetics); Image (mathematics); Topology (electrical circuits); Algorithm; Mathematics","score_opus":0.016081578850485976,"score_gpt":0.30357207305140727,"score_spread":0.28749049420092126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2275448079","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10014347,0.000017084798,0.8964969,0.0027821728,0.00018017205,0.00026409974,1.8131722e-7,0.000084935295,0.000030967076],"genre_scores_gemma":[0.5625459,0.000002796346,0.43661684,0.00078894605,0.000035697325,0.0000068548597,7.314288e-7,0.000001810395,4.3911928e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981183,0.00023739549,0.0003234426,0.0006708033,0.00036548678,0.00028456078],"domain_scores_gemma":[0.998719,0.0005094649,0.000085868945,0.00050840725,0.00008850954,0.00008877145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00212763,0.000116178286,0.00017837467,0.00024881784,0.00007619579,0.00023049754,0.00078622176,0.00007955204,0.000005919358],"category_scores_gemma":[0.0008554443,0.00010190995,0.000018382343,0.0009137458,0.00055482367,0.0012579425,0.00033437842,0.00022268303,0.0000022077704],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031168383,0.00012676997,0.025649574,0.000035595745,0.0000014606674,0.000013918494,0.0013969837,0.00095815654,0.025881903,0.003938804,0.000041004503,0.94192463],"study_design_scores_gemma":[0.00079447974,0.00009244039,0.03876449,0.000023004572,6.824823e-7,0.00001294879,3.7546818e-7,0.8526508,0.047035486,0.060483743,0.00001206432,0.00012944794],"about_ca_topic_score_codex":0.00010092147,"about_ca_topic_score_gemma":0.000107255044,"teacher_disagreement_score":0.94179523,"about_ca_system_score_codex":0.00010192597,"about_ca_system_score_gemma":0.00010578261,"threshold_uncertainty_score":0.41557673},"labels":[],"label_agreement":null},{"id":"W2277756578","doi":"","title":"Genetic algorithm driven statistically deformed models for medical image segmentation","year":2006,"lang":"en","type":"article","venue":"Genetic and Evolutionary Computation Conference","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Initialization; Maxima and minima; Segmentation; Artificial intelligence; Image segmentation; Computer science; Genetic algorithm; Computer vision; Pixel; Pattern recognition (psychology); Deformation (meteorology); Algorithm; Mathematics; Machine learning; Physics","score_opus":0.013837638655800757,"score_gpt":0.2685476613573816,"score_spread":0.2547100227015809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2277756578","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014892345,0.0001767745,0.99637634,0.00066087506,0.00015592272,0.0006155932,0.0000397243,0.00024134012,0.00024418192],"genre_scores_gemma":[0.088402666,0.00008397608,0.91067165,0.00034529666,0.0000993026,0.00016300623,0.0001399852,0.000012446906,0.00008168291],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977976,0.00010995129,0.0005571886,0.00052234007,0.0006914457,0.00032146418],"domain_scores_gemma":[0.99872065,0.00027927035,0.00015425004,0.0001785657,0.00044891247,0.00021838346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018359536,0.0001973554,0.00019839776,0.00013401415,0.00022911567,0.00017209194,0.0003644311,0.00010866292,0.000072193696],"category_scores_gemma":[0.00006354277,0.00019779117,0.000045197485,0.00018917027,0.00023474036,0.00047006606,0.00014280119,0.00010397542,0.00001614366],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013580334,0.00016385765,0.00023888041,0.00008391779,0.00002587448,0.000026898031,0.00025748115,0.0075779995,0.0011703519,0.018464355,0.009094123,0.9628827],"study_design_scores_gemma":[0.00063285005,0.00012497956,0.013204636,0.00002416583,0.000012107582,0.000057865564,0.000024900019,0.83757716,0.00027603595,0.14783335,0.000035724053,0.0001961963],"about_ca_topic_score_codex":0.00008230999,"about_ca_topic_score_gemma":0.000009177209,"teacher_disagreement_score":0.9626865,"about_ca_system_score_codex":0.00007588271,"about_ca_system_score_gemma":0.00037831016,"threshold_uncertainty_score":0.806569},"labels":[],"label_agreement":null},{"id":"W2280367348","doi":"10.1007/s10278-015-9844-y","title":"Sequential Registration-Based Segmentation of the Prostate Gland in MR Image Volumes","year":2015,"lang":"en","type":"article","venue":"Journal of Digital Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University; University of Waterloo; University of Toronto; Sunnybrook Health Science Centre","funders":"FedDev Ontario","keywords":"Prostate gland; Prostate; Computer vision; Artificial intelligence; Segmentation; Computer science; Image registration; Image segmentation; Magnetic resonance imaging; Image (mathematics); Medicine; Radiology; Internal medicine","score_opus":0.019878576584126152,"score_gpt":0.2890450480033686,"score_spread":0.26916647141924244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2280367348","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0829416,0.00006165184,0.91358703,0.0021985937,0.00024566025,0.00015770382,0.000004156421,0.0000223412,0.0007812513],"genre_scores_gemma":[0.93338686,0.0000018677237,0.06632916,0.00017861425,0.00003582331,0.0000019084396,0.0000020206949,0.0000052800897,0.00005845107],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986447,0.00006237016,0.00052724377,0.00009584156,0.0005566189,0.000113197624],"domain_scores_gemma":[0.99880964,0.000050949315,0.000572284,0.00016361897,0.00032532364,0.00007816146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000529753,0.00007453335,0.0001246395,0.00013356212,0.00002113024,0.00033150474,0.000424048,0.000013756163,0.0000029299451],"category_scores_gemma":[0.00032085727,0.00005320646,0.00006469338,0.00027774848,0.00010479284,0.002542995,0.00006504525,0.00012802772,0.0000013046397],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001872002,0.00087088876,0.34139732,0.00020210174,0.0000707655,0.00043328505,0.00871688,0.0010757908,0.23400514,0.000532972,0.02339047,0.38911718],"study_design_scores_gemma":[0.0048570726,0.0003394456,0.009774715,0.00056112645,0.000028645107,0.00029455204,0.0010214938,0.07513143,0.89538175,0.011920078,0.00032494622,0.00036473904],"about_ca_topic_score_codex":0.000013698237,"about_ca_topic_score_gemma":0.0000026130333,"teacher_disagreement_score":0.8504453,"about_ca_system_score_codex":0.00009348684,"about_ca_system_score_gemma":0.00032305383,"threshold_uncertainty_score":0.31967077},"labels":[],"label_agreement":null},{"id":"W2280845481","doi":"10.1109/tmi.2015.2485299","title":"Towards Personalized Statistical Deformable Model and Hybrid Point Matching for Robust MR-TRUS Registration","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Image registration; Fiducial marker; Computer science; Artificial intelligence; Computer vision; Matching (statistics); Feature (linguistics); Magnetic resonance imaging; Point set registration; Orientation (vector space); Pattern recognition (psychology); Point (geometry); Radiology; Medicine; Mathematics; Image (mathematics)","score_opus":0.037373095106781634,"score_gpt":0.3110396223142665,"score_spread":0.27366652720748486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2280845481","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00038747428,0.00004351923,0.99364024,0.0046000252,0.00030918533,0.00032998895,0.000031617823,0.00032819947,0.0003297343],"genre_scores_gemma":[0.1757154,0.000042477615,0.8212575,0.0025776082,0.00005061353,0.00012517111,0.000013517028,0.00002115477,0.00019657188],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977327,0.00008761122,0.00041045362,0.00043482534,0.0009818192,0.00035261025],"domain_scores_gemma":[0.9986576,0.00020006596,0.0000836359,0.0002634775,0.00014942148,0.0006457941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011895293,0.00018326692,0.00022319487,0.00013697021,0.0002101093,0.00021498249,0.00035817607,0.000058346814,0.00005711292],"category_scores_gemma":[0.00016170359,0.0001667585,0.000062440195,0.00013215764,0.00024008729,0.0009319193,0.000008098326,0.00035750965,0.000008132475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011111688,0.00028963247,0.000003101846,0.00013061709,0.00004353541,0.000093939045,0.002208871,0.0121266125,0.0010155934,0.0064300383,0.010186739,0.9673602],"study_design_scores_gemma":[0.0011393713,0.000065489374,0.0000018231782,0.00006872018,0.000021631517,0.000151759,0.00018722625,0.9779391,0.0071027973,0.0130429035,0.00009315482,0.00018598171],"about_ca_topic_score_codex":0.00010225726,"about_ca_topic_score_gemma":0.000009552937,"teacher_disagreement_score":0.96717423,"about_ca_system_score_codex":0.00014114413,"about_ca_system_score_gemma":0.0004030692,"threshold_uncertainty_score":0.6800214},"labels":[],"label_agreement":null},{"id":"W2283431359","doi":"","title":"A novel method for spatial smoothing","year":2012,"lang":"en","type":"article","venue":"Virtual Community of Pathological Anatomy (University of Castilla La Mancha)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Smoothing; A priori and a posteriori; Computer science; Domain (mathematical analysis); Algorithm; Spatial analysis; Exploit; Planar; Surface (topology); Data mining; Mathematics; Computer vision; Geometry; Computer graphics (images); Statistics","score_opus":0.042845042663295946,"score_gpt":0.30618637696287704,"score_spread":0.26334133429958106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2283431359","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04927256,0.000032675303,0.94911486,0.00025696907,0.00007204701,0.00021226035,0.000028258448,0.00013012548,0.0008802621],"genre_scores_gemma":[0.5101934,0.000008410867,0.48965177,0.000094947594,0.000010648864,7.1196683e-7,0.000005003974,0.000003564195,0.00003151274],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982952,0.00071969343,0.00021451719,0.00016984774,0.00032730115,0.00027345345],"domain_scores_gemma":[0.9975762,0.001283115,0.00027980426,0.0004965401,0.00019301174,0.00017135368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021335268,0.0001446311,0.00037137282,0.00017438942,0.0002829703,0.000015394411,0.0012694943,0.00017316983,0.00005276352],"category_scores_gemma":[0.0003778964,0.00015037165,0.0001677822,0.00027109924,0.00041785938,0.0005167614,0.000956071,0.00041665294,0.0000030162948],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016726638,0.0020115366,0.00088858296,0.00027380852,0.00009292398,0.000034274264,0.022482699,0.000039465267,0.11596974,0.13068052,0.00075464894,0.7266045],"study_design_scores_gemma":[0.026187692,0.018147038,0.22506088,0.0015525516,0.0008240897,0.0016819645,0.081558116,0.30130354,0.24542063,0.053946383,0.03806664,0.006250472],"about_ca_topic_score_codex":0.00055537233,"about_ca_topic_score_gemma":0.000036856567,"teacher_disagreement_score":0.7203541,"about_ca_system_score_codex":0.00003277447,"about_ca_system_score_gemma":0.00003356436,"threshold_uncertainty_score":0.6131978},"labels":[],"label_agreement":null},{"id":"W2289134797","doi":"","title":"2D/3D Discrete Duality Finite Volume (DDFV) scheme for anisotropic- heterogeneous elliptic equations, application to the electrocardiogram simulation.","year":2008,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Duality (order theory); Finite volume method; Mathematics; Operator (biology); Tensor (intrinsic definition); Scheme (mathematics); Divergence (linguistics); Applied mathematics; Mathematical analysis; Pure mathematics; Physics","score_opus":0.019164960572508997,"score_gpt":0.27671162172811903,"score_spread":0.25754666115561003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2289134797","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00080587517,0.00041988466,0.98315674,0.011194584,0.00018798144,0.0026478688,0.00008673184,0.00076715904,0.0007331958],"genre_scores_gemma":[0.33074316,0.0003741027,0.66404086,0.0008141169,0.00008462542,0.0015089328,0.00077411864,0.000056195193,0.0016038644],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9929683,0.0032809023,0.0009502223,0.0013232352,0.00090982206,0.000567502],"domain_scores_gemma":[0.9893885,0.0030152278,0.000728285,0.003963833,0.0025946938,0.0003094069],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003970858,0.00045941735,0.00047234623,0.00025972078,0.00083119253,0.00086201244,0.0031890392,0.00030763735,0.00002621178],"category_scores_gemma":[0.0033268698,0.0004336541,0.00037446996,0.0008152948,0.00021823736,0.00033605404,0.0017578305,0.0005992061,0.00009877052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006823362,0.0014403905,0.00095077255,0.00062176783,0.0006109935,0.000011426656,0.018663237,0.14403921,0.006730585,0.0762605,0.010232548,0.74037033],"study_design_scores_gemma":[0.00031598908,0.0000027550147,0.0002651363,0.00021992154,0.000042493077,0.0000056041063,0.000014874734,0.95409083,0.0189887,0.0035466752,0.022015885,0.000491103],"about_ca_topic_score_codex":0.0003098199,"about_ca_topic_score_gemma":0.00015415387,"teacher_disagreement_score":0.8100516,"about_ca_system_score_codex":0.0002671343,"about_ca_system_score_gemma":0.00035212812,"threshold_uncertainty_score":0.99981153},"labels":[],"label_agreement":null},{"id":"W2293107716","doi":"10.1109/icip.2015.7351191","title":"Probabilistic continuous edge detection using local symmetry","year":2015,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Local symmetry; Invariant (physics); Homogeneous space; Symmetry (geometry); Gaussian; Lie algebra; Lie group; Mathematics; Markov random field; Probabilistic logic; Image (mathematics); Pure mathematics; Action (physics); Artificial intelligence; Computer science; Physics; Mathematical physics; Geometry; Image segmentation; Quantum mechanics","score_opus":0.04200186255399984,"score_gpt":0.2991407051925157,"score_spread":0.25713884263851583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293107716","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053971657,0.00001990776,0.9907732,0.00007087222,0.00029162387,0.00016943962,1.5635624e-7,0.0005476622,0.002729959],"genre_scores_gemma":[0.671665,4.3075363e-7,0.32764387,0.00043411422,0.000044372613,0.000008979983,4.3491838e-7,0.0000050068006,0.00019776121],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991007,0.00006820883,0.00017304519,0.00021795882,0.00028432,0.00015576235],"domain_scores_gemma":[0.99933314,0.000043224005,0.000048324353,0.0002599473,0.000142731,0.00017262701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037565976,0.00007419404,0.00009484014,0.000076344,0.000036642126,0.00009154175,0.00029992027,0.00004737014,0.000018033315],"category_scores_gemma":[0.00023153514,0.00006315588,0.000022917751,0.00029198718,0.000076504286,0.0003888047,0.0001313507,0.00008454328,0.000054817294],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000045610327,0.00008630124,0.00009737634,0.000017524657,0.0000075823527,0.000021639156,0.00025144237,0.000038370003,0.006599586,0.006340861,0.001802431,0.98473233],"study_design_scores_gemma":[0.0004886138,0.00025356878,0.00012218567,0.000023446077,0.00000944251,0.000102457816,0.00019364832,0.49877575,0.48542047,0.01381242,0.00053418643,0.00026379226],"about_ca_topic_score_codex":0.00011043952,"about_ca_topic_score_gemma":0.00000789504,"teacher_disagreement_score":0.9844685,"about_ca_system_score_codex":0.00013356493,"about_ca_system_score_gemma":0.00007833285,"threshold_uncertainty_score":0.25754222},"labels":[],"label_agreement":null},{"id":"W2293451754","doi":"10.1109/icip.2015.7350805","title":"User-guided graph reduction for fast image segmentation","year":2015,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Pixel; Artificial intelligence; Image segmentation; Segmentation; Cut; Computer vision; Graph; Computation; Random walker algorithm; Pattern recognition (psychology); Algorithm; Theoretical computer science","score_opus":0.04675275421292332,"score_gpt":0.34093202605484874,"score_spread":0.29417927184192544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293451754","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00091255887,0.000007728782,0.9941275,0.001111927,0.0003833558,0.0004751747,0.0000015396865,0.00059868,0.0023814938],"genre_scores_gemma":[0.0059549776,0.0000050238177,0.9913614,0.0005345516,0.00007936429,0.0001381116,0.000020954893,0.000008047977,0.0018975881],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99900293,0.000045745455,0.00023012978,0.00026345544,0.00029781752,0.0001599343],"domain_scores_gemma":[0.9992068,0.000026495172,0.00008152639,0.00027216066,0.00025980666,0.00015320582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043608408,0.00008687736,0.00008607355,0.00011688258,0.0000550299,0.00015103194,0.00033090723,0.000037799975,0.000034041703],"category_scores_gemma":[0.00009683324,0.000077608136,0.000043001823,0.00026714941,0.000044247216,0.0011856958,0.00006684935,0.00004481193,0.000045348163],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001207695,0.000113109134,0.0000419058,0.000025215704,0.000017498887,0.0000027096216,0.0012893964,0.000010179203,0.21959035,0.016005801,0.5693315,0.19356023],"study_design_scores_gemma":[0.0008133813,0.00016631681,0.00005165761,0.000008030821,0.000006515729,0.000023155175,0.00042260616,0.0053887255,0.9778239,0.014080915,0.0010495128,0.00016526174],"about_ca_topic_score_codex":0.00003420542,"about_ca_topic_score_gemma":0.0000015726379,"teacher_disagreement_score":0.75823355,"about_ca_system_score_codex":0.00006544073,"about_ca_system_score_gemma":0.00005796218,"threshold_uncertainty_score":0.3164768},"labels":[],"label_agreement":null},{"id":"W2293462034","doi":"10.1109/icip.2015.7351356","title":"A sensitive and efficient method for measuring change in cortical thickness using fuzzy correspondence in Alzheimer's disease","year":2015,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Voxel; Artificial intelligence; Computer science; Pattern recognition (psychology); Feature extraction; Sensitivity (control systems); Computer vision; Matching (statistics); Fuzzy logic; Mathematics; Statistics","score_opus":0.16644746997799367,"score_gpt":0.3942644353591158,"score_spread":0.22781696538112214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293462034","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026898175,0.00016700687,0.97167784,0.00037025282,0.00010628187,0.00063501106,0.0000019075226,0.00008541327,0.00005813749],"genre_scores_gemma":[0.43219158,0.0000015439035,0.5670303,0.00068147504,0.00001922458,0.00006558235,7.93705e-7,0.0000057367274,0.0000037482853],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984096,0.0003474863,0.00023252153,0.00036549265,0.00040080873,0.00024406982],"domain_scores_gemma":[0.9988866,0.0003684156,0.000047803056,0.00018614142,0.000153665,0.00035741614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002368129,0.00010599312,0.00017029251,0.00019663881,0.00002806166,0.000068068664,0.00019084956,0.0000432809,0.0000014851661],"category_scores_gemma":[0.00085658784,0.00009544575,0.000018234883,0.00038494196,0.000063490355,0.00030489362,0.0002334725,0.0001233964,0.000001687111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002039588,0.0018434651,0.022305807,0.0002519736,0.000075453405,0.0023351253,0.088607654,0.0054256804,0.026582338,0.04672767,0.0006024999,0.80320275],"study_design_scores_gemma":[0.00067034067,0.000047094214,0.0049794624,0.00008672657,0.000010820109,0.000017014592,0.0003284161,0.9835992,0.008965801,0.0011445094,0.0000038083815,0.00014684504],"about_ca_topic_score_codex":0.00031823,"about_ca_topic_score_gemma":0.00005255273,"teacher_disagreement_score":0.9781735,"about_ca_system_score_codex":0.00010499719,"about_ca_system_score_gemma":0.00016095537,"threshold_uncertainty_score":0.38921648},"labels":[],"label_agreement":null},{"id":"W2293817981","doi":"10.1007/s11042-015-3148-6","title":"Segmentation data visualizing and clustering","year":2015,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Cluster analysis; Segmentation; Artificial intelligence; Data mining; Information retrieval","score_opus":0.1412976126198688,"score_gpt":0.3763502841596905,"score_spread":0.2350526715398217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293817981","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00047008012,0.00018732852,0.9979928,0.0004765476,0.000029759989,0.00034959853,0.000018821434,0.00017120993,0.00030383846],"genre_scores_gemma":[0.01750671,0.00013647729,0.98132664,0.00049023086,0.000092640956,0.00022966092,0.00015056698,0.000007081349,0.000059981194],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927247,0.000024706713,0.00015123954,0.0003008787,0.00014967773,0.00010102347],"domain_scores_gemma":[0.9992264,0.00009555235,0.00005298761,0.0004126072,0.000041234238,0.0001712303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002730747,0.00006807191,0.00007292489,0.00003979493,0.00008289947,0.000226676,0.00034285424,0.000028262557,0.0000039029346],"category_scores_gemma":[0.00006576651,0.00006445432,0.0000050134317,0.00012412139,0.00005841172,0.00084432046,0.0005090305,0.000054022166,0.000012068453],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.6160675e-7,0.000019399195,0.0002360838,0.000010297055,0.000003725292,5.785078e-7,0.00046981033,0.000001390354,0.0037548684,0.0006666046,0.0011415363,0.993695],"study_design_scores_gemma":[0.0014561295,0.00008098862,0.0037944126,0.00004367622,0.000027993332,0.00004289704,0.0009315455,0.93895566,0.02099439,0.0029515808,0.030229194,0.0004915369],"about_ca_topic_score_codex":0.000022614551,"about_ca_topic_score_gemma":0.000005096028,"teacher_disagreement_score":0.9932035,"about_ca_system_score_codex":0.000013504525,"about_ca_system_score_gemma":0.000023660727,"threshold_uncertainty_score":0.26283708},"labels":[],"label_agreement":null},{"id":"W2293943348","doi":"10.1109/embc.2015.7319004","title":"Atlas to patient registration with brain tumor based on a mesh-free method","year":2015,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Cisco Systems","keywords":"Atlas (anatomy); Computer science; Image registration; Segmentation; Artificial intelligence; Brain atlas; Computer vision; Image segmentation; Deformation (meteorology); Pattern recognition (psychology); Ranking (information retrieval); Algorithm; Image (mathematics); Geology","score_opus":0.02822805091077452,"score_gpt":0.31363418434930135,"score_spread":0.28540613343852683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293943348","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022991456,9.979753e-7,0.9690811,0.012531198,0.000050813436,0.0003573837,0.0000010446265,0.00037030072,0.017377248],"genre_scores_gemma":[0.014153132,4.1331045e-8,0.9635642,0.021623002,0.00001719724,0.0000705681,0.0000030204833,0.0000066036837,0.00056219194],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984774,0.00015898743,0.00018550744,0.00032833806,0.0006950116,0.00015475054],"domain_scores_gemma":[0.9985933,0.00015782283,0.00007373022,0.0007389535,0.00014486155,0.0002913351],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006262367,0.000103340404,0.000102552374,0.0001054671,0.000030077303,0.00011393452,0.0005434626,0.000021877193,0.000041171854],"category_scores_gemma":[0.0006067962,0.00007291074,0.00001951612,0.00035971572,0.00002057117,0.0002473673,0.00009852186,0.00007228658,0.0000481875],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001126532,0.00028119056,0.000059505892,0.000015126004,0.000009639641,0.00012356523,0.0014526952,0.00053093303,0.003958345,0.027680699,0.73403144,0.23174421],"study_design_scores_gemma":[0.0019274908,0.0065493225,0.00016287732,0.000105638814,0.0000073901274,0.000042880612,0.0002176535,0.28106493,0.69427246,0.005520611,0.009587363,0.0005414029],"about_ca_topic_score_codex":0.000092772345,"about_ca_topic_score_gemma":0.000027937425,"teacher_disagreement_score":0.7244441,"about_ca_system_score_codex":0.000077091914,"about_ca_system_score_gemma":0.00015940561,"threshold_uncertainty_score":0.29732138},"labels":[],"label_agreement":null},{"id":"W2294136007","doi":"10.1007/978-3-319-20801-5_40","title":"Statistical Textural Distinctiveness in Multi-Parametric Prostate MRI for Suspicious Region Detection","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Optimal distinctiveness theory; Computer science; Artificial intelligence; Parametric statistics; Pattern recognition (psychology); Prostate cancer; Homogeneous; Computer vision; Texture (cosmology); Cancer; Image (mathematics); Medicine; Mathematics; Statistics","score_opus":0.04469763455020679,"score_gpt":0.3205198136397765,"score_spread":0.2758221790895697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294136007","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000047706002,0.00017263967,0.99678886,0.00022636459,0.0009792068,0.0014561991,0.00001210694,0.00022533012,0.00009159793],"genre_scores_gemma":[0.13020848,0.000020094563,0.8689342,0.00035564697,0.00013699092,0.000107558684,0.00001818034,0.000034529494,0.0001842872],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961926,0.00008183714,0.0006568881,0.0014773036,0.00094756874,0.00064384064],"domain_scores_gemma":[0.99732214,0.0008789878,0.00032534768,0.0007579578,0.0004778108,0.00023772604],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012869765,0.00044753557,0.00052901154,0.0012832455,0.0001439495,0.00037154806,0.0016702857,0.0002987029,0.0000047259646],"category_scores_gemma":[0.00084879453,0.00040296954,0.0000747632,0.0010014895,0.00072932284,0.00064749876,0.0006160253,0.00071349076,0.000010078046],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028386292,0.00005454735,0.00009225971,0.00006754168,0.000004226231,0.00011108982,0.0003570534,0.0027950061,0.00010286669,0.0016366475,0.000025544445,0.9947248],"study_design_scores_gemma":[0.0010701482,0.00060767535,0.00075660844,0.00027842107,0.000009162876,0.00009364281,6.005154e-7,0.8805906,0.0052501047,0.11032354,0.00028112018,0.0007383637],"about_ca_topic_score_codex":0.00006853303,"about_ca_topic_score_gemma":0.00018112236,"teacher_disagreement_score":0.9939865,"about_ca_system_score_codex":0.0009064908,"about_ca_system_score_gemma":0.000490671,"threshold_uncertainty_score":0.9998422},"labels":[],"label_agreement":null},{"id":"W2295452827","doi":"10.1007/978-3-319-10470-6_73","title":"Direct Estimation of Cardiac Bi-ventricular Volumes with Regression Forests","year":2014,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CARE Canada; London Health Sciences Centre; St Joseph's Health Care; Western University","funders":"","keywords":"Segmentation; Computer science; Regression; Ejection fraction; Estimation; Ventricle; Artificial intelligence; Left Ventricles; Cardiac Ventricle; Image segmentation; Pattern recognition (psychology); Representation (politics); Computer vision; Statistics; Mathematics; Cardiology; Medicine; Heart failure","score_opus":0.00706734484611754,"score_gpt":0.26195400550797937,"score_spread":0.25488666066186183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295452827","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020152899,0.00007868102,0.9788575,0.00024439782,0.0002760673,0.00021348963,4.508295e-7,0.00015071857,0.000025752985],"genre_scores_gemma":[0.5188387,0.0000026205034,0.48100132,0.00012057233,0.00002552785,0.0000067019896,8.708997e-7,0.0000031809755,4.879233e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795663,0.00012934454,0.0002613014,0.00053759915,0.00082067755,0.0002944427],"domain_scores_gemma":[0.9985807,0.0003338438,0.00016880434,0.000658809,0.0001535338,0.0001043347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010109862,0.00015056111,0.00024399643,0.00034389514,0.00010183758,0.00014106485,0.0011026433,0.000054105123,0.0000029197672],"category_scores_gemma":[0.0003475814,0.00010352937,0.00004166145,0.0017877774,0.00035617754,0.0007014901,0.00030738494,0.00012940036,0.000003183174],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024860674,0.000030231115,0.0032997334,0.000020584723,0.0000023806206,0.000004972118,0.00027682047,0.022226175,0.0014897157,0.000117887335,0.000021978221,0.97250706],"study_design_scores_gemma":[0.000099413046,0.00015106195,0.0048547266,0.00013997656,0.0000022164973,0.0000067261767,1.652753e-7,0.678255,0.3143291,0.0020425322,0.000010432806,0.00010861136],"about_ca_topic_score_codex":0.000040201925,"about_ca_topic_score_gemma":0.000009835306,"teacher_disagreement_score":0.9723984,"about_ca_system_score_codex":0.00006398228,"about_ca_system_score_gemma":0.00011069208,"threshold_uncertainty_score":0.42218053},"labels":[],"label_agreement":null},{"id":"W2295974935","doi":"10.1109/embc.2015.7319041","title":"Ground truth delineation for medical image segmentation based on Local Consistency and Distribution Map analysis","year":2015,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Ground truth; Computer science; Segmentation; Artificial intelligence; Preprocessor; Robustness (evolution); Image segmentation; Consistency (knowledge bases); Computer vision; Pixel; Medical imaging; Data mining; Pattern recognition (psychology)","score_opus":0.022696235935720323,"score_gpt":0.31587556211669315,"score_spread":0.2931793261809728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295974935","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004893833,0.0000131502875,0.9964955,0.002208047,0.00011322332,0.00030104283,0.000022954768,0.00021367015,0.00014301695],"genre_scores_gemma":[0.39366636,0.0000047856124,0.6028086,0.0020960874,0.000055258082,0.00011664565,0.0011502029,0.0000073598853,0.000094735464],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832785,0.00011233783,0.000340971,0.0003500033,0.0007110521,0.00015781015],"domain_scores_gemma":[0.9987558,0.0003316778,0.000096891024,0.00023625528,0.00028520194,0.00029414453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010146423,0.00011345915,0.00016587792,0.00014577855,0.0000781922,0.0001465447,0.00020284207,0.00008194741,0.000059348688],"category_scores_gemma":[0.00057444634,0.00009541876,0.00006458484,0.00042589463,0.00013075273,0.00044223425,0.00004621372,0.00006952314,0.000009350574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015864421,0.0005602709,0.0010843374,0.000107649146,0.00020707579,0.000024327966,0.0003952221,0.00032405628,0.0006394183,0.05006351,0.030713113,0.91572237],"study_design_scores_gemma":[0.0010726963,0.00025117677,0.000421892,0.000011302866,0.00007945657,0.0000020834925,0.00013858447,0.98526347,0.011002264,0.00145648,0.00017148933,0.0001291016],"about_ca_topic_score_codex":0.00006143742,"about_ca_topic_score_gemma":0.000018568302,"teacher_disagreement_score":0.9849394,"about_ca_system_score_codex":0.0001478248,"about_ca_system_score_gemma":0.00014254263,"threshold_uncertainty_score":0.38910642},"labels":[],"label_agreement":null},{"id":"W2296554695","doi":"10.1109/embc.2015.7318320","title":"Robust deformable registration of pre- and post-resection ultrasound volumes for visualization of residual tumor in neurosurgery","year":2015,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Ultrasound; Computer science; Minification; Residual; Neurosurgery; Free-form deformation; Resection; Computer vision; Visualization; Artificial intelligence; Image registration; Radiology; Deformation (meteorology); Medicine; Surgery; Algorithm; Image (mathematics); Materials science","score_opus":0.03587945865483571,"score_gpt":0.2979179056036933,"score_spread":0.2620384469488576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2296554695","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24358322,0.000017022301,0.75579774,0.00005897915,0.000057704976,0.00024792034,0.0000021555243,0.000042761443,0.00019250634],"genre_scores_gemma":[0.90646,0.000011581461,0.09304728,0.000115981486,0.000016433558,0.000027639884,0.000019847765,0.0000054810907,0.00029576937],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99907905,0.000060873615,0.00037562285,0.00015343212,0.0002411943,0.000089813984],"domain_scores_gemma":[0.9991744,0.00015399439,0.00020194388,0.00014233119,0.0002801242,0.00004719577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075580744,0.00005600593,0.000113475035,0.00015258293,0.000017637822,0.00003598635,0.00011192972,0.00003140084,0.000002895062],"category_scores_gemma":[0.00089392933,0.00005174543,0.000015232114,0.00024166192,0.000052429594,0.00082323793,0.00003121109,0.000029134413,1.5590079e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010481431,0.0010085534,0.07403669,0.0013947454,0.000040707164,0.0000071361183,0.007818209,0.003445522,0.7923613,0.045947652,0.03246502,0.040426295],"study_design_scores_gemma":[0.00077507866,0.0008533279,0.032735754,0.00006179211,0.0000061265205,0.0000213954,0.00024903097,0.101577915,0.861583,0.0019807178,0.000028520926,0.00012734518],"about_ca_topic_score_codex":0.00036308877,"about_ca_topic_score_gemma":0.00010065735,"teacher_disagreement_score":0.6628768,"about_ca_system_score_codex":0.000022300794,"about_ca_system_score_gemma":0.00009132186,"threshold_uncertainty_score":0.21101174},"labels":[],"label_agreement":null},{"id":"W2296753965","doi":"10.1515/macro-2015-0008","title":"An Atlas Based Performance Evaluation of Inhomogeneity Correcting Effects","year":2015,"lang":"en","type":"article","venue":"MACRo 2015","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Intensity (physics); Computer science; Cluster analysis; Artificial intelligence; Set (abstract data type); Fuzzy logic; Compensation (psychology); Fuzzy clustering; Algorithm; Computer vision; Pattern recognition (psychology); Data mining; Optics; Physics","score_opus":0.05025427991001209,"score_gpt":0.3598622696663014,"score_spread":0.30960798975628934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2296753965","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3928629,0.00004036067,0.60590416,0.00004205855,0.00028180706,0.0002136021,5.2309446e-7,0.00012840283,0.00052622607],"genre_scores_gemma":[0.9041603,6.9963534e-7,0.095569365,0.00017462451,0.00003645637,0.000029454055,0.0000065351837,0.000005403404,0.000017134109],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982718,0.00032397118,0.00019075088,0.00021088897,0.0008588868,0.00014367612],"domain_scores_gemma":[0.9986778,0.00007220494,0.00013122756,0.0004287754,0.00053285965,0.00015716391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029019455,0.000082452985,0.00011627837,0.00007932675,0.00003703632,0.000042565283,0.00043992314,0.000044091263,0.000020714064],"category_scores_gemma":[0.00045841374,0.00007593256,0.000021164557,0.00026069777,0.00004298915,0.00048184118,0.000070423965,0.00007831447,0.000028396536],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001248988,0.00013868733,0.010209483,0.000047907488,0.000006526683,0.0000025661775,0.0008637746,0.0007634684,0.017045565,0.00003232049,0.0057530557,0.96512413],"study_design_scores_gemma":[0.00045094482,0.0001872667,0.003771555,0.000023917133,0.0000075165726,0.000002480891,0.000012694753,0.4653645,0.52995735,0.0001221412,0.000030042474,0.00006961149],"about_ca_topic_score_codex":0.00004614654,"about_ca_topic_score_gemma":0.0000063049006,"teacher_disagreement_score":0.9650546,"about_ca_system_score_codex":0.00009700384,"about_ca_system_score_gemma":0.00025323383,"threshold_uncertainty_score":0.309644},"labels":[],"label_agreement":null},{"id":"W2297514228","doi":"10.1016/j.compmedimag.2016.03.001","title":"Statistical shape analysis of subcortical structures using spectral matching","year":2016,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Hôpital Notre-Dame; Polytechnique Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"Janssen Research and Development; National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; Eisai; Servier; U.S. Department of Defense; Eli Lilly and Company; Stanley Foundation; Lundbeckfonden; Canada Research Chairs; Centre de recherche du CHU Sainte-Justine; Pfizer; BioClinica; Biogen; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; F. Hoffmann-La Roche; Roche; Merck; Alzheimer's Drug Discovery Foundation; Takeda Pharmaceutical Company; AbbVie; Fujirebio Europe; Alzheimer's Association; GE Healthcare; Alzheimer's Disease Neuroimaging Initiative; Johnson and Johnson; Meso Scale Diagnostics","keywords":"Pattern recognition (psychology); Shape analysis (program analysis); Artificial intelligence; Computer science; Polygon mesh; Curvature; Matching (statistics); Perimeter; Population; Mathematics; Geometry; Medicine; Statistics","score_opus":0.016715399119284543,"score_gpt":0.3118269522216882,"score_spread":0.29511155310240367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2297514228","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.107024394,0.00010220371,0.89144415,0.0009917599,0.00017413509,0.00006875178,0.000010393958,0.00017138195,0.000012842654],"genre_scores_gemma":[0.60250044,0.0001196128,0.39641535,0.00089352875,0.000053330285,0.0000020357627,0.0000053084336,0.000008664773,0.0000017361092],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99758106,0.00017795929,0.0005540276,0.00046907994,0.000868419,0.00034945627],"domain_scores_gemma":[0.9982149,0.0007165339,0.00013756061,0.00035903123,0.0000960646,0.00047591855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005933946,0.00018090414,0.00047522783,0.00047381368,0.00008892995,0.000093891875,0.0005861106,0.00008272016,0.00014973377],"category_scores_gemma":[0.00031334453,0.00012250862,0.00012040073,0.0008181587,0.00070883357,0.00028654424,0.0003748212,0.00020826644,7.3672345e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028532455,0.00013503952,0.013732582,0.000079296886,0.0005591466,0.00027897485,0.00039351278,0.0000038051612,0.025966886,0.20597062,0.0003745403,0.75247705],"study_design_scores_gemma":[0.0011383104,0.00006099906,0.050109874,0.00017997508,0.00033301165,0.00010711516,0.000019609939,0.90275973,0.0033368943,0.041526552,0.000049961636,0.0003779881],"about_ca_topic_score_codex":0.00004247016,"about_ca_topic_score_gemma":0.0000020256746,"teacher_disagreement_score":0.9027559,"about_ca_system_score_codex":0.000020443846,"about_ca_system_score_gemma":0.000099322126,"threshold_uncertainty_score":0.49957564},"labels":[],"label_agreement":null},{"id":"W2303292413","doi":"","title":"Filtering for Detecting Multiple Targets Trajectories on a One-Dimensional Torus","year":2003,"lang":"en","type":"article","venue":"Les Cahiers du GERAD","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Torus; Markov chain; Filter (signal processing); Computer science; Algorithm; Artificial intelligence; Mathematics; Computer vision; Machine learning","score_opus":0.020840896739951514,"score_gpt":0.2506326606169617,"score_spread":0.2297917638770102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2303292413","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060979705,0.00005990498,0.93740743,0.00019263725,0.00043690452,0.00029544448,0.0000039941724,0.0003555565,0.0002684466],"genre_scores_gemma":[0.36691985,0.0000031478025,0.63200027,0.0007781712,0.00006742413,0.00008422468,0.0000030361803,0.000013076654,0.00013080041],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99875754,0.00008188337,0.00022094471,0.00035465788,0.0002868827,0.00029810058],"domain_scores_gemma":[0.9989509,0.00051671127,0.00008362611,0.00026389284,0.000069026166,0.00011584041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034111794,0.0001448506,0.0001551157,0.000093901595,0.00032732822,0.000095230425,0.00028180148,0.00008919747,0.000028279272],"category_scores_gemma":[0.00090822886,0.00014283885,0.00007708578,0.00015351322,0.000079613645,0.00025757385,0.000033546497,0.00018336793,0.000008008153],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014015229,0.0005393048,0.0014657086,0.00022498758,0.0001706202,0.00010528856,0.01138945,0.0002577377,0.47184318,0.13395877,0.007164965,0.37273982],"study_design_scores_gemma":[0.0007820344,0.0002625808,0.00037785945,0.000040957304,0.0000063772677,0.000018127368,0.000093855466,0.004947891,0.9831084,0.0069119385,0.0031399566,0.0003100416],"about_ca_topic_score_codex":0.000018417224,"about_ca_topic_score_gemma":0.0000065613467,"teacher_disagreement_score":0.51126516,"about_ca_system_score_codex":0.000120584766,"about_ca_system_score_gemma":0.000039628703,"threshold_uncertainty_score":0.5824799},"labels":[],"label_agreement":null},{"id":"W2313350414","doi":"10.1109/embc.2014.6944884","title":"Multi-modal image registration using structural features","year":2014,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Image registration; Artificial intelligence; Computer science; Modal; Mutual information; Similarity (geometry); Computer vision; Pattern recognition (psychology); Medical imaging; Image (mathematics)","score_opus":0.022535472769611195,"score_gpt":0.3256243365648871,"score_spread":0.3030888637952759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2313350414","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050640455,0.0000057159864,0.99245113,0.00032654215,0.00013307294,0.00009297408,3.205307e-7,0.00037246733,0.001553732],"genre_scores_gemma":[0.1852694,5.374823e-7,0.81366473,0.0006031947,0.00004783764,0.0000021218789,0.0000022747843,0.0000036075257,0.00040629215],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992013,0.000063352796,0.0001451818,0.00021960275,0.00023336663,0.00013722359],"domain_scores_gemma":[0.99946076,0.000032871267,0.00006700792,0.00030367804,0.00006270747,0.00007295556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019757809,0.00007933561,0.00007423576,0.000048729104,0.00008077227,0.00020312214,0.00037582475,0.000040259554,0.00005348526],"category_scores_gemma":[0.0001205504,0.000063754225,0.000027863565,0.000112926464,0.000055669152,0.0007074433,0.00008419533,0.00008514115,0.000011768322],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031486452,0.000025610954,0.0002056466,0.000020816029,0.000008168113,0.0000076249644,0.00040583315,0.000021184474,0.80418694,0.023985647,0.0059826365,0.16514675],"study_design_scores_gemma":[0.00031399954,0.000043743516,0.0060708136,0.000010132602,0.0000031297584,0.000034789475,0.000019046925,0.58927584,0.40136012,0.0025953369,0.000092943446,0.00018010121],"about_ca_topic_score_codex":0.000109562025,"about_ca_topic_score_gemma":0.000013550682,"teacher_disagreement_score":0.5892547,"about_ca_system_score_codex":0.000026373922,"about_ca_system_score_gemma":0.00002372888,"threshold_uncertainty_score":0.25998217},"labels":[],"label_agreement":null},{"id":"W2313657968","doi":"","title":"Advances in computational image segmentation and perceptual grouping","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Segmentation; Artificial intelligence; Robustness (evolution); Image segmentation; Scale-space segmentation; Computer science; Segmentation-based object categorization; Embedding; Pattern recognition (psychology); Computer vision; Smoothing; Mathematics","score_opus":0.008412090216912428,"score_gpt":0.2950634303887146,"score_spread":0.28665134017180216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2313657968","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054163933,0.00010805302,0.9904219,0.001125587,0.000024125547,0.00010950495,3.2570097e-7,0.00015364328,0.002640498],"genre_scores_gemma":[0.18148519,0.00008247045,0.8172674,0.0010839091,0.000021698826,0.000011960394,0.0000042924125,0.0000025525007,0.00004051077],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992823,0.00003794453,0.00017506133,0.00019604336,0.00019942131,0.000109236586],"domain_scores_gemma":[0.9997425,0.00007166819,0.000033859866,0.0000732529,0.000028558003,0.000050135845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016963288,0.0000618768,0.00006487249,0.00010042279,0.000037694288,0.00008393398,0.00014374973,0.000018824408,0.00010772519],"category_scores_gemma":[0.00002113013,0.000058038946,0.000010199768,0.00014519737,0.00005524022,0.0020870855,0.00007277849,0.000058903104,0.000023758026],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014622304,0.000037030997,0.0005665586,0.000008959491,9.5304665e-7,0.0000030420208,0.0011645426,0.00016193421,0.0037287564,0.00916313,0.00029811947,0.9848655],"study_design_scores_gemma":[0.0022924016,0.00019464106,0.033493992,0.00008294118,0.0000044518083,0.00006904985,0.0015909537,0.89050853,0.046767432,0.023202756,0.0011325775,0.00066028634],"about_ca_topic_score_codex":0.0000072837,"about_ca_topic_score_gemma":0.000016515194,"teacher_disagreement_score":0.98420525,"about_ca_system_score_codex":0.00004027224,"about_ca_system_score_gemma":0.000014162769,"threshold_uncertainty_score":0.23667595},"labels":[],"label_agreement":null},{"id":"W2316739831","doi":"10.2316/j.2013.210-1055","title":"SIGNAL ANALYSIS OF MULTI-PARAMETRIC MR IMAGES IN HIGHER ORDER FOURIER SPACES","year":2013,"lang":"en","type":"article","venue":"International Journal of Computational Bioscience","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fourier analysis; Fourier transform; SIGNAL (programming language); Computer science; Parametric statistics; Order (exchange); Computer vision; Artificial intelligence; Mathematics; Mathematical analysis; Statistics","score_opus":0.027994942117398086,"score_gpt":0.32813263214338995,"score_spread":0.3001376900259919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2316739831","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05369026,0.000086158616,0.94255304,0.0029430087,0.0004621926,0.00009862155,0.0000070838582,0.000019444458,0.00014020535],"genre_scores_gemma":[0.56684613,0.000011908672,0.4326051,0.00043037772,0.000029013485,0.0000032690164,0.0000025833906,0.0000027202993,0.00006890528],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99683344,0.000113072194,0.0008613233,0.0002473499,0.0017660941,0.00017874356],"domain_scores_gemma":[0.99621063,0.00055722945,0.00077110075,0.00014036785,0.0021928006,0.0001278867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072565227,0.00012832665,0.00029474444,0.002513418,0.000032016982,0.00026392395,0.0017606698,0.000048010083,0.0005318571],"category_scores_gemma":[0.00038349006,0.00010712447,0.00015453306,0.0034419196,0.00026240954,0.0016720458,0.00019335862,0.0001787601,0.00001562085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008817008,0.0032786147,0.11254729,0.000039222556,0.0021142412,0.0003481571,0.0021683264,0.4508433,0.08612455,0.020206414,0.006691017,0.31555068],"study_design_scores_gemma":[0.0011076991,0.00019244579,0.3513757,0.00007822279,0.000061093066,0.000046462686,0.000074258685,0.6131979,0.024342544,0.009115846,0.00012330477,0.0002845221],"about_ca_topic_score_codex":0.00006962874,"about_ca_topic_score_gemma":0.0000027370268,"teacher_disagreement_score":0.5131559,"about_ca_system_score_codex":0.000109570785,"about_ca_system_score_gemma":0.000231512,"threshold_uncertainty_score":0.5823462},"labels":[],"label_agreement":null},{"id":"W2317689312","doi":"10.1117/12.2217079","title":"3D prostate MR-TRUS non-rigid registration using dual optimization with volume-preserving constraint","year":2016,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Constraint (computer-aided design); Volume (thermodynamics); Dual (grammatical number); Computer science; Image registration; Computer vision; Artificial intelligence; Mathematics; Geometry; Physics; Image (mathematics)","score_opus":0.012266816311921025,"score_gpt":0.23920004775557677,"score_spread":0.22693323144365574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2317689312","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7248211,0.000014023525,0.27107537,0.0023086981,0.00013008805,0.0006615564,0.000019885943,0.00017488743,0.00079440564],"genre_scores_gemma":[0.09896451,0.000044487853,0.9003218,0.00011226215,0.00018285574,0.00011611661,0.0000050351155,0.000041455518,0.00021150628],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972872,4.5067598e-8,0.0007602474,0.00052529544,0.0010119902,0.00041522464],"domain_scores_gemma":[0.99684894,0.00012544761,0.00064968615,0.0001145158,0.0020994449,0.00016198454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007937109,0.00031156174,0.00034011077,0.00013152408,0.000117482894,0.0002663101,0.0011573919,0.00015672203,0.000022181888],"category_scores_gemma":[0.00059332926,0.00021612244,0.00024383642,0.00042245194,0.00038650932,0.0019743317,0.00026949085,0.00021138825,0.0000011480344],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007754899,0.00011569384,0.00042190048,0.00034350835,0.00024753957,5.9197964e-7,0.00035627713,0.0013457892,0.87269443,0.1193361,0.001941831,0.0031188151],"study_design_scores_gemma":[0.0015696294,0.0005423864,0.00029544148,0.0009234169,0.00009135434,0.000064917454,0.00045888024,0.63778836,0.35665393,0.00085655705,0.0002567512,0.0004984092],"about_ca_topic_score_codex":0.000015241895,"about_ca_topic_score_gemma":1.6037669e-7,"teacher_disagreement_score":0.63644254,"about_ca_system_score_codex":0.00023957182,"about_ca_system_score_gemma":0.00010460648,"threshold_uncertainty_score":0.8813217},"labels":[],"label_agreement":null},{"id":"W2320936101","doi":"10.1107/s0108767311097388","title":"<i>QFocus</i>: structure reconstruction from focal series of HRTEM images","year":2011,"lang":"en","type":"article","venue":"Acta Crystallographica Section A Foundations of Crystallography","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Series (stratigraphy); High-resolution transmission electron microscopy; Geology; Optics; Physics; Paleontology; Transmission electron microscopy","score_opus":0.015993306483312278,"score_gpt":0.23457328032031433,"score_spread":0.21857997383700206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2320936101","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14423566,0.000100778336,0.85080326,0.00023680432,0.00081659015,0.0005686355,0.00016826444,0.00060744345,0.0024625498],"genre_scores_gemma":[0.7544414,0.00015912746,0.24513061,0.000064571956,0.000052423115,0.000041261035,0.00007370022,0.000022477805,0.000014473558],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9968531,0.00027364827,0.0011560705,0.00069897587,0.0006605218,0.0003576964],"domain_scores_gemma":[0.99684256,0.00017171426,0.0009832433,0.0010851691,0.00072334066,0.0001939483],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034097972,0.00036580773,0.0005303385,0.0016231108,0.0002370556,0.00010429493,0.0010867406,0.0003024711,0.0005623072],"category_scores_gemma":[0.00013925595,0.00036733475,0.00044421217,0.0028405692,0.0011953535,0.0026861944,0.00022097945,0.00036984065,2.3735107e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010058107,0.00036764797,0.008872245,0.00008207698,0.00039918642,0.000005629929,0.0012196498,0.0000030054055,0.96566236,0.0032205104,0.00083386316,0.01923327],"study_design_scores_gemma":[0.0036446971,0.0030866005,0.1640817,0.0005180029,0.0005569183,0.0004743808,0.0027842163,0.0004813628,0.5283743,0.28460088,0.009026599,0.0023703089],"about_ca_topic_score_codex":0.0005235514,"about_ca_topic_score_gemma":0.00018543117,"teacher_disagreement_score":0.6102057,"about_ca_system_score_codex":0.000028571725,"about_ca_system_score_gemma":0.000121848156,"threshold_uncertainty_score":0.99987787},"labels":[],"label_agreement":null},{"id":"W2323258221","doi":"10.4310/cms.2015.v13.n1.a13","title":"Optimal transport for particle image velocimetry","year":2014,"lang":"en","type":"article","venue":"Communications in Mathematical Sciences","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Western Canada Research Grid; Compute Canada","keywords":"Particle image velocimetry; Particle tracking velocimetry; Velocimetry; Particle (ecology); Physics; Image (mathematics); Mechanics; Classical mechanics; Computer science; Geology; Computer vision; Turbulence","score_opus":0.0668681544438212,"score_gpt":0.3869730036761329,"score_spread":0.3201048492323117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2323258221","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037397374,0.000039324234,0.9863991,0.003671102,0.000022603828,0.0002722892,9.425362e-7,0.00015754238,0.005697343],"genre_scores_gemma":[0.28678384,0.0000117446525,0.7127615,0.00026623363,0.0000061981523,0.00012748674,0.0000011806153,0.0000029148343,0.000038845177],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876946,0.00012379258,0.00035774094,0.00023474508,0.00027054801,0.00024368544],"domain_scores_gemma":[0.9976565,0.0011241045,0.00006548296,0.0010134076,0.000055763496,0.00008469509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023361829,0.000080308106,0.00014500794,0.00007747576,0.00024380023,0.00010755417,0.0029826767,0.00003671005,0.000040022256],"category_scores_gemma":[0.000754878,0.000066431676,0.00004637232,0.0006600191,0.0007690402,0.00053522806,0.000267024,0.0001029431,0.00004392296],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014770537,0.00035297012,0.0003282916,0.000041940224,0.0000032404248,3.7584957e-7,0.0013423956,0.000023851446,0.0028911985,0.9317023,0.00033422187,0.062977724],"study_design_scores_gemma":[0.00031781086,0.00013068072,0.00075915543,0.00006812007,0.000005740336,0.0000048087086,0.00021963632,0.7571217,0.021584896,0.21859284,0.0009871122,0.0002075333],"about_ca_topic_score_codex":0.000004208781,"about_ca_topic_score_gemma":0.0000036858166,"teacher_disagreement_score":0.75709784,"about_ca_system_score_codex":0.000024819788,"about_ca_system_score_gemma":0.000041405347,"threshold_uncertainty_score":0.55426055},"labels":[],"label_agreement":null},{"id":"W2327436943","doi":"10.2316/p.2011.737-023","title":"Multi-Parametric MR Image Processing using Higher Dimensional Vector Algebra","year":2011,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Parametric statistics; Computer science; Image processing; Algebra over a field; Computer vision; Artificial intelligence; Mathematics; Image (mathematics); Pure mathematics; Statistics","score_opus":0.07526901277797293,"score_gpt":0.317217475454278,"score_spread":0.24194846267630504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2327436943","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00786155,0.00006496483,0.98973745,0.000070779126,0.000197357,0.00014991041,4.3897558e-7,0.00059387874,0.0013236898],"genre_scores_gemma":[0.15060495,0.000001038016,0.8480095,0.0008457395,0.000028006913,0.000008585129,9.384935e-7,0.000010708574,0.0004905185],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986709,0.000054491244,0.00025512505,0.00037385296,0.00038218906,0.00026345142],"domain_scores_gemma":[0.9992522,0.000044191806,0.00010018955,0.00031110394,0.00013586444,0.00015645397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022474668,0.00013491206,0.0001349005,0.00021218811,0.000101792524,0.00012454708,0.0005391738,0.000058609425,0.0008674761],"category_scores_gemma":[0.00007061088,0.000109487424,0.00004492286,0.00072939595,0.00009414358,0.0010719362,0.00025587744,0.00012603152,0.00010818755],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021516631,0.00151588,0.001751017,0.00014634302,0.00006354259,0.00027292583,0.002047088,0.000005386851,0.6970395,0.007857567,0.007970916,0.28130832],"study_design_scores_gemma":[0.00063841656,0.00009363404,0.012579445,0.000061628234,0.000015371379,0.000034274475,0.000020257918,0.25347403,0.73112893,0.0013658236,0.000094342155,0.0004938417],"about_ca_topic_score_codex":0.00009216105,"about_ca_topic_score_gemma":7.8973886e-7,"teacher_disagreement_score":0.2808145,"about_ca_system_score_codex":0.00005234302,"about_ca_system_score_gemma":0.000079226294,"threshold_uncertainty_score":0.9498255},"labels":[],"label_agreement":null},{"id":"W2329319890","doi":"10.5594/m001453","title":"Unconstrained 2D to Stereoscopic 3D Image and Video Conversion Using Semi-Automatic Energy Minimization Techniques","year":2012,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Energy minimization; Computer science; Stereoscopy; Computer vision; Minification; Artificial intelligence; Energy (signal processing); Computer graphics (images); Image (mathematics); Mathematics; Physics","score_opus":0.01566061793447373,"score_gpt":0.28111839016201434,"score_spread":0.26545777222754063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2329319890","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00845636,0.000029048122,0.98914695,0.0002556437,0.00011023301,0.00022334239,0.0000010325225,0.0007165314,0.0010608564],"genre_scores_gemma":[0.14132309,0.000010208813,0.8563302,0.0021192015,0.000039546365,0.000017944663,0.0000038424814,0.000009195138,0.0001467392],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988546,0.000097532415,0.0002752035,0.00024835177,0.0002599122,0.00026439482],"domain_scores_gemma":[0.99920803,0.000088151115,0.00008780901,0.00029078414,0.00007799421,0.00024720037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024354133,0.00013962109,0.00015726406,0.00020048954,0.000079938516,0.00013407081,0.0002575705,0.00006856015,0.00018445704],"category_scores_gemma":[0.00007846915,0.00012447278,0.000020819898,0.00031053592,0.000073871015,0.0012439756,0.00025556097,0.000052130625,0.000010346652],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005476914,0.00016458383,0.0017557607,0.00017403277,0.000027888944,0.000010739222,0.0019390158,0.0000014551584,0.3694854,0.0039329245,0.010936375,0.61156636],"study_design_scores_gemma":[0.00022160615,0.000097608725,0.00022461823,0.00011747588,0.0000132812265,0.0000336349,0.000112332404,0.06633616,0.9316813,0.00021582641,0.0006828152,0.0002633432],"about_ca_topic_score_codex":0.000079068355,"about_ca_topic_score_gemma":0.000001839997,"teacher_disagreement_score":0.611303,"about_ca_system_score_codex":0.000069163405,"about_ca_system_score_gemma":0.00003978148,"threshold_uncertainty_score":0.5075852},"labels":[],"label_agreement":null},{"id":"W2330616591","doi":"10.1117/12.2216486","title":"Ultrafast superpixel segmentation of large 3D medical datasets","year":2016,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal","funders":"","keywords":"Computer science; Segmentation; CUDA; Wavefront; Field-programmable gate array; Thread (computing); Acceleration; Real-time computing; Parallel computing; Artificial intelligence; Computer hardware","score_opus":0.010689911308479186,"score_gpt":0.2604559564364401,"score_spread":0.2497660451279609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2330616591","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94746464,0.0000326202,0.046862412,0.004074567,0.0002291786,0.00048680074,0.00019963893,0.00015232114,0.000497803],"genre_scores_gemma":[0.2664461,0.00027080093,0.73195106,0.0005422373,0.00034127454,0.00020710556,0.00004184723,0.00005707722,0.00014248265],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9965883,4.489259e-8,0.00093032845,0.00045444348,0.001621014,0.00040585577],"domain_scores_gemma":[0.9976458,0.00030955728,0.00046595826,0.00011634676,0.0012437331,0.00021862205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013202316,0.00026837926,0.00039145385,0.00013216378,0.00006169852,0.00007718235,0.0021583873,0.00019871503,0.0000722581],"category_scores_gemma":[0.0014900968,0.0001857869,0.00037085486,0.00037867847,0.0003190653,0.0013391766,0.0004077269,0.0002185712,0.0000033072174],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025922574,0.00018421552,0.00031403627,0.00026493563,0.00017664477,1.8509769e-7,0.00018648511,0.0000013060196,0.74241275,0.2468387,0.0056425,0.0039523467],"study_design_scores_gemma":[0.0015191055,0.00029067992,0.00047310386,0.00052375894,0.00005506173,0.000020219004,0.00034607886,0.010555768,0.9837283,0.0010092969,0.0011993058,0.00027931776],"about_ca_topic_score_codex":0.0000067231085,"about_ca_topic_score_gemma":1.6965356e-7,"teacher_disagreement_score":0.68508863,"about_ca_system_score_codex":0.0001272488,"about_ca_system_score_gemma":0.00007430748,"threshold_uncertainty_score":0.75761706},"labels":[],"label_agreement":null},{"id":"W2331809639","doi":"10.1109/tsmc.2016.2531645","title":"A Novel Fusion Approach Based on the Global Consistency Criterion to Fusing Multiple Segmentations","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Segmentation; Fusion; Consistency (knowledge bases); Artificial intelligence; Computer science; Scale-space segmentation; Pattern recognition (psychology); Image segmentation; Ground truth; Segmentation-based object categorization; Image fusion; Energy (signal processing); Fuse (electrical); Image (mathematics); Computer vision; Mathematics; Statistics; Physics","score_opus":0.03112015166034882,"score_gpt":0.2589120932345474,"score_spread":0.22779194157419858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2331809639","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022786334,0.000044767858,0.9922417,0.00087448384,0.0011228645,0.0014144087,0.00010728349,0.00029311408,0.0016227886],"genre_scores_gemma":[0.9832515,0.00001322425,0.01479837,0.0008383546,0.000057768826,0.00043461085,0.0000023002692,0.000019116895,0.00058473245],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975975,0.0002891475,0.0005535807,0.00056779094,0.00067831494,0.00031367983],"domain_scores_gemma":[0.9982999,0.00037879043,0.00015966868,0.00071127404,0.00019307333,0.0002573177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005606398,0.00026287322,0.0002641453,0.00017345301,0.00040337988,0.00044785393,0.00044093287,0.00011032296,0.000011197421],"category_scores_gemma":[0.000031118165,0.00016439184,0.0000805087,0.0004358766,0.00010653821,0.00018901865,0.000008785359,0.00012288452,0.000042627464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005303784,0.006438873,0.0009537455,0.002342216,0.0006369697,0.00007312834,0.009062402,0.046696555,0.4245271,0.07450544,0.020214636,0.41401854],"study_design_scores_gemma":[0.005645365,0.0018439491,0.00079127477,0.0044972934,0.00014738042,0.00043857293,0.004490477,0.9379064,0.037875947,0.00013823301,0.0043358137,0.0018892944],"about_ca_topic_score_codex":0.00051461527,"about_ca_topic_score_gemma":0.000027063223,"teacher_disagreement_score":0.9809729,"about_ca_system_score_codex":0.00030532232,"about_ca_system_score_gemma":0.00005733153,"threshold_uncertainty_score":0.67037046},"labels":[],"label_agreement":null},{"id":"W2331979674","doi":"10.1103/physreve.90.023306","title":"Stochastic reconstruction using multiple correlation functions with different-phase-neighbor-based pixel selection","year":2014,"lang":"en","type":"article","venue":"Physical Review E","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Alberta Innovates; University of Alberta","keywords":"Pixel; Simulated annealing; Algorithm; Minification; Energy (signal processing); Computer science; Convergence (economics); Random walker algorithm; Mathematics; Mathematical optimization; Artificial intelligence; Statistics","score_opus":0.024840350739638258,"score_gpt":0.3130834033444961,"score_spread":0.28824305260485783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2331979674","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013074354,0.00011436144,0.9856333,0.00017098717,0.00015270452,0.0004550161,0.0000015825677,0.00032432683,0.00007334923],"genre_scores_gemma":[0.9323472,0.00002969428,0.066702746,0.00060929003,0.00015954004,0.000096314456,0.000022802891,0.000015598423,0.000016845897],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866414,0.00020217648,0.0002509286,0.00035447587,0.00034595234,0.00018234698],"domain_scores_gemma":[0.9989601,0.00027769798,0.00020400486,0.00028567022,0.00014972081,0.00012282918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017391064,0.00016476445,0.0002693334,0.00007319746,0.0001613907,0.000057375608,0.00017824875,0.00002816681,0.000034016284],"category_scores_gemma":[0.00030460808,0.00012492563,0.000079828555,0.00048959634,0.00006753169,0.0004538004,0.000028075405,0.00019601823,0.000032227945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020646448,0.0005151096,0.00036833689,0.000368003,0.000025024623,3.7149124e-7,0.00003857799,0.0014494723,0.021753464,0.0019244441,0.00028095674,0.9732556],"study_design_scores_gemma":[0.00056388247,0.0002782173,0.0002037524,0.00083099294,0.00006417466,0.000012481265,0.0000019394895,0.993981,0.0031480326,0.000665612,0.0000851127,0.0001648174],"about_ca_topic_score_codex":0.000011097559,"about_ca_topic_score_gemma":0.000003703907,"teacher_disagreement_score":0.99253154,"about_ca_system_score_codex":0.00009636857,"about_ca_system_score_gemma":0.00004827485,"threshold_uncertainty_score":0.50943196},"labels":[],"label_agreement":null},{"id":"W2333027161","doi":"10.9790/2834-0928110115","title":"Detection of Tumor in Liver Using Image Segmentation and Registration Technique","year":2014,"lang":"en","type":"article","venue":"IOSR Journal of Electronics and Communication Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Image registration; Segmentation; Image segmentation; Image (mathematics); Pattern recognition (psychology)","score_opus":0.007345343232606211,"score_gpt":0.239914937540088,"score_spread":0.23256959430748178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2333027161","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.095723465,0.0010111175,0.9030979,0.00005053324,0.0000105706895,0.000079348036,7.9707554e-8,0.000013053699,0.000013907962],"genre_scores_gemma":[0.6945015,0.0006295472,0.3048439,0.000012698398,0.000005284523,0.0000029551677,2.6984063e-7,0.0000033323772,4.7331105e-7],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99934214,0.00007515927,0.00032564695,0.00006330668,0.0001179244,0.00007581159],"domain_scores_gemma":[0.9993504,0.000079992824,0.00028029922,0.00015830509,0.000100291465,0.000030701893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008528327,0.000057008536,0.000106749445,0.00018133101,0.00002812389,0.000038669055,0.00017772062,0.00002761866,5.630248e-7],"category_scores_gemma":[0.00009390245,0.000058611367,0.000015722926,0.00015925961,0.000022805856,0.00050530693,0.000044940276,0.00018205706,3.087529e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037683199,0.000016602267,0.00003997107,0.000031346,0.0000048014117,6.2969696e-7,0.00017630761,0.00030550716,0.9569429,0.0024634537,0.0000011709095,0.040013507],"study_design_scores_gemma":[0.0002422516,0.00015226453,0.00074646424,0.00011509709,0.0000066039793,0.00012407033,0.000022726363,0.32636347,0.6710337,0.0010886589,0.000037256978,0.00006745831],"about_ca_topic_score_codex":0.000013929414,"about_ca_topic_score_gemma":0.000006209224,"teacher_disagreement_score":0.59877807,"about_ca_system_score_codex":0.00005910379,"about_ca_system_score_gemma":0.000026612894,"threshold_uncertainty_score":0.23901021},"labels":[],"label_agreement":null},{"id":"W2336780136","doi":"10.1109/jbhi.2016.2554122","title":"Registration of Pre- and Postresection Ultrasound Volumes With Noncorresponding Regions in Neurosurgery","year":2016,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ultrasound; Computer science; Artificial intelligence; Outlier; Image registration; Computer vision; Image quality; Robustness (evolution); Radiology; Medicine; Image (mathematics)","score_opus":0.024465661537519515,"score_gpt":0.3106416093958324,"score_spread":0.2861759478583129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2336780136","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21195303,0.000052789874,0.7839629,0.0037892887,0.0001366569,0.00007542443,0.0000017383898,0.000010024158,0.000018131306],"genre_scores_gemma":[0.89092004,0.0021277505,0.105935544,0.0008911423,0.00007845823,0.0000021751393,7.905145e-7,0.0000040465934,0.000040049035],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9984121,0.000053840497,0.00095224974,0.00004990113,0.00040380177,0.00012815959],"domain_scores_gemma":[0.99847627,0.00029269874,0.0008461137,0.00008566925,0.00008878971,0.0002104564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00134267,0.00006096706,0.00018724652,0.0003206617,0.000043462176,0.000032732285,0.00011759492,0.000045914883,0.0000017861],"category_scores_gemma":[0.00020674863,0.00003494584,0.000015624815,0.00024741073,0.00019147553,0.0007896834,0.000018086062,0.00012779972,1.5981743e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019288076,0.00018779148,0.015239,0.0007308396,0.00002163812,0.000027096146,0.0066648778,0.0000019505658,0.0066229952,0.0008862621,0.009071688,0.96035296],"study_design_scores_gemma":[0.01847087,0.034432713,0.78003454,0.022075266,0.000081801656,0.017328164,0.0050443243,0.031286124,0.060120903,0.016535245,0.012972173,0.0016178882],"about_ca_topic_score_codex":0.00002568958,"about_ca_topic_score_gemma":0.00000962487,"teacher_disagreement_score":0.9587351,"about_ca_system_score_codex":0.00003973477,"about_ca_system_score_gemma":0.0002423391,"threshold_uncertainty_score":0.142505},"labels":[],"label_agreement":null},{"id":"W2339205893","doi":"","title":"A unified approach to surface-based registration for image-guided surgery","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Image registration; Computer vision; Rigid transformation; Artificial intelligence; Point set registration; Rotation (mathematics); Fiducial marker; Computer science; Point (geometry); Frame (networking); Coordinate system; Invariant (physics); Algorithm; Mathematics; Image (mathematics); Geometry","score_opus":0.06622432652425807,"score_gpt":0.3195413177806227,"score_spread":0.25331699125636464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2339205893","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006649292,0.000004805384,0.982529,0.00564186,0.000056909103,0.0006148619,0.0000018509259,0.0006343396,0.009851464],"genre_scores_gemma":[0.03165216,0.0000010504551,0.9612923,0.005049729,0.00005531314,0.00009586448,0.000023615392,0.000009005527,0.0018209318],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998704,0.000058370817,0.00033972316,0.00036138706,0.00030764204,0.0002288831],"domain_scores_gemma":[0.99884737,0.0002655514,0.00008241221,0.000464501,0.00017904464,0.00016113733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087798695,0.000109956825,0.00014758136,0.0001035654,0.000066536326,0.0001856762,0.00041448252,0.00004985362,0.000031227166],"category_scores_gemma":[0.00027996217,0.00009923109,0.00007358015,0.00034935534,0.00003131312,0.0005034643,0.000041494237,0.000050871735,0.000036241046],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022398148,0.00038267724,0.0000490648,0.000087675326,0.000013083762,0.0000027217516,0.00023799938,0.00079094665,0.053586885,0.023663888,0.7970904,0.124072276],"study_design_scores_gemma":[0.00038930486,0.000054945343,0.000108447515,0.00001512795,0.000004650736,0.000005422213,0.000020190146,0.3022854,0.68888044,0.0006541194,0.0072995066,0.0002824226],"about_ca_topic_score_codex":0.000029750016,"about_ca_topic_score_gemma":0.000004639034,"teacher_disagreement_score":0.78979087,"about_ca_system_score_codex":0.00006515678,"about_ca_system_score_gemma":0.00015895978,"threshold_uncertainty_score":0.40465263},"labels":[],"label_agreement":null},{"id":"W2342719188","doi":"10.1109/tmi.2015.2511062","title":"Kernel Bundle Diffeomorphic Image Registration Using Stationary Velocity Fields and Wendland Basis Functions","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Højteknologifonden; National Institute on Aging; Villum Fonden","keywords":"Kernel (algebra); Reproducing kernel Hilbert space; Image registration; WKB approximation; Mathematics; Pointwise; Artificial intelligence; Computer science; Algorithm; Hilbert space; Mathematical analysis; Image (mathematics); Pure mathematics","score_opus":0.03393778996207534,"score_gpt":0.29903992417765274,"score_spread":0.2651021342155774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2342719188","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005358091,0.000056693512,0.9885963,0.0044268556,0.000632126,0.00018306074,0.000011829557,0.0003517255,0.00038331313],"genre_scores_gemma":[0.7632401,0.00007885809,0.2335311,0.0025146715,0.00013261946,0.00005597767,0.000013028126,0.000024267667,0.00040937928],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997789,0.0001693583,0.00037721638,0.0004373341,0.0009621605,0.0002649026],"domain_scores_gemma":[0.99861515,0.00024009892,0.000104562845,0.00034028754,0.00017126286,0.00052862166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006805244,0.00017667051,0.00017849727,0.0002015439,0.00028595442,0.00019480943,0.0003147362,0.00010077744,0.00018881357],"category_scores_gemma":[0.00014882396,0.00017097444,0.0000557022,0.00036054364,0.0002847068,0.0010465309,0.000010118857,0.00047293282,0.000025468722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007894416,0.0012913537,0.0005766996,0.00013201204,0.000118453165,0.00028619898,0.0028044381,0.0009265532,0.0064626317,0.00043433587,0.019088117,0.96780026],"study_design_scores_gemma":[0.001552418,0.00015764486,0.00039242467,0.00014955987,0.000058305206,0.000365882,0.00047983142,0.9765507,0.017332433,0.002173121,0.0003509699,0.0004367268],"about_ca_topic_score_codex":0.00026764363,"about_ca_topic_score_gemma":0.000034752535,"teacher_disagreement_score":0.97562414,"about_ca_system_score_codex":0.00012333029,"about_ca_system_score_gemma":0.00027809883,"threshold_uncertainty_score":0.69721353},"labels":[],"label_agreement":null},{"id":"W2343237073","doi":"10.1109/jbhi.2016.2519686","title":"Evaluation of Three Algorithms for the Segmentation of Overlapping Cervical Cells","year":2016,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":161,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Australian Research Council; Natural Sciences and Engineering Research Council of Canada; Office of Science; Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; U.S. Department of Energy","keywords":"Computer science; Segmentation; Artificial intelligence; Algorithm; Image segmentation; Pattern recognition (psychology)","score_opus":0.11091829726378473,"score_gpt":0.38623803174834204,"score_spread":0.2753197344845573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2343237073","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030702145,0.00018758947,0.9916012,0.004417146,0.0003917879,0.00030804132,0.000008478705,0.000006306143,0.000009226453],"genre_scores_gemma":[0.1301426,0.0013906668,0.86650836,0.0016716811,0.00025726552,0.000016124448,0.0000015324537,0.0000061234364,0.000005673819],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971097,0.000067167064,0.0013439232,0.000041635718,0.001305807,0.00013172647],"domain_scores_gemma":[0.997308,0.0004275244,0.0013472013,0.000119864795,0.00064715696,0.00015025784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0069362125,0.00006264246,0.0002159187,0.00013032772,0.000056122975,0.000015485111,0.00030223856,0.000050570547,0.000014056156],"category_scores_gemma":[0.00016917333,0.00003068763,0.00005488568,0.00015640764,0.00016513806,0.00041536492,0.000037308288,0.000080000515,5.020377e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000070074875,0.000041867774,0.000021429154,0.00021319321,0.000028141503,9.620721e-8,0.0011325941,0.0000041903827,0.002945251,0.00017344352,0.0019298261,0.993503],"study_design_scores_gemma":[0.010364709,0.005523945,0.005821369,0.00234425,0.0002671302,0.00012223855,0.0016620385,0.7480172,0.19872409,0.024189936,0.00263391,0.00032918341],"about_ca_topic_score_codex":0.0000076013825,"about_ca_topic_score_gemma":0.0000014429723,"teacher_disagreement_score":0.9931738,"about_ca_system_score_codex":0.000072357354,"about_ca_system_score_gemma":0.00046214627,"threshold_uncertainty_score":0.24039666},"labels":[],"label_agreement":null},{"id":"W2344281687","doi":"10.1017/cbo9781139523967","title":"Geometric Methods in Signal and Image Analysis","year":2015,"lang":"en","type":"book","venue":"Cambridge University Press eBooks","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Key (lock); Artificial intelligence; SIGNAL (programming language); Robotics; Computer graphics; Image processing; Graphics; Signal processing; Data science; Range (aeronautics); Resource (disambiguation); Image (mathematics); Computer graphics (images); Digital signal processing; Engineering; Robot; Computer security; Programming language","score_opus":0.030651001181798296,"score_gpt":0.2946489123592252,"score_spread":0.2639979111774269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2344281687","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000030470328,0.00011798231,0.5486398,0.0000061973146,0.000030691786,0.00016295929,0.00001773898,0.00012320057,0.45089838],"genre_scores_gemma":[0.000009612787,0.000044941244,0.29803666,0.00007060976,0.000021825752,0.0000012179951,0.000025754965,0.000010620003,0.70177877],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979504,0.00038083593,0.00025697454,0.0006857285,0.00044813944,0.0002779622],"domain_scores_gemma":[0.9983313,0.00027589663,0.00023581827,0.00062823587,0.00022438816,0.00030436544],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008893628,0.00026966693,0.00058498257,0.0021758024,0.00005998543,0.00013990232,0.0011706061,0.0002716308,0.0000053242497],"category_scores_gemma":[0.00006642163,0.00031104137,0.00015018898,0.0003656578,0.00024138263,0.0003355169,0.0011058225,0.00048691937,0.0000037342918],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047776943,0.00009868692,0.000038108432,0.00031047725,0.0013156896,0.0021925592,0.00048377697,0.000008031739,0.00070845935,0.13198896,0.6453482,0.21745928],"study_design_scores_gemma":[0.0033863198,0.00042623671,0.0007405538,0.00030527,0.002918494,0.00005232331,0.00012918093,0.030541582,0.021795154,0.00029949215,0.9363802,0.0030251846],"about_ca_topic_score_codex":0.00021133307,"about_ca_topic_score_gemma":0.0000020711607,"teacher_disagreement_score":0.29103202,"about_ca_system_score_codex":0.0004447808,"about_ca_system_score_gemma":0.0003322118,"threshold_uncertainty_score":0.9999342},"labels":[],"label_agreement":null},{"id":"W2353048332","doi":"10.1016/j.nicl.2016.05.004","title":"Classification of amyloid status using machine learning with histograms of oriented 3D gradients","year":2016,"lang":"en","type":"article","venue":"NeuroImage Clinical","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Canadian Institutes of Health Research; National Institute on Aging; Center for Advanced Systems and Engineering, Syracuse University","keywords":"Voxel; Artificial intelligence; Histogram; Positron emission tomography; Standardized uptake value; Pattern recognition (psychology); Feature (linguistics); Computer science; Set (abstract data type); Reliability (semiconductor); Mathematics; Nuclear medicine; Image (mathematics); Machine learning; Medicine; Physics","score_opus":0.07444796800944754,"score_gpt":0.3620680782374885,"score_spread":0.28762011022804096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2353048332","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2813671,0.000015933983,0.71800697,0.00009951306,0.00014129074,0.00014353242,0.000004615506,0.00011477524,0.00010629203],"genre_scores_gemma":[0.7045991,0.00007009945,0.29508892,0.00010594825,0.000023541457,0.0000047567887,0.000004859935,0.000015836493,0.00008695419],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99757105,0.00040415936,0.0008340899,0.0004639632,0.0004886666,0.00023807857],"domain_scores_gemma":[0.99781203,0.0005336547,0.00064850267,0.0005748139,0.0002624524,0.00016857048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006639316,0.00012647,0.00030472956,0.000118970005,0.000043780576,0.000015165809,0.0004328306,0.00006597388,0.00003351362],"category_scores_gemma":[0.0012097631,0.00008404468,0.00008208455,0.0003802784,0.0004851514,0.00035319792,0.00016100283,0.00022890446,0.0000055458395],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001423961,0.00067962654,0.35823905,0.00004910665,0.000029777637,0.000027825023,0.00017915528,0.0000039230113,0.28025112,0.0006774356,0.00011850511,0.3596021],"study_design_scores_gemma":[0.009629198,0.007895108,0.6408876,0.0008081892,0.00016043468,0.0000750677,0.00006889901,0.13766493,0.19420613,0.00048797234,0.007100702,0.0010157615],"about_ca_topic_score_codex":0.00004616387,"about_ca_topic_score_gemma":0.000004309335,"teacher_disagreement_score":0.423232,"about_ca_system_score_codex":0.00003752879,"about_ca_system_score_gemma":0.00009367433,"threshold_uncertainty_score":0.34272426},"labels":[],"label_agreement":null},{"id":"W2361328064","doi":"","title":"Automatic Left Ventricle Segmentation Based on Improved Coupled Level Set Approach from MSCT Dataset","year":2011,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Segmentation; Endocardium; Artificial intelligence; Level set (data structures); Computer science; Ventricle; Pattern recognition (psychology); Similarity (geometry); Set (abstract data type); Computer vision; Image (mathematics); Medicine; Cardiology","score_opus":0.07943496885233027,"score_gpt":0.29560206154233437,"score_spread":0.21616709269000411,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2361328064","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00312809,0.0000051091233,0.9946603,0.00007391445,0.00011014112,0.0004835178,0.000312133,0.00050438294,0.00072237034],"genre_scores_gemma":[0.12932596,0.0000015400068,0.86580354,0.0024679676,0.00002183003,0.00005409231,0.0022504185,0.000012843803,0.000061821775],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840873,0.00011865969,0.00032284769,0.00047310823,0.0004444575,0.00023216577],"domain_scores_gemma":[0.9987778,0.000107187756,0.00013497706,0.0007832343,0.000041694475,0.00015507091],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003654833,0.00016706655,0.00015623716,0.00012560983,0.0000793984,0.00011349879,0.00079433614,0.000062637766,0.0014275633],"category_scores_gemma":[0.00007049503,0.00014327612,0.000040324565,0.00020885226,0.00004637651,0.00044718012,0.00014000609,0.00011354354,0.00014364251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001390437,0.0040108524,0.002416828,0.00020731837,0.00022040943,0.00007840949,0.0068554957,0.000078860736,0.19528688,0.0019582873,0.2532482,0.5354994],"study_design_scores_gemma":[0.0006397975,0.00012784901,0.002064344,0.000010482091,0.000009764385,0.0000016714602,0.00007382456,0.83841926,0.15814194,0.0003034302,0.000042848573,0.00016476754],"about_ca_topic_score_codex":0.00041952965,"about_ca_topic_score_gemma":0.000007141895,"teacher_disagreement_score":0.8383404,"about_ca_system_score_codex":0.000067061635,"about_ca_system_score_gemma":0.00007259271,"threshold_uncertainty_score":0.99948525},"labels":[],"label_agreement":null},{"id":"W2381541746","doi":"","title":"A New Strategy of Edge Detection","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Edge detection; Boundary (topology); Enhanced Data Rates for GSM Evolution; Dimension (graph theory); Image (mathematics); Track (disk drive); Algorithm; Topology (electrical circuits); Computer vision; Artificial intelligence; Image processing; Mathematical analysis; Mathematics; Pure mathematics","score_opus":0.018077139414752797,"score_gpt":0.2641859274908245,"score_spread":0.24610878807607167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2381541746","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005936511,0.00006524021,0.99798703,0.00010603048,0.000013004902,0.00037498522,0.0000012790827,0.00030543213,0.0005533579],"genre_scores_gemma":[0.048431743,0.000021256188,0.95084316,0.00020493778,0.000087617824,0.000110082845,0.00000400274,0.000006628489,0.00029060166],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99917567,0.000022931556,0.00026112012,0.00025952855,0.00015036989,0.0001303538],"domain_scores_gemma":[0.99928993,0.000036009158,0.00010244547,0.00037616733,0.000088941335,0.00010648142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006431347,0.00008767378,0.00011127041,0.0001130974,0.00009348918,0.000025598589,0.00061963033,0.00004634232,0.00002477198],"category_scores_gemma":[5.9495983e-7,0.00008942884,0.00005151669,0.00046178405,0.00005131583,0.00023738312,0.00012677922,0.000085731,0.00007245344],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.538759e-7,0.00004466546,0.000021354783,0.0000070063925,0.0000060652665,9.460937e-7,0.00017449314,0.0000062909407,0.09939513,0.0013873142,0.004454722,0.89450145],"study_design_scores_gemma":[0.00028787367,0.00005751757,0.0025485645,0.000008227883,0.000004595388,0.00009767951,0.00000581831,0.0016383063,0.9538886,0.0031545376,0.038144432,0.00016386303],"about_ca_topic_score_codex":0.000047448975,"about_ca_topic_score_gemma":0.0000026901748,"teacher_disagreement_score":0.8943376,"about_ca_system_score_codex":0.0000255439,"about_ca_system_score_gemma":0.00009852937,"threshold_uncertainty_score":0.3646802},"labels":[],"label_agreement":null},{"id":"W2384015905","doi":"","title":"A New Multiphase Image Segmentation Model by Piecewise Constant Level Set Method","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Piecewise; Computer science; Constant (computer programming); Segmentation; Noise (video); Algorithm; Convergence (economics); Image segmentation; Function (biology); Rate of convergence; Image (mathematics); Minification; Set (abstract data type); Mathematical optimization; Level set method; Level set (data structures); Artificial intelligence; Mathematics; Key (lock)","score_opus":0.04333500166671185,"score_gpt":0.3361468029685669,"score_spread":0.2928118013018551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2384015905","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000057209745,0.00008261414,0.99679595,0.00077791535,0.000015895885,0.0011861668,0.00010591342,0.00060699624,0.0003713586],"genre_scores_gemma":[0.0004896307,0.000049590602,0.99534965,0.002283717,0.00006326068,0.0005306304,0.00017089806,0.000023946623,0.0010386676],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99802995,0.00008543492,0.00048656375,0.00070982566,0.0003541796,0.00033404646],"domain_scores_gemma":[0.9985497,0.00010363217,0.00018298626,0.0006636411,0.00016536482,0.00033466905],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002244634,0.00025112982,0.00022210463,0.00015839981,0.00030107534,0.0001485475,0.0010334573,0.000087404675,0.000036104066],"category_scores_gemma":[0.0000026834846,0.00026268492,0.00009244419,0.0005042073,0.00010193894,0.0005556317,0.00030515855,0.00018462032,0.00014243771],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000299783,0.00015218499,0.000011719291,0.000011389994,0.000020592897,0.0000058514133,0.0009853473,0.00012788502,0.4009582,0.0011448883,0.22139981,0.3751791],"study_design_scores_gemma":[0.0017725412,0.00005824566,0.000028966748,0.000017922901,0.000022082562,0.0002872829,0.000033408563,0.29945746,0.6667177,0.0036428971,0.027340343,0.00062116445],"about_ca_topic_score_codex":0.00006306095,"about_ca_topic_score_gemma":0.000001789283,"teacher_disagreement_score":0.37455797,"about_ca_system_score_codex":0.00010486902,"about_ca_system_score_gemma":0.00025707242,"threshold_uncertainty_score":0.99998254},"labels":[],"label_agreement":null},{"id":"W2396856533","doi":"10.82308/1390","title":"Efficient and reliable methods for direct parameterized image registration","year":2008,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Parameterized complexity; Computer science; Hessian matrix; Image registration; Measure (data warehouse); Context (archaeology); Mathematical optimization; Algorithm; Range (aeronautics); Pixel; Reliability (semiconductor); Image (mathematics); Mathematics; Artificial intelligence; Data mining","score_opus":0.033609487326116835,"score_gpt":0.31520642758494954,"score_spread":0.2815969402588327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2396856533","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19530548,0.00029838004,0.7719419,0.00030198903,0.0008764557,0.002632562,0.0001424503,0.0022348685,0.026265914],"genre_scores_gemma":[0.116533145,0.00009221468,0.88178396,0.0006080639,0.000014591338,0.00024528455,0.000012859506,0.000034268283,0.000675623],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9971321,0.00044154158,0.0006122812,0.00089019304,0.0004358101,0.00048805],"domain_scores_gemma":[0.9976306,0.0007110015,0.0002901291,0.00078304834,0.00026843543,0.00031680445],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0022960883,0.00030027432,0.00039967278,0.00018800805,0.00089898787,0.00013562228,0.000648518,0.0001652931,0.00002649189],"category_scores_gemma":[0.001985231,0.0002915787,0.00013735697,0.00048873515,0.00019743569,0.0007723845,0.00027538138,0.0003125424,0.000023559562],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050810813,0.00017319071,0.0000063395,0.00008020956,0.00003683058,0.000035350007,0.000015782212,0.000019504974,0.56805634,0.030291596,0.000073371484,0.4011607],"study_design_scores_gemma":[0.0012167956,0.00032761903,0.0001521962,0.00006177485,0.000029266765,0.00012324369,0.000011630563,0.017221056,0.9482356,0.016080108,0.016004512,0.00053624593],"about_ca_topic_score_codex":0.00005996257,"about_ca_topic_score_gemma":0.000002967264,"teacher_disagreement_score":0.40062445,"about_ca_system_score_codex":0.00018420209,"about_ca_system_score_gemma":0.000035277815,"threshold_uncertainty_score":0.9999536},"labels":[],"label_agreement":null},{"id":"W2404064205","doi":"10.1016/j.patrec.2016.05.008","title":"On the computation of integrals over fixed-size rectangles of arbitrary dimension","year":2016,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Consejo Nacional de Ciencia y Tecnología","keywords":"Normalization (sociology); Computation; Dimension (graph theory); Mathematics; Metric (unit); Constant (computer programming); Algorithm; Computer science; Combinatorics","score_opus":0.02241432821737332,"score_gpt":0.2587853906969715,"score_spread":0.23637106247959816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2404064205","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39920616,0.000004435832,0.59567815,0.004667768,0.00013880018,0.00015512452,0.000011879734,0.000060024628,0.00007764213],"genre_scores_gemma":[0.97137094,0.000014698993,0.017901102,0.010637821,0.000025595658,0.000025749478,0.00000670944,0.000009201199,0.000008175253],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998701,0.0002315035,0.00035752435,0.00021880146,0.00036755315,0.00012363661],"domain_scores_gemma":[0.99808997,0.0012530114,0.00029282316,0.00023703113,0.000088753775,0.000038392016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034270113,0.00010825242,0.00014625776,0.000101759004,0.00003178324,0.000017723762,0.00028069073,0.000038706905,0.00021072703],"category_scores_gemma":[0.00026102792,0.00006166766,0.00007728631,0.00016596481,0.00012246081,0.0002725609,0.000061763436,0.000084862986,0.000039712293],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000145081685,0.00006908435,0.00052734185,0.000030425446,0.000021781989,0.0000041428457,0.0002571294,5.965502e-7,0.36889675,0.00012874391,0.0101989135,0.6198506],"study_design_scores_gemma":[0.0004749441,0.00015715629,0.00572414,0.00078880234,0.0000098433675,0.0000043504133,0.000020761316,0.00044731065,0.9842717,0.007943735,0.000011271305,0.00014598451],"about_ca_topic_score_codex":0.000024048617,"about_ca_topic_score_gemma":0.0000012342863,"teacher_disagreement_score":0.6197046,"about_ca_system_score_codex":0.000028528846,"about_ca_system_score_gemma":0.00001170158,"threshold_uncertainty_score":0.25147343},"labels":[],"label_agreement":null},{"id":"W2404602184","doi":"10.1007/978-3-319-24553-9_82","title":"Direct and Simultaneous Four-Chamber Volume Estimation by Multi-Output Regression","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CARE Canada; London Health Sciences Centre; St Joseph's Health Care; Western University","funders":"","keywords":"Discriminative model; Segmentation; Artificial intelligence; Computer science; Pattern recognition (psychology); Lasso (programming language); Mathematics","score_opus":0.029372672749977957,"score_gpt":0.2914831438326214,"score_spread":0.26211047108264346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2404602184","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000013248678,0.00078434177,0.9958638,0.0005307844,0.0008538702,0.000488124,0.000010573856,0.0004113104,0.0010439268],"genre_scores_gemma":[0.019536193,0.000072772455,0.9741934,0.0014395782,0.00013471227,0.000014152337,0.000021516937,0.000039799026,0.0045478935],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961056,0.000055014305,0.0005262566,0.0014581985,0.0013534839,0.0005014227],"domain_scores_gemma":[0.99727243,0.00057773414,0.0003572455,0.00097513327,0.00045297248,0.0003644852],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001087087,0.00051726185,0.00051262375,0.00049668556,0.00020196348,0.00053161295,0.0013207021,0.00037332487,0.000022653232],"category_scores_gemma":[0.00077536644,0.00043539956,0.00006160511,0.00035884598,0.0007942371,0.0008048128,0.0011739609,0.0006074802,0.000043331453],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000036426009,0.000024302431,0.000006741153,0.000029319956,0.0000050552917,0.00015281812,0.00045583292,0.003179759,0.00026388754,0.00010030931,0.0018788483,0.99389946],"study_design_scores_gemma":[0.00031598413,0.00016770334,0.0000068293316,0.00042365593,0.000007637895,0.00012611074,1.4707719e-7,0.9842598,0.0049556633,0.0070690503,0.0021189423,0.000548482],"about_ca_topic_score_codex":0.000033744538,"about_ca_topic_score_gemma":0.000014787124,"teacher_disagreement_score":0.993351,"about_ca_system_score_codex":0.00031645928,"about_ca_system_score_gemma":0.00029311058,"threshold_uncertainty_score":0.9998098},"labels":[],"label_agreement":null},{"id":"W2404982427","doi":"","title":"Probabilistic Anatomical Labeling of Brain Structures Using Statistical Probabilistic Anatomical Maps","year":2002,"lang":"en","type":"article","venue":"The Korean Journal of Nuclear Medicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Statistical parametric mapping; Probabilistic logic; Computer science; Artificial intelligence; Parametric statistics; Pattern recognition (psychology); Statistical model; Statistical analysis; Medicine; Mathematics; Statistics","score_opus":0.0367715052562237,"score_gpt":0.29828693915798044,"score_spread":0.26151543390175674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2404982427","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2991229,0.0008547276,0.67935014,0.018530972,0.0007685537,0.0007410346,0.000018610652,0.0001734856,0.00043959182],"genre_scores_gemma":[0.78712505,0.000039238636,0.21083307,0.0016418161,0.00031788295,8.4758335e-7,0.0000012859525,0.000030784046,0.000010040316],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969408,0.0004354861,0.0010882631,0.00023749584,0.0009923055,0.00030565055],"domain_scores_gemma":[0.9973161,0.0009784581,0.0006106383,0.00046682145,0.00032665895,0.00030129834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015472162,0.0001981085,0.00054133555,0.0002127305,0.00010399169,0.000043006596,0.0012096114,0.00008090068,0.0005005521],"category_scores_gemma":[0.0028982325,0.00011754983,0.00008339806,0.0004115054,0.0010602685,0.00023507129,0.00017178371,0.0005863278,0.000005714031],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044663646,0.0010996677,0.0004927199,0.0008719149,0.000555583,0.0012046827,0.016120726,0.00091667526,0.12089571,0.5243861,0.11739764,0.21561193],"study_design_scores_gemma":[0.006416345,0.004173764,0.0033565494,0.0020116484,0.00056304166,0.0039782287,0.0012908934,0.62017816,0.004200092,0.35117087,0.0017561008,0.00090433215],"about_ca_topic_score_codex":0.000019237405,"about_ca_topic_score_gemma":9.01406e-7,"teacher_disagreement_score":0.61926144,"about_ca_system_score_codex":0.000118218835,"about_ca_system_score_gemma":0.000066212626,"threshold_uncertainty_score":0.5480695},"labels":[],"label_agreement":null},{"id":"W2406502016","doi":"","title":"Adapted MRF Segmentation of Multiple Sclerosis Lesions Using Local Contextual Information.","year":2011,"lang":"en","type":"article","venue":"MIUA","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Voxel; Artificial intelligence; Pattern recognition (psychology); Markov random field; Segmentation; Computer science; Outlier; Bayesian probability; Multiple sclerosis; Image segmentation; Computer vision; Medicine","score_opus":0.11183892526492925,"score_gpt":0.27498513046577616,"score_spread":0.1631462052008469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2406502016","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016927473,0.000015635422,0.9820841,0.000026145945,0.00008901134,0.00018326509,0.0000067581555,0.00014304183,0.0005245874],"genre_scores_gemma":[0.5924444,0.0000046811633,0.4072589,0.0002574324,0.000005357032,0.000008952966,0.000007696114,0.0000026427026,0.0000099265535],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99909157,0.000052997944,0.00034292316,0.00010957363,0.00027506112,0.00012788153],"domain_scores_gemma":[0.99931973,0.000064050175,0.00015799877,0.00022913571,0.00015180613,0.000077287135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017952711,0.000078117206,0.00010738033,0.00012582722,0.00006756956,0.000027866197,0.0003149473,0.000046615107,0.0001424019],"category_scores_gemma":[0.00010021484,0.00007389823,0.000037305927,0.0002482275,0.00009904915,0.0013007766,0.00011425879,0.00006456002,0.000040159124],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025061,0.00018498984,0.0010538526,0.00004099402,0.000031637817,0.0000027654453,0.011624513,0.00006511174,0.09100685,0.0031537556,0.002550985,0.8902595],"study_design_scores_gemma":[0.0007050904,0.00010890579,0.0045519504,0.00007024833,0.000010051879,0.0000045574534,0.0011567498,0.081091285,0.91183543,0.0002157218,0.00010164737,0.00014838485],"about_ca_topic_score_codex":0.00037277027,"about_ca_topic_score_gemma":0.00001012864,"teacher_disagreement_score":0.8901111,"about_ca_system_score_codex":0.00004382464,"about_ca_system_score_gemma":0.000053076536,"threshold_uncertainty_score":0.3013482},"labels":[],"label_agreement":null},{"id":"W2415818926","doi":"10.3233/978-1-61499-375-9-436","title":"Real-Time Simulation of Transesophageal Echocardiography","year":2014,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Haptic technology; Context (archaeology); Ultrasound; Computer vision; Computer graphics (images); Artificial intelligence; Human–computer interaction; Radiology; Medicine","score_opus":0.024005228147122103,"score_gpt":0.35336736110096895,"score_spread":0.32936213295384686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2415818926","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01760264,0.00044065126,0.9798891,0.0007494778,0.00008209157,0.0002643398,0.0000012169038,0.00031053732,0.0006599769],"genre_scores_gemma":[0.6867378,0.002816362,0.31002945,0.00038183262,0.000006122603,0.000019262858,0.0000010656531,0.000002817021,0.0000053289555],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998903,0.000053415635,0.0006595397,0.00008195305,0.00013014121,0.00017199368],"domain_scores_gemma":[0.99921536,0.0002189265,0.00022139496,0.00024133544,0.00007142997,0.00003152126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008518891,0.000079646576,0.00028809923,0.00064611214,0.000076031414,0.000005320635,0.00022707574,0.00008877427,0.0000010484467],"category_scores_gemma":[0.00021649434,0.00007039437,0.000021768878,0.0008281787,0.0003607594,0.00026801412,0.00011234401,0.00013531045,0.000001881229],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010744461,0.00007079198,0.0074442863,0.0020248503,0.00007478105,0.0000017299502,0.015624854,0.0011315353,0.000071629176,0.06924308,0.0006651438,0.9036366],"study_design_scores_gemma":[0.0022030387,0.0025939906,0.010984841,0.000915946,0.000016548414,0.000018925926,0.004252186,0.83390266,0.0075179106,0.13565381,0.0013964226,0.00054370105],"about_ca_topic_score_codex":0.0000044733565,"about_ca_topic_score_gemma":5.8586625e-7,"teacher_disagreement_score":0.90309286,"about_ca_system_score_codex":0.000022615079,"about_ca_system_score_gemma":0.000023138111,"threshold_uncertainty_score":0.28705993},"labels":[],"label_agreement":null},{"id":"W2426986800","doi":"10.1109/isbi.2016.7493197","title":"Local discriminative characterization of MRI for Alzheimer's disease","year":2016,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Discriminative model; Artificial intelligence; Pattern recognition (psychology); Random forest; Texture (cosmology); Computer science; Feature (linguistics); Image texture; Feature extraction; Contextual image classification; Brain disease; Disease; Image (mathematics); Image processing; Medicine; Pathology","score_opus":0.024449591312603762,"score_gpt":0.29693723797624516,"score_spread":0.2724876466636414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2426986800","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025741835,0.0000069633033,0.9968616,0.0022562086,0.000060378643,0.0002498235,0.000016375036,0.00010964812,0.00018162616],"genre_scores_gemma":[0.6930071,0.000014200641,0.30549732,0.00068624015,0.000031106512,0.00012951318,0.000021897742,0.000008517095,0.00060411694],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99941707,0.000026899039,0.00014438342,0.00015055206,0.00017580367,0.00008527689],"domain_scores_gemma":[0.99941283,0.00006544834,0.00006693864,0.00018129675,0.00015676203,0.00011674397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013274785,0.000051161765,0.000069617505,0.0000417649,0.000015002846,0.000012104911,0.0002507944,0.000014912694,0.000042720112],"category_scores_gemma":[0.00006505968,0.000030428768,0.000027530516,0.00007745567,0.00008302897,0.000485121,0.00006960636,0.00001036574,0.0000070583196],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019952464,0.0000815454,0.0000674698,0.00001246943,0.000015251554,0.0000013200221,0.00022510804,1.4932365e-7,0.07124892,0.054496944,0.0024157094,0.87141514],"study_design_scores_gemma":[0.00039953933,0.000115398536,0.0028301326,0.00003770434,0.000015914526,3.3510196e-7,0.000018243269,0.006845583,0.97936285,0.009748987,0.00051951373,0.00010581687],"about_ca_topic_score_codex":0.0000019956317,"about_ca_topic_score_gemma":2.97633e-7,"teacher_disagreement_score":0.9081139,"about_ca_system_score_codex":0.000015710442,"about_ca_system_score_gemma":0.00005217585,"threshold_uncertainty_score":0.12408491},"labels":[],"label_agreement":null},{"id":"W2431976432","doi":"10.1049/iet-ipr.2016.0271","title":"Non‐local‐based spatially constrained hierarchical fuzzy <i>C</i> ‐means method for brain magnetic resonance imaging segmentation","year":2016,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Science Foundation of Jiangsu Province; National Research Foundation of Korea","keywords":"Segmentation; Magnetic resonance imaging; Fuzzy logic; Image segmentation; Computer science; Artificial intelligence; Computer vision; Nuclear magnetic resonance; Physics; Pattern recognition (psychology); Radiology; Medicine","score_opus":0.011690079388820848,"score_gpt":0.3085751235614408,"score_spread":0.29688504417261996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2431976432","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000050996525,0.00027302728,0.98543525,0.012166798,0.00012266447,0.0007474777,0.000024503734,0.0005840623,0.00059523614],"genre_scores_gemma":[0.020636674,0.0000059414797,0.972013,0.006692541,0.00010360117,0.00027501537,0.000013141193,0.0000404291,0.00021964572],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9971962,0.00019913528,0.0006067486,0.00081605546,0.00058814953,0.0005937138],"domain_scores_gemma":[0.99793077,0.0007822993,0.00025378863,0.00043674838,0.00036614592,0.00023023075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001330575,0.00029651888,0.00029723652,0.00020195966,0.00026692782,0.00043341907,0.00080317614,0.00007893286,0.00004802138],"category_scores_gemma":[0.00048594715,0.00023599085,0.00010785398,0.00044369078,0.00043618362,0.0014934003,0.0001366501,0.00017245053,0.000016267524],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022545642,0.00004374877,0.000049069688,0.00007711491,0.0000016829105,0.0000150892,0.00021377116,0.0000026097753,0.26976702,0.00014254916,0.0013668104,0.728298],"study_design_scores_gemma":[0.0030023118,0.00021294194,0.00022502833,0.0005434302,0.000021135735,0.000036619607,0.00006302631,0.38396356,0.60123,0.009168848,0.0009993427,0.00053371204],"about_ca_topic_score_codex":0.000015549169,"about_ca_topic_score_gemma":0.0000051961024,"teacher_disagreement_score":0.7277643,"about_ca_system_score_codex":0.0001278656,"about_ca_system_score_gemma":0.0004747721,"threshold_uncertainty_score":0.9623428},"labels":[],"label_agreement":null},{"id":"W2432368163","doi":"10.1007/978-3-642-59758-9_222","title":"Digital Enhancement of Portal Images by Anisotropic Diffusion","year":2000,"lang":"en","type":"book-chapter","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Northeast Cancer Centre","funders":"","keywords":"Anisotropic diffusion; Computer vision; Computer science; Homogeneous; Artificial intelligence; Noise (video); Image quality; Image enhancement; Digital image processing; Contrast (vision); Digital image; Image processing; Image (mathematics); Mathematics","score_opus":0.007778929662698926,"score_gpt":0.2352220871041581,"score_spread":0.22744315744145918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2432368163","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000005717705,0.00010626431,0.4836671,0.000054845692,0.000048689773,0.00013537738,0.00002141825,0.00012341648,0.51583713],"genre_scores_gemma":[0.0011389749,0.0007719768,0.05140367,0.00038304357,0.000040124378,0.000009772452,0.00012975054,0.000024327679,0.9460984],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983093,0.000006344349,0.00047249583,0.00041626257,0.000631746,0.00016381251],"domain_scores_gemma":[0.9990582,0.000030876483,0.00022633084,0.00051244313,0.000061304934,0.00011084339],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00004725015,0.00023917464,0.00029100996,0.000101774946,0.000028651299,0.00010467495,0.0005456089,0.0001370923,0.0063567976],"category_scores_gemma":[0.0000068096465,0.00020421139,0.000108952074,0.000028000128,0.00012623116,0.00040596086,0.0002535448,0.0001486261,0.00015948043],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000039302113,0.00010589136,0.0000035252772,0.00003697179,0.0000360681,0.00004864218,0.000040185176,2.3299584e-8,0.008872101,0.033001453,0.13587597,0.82197523],"study_design_scores_gemma":[0.0008476398,0.0010822796,0.000017253014,0.00046699113,0.000043075397,0.00004510977,0.000009641937,0.00018347052,0.6814842,0.046120647,0.26838103,0.001318612],"about_ca_topic_score_codex":0.0000064708906,"about_ca_topic_score_gemma":3.6944706e-7,"teacher_disagreement_score":0.8206566,"about_ca_system_score_codex":0.000031513868,"about_ca_system_score_gemma":0.00004833574,"threshold_uncertainty_score":0.99455154},"labels":[],"label_agreement":null},{"id":"W2460464989","doi":"10.1007/978-3-319-28712-6_16","title":"Detecting Myocardial Infarction Using Medial Surfaces","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Context (archaeology); Medial axis; Endocardium; Myocardial infarction; RADIUS; Surface (topology); Geometry; Artificial intelligence; Computer science; Anatomy; Mathematics; Medicine; Cardiology; Geology","score_opus":0.023704478010662052,"score_gpt":0.281636014942724,"score_spread":0.25793153693206194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2460464989","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002141274,0.00012653867,0.99392045,0.00035621595,0.003179995,0.00035685615,0.0000031026634,0.00039062544,0.0014520857],"genre_scores_gemma":[0.049729295,0.00005010933,0.94744134,0.0013085972,0.0012972334,0.0000082594925,0.0000016984608,0.000041399377,0.00012205314],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959187,0.00007200146,0.0005950002,0.0013265715,0.0014548722,0.0006328543],"domain_scores_gemma":[0.9975329,0.00062802335,0.00039034066,0.00095390424,0.00027311585,0.00022172373],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013776986,0.00045040838,0.00046035182,0.0008578686,0.00029411318,0.00049894146,0.0023036483,0.0003555784,0.000050217324],"category_scores_gemma":[0.0003270059,0.00037349862,0.00013014712,0.0004905594,0.00083120115,0.0011590443,0.0012494627,0.00068286434,0.000036742043],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033682159,0.0000072770226,0.000044407865,0.00001564783,0.000009948295,0.000034808927,0.00034748885,0.0013581335,0.008539478,0.0009850826,0.000009628848,0.9886447],"study_design_scores_gemma":[0.0009402696,0.00041571827,0.00020857011,0.0015955196,0.00003342817,0.00029207495,4.972268e-7,0.6200235,0.14106618,0.23243628,0.00079205644,0.0021958943],"about_ca_topic_score_codex":0.000026479278,"about_ca_topic_score_gemma":0.00002166923,"teacher_disagreement_score":0.9864488,"about_ca_system_score_codex":0.00052249886,"about_ca_system_score_gemma":0.00059947587,"threshold_uncertainty_score":0.9998717},"labels":[],"label_agreement":null},{"id":"W2460596903","doi":"","title":"Un modèle déformable intégrant des relations spatiales pour la segmentation de structures cérébrales","year":2005,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"","keywords":"Humanities; Physics; Philosophy","score_opus":0.024822221595514303,"score_gpt":0.2587744543087526,"score_spread":0.2339522327132383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2460596903","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035340093,0.0019285511,0.92851126,0.015078637,0.00024880221,0.0008487298,0.000093183015,0.0006915819,0.017259143],"genre_scores_gemma":[0.21555875,0.0021444086,0.7686196,0.00030636694,0.00007443092,0.00018675024,0.0004776983,0.00006931246,0.012562692],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.98487484,0.01068647,0.001308972,0.001271081,0.0009974202,0.0008612013],"domain_scores_gemma":[0.9905947,0.0028071932,0.0011274586,0.0022967826,0.0026169813,0.0005569038],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.007742794,0.0007000776,0.0005979278,0.00042647173,0.0011146944,0.001482848,0.0026398234,0.00061777094,0.001026502],"category_scores_gemma":[0.0024870993,0.0007727714,0.00035553696,0.00080668443,0.001199554,0.0014084944,0.0019358686,0.0012458997,0.00017165592],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015769445,0.0008251786,0.0027401277,0.00028133622,0.00017555825,0.00002151045,0.032363042,0.0018315567,0.024333542,0.29604974,0.0026801005,0.63868254],"study_design_scores_gemma":[0.0012536285,0.0000025755305,0.021251537,0.0028610663,0.00017338063,0.00013720265,0.0004435325,0.25400874,0.5943451,0.11588648,0.008472502,0.0011642314],"about_ca_topic_score_codex":0.0035683115,"about_ca_topic_score_gemma":0.0023267977,"teacher_disagreement_score":0.6375183,"about_ca_system_score_codex":0.00077576214,"about_ca_system_score_gemma":0.001190083,"threshold_uncertainty_score":0.9998867},"labels":[],"label_agreement":null},{"id":"W2464080977","doi":"10.1016/j.cviu.2016.01.001","title":"Inference and Learning of Graphical Models: Theory and Applications in Computer Vision and Image Analysis","year":2016,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Artificial intelligence; Inference; Computer science; Computer vision; Active appearance model; Graphical model; Object (grammar); Tracking (education); Graph; Eye tracking; Video tracking; Process (computing); Pattern recognition (psychology); Image (mathematics); Theoretical computer science","score_opus":0.0243846818271599,"score_gpt":0.31022235628894307,"score_spread":0.28583767446178315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2464080977","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021657191,0.00018801924,0.97748786,0.00029846872,0.000019365441,0.00020047724,0.0000021209814,0.00009123003,0.00005524895],"genre_scores_gemma":[0.7326132,0.00060708774,0.2666253,0.000117419746,0.000015098042,0.0000054130114,0.0000023420105,0.0000071581894,0.000006978075],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829495,0.00031728952,0.0003685624,0.0005808614,0.00023424685,0.00020411938],"domain_scores_gemma":[0.9982623,0.0011159843,0.00013705679,0.00024393172,0.00006478931,0.0001759661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009782199,0.00019035401,0.0003445552,0.000624821,0.00019120921,0.0003087107,0.00019947453,0.00007859164,0.0000072104444],"category_scores_gemma":[0.000040974486,0.00013235524,0.000043100805,0.0005587751,0.00052976527,0.0012055063,0.0006501319,0.00018540196,3.779955e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060698923,0.00015005177,0.0052518733,0.00018608214,0.00012216195,0.000024542234,0.0029122378,0.000034242217,0.023796856,0.23999363,0.00012306841,0.7273446],"study_design_scores_gemma":[0.0012051564,0.00044572543,0.009836474,0.0003481026,0.000058911646,0.000021491085,0.0002800738,0.861344,0.0010512688,0.12503333,0.000018567636,0.00035691215],"about_ca_topic_score_codex":0.000009078307,"about_ca_topic_score_gemma":0.0000029972016,"teacher_disagreement_score":0.86130977,"about_ca_system_score_codex":0.000036222085,"about_ca_system_score_gemma":0.000013705409,"threshold_uncertainty_score":0.539729},"labels":[],"label_agreement":null},{"id":"W2465779640","doi":"10.3233/978-1-58603-888-5-96","title":"Constrained Intensity-based Image Registration: Application to Aligning Human Back Images","year":2008,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Image registration; Computer science; Computer vision; Artificial intelligence; Image (mathematics); Intensity (physics); Optics","score_opus":0.050430034491730516,"score_gpt":0.3756567660250492,"score_spread":0.3252267315333187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2465779640","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003638181,0.00017798306,0.9833573,0.011129203,0.0000611804,0.0005349223,0.0000018189132,0.0003715845,0.0007278396],"genre_scores_gemma":[0.18769087,0.0002978202,0.8059734,0.005880348,0.000010891997,0.00010597397,0.000005859837,0.0000041119047,0.000030726576],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99868023,0.00002827058,0.0007374317,0.00015604508,0.00014947276,0.00024852256],"domain_scores_gemma":[0.99906397,0.0000718649,0.00026090452,0.00034746007,0.00018686953,0.00006890662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005732257,0.00011968702,0.0002717694,0.0004754185,0.00034353248,0.000018058252,0.0003117614,0.00008660844,0.0000020347138],"category_scores_gemma":[0.00027680237,0.000114071634,0.00001393528,0.0007617773,0.00080605975,0.0003592898,0.0002181253,0.00022108466,0.000015670452],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006521767,0.000546509,0.024281055,0.0070043327,0.00018178637,0.00013818723,0.073387675,0.00011531285,0.008436413,0.30392036,0.15217398,0.42974916],"study_design_scores_gemma":[0.012927488,0.010272644,0.018968066,0.005398184,0.000044875687,0.0021714633,0.07950852,0.087785,0.5973926,0.1441766,0.036270745,0.0050837835],"about_ca_topic_score_codex":0.000012135279,"about_ca_topic_score_gemma":0.0000063699763,"teacher_disagreement_score":0.58895624,"about_ca_system_score_codex":0.0000861004,"about_ca_system_score_gemma":0.000088487424,"threshold_uncertainty_score":0.46517062},"labels":[],"label_agreement":null},{"id":"W2466879219","doi":"10.82308/28321","title":"Shape analysis of cortical folds","year":2011,"lang":"en","type":"article","venue":"Open MIND","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Geometry; Surface (topology); Shape analysis (program analysis); Fold (higher-order function); Mathematics; Scalar field; Geology; Artificial intelligence; Computer science","score_opus":0.10344943949442528,"score_gpt":0.36010372810339136,"score_spread":0.2566542886089661,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2466879219","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04553234,0.0000060882435,0.9227263,0.00004165217,0.000030065754,0.00011436187,0.0000021410283,0.000004610277,0.03154245],"genre_scores_gemma":[0.42252353,0.00000144753,0.5771547,0.00008512974,0.000002609422,0.0000044069666,0.000001961499,0.0000013999818,0.00022484147],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993128,0.000045617282,0.00019818274,0.00018320503,0.00017078372,0.00008938268],"domain_scores_gemma":[0.999424,0.00004163852,0.00006845929,0.00034350736,0.000048138485,0.000074295844],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00031307724,0.000043823966,0.00014650621,0.000110623565,0.000019531393,0.000048699683,0.0010612189,0.0000273501,0.009068861],"category_scores_gemma":[0.00005494886,0.00003742288,0.000047610443,0.00061379536,0.000053391726,0.00030909426,0.000369191,0.000044329725,0.00009158744],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005590935,0.00015212683,0.0025944994,0.000001544332,0.0001979553,0.000017005263,0.0021033075,3.127752e-7,0.0054139774,0.0010685943,0.00044856145,0.9879965],"study_design_scores_gemma":[0.00036614938,0.0002732621,0.06852274,0.000019157627,0.0004295286,0.0000034065204,0.0001363167,0.076923594,0.851332,0.00070359604,0.0010287075,0.0002615159],"about_ca_topic_score_codex":0.000045508197,"about_ca_topic_score_gemma":0.000009721462,"teacher_disagreement_score":0.98773503,"about_ca_system_score_codex":0.0000073509705,"about_ca_system_score_gemma":0.000030851006,"threshold_uncertainty_score":0.99183697},"labels":[],"label_agreement":null},{"id":"W2486361","doi":"10.1007/978-3-319-10404-1_99","title":"3D Prostate TRUS Segmentation Using Globally Optimized Volume-Preserving Prior","year":2014,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Segmentation; 3D ultrasound; Volume (thermodynamics); Artificial intelligence; Prostate biopsy; Image segmentation; Relaxation (psychology); Prostate; Mathematical optimization; Ultrasound; Algorithm; Computer vision; Mathematics; Medicine; Radiology","score_opus":0.013810641207331158,"score_gpt":0.2869940784312397,"score_spread":0.27318343722390853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2486361","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014653844,0.00003516879,0.9830642,0.00066637085,0.00070437003,0.000483012,8.153413e-7,0.00035569607,0.000036555615],"genre_scores_gemma":[0.17923316,0.0000042993724,0.8186773,0.00196006,0.000097069824,0.000014789724,0.0000015699089,0.000009472762,0.0000022606018],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966627,0.00022416958,0.0004716209,0.000941927,0.0010781705,0.00062145875],"domain_scores_gemma":[0.99825686,0.00025852828,0.00021475945,0.00084731856,0.00022384278,0.00019865911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019034815,0.00024154285,0.000265824,0.00037183837,0.000312515,0.0009115274,0.0025778406,0.00007991071,0.000024746954],"category_scores_gemma":[0.0005248142,0.00022050155,0.000050377188,0.001964505,0.0003488601,0.0017961767,0.0011357038,0.00025423375,0.000013773666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000066022267,0.000041835086,0.0009030564,0.000020970852,0.0000029565556,0.000009798101,0.001028025,0.07409391,0.018273462,0.000053052892,0.00001820479,0.9055481],"study_design_scores_gemma":[0.00051595597,0.00010275587,0.00085341773,0.00007868726,0.0000030401259,0.000023579694,8.4850666e-7,0.9300041,0.064847685,0.0033098964,0.00001654615,0.00024348624],"about_ca_topic_score_codex":0.000105141684,"about_ca_topic_score_gemma":0.000011944738,"teacher_disagreement_score":0.9053046,"about_ca_system_score_codex":0.00027741588,"about_ca_system_score_gemma":0.00025698688,"threshold_uncertainty_score":0.8991792},"labels":[],"label_agreement":null},{"id":"W2490911995","doi":"10.1007/978-3-319-31808-0_3","title":"Atlas-Guided Transcranial Doppler Ultrasound Examination with a Neuro-Surgical Navigation System: Case Study","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"NeuroRx Research (Canada); Montreal Neurological Institute and Hospital","funders":"","keywords":"Transcranial Doppler; Computer science; Neurovascular bundle; Computer vision; Landmark; Artificial intelligence; Atlas (anatomy); Brain atlas; Ultrasound; Population; Human skull; Skull; Radiology; Medicine; Anatomy","score_opus":0.018891963140317332,"score_gpt":0.2747876160705909,"score_spread":0.25589565293027355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2490911995","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055696345,0.000031393633,0.99031717,0.00015675167,0.00072333403,0.0015574102,0.0000071521044,0.00051369454,0.0011234861],"genre_scores_gemma":[0.7466597,0.0000038456024,0.25251564,0.00016954361,0.00039718722,0.000042385003,0.000006721667,0.000043444146,0.00016153029],"study_design_codex":"design_other","study_design_gemma":"case_report","domain_scores_codex":[0.9946748,0.00022672035,0.0007929216,0.0018093404,0.0019393563,0.0005568814],"domain_scores_gemma":[0.9964869,0.0011708142,0.0004029919,0.0012196652,0.00044842955,0.00027122008],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016309726,0.00059926766,0.0005677143,0.00066867325,0.00032978057,0.00065254327,0.001898409,0.00026836814,0.00002824044],"category_scores_gemma":[0.00007332977,0.0004289698,0.000094968156,0.00068602344,0.0007210054,0.0010980833,0.00031417716,0.0006641214,0.00002707162],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038434893,0.00033198786,0.00018657699,0.00025187107,0.00005774461,0.061541863,0.0069421413,0.0011065996,0.0017135506,0.0072574685,0.000048749906,0.920523],"study_design_scores_gemma":[0.016956188,0.011650655,0.00087057945,0.008999481,0.00038622651,0.46282148,0.00006115992,0.43120083,0.0364855,0.021245651,0.0005625277,0.008759724],"about_ca_topic_score_codex":0.000064859785,"about_ca_topic_score_gemma":0.00003904864,"teacher_disagreement_score":0.9117633,"about_ca_system_score_codex":0.00047944707,"about_ca_system_score_gemma":0.0003719676,"threshold_uncertainty_score":0.99981624},"labels":[],"label_agreement":null},{"id":"W2495563369","doi":"10.1142/9789814343008_0007","title":"NONPARAMETRIC SAMPLE-BASED METHODS FOR IMAGE UNDERSTANDING","year":2011,"lang":"en","type":"book-chapter","venue":"Series in computer vision","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Nonparametric statistics; Sample (material); Image (mathematics); Computer science; Artificial intelligence; Statistics; Mathematics; Pattern recognition (psychology); Chemistry; Chromatography","score_opus":0.08641229262845973,"score_gpt":0.3803471155718827,"score_spread":0.293934822943423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2495563369","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.6756803e-7,0.00010417034,0.98681885,0.00021797433,0.0010999149,0.00088658085,0.00001761571,0.00045189034,0.010402829],"genre_scores_gemma":[0.000027614074,0.00008371059,0.99579304,0.0007179408,0.00014331877,0.000047040132,0.000053242577,0.00006285728,0.0030712467],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99745595,0.00012895149,0.000721079,0.00091616786,0.00038376523,0.00039410134],"domain_scores_gemma":[0.99668914,0.001662646,0.00035248158,0.0009871108,0.00015631152,0.00015231947],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012072648,0.00045184296,0.00060098484,0.0010234739,0.00012446086,0.00030130838,0.001359641,0.00037499663,0.0001587633],"category_scores_gemma":[0.00014706967,0.00044342654,0.00022546416,0.00025444603,0.00023607578,0.0007662279,0.00064481515,0.00042548546,0.000019298779],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043612323,0.000039983963,0.0000012469616,0.00018354811,0.000021750622,0.000029660021,0.00017395795,0.0000070345727,0.000095015836,0.26046732,0.0033822013,0.7355547],"study_design_scores_gemma":[0.00088580046,0.00146174,0.000006772024,0.0007935597,0.000024813051,0.000020986081,0.0000043831737,0.08935669,0.006710771,0.8750213,0.024779886,0.0009333441],"about_ca_topic_score_codex":0.000013611414,"about_ca_topic_score_gemma":0.0000044347207,"teacher_disagreement_score":0.73462135,"about_ca_system_score_codex":0.00042801062,"about_ca_system_score_gemma":0.00014627283,"threshold_uncertainty_score":0.99980175},"labels":[],"label_agreement":null},{"id":"W2498622815","doi":"10.1090/cbms/096/07","title":"The variational bi-complex","year":2002,"lang":"en","type":"book-chapter","venue":"Regional conference series in mathematics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":102,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Mathematics; Computer science; Pure mathematics","score_opus":0.09682261704876434,"score_gpt":0.291586131605099,"score_spread":0.19476351455633467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2498622815","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.8582168e-7,0.00009678045,0.6199072,0.005000709,0.00013225827,0.0003113377,0.000008193803,0.00016393924,0.3743794],"genre_scores_gemma":[0.00012499765,0.0020853863,0.45373878,0.00067810493,0.00016303256,0.00007657254,0.000043693184,0.000045015193,0.54304445],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976272,0.00004000774,0.00068874843,0.0003852398,0.0009836928,0.00027513062],"domain_scores_gemma":[0.99777097,0.00059865206,0.0004355974,0.0008163477,0.0002844457,0.000094016956],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00044353603,0.000325754,0.0003553062,0.00014530614,0.00018980973,0.00032190318,0.0016938851,0.0002294973,0.0009575545],"category_scores_gemma":[0.00015297053,0.0002477706,0.00010746598,0.00008499919,0.0004729945,0.00035899473,0.00035181906,0.0004694423,0.0002089253],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016006895,0.000019995556,2.1940693e-7,0.000046010722,0.000019164352,0.000014755719,0.000357093,0.0000011746791,0.000014428455,0.97308993,0.015995914,0.01043971],"study_design_scores_gemma":[0.00010019843,0.000042615542,0.000011441613,0.0002508809,0.000007352099,0.000068825524,0.00002646633,0.00870345,0.000053002732,0.8576505,0.13279682,0.00028840743],"about_ca_topic_score_codex":0.0000022454806,"about_ca_topic_score_gemma":0.000011327175,"teacher_disagreement_score":0.16866502,"about_ca_system_score_codex":0.00011704024,"about_ca_system_score_gemma":0.00021391323,"threshold_uncertainty_score":0.99999744},"labels":[],"label_agreement":null},{"id":"W2501195497","doi":"10.3389/fnins.2016.00325","title":"Manual-Protocol Inspired Technique for Improving Automated MR Image Segmentation during Label Fusion","year":2016,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children; University of Toronto; SickKids Foundation; Centre for Addiction and Mental Health; McGill University; Douglas Mental Health University Institute","funders":"National Institute of Mental Health; National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; IXICO; Servier; Eisai; Bristol-Myers Squibb; Government of Ontario; Eli Lilly and Company; Compute Canada; Weston Brain Institute; Pfizer; Biogen; BioClinica; Alzheimer's Association; Amorfix Life Sciences; Alzheimer's Society; Synarc; F. Hoffmann-La Roche; University of Toronto; Brain and Behavior Research Foundation; Medpace; Novartis Pharmaceuticals Corporation; AstraZeneca; Bayer HealthCare; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Michael J. Fox Foundation for Parkinson's Research","keywords":"Computer science; Artificial intelligence; Segmentation; Pattern recognition (psychology); Markov random field; Robustness (evolution); Markov chain; Inference; Probabilistic logic; Image segmentation; Machine learning","score_opus":0.014674293839928697,"score_gpt":0.31075282528392834,"score_spread":0.29607853144399965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2501195497","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024101748,0.000002150338,0.970113,0.00033767943,0.0005373486,0.025432734,0.000007167184,0.0011118997,0.0000478901],"genre_scores_gemma":[0.011989119,0.0000045312017,0.9318282,0.0004751765,0.000026521651,0.055450093,0.0000011210439,0.000020614345,0.00020465198],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976529,0.00012179008,0.00045903947,0.00079730875,0.000483719,0.00048523393],"domain_scores_gemma":[0.9989768,0.00005741908,0.00024063869,0.0005009384,0.000091357804,0.00013282987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006812267,0.00019420525,0.00018466263,0.00041995477,0.00021622283,0.00017541042,0.0011818113,0.00008168171,0.000004486216],"category_scores_gemma":[0.00049490656,0.00015262344,0.00004164589,0.0007927684,0.00022824845,0.0020224669,0.00037026143,0.00010971394,0.0000031522925],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021155807,0.0000572347,0.00044592083,0.00006444828,3.9411628e-7,0.00001426444,0.000069386064,9.0991e-7,0.9591067,0.00004436301,0.0013897845,0.038785435],"study_design_scores_gemma":[0.0012846446,0.00020394441,0.00217988,0.00008461666,0.0000015268473,0.000010379625,0.000014074076,0.03381709,0.96146226,0.00061758567,0.000105565196,0.0002184577],"about_ca_topic_score_codex":0.000008886907,"about_ca_topic_score_gemma":9.1034633e-7,"teacher_disagreement_score":0.03856698,"about_ca_system_score_codex":0.00023102036,"about_ca_system_score_gemma":0.00009438296,"threshold_uncertainty_score":0.6223803},"labels":[],"label_agreement":null},{"id":"W2503958973","doi":"10.1109/ivmspw.2016.7528181","title":"Total variation constrained graph regularized NMF for medical image registration","year":2016,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Non-negative matrix factorization; Matrix decomposition; Computer science; Graph; Artificial intelligence; Adjacency matrix; Pattern recognition (psychology); Variation (astronomy); Manifold (fluid mechanics); Regularization (linguistics); Theoretical computer science","score_opus":0.010529255093430563,"score_gpt":0.2796983639659691,"score_spread":0.26916910887253853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2503958973","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002228587,0.0000027311808,0.9836308,0.012900202,0.00018565076,0.00040167975,0.0000041266203,0.0005537161,0.0020982816],"genre_scores_gemma":[0.04034898,0.000008074106,0.9565271,0.0010007237,0.000097308555,0.00011424138,0.000010927021,0.000007749096,0.0018848652],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99845374,0.00007880594,0.00036494195,0.00033168646,0.0005731741,0.00019764299],"domain_scores_gemma":[0.99882275,0.0003504447,0.00012497761,0.00035124563,0.00017616531,0.00017441552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009499392,0.000100674,0.00012610228,0.00008596994,0.000069860595,0.000101114056,0.0004019604,0.00011007889,0.0005177611],"category_scores_gemma":[0.0012381221,0.00006598267,0.000070109854,0.00016633165,0.00015245227,0.0007812223,0.00006248973,0.000049417184,0.0000227526],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021174823,0.000079309655,0.000009039608,0.00001710468,0.000020350093,0.000009190274,0.00011596525,4.378446e-8,0.38930532,0.3386617,0.018013239,0.25374755],"study_design_scores_gemma":[0.008362511,0.000692489,0.0019007374,0.00018462696,0.000029318957,0.000164496,0.00004063892,0.038565405,0.7169949,0.23132768,0.00095149846,0.0007857247],"about_ca_topic_score_codex":0.000014498097,"about_ca_topic_score_gemma":0.0000042783777,"teacher_disagreement_score":0.32768953,"about_ca_system_score_codex":0.000036596564,"about_ca_system_score_gemma":0.00016864765,"threshold_uncertainty_score":0.5669121},"labels":[],"label_agreement":null},{"id":"W2505927621","doi":"10.5539/mas.v10n11p30","title":"Hybrid Methodology for Image Segmentation Based on Active Contour Module and Alpha-Shape Theory","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Baghdad","keywords":"Active contour model; Artificial intelligence; Computer science; Computer vision; Segmentation; Image segmentation; Level set (data structures); Contour line; Process (computing); Computation; Image processing; Image (mathematics); Pattern recognition (psychology); Algorithm; Physics","score_opus":0.04004023012615147,"score_gpt":0.32551141267812767,"score_spread":0.2854711825519762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2505927621","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004164728,0.0000056364006,0.9932668,0.00074588956,0.00009250258,0.00067788514,0.000011075586,0.00020629691,0.00082915695],"genre_scores_gemma":[0.46740958,0.0000024975545,0.53072923,0.0015819483,0.000017143177,0.00019991916,0.0000012563719,0.000007033309,0.000051396597],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99803716,0.00013898194,0.00020415292,0.0007699357,0.00048321323,0.0003665594],"domain_scores_gemma":[0.99794054,0.0012351838,0.00013641498,0.0003906135,0.00012697744,0.00017024894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024926416,0.00015361265,0.00017496053,0.00020314951,0.00025053124,0.00013258372,0.00074382883,0.000035254972,0.000033869757],"category_scores_gemma":[0.00041881745,0.000108038286,0.000031209383,0.00018936192,0.0007250926,0.00068514294,0.0001702561,0.00006981778,0.000013563193],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033646695,0.00002161465,0.0000012706879,0.0000038080334,0.0000016095028,9.656992e-7,0.00012138156,0.00000187546,0.5603974,0.007725053,0.00005567966,0.4316357],"study_design_scores_gemma":[0.00057674333,0.00009922452,0.0001180534,0.00000917625,0.0000032235732,0.0000021981214,0.00001828987,0.25028893,0.661416,0.08734528,0.000009752224,0.00011314998],"about_ca_topic_score_codex":0.0000021416572,"about_ca_topic_score_gemma":4.3849627e-7,"teacher_disagreement_score":0.46324486,"about_ca_system_score_codex":0.00011359843,"about_ca_system_score_gemma":0.00013619308,"threshold_uncertainty_score":0.44056734},"labels":[],"label_agreement":null},{"id":"W2510837554","doi":"10.1109/icip.2016.7533211","title":"Self-similarity measure for multi-modal image registration","year":2016,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Similarity (geometry); Artificial intelligence; Similarity measure; Computer science; Modal; Image registration; Pattern recognition (psychology); Measure (data warehouse); Computer vision; Mutual information; Image (mathematics); Data mining","score_opus":0.046861079791932755,"score_gpt":0.32750555876400395,"score_spread":0.2806444789720712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2510837554","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000051335264,0.0000061893265,0.9931862,0.0035978337,0.000098492135,0.00037695083,0.0000034227853,0.0008815564,0.0017980186],"genre_scores_gemma":[0.012417953,0.000005283215,0.98569095,0.0006202638,0.000041483094,0.00006868261,0.0000015966282,0.0000055192827,0.0011482943],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99906826,0.000044221968,0.00018588251,0.0002801815,0.0002556775,0.00016579735],"domain_scores_gemma":[0.99920124,0.00008854527,0.000067456356,0.00035920224,0.0001902021,0.00009334076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049414515,0.00008233219,0.00008200807,0.0000391437,0.000065979875,0.00008863644,0.0004342025,0.00005690605,0.000046502115],"category_scores_gemma":[0.00027212204,0.000052381594,0.0000477694,0.000086164,0.000040600506,0.00083744753,0.00006283173,0.000039465533,0.00003090438],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001548267,0.0003729162,0.00027138344,0.00007550174,0.000032461416,0.000008965168,0.00038416413,5.124456e-8,0.32157385,0.07002917,0.082840264,0.52439576],"study_design_scores_gemma":[0.0015695769,0.00015671662,0.0008291771,0.000030349369,0.0000084142175,0.00000852978,0.00001213928,0.024303334,0.9623831,0.005888105,0.0045273555,0.00028321779],"about_ca_topic_score_codex":0.000007971262,"about_ca_topic_score_gemma":0.00001569298,"teacher_disagreement_score":0.64080924,"about_ca_system_score_codex":0.00005047578,"about_ca_system_score_gemma":0.00006928549,"threshold_uncertainty_score":0.21360594},"labels":[],"label_agreement":null},{"id":"W2513714682","doi":"10.1109/icip.2016.7532772","title":"Notice of Removal A Bag-of-shapes descriptor for medical imaging","year":2016,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Notice; Medical imaging; Computer science; Computer vision; Artificial intelligence; Law; Political science","score_opus":0.016728676639255577,"score_gpt":0.2975483464475479,"score_spread":0.2808196698082923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2513714682","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00065664464,0.000038344548,0.9947784,0.0030284573,0.00012899828,0.00014997437,0.000002195767,0.00013003741,0.0010869366],"genre_scores_gemma":[0.07373156,0.00001181669,0.9248866,0.00088733196,0.00004041187,0.000014901799,3.2820424e-7,0.0000056304343,0.0004214589],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987834,0.000040092254,0.00034279135,0.00019396002,0.00048874697,0.00015099929],"domain_scores_gemma":[0.9988942,0.0004923671,0.00010795938,0.00021886027,0.00017040603,0.000116220035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006487684,0.00006592302,0.00014453087,0.00007486354,0.000017654233,0.000012172099,0.0006794813,0.000036044174,0.0003577221],"category_scores_gemma":[0.0012117145,0.000040192062,0.00005887884,0.00012162494,0.00014273814,0.00034678399,0.00015607208,0.000032295855,0.000006394243],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000078298335,0.00005445508,0.0001069619,0.000044663073,0.000008900213,0.0000071178924,0.000117615564,3.2646806e-8,0.11421216,0.022961332,0.007607112,0.8548718],"study_design_scores_gemma":[0.0008722981,0.00009233442,0.00019278804,0.00020974329,0.000008216546,0.000029732459,0.000032160653,0.025209947,0.96579736,0.005065029,0.0023567667,0.00013360048],"about_ca_topic_score_codex":0.000020130297,"about_ca_topic_score_gemma":0.0000016075561,"teacher_disagreement_score":0.85473824,"about_ca_system_score_codex":0.000016181311,"about_ca_system_score_gemma":0.00010494447,"threshold_uncertainty_score":0.3916806},"labels":[],"label_agreement":null},{"id":"W2513927335","doi":"10.1118/1.4961403","title":"A particle filter based autocontouring algorithm for lung tumor tracking using dynamic magnetic resonance imaging","year":2016,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Contouring; Artificial intelligence; Thresholding; Context (archaeology); Algorithm; Centroid; Magnetic resonance imaging; Tracking (education); ModelSim; Filter (signal processing); Computer science; Mathematics; Nuclear medicine; Computer vision; Image (mathematics); Medicine; Radiology","score_opus":0.017476313034603914,"score_gpt":0.3002081090736976,"score_spread":0.2827317960390937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2513927335","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00089064153,0.0003292886,0.9964829,0.0013132879,0.00019457874,0.00040590198,0.0000074136974,0.00035591098,0.000020090438],"genre_scores_gemma":[0.32471085,0.0000061021838,0.67121565,0.0034190202,0.00031656527,0.00019994758,0.000002520411,0.000040025072,0.0000893029],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979142,0.00007534545,0.00035106522,0.00044873013,0.0007202898,0.0004903679],"domain_scores_gemma":[0.99870515,0.00042000617,0.000102764105,0.00040044435,0.00010342819,0.00026819803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055514585,0.00016597359,0.00020286826,0.000034336757,0.00012746736,0.00010192327,0.0006599542,0.000038977098,0.0000986081],"category_scores_gemma":[0.00033743554,0.00012519094,0.00009470155,0.00021788615,0.00016534646,0.0006208097,0.0001468275,0.0001295103,0.0000074668674],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002363125,0.00005636121,0.00017636847,0.000019414261,0.0000020268658,0.000036066624,0.000057772722,0.0000016242451,0.0057512126,0.00009017565,0.00022402927,0.9935826],"study_design_scores_gemma":[0.001013505,0.000034846347,0.00024211787,0.00032590772,0.000008601576,0.0000076263364,0.0000033055571,0.8819998,0.114215076,0.0018250592,0.00014331,0.00018084834],"about_ca_topic_score_codex":0.000010460847,"about_ca_topic_score_gemma":0.0000011145052,"teacher_disagreement_score":0.99340177,"about_ca_system_score_codex":0.00014240711,"about_ca_system_score_gemma":0.00017955367,"threshold_uncertainty_score":0.51051384},"labels":[],"label_agreement":null},{"id":"W2517494255","doi":"","title":"Low and mid-level shape priors for image segmentation","year":2010,"lang":"en","type":"dissertation","venue":"Library and Archives Canada (Government of Canada)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Segmentation; Boundary (topology); Compact space; Artificial intelligence; Closure (psychology); Mathematics; Image segmentation; Image (mathematics); Pattern recognition (psychology); Geometric flow; Measure (data warehouse); Level set (data structures); Computer vision; Computer science; Flow (mathematics); Geometry; Pure mathematics; Mathematical analysis","score_opus":0.004788741715409795,"score_gpt":0.19031784182686678,"score_spread":0.18552910011145698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2517494255","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20971256,0.001370712,0.58285564,0.013214554,0.0055386787,0.0068388805,0.002942801,0.00039070728,0.17713547],"genre_scores_gemma":[0.14693041,0.0007077867,0.80781525,0.0043519707,0.00021007478,0.00026723396,0.00068302633,0.00010904404,0.038925223],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99759585,0.000037566035,0.00036686374,0.00040443413,0.0013479183,0.00024738864],"domain_scores_gemma":[0.9989076,0.00027144764,0.00032294687,0.00023302587,0.0000023402986,0.00026258995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000020909021,0.00024305044,0.00027114904,0.00004645219,0.00016354569,0.000096280135,0.00042827651,0.00007389116,0.000029874956],"category_scores_gemma":[0.000012571961,0.00024404361,0.00003062245,0.00006885347,0.00006189122,0.00076738646,0.00010254296,0.00021253685,1.8212358e-9],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022258995,0.00004704358,0.00026959315,0.0016714009,0.00008508841,0.000044469332,0.0006033564,6.099253e-7,0.5511448,0.016090624,0.0027323114,0.42708814],"study_design_scores_gemma":[0.00036784197,0.00007484103,0.005883709,0.00019560196,0.000021022268,0.0000040704736,0.00096140336,0.0024540739,0.98715436,0.0017607582,0.00081968214,0.00030261258],"about_ca_topic_score_codex":0.00061672233,"about_ca_topic_score_gemma":0.0156181315,"teacher_disagreement_score":0.43600962,"about_ca_system_score_codex":0.000007063833,"about_ca_system_score_gemma":0.0015042513,"threshold_uncertainty_score":0.99518096},"labels":[],"label_agreement":null},{"id":"W2523272928","doi":"10.1088/0031-9155/61/19/7162","title":"Estimating the brain pathological age of Alzheimer’s disease patients from MR image data based on the separability distance criterion","year":2016,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; Canadian Institutes of Health Research; National Natural Science Foundation of China","keywords":"Pathological; Brain disease; Brain aging; Medicine; Disease; Estimation; Artificial intelligence; Pattern recognition (psychology); Computer science; Pathology","score_opus":0.23193221336439152,"score_gpt":0.4240597112088768,"score_spread":0.1921274978444853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2523272928","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06733208,0.000055273165,0.91703486,0.015146183,0.000088300374,0.00020545462,0.00007132175,0.000024568688,0.000041945124],"genre_scores_gemma":[0.9706281,0.000010963254,0.024827696,0.0043356963,0.00008215751,0.000019746709,0.00009210126,0.0000026452067,8.834372e-7],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987471,0.00042825047,0.00023692251,0.00033748048,0.00013245312,0.00011777403],"domain_scores_gemma":[0.9972292,0.0017994144,0.000112117224,0.0007783554,0.000036379846,0.000044504814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007926463,0.000088507455,0.00016386539,0.000014230718,0.000047941765,0.000008525225,0.0007037165,0.000025884072,0.000017085777],"category_scores_gemma":[0.0016129614,0.000033125903,0.000015055734,0.00010424515,0.00083470735,0.00011347764,0.00030010485,0.00010108457,0.0000010660812],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001044956,0.00055605423,0.071459785,0.000036821348,0.000020923177,0.00002493187,0.0009660738,0.0000034851575,0.048672315,0.010750206,0.0058742156,0.8615307],"study_design_scores_gemma":[0.003005214,0.0011714001,0.34308153,0.0008806935,0.00006671838,5.659158e-7,0.00007526838,0.24652004,0.006834398,0.39746696,0.00046330784,0.0004339062],"about_ca_topic_score_codex":0.00003508914,"about_ca_topic_score_gemma":0.0000023379293,"teacher_disagreement_score":0.90329605,"about_ca_system_score_codex":0.00000819406,"about_ca_system_score_gemma":0.00001637354,"threshold_uncertainty_score":0.30755138},"labels":[],"label_agreement":null},{"id":"W2525566051","doi":"10.1007/978-3-319-24726-7_6","title":"Shape Distances for Binary Image Segmentation","year":2016,"lang":"en","type":"book-chapter","venue":"Mathematics and visualization","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal; Western University","funders":"","keywords":"Task (project management); Segmentation; Artificial intelligence; Similarity (geometry); Image (mathematics); Shape analysis (program analysis); Measure (data warehouse); Computer science; Binary number; Computer vision; Image segmentation; Active shape model; Pattern recognition (psychology); Mathematics; Data mining; Engineering","score_opus":0.02906222491048313,"score_gpt":0.32943558096239983,"score_spread":0.30037335605191673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2525566051","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000058468336,0.00013932514,0.9758391,0.00012725845,0.00010362634,0.0006347429,0.00003070288,0.00020014086,0.022919293],"genre_scores_gemma":[0.000090914036,0.0009064579,0.8919101,0.00035165806,0.0001642644,0.00015482241,0.0001832632,0.00007657027,0.10616195],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99889207,0.000008724054,0.0003840407,0.00031053036,0.00028247127,0.00012216253],"domain_scores_gemma":[0.9990449,0.00014740037,0.00035471583,0.00023542534,0.00015203025,0.000065541986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024330107,0.00019858562,0.0002240244,0.00012883786,0.00010117124,0.0001845211,0.0002234865,0.00012353428,0.00013067013],"category_scores_gemma":[0.000049793965,0.0001571147,0.00005324398,0.00003147219,0.00007286277,0.0003872346,0.0000964972,0.000041435855,0.000018563707],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020919435,0.000024718758,4.2989248e-7,0.0004754073,0.000018921432,0.0000014949729,0.0004563215,4.01792e-8,0.0031522666,0.9118161,0.0031917065,0.08086047],"study_design_scores_gemma":[0.00085959723,0.00041855898,0.000003441487,0.0014834356,0.00009583011,0.000010922857,0.000065931534,0.1000566,0.018839167,0.8621818,0.015121578,0.0008631693],"about_ca_topic_score_codex":2.0195037e-7,"about_ca_topic_score_gemma":4.02882e-7,"teacher_disagreement_score":0.10005657,"about_ca_system_score_codex":0.000041716456,"about_ca_system_score_gemma":0.000027868162,"threshold_uncertainty_score":0.64069515},"labels":[],"label_agreement":null},{"id":"W2525893227","doi":"10.1007/978-3-319-46726-9_32","title":"Basal Slice Detection Using Long-Axis Segmentation for Cardiac Analysis","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Cardiac cycle; Basal (medicine); Ventricle; Computer science; Segmentation; Diastole; Systole; Artificial intelligence; Cardiology; Medicine; Internal medicine; Blood pressure","score_opus":0.02160193631687243,"score_gpt":0.2963481561469777,"score_spread":0.2747462198301053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2525893227","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000095982265,0.000113909606,0.9969357,0.0001869153,0.0013695422,0.00081205345,0.000017527329,0.00024198799,0.00022632777],"genre_scores_gemma":[0.025894424,0.000032678203,0.97222537,0.0009685893,0.0005245345,0.000048876896,0.000012972638,0.000035144967,0.00025742725],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99622136,0.00006836153,0.00060527574,0.0014711736,0.0010931409,0.0005407141],"domain_scores_gemma":[0.997283,0.00063142343,0.00043835136,0.0010270956,0.00042997135,0.00019019359],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012618776,0.00043193804,0.0005811641,0.001614938,0.00029106304,0.0004663752,0.0016404885,0.00029434563,0.000030487423],"category_scores_gemma":[0.00013820585,0.00037404778,0.00032966962,0.0011866795,0.00042590778,0.0010260262,0.00052649237,0.0003231975,0.000011941879],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006469755,0.000012960157,0.00008973939,0.000034816425,0.0000896119,0.000008000934,0.00024111533,0.0023354366,0.0073166164,0.00040594334,0.0000074491163,0.9894518],"study_design_scores_gemma":[0.00041273705,0.00026413202,0.00027624896,0.0002526201,0.00022649695,0.000010605127,3.8136346e-7,0.7265608,0.23169506,0.039213195,0.00011331661,0.0009744482],"about_ca_topic_score_codex":0.000034446923,"about_ca_topic_score_gemma":0.000060236394,"teacher_disagreement_score":0.9884774,"about_ca_system_score_codex":0.0006964572,"about_ca_system_score_gemma":0.00031060286,"threshold_uncertainty_score":0.99987113},"labels":[],"label_agreement":null},{"id":"W2528678964","doi":"10.1016/j.compbiomed.2016.10.001","title":"A graph-based approach for spatio-temporal segmentation of coronary arteries in X-ray angiographic sequences","year":2016,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; École de Technologie Supérieure","funders":"","keywords":"Coronary arteries; Segmentation; Computer science; Artificial intelligence; Tracking (education); Computer vision; Computation; Pattern recognition (psychology); Algorithm; Artery; Medicine; Cardiology","score_opus":0.02509959079633302,"score_gpt":0.3139974439944408,"score_spread":0.2888978531981078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2528678964","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044618152,0.00030723354,0.9533148,0.0012037709,0.00015017607,0.00034899687,0.0000033840001,0.000039190407,0.000014313693],"genre_scores_gemma":[0.6444057,0.00007346899,0.3549748,0.0004349221,0.000016847154,0.00005903317,0.00003140786,0.0000020603864,0.0000017295332],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990575,0.00012577085,0.00032268572,0.0002718379,0.000076545635,0.00014571444],"domain_scores_gemma":[0.99932516,0.00033195817,0.00012015675,0.00013990927,0.000037342303,0.000045491808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051782676,0.00009648609,0.00022984557,0.00043614686,0.000023055612,0.0000035088037,0.00022709671,0.0000709079,0.000004388339],"category_scores_gemma":[0.000046225647,0.00006101953,0.000025568503,0.00031791462,0.0006622306,0.00013826706,0.00003827239,0.000044448676,7.8235544e-8],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017376861,0.0002052044,0.5209538,0.00019979033,0.000031221818,0.000013897899,0.0016148443,0.000020789701,0.02756806,0.016319,0.0008613088,0.43203834],"study_design_scores_gemma":[0.03344221,0.0135439765,0.6000663,0.0033581846,0.00006765978,0.000072432405,0.001119785,0.08954698,0.0677524,0.18882926,0.00065195904,0.0015488783],"about_ca_topic_score_codex":0.0000385419,"about_ca_topic_score_gemma":0.000015792411,"teacher_disagreement_score":0.5997876,"about_ca_system_score_codex":0.000015586662,"about_ca_system_score_gemma":0.00002949972,"threshold_uncertainty_score":0.24883042},"labels":[],"label_agreement":null},{"id":"W2528836712","doi":"10.1016/j.ijrobp.2016.06.2380","title":"Automatic Skull Stripping in Computed Tomographic Images Based on K-Means Statistical Classifier for Radiation Therapy Planning","year":2016,"lang":"en","type":"article","venue":"International Journal of Radiation Oncology*Biology*Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Voxel; Artificial intelligence; Segmentation; Medicine; Pattern recognition (psychology); Partial volume; Hounsfield scale; Skull; Nuclear medicine; Computer science; White matter; Computer vision; Radiology; Magnetic resonance imaging; Computed tomography; Anatomy","score_opus":0.031664223120489436,"score_gpt":0.3586908892096504,"score_spread":0.327026666089161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2528836712","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011623098,0.00008535572,0.98211044,0.004242776,0.001347819,0.0003427151,0.00005850872,0.00011535946,0.00007394087],"genre_scores_gemma":[0.7502463,0.00013925412,0.24642147,0.0023390234,0.00071396725,0.000047051224,0.00006587064,0.000018879462,0.000008173299],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975797,0.00046711115,0.0009256271,0.00032143554,0.00042352802,0.00028259068],"domain_scores_gemma":[0.99543065,0.002759209,0.0010067996,0.00019517328,0.00048306433,0.00012511163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011560353,0.00020589824,0.00038052668,0.0005333895,0.00006948792,0.00008673046,0.0008892518,0.00018846318,0.000044649452],"category_scores_gemma":[0.00072336016,0.00015687752,0.00014754402,0.00029781184,0.00016299287,0.00076994236,0.00004689489,0.0002928535,0.0000051649654],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011259516,0.00032061682,0.022355458,0.0000059295144,0.0000959681,0.000023108625,0.00020410726,0.0008445375,0.0040280493,0.01218334,0.0018256861,0.9580006],"study_design_scores_gemma":[0.019496495,0.0039013,0.079173505,0.00047895766,0.000035978017,0.000051040148,0.000044586654,0.7726156,0.03202755,0.0850173,0.0064989673,0.00065869174],"about_ca_topic_score_codex":0.00000444902,"about_ca_topic_score_gemma":6.533782e-7,"teacher_disagreement_score":0.9573419,"about_ca_system_score_codex":0.0005879637,"about_ca_system_score_gemma":0.00042927667,"threshold_uncertainty_score":0.63972795},"labels":[],"label_agreement":null},{"id":"W2529336359","doi":"10.1088/0031-9155/61/20/7236","title":"Automatic landmark generation for deformable image registration evaluation for 4D CT images of lung","year":2016,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"London Health Sciences Centre; Western University","funders":"","keywords":"Landmark; Image registration; Computer vision; Artificial intelligence; Computer science; Image (mathematics); Nuclear medicine; Medicine","score_opus":0.21891730708733434,"score_gpt":0.45072053985200466,"score_spread":0.23180323276467033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2529336359","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019377511,0.00008851374,0.978146,0.0016221055,0.000099366895,0.0005610595,0.0000061398996,0.000025621415,0.000073645344],"genre_scores_gemma":[0.70438117,0.00012038229,0.2943542,0.00040388532,0.00028349363,0.00033729436,0.00009008528,0.000005209673,0.00002429451],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993257,0.000065052904,0.00025563946,0.0001670809,0.00008387417,0.00010267619],"domain_scores_gemma":[0.99924463,0.00028884772,0.00015041053,0.00013368667,0.00016108407,0.00002134724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010935628,0.000061866296,0.00014939714,0.00005335058,0.00003606112,0.00000860645,0.00010713675,0.000020816531,0.0000072554476],"category_scores_gemma":[0.00043239928,0.000036946854,0.000017422683,0.00008151215,0.0001291563,0.0002591271,0.000019993886,0.000021747153,1.718569e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000057752177,0.000021379745,0.00027363456,0.0000836572,0.0000073426145,1.0378396e-7,0.00013169473,0.0000011711692,0.40426618,0.011730324,0.0035614509,0.57991725],"study_design_scores_gemma":[0.0021734245,0.0004922115,0.00030138792,0.00012212321,0.00002786404,0.000002859404,0.000024897055,0.62294674,0.27302456,0.10067582,0.00011034225,0.000097805896],"about_ca_topic_score_codex":0.000018835173,"about_ca_topic_score_gemma":0.0000055550026,"teacher_disagreement_score":0.68500364,"about_ca_system_score_codex":0.000027137965,"about_ca_system_score_gemma":0.00004680897,"threshold_uncertainty_score":0.1506649},"labels":[],"label_agreement":null},{"id":"W2533845842","doi":"10.1049/iet-cvi.2016.0301","title":"Enhanced X‐ray image segmentation method using prior shape","year":2016,"lang":"en","type":"article","venue":"IET Computer Vision","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Artificial intelligence; Computer vision; Boundary (topology); Pixel; Image segmentation; Path (computing); Segmentation; Image (mathematics); Computer science; Object (grammar); Function (biology); Pattern recognition (psychology); Mathematics","score_opus":0.020012066608715365,"score_gpt":0.35754205041474246,"score_spread":0.3375299838060271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2533845842","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007832479,0.000014597054,0.989947,0.00064701575,0.00055226235,0.000335665,0.000002139863,0.00057680946,0.00009201516],"genre_scores_gemma":[0.026849076,0.00001526481,0.97180647,0.0010390824,0.000177982,0.000012924507,0.0000030505087,0.00001750735,0.00007866788],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997818,0.00027376288,0.00041815048,0.00062476244,0.0005510582,0.00031425146],"domain_scores_gemma":[0.99864143,0.0002623751,0.00019897005,0.00056146225,0.00017406033,0.00016167943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006237161,0.00020103175,0.00021197047,0.00017746586,0.00013419977,0.00024215355,0.00074977,0.00008200568,0.00019464719],"category_scores_gemma":[0.000037802813,0.0001413523,0.00008659842,0.00032542023,0.000060532468,0.0016850686,0.0004699137,0.00009685028,0.00015305322],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000038557255,0.000028524151,0.0000023886698,0.0000067316128,0.0000046874725,0.00000682453,0.00013127652,0.0000052155747,0.4809063,0.000067323774,0.0009248222,0.51791203],"study_design_scores_gemma":[0.00066461356,0.00022456853,0.0003955667,0.00015870684,0.000006434668,0.000016429894,0.000004735308,0.25306162,0.74419916,0.00083904614,0.00018985885,0.00023923985],"about_ca_topic_score_codex":0.000005727674,"about_ca_topic_score_gemma":3.5015728e-7,"teacher_disagreement_score":0.51767284,"about_ca_system_score_codex":0.00013165279,"about_ca_system_score_gemma":0.000053880103,"threshold_uncertainty_score":0.576418},"labels":[],"label_agreement":null},{"id":"W2535796625","doi":"10.1109/embc.2016.7590908","title":"Sorted self-similarity for multi-modal image registration","year":2016,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Similarity (geometry); Artificial intelligence; Image registration; Pixel; Computer science; Computer vision; Modal; Mutual information; Pattern recognition (psychology); Similarity measure; Image (mathematics)","score_opus":0.04402158191451909,"score_gpt":0.3374296229496943,"score_spread":0.2934080410351752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2535796625","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017864627,0.0000026393816,0.9946701,0.0023843152,0.00009166055,0.0003591333,0.0000035770104,0.001011214,0.0012987446],"genre_scores_gemma":[0.011250324,0.0000066323273,0.9859011,0.00082198926,0.0000341954,0.00006828851,0.0000028831607,0.000005408659,0.0019091717],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991224,0.000035247274,0.00020147144,0.00027772697,0.000194469,0.00016869648],"domain_scores_gemma":[0.99924994,0.00009640652,0.000072344075,0.00034923916,0.0001434372,0.00008864414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003425405,0.000079624115,0.00008078417,0.000043826913,0.000062527186,0.00008333475,0.0004019639,0.000052214924,0.00007250717],"category_scores_gemma":[0.00019633534,0.00005160986,0.00004414128,0.000092642746,0.00004350442,0.000810602,0.000067422836,0.000035128658,0.00003234057],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013634389,0.00041772658,0.0003036751,0.00004819528,0.000032192376,0.0000098823275,0.0002947174,6.932939e-8,0.4666922,0.0661567,0.07318998,0.392841],"study_design_scores_gemma":[0.0015742396,0.00015317282,0.0017483267,0.000020006653,0.0000075112157,0.0000067468736,0.000010315396,0.047597647,0.9392079,0.0064053587,0.003011509,0.00025727952],"about_ca_topic_score_codex":0.000013079929,"about_ca_topic_score_gemma":0.000011674608,"teacher_disagreement_score":0.4725157,"about_ca_system_score_codex":0.00004341191,"about_ca_system_score_gemma":0.00005991571,"threshold_uncertainty_score":0.2104589},"labels":[],"label_agreement":null},{"id":"W2536315965","doi":"10.1109/embc.2016.7592203","title":"A multi-criteria evaluation platform for segmentation algorithms","year":2016,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Segmentation; Computer science; Robustness (evolution); Ground truth; Algorithm; Image segmentation; Reliability (semiconductor); Data mining; Artificial intelligence; Outlier; Sensitivity (control systems); Graph; Machine learning; Theoretical computer science","score_opus":0.08988778626439302,"score_gpt":0.39486476551874017,"score_spread":0.30497697925434714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2536315965","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004552907,0.000011095703,0.9970233,0.0008311843,0.00027484022,0.00083806366,0.000004671859,0.00033849676,0.0002230447],"genre_scores_gemma":[0.013741253,0.000008125762,0.98433864,0.00064382463,0.00004639474,0.00044796287,0.000010833759,0.00000653131,0.00075644534],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99894583,0.000035960376,0.0002207709,0.000269266,0.000366259,0.00016193745],"domain_scores_gemma":[0.999223,0.00013129097,0.00007278804,0.0002471882,0.00025167622,0.000074045005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079987786,0.00008268626,0.000076786775,0.00008272713,0.000059667087,0.00008217498,0.00030270143,0.00003988553,0.00032640732],"category_scores_gemma":[0.00020858465,0.000052498675,0.000037616275,0.00010968975,0.000027043483,0.0011179314,0.00006130677,0.000019098254,0.000051960284],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002473136,0.00003029961,0.000008839896,0.0000045016523,0.0000049047903,2.333791e-7,0.00017228434,1.2960773e-7,0.15422243,0.0004716121,0.0038822861,0.8412],"study_design_scores_gemma":[0.002169064,0.00013613279,0.0003201606,0.000026010812,0.000008760639,0.0000037870423,0.00004170786,0.23902579,0.7536597,0.003960937,0.0004870839,0.0001608428],"about_ca_topic_score_codex":0.000008025204,"about_ca_topic_score_gemma":0.0000037489135,"teacher_disagreement_score":0.8410392,"about_ca_system_score_codex":0.00010774401,"about_ca_system_score_gemma":0.000050884453,"threshold_uncertainty_score":0.35739312},"labels":[],"label_agreement":null},{"id":"W2537371119","doi":"10.1109/tic-sth.2009.5444517","title":"Deformable modeling of human liver with contact surface","year":2009,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Princess Margaret Cancer Centre","funders":"Terry Fox Foundation; National Cancer Institute; Cancer Care Ontario; Foundation for the National Institutes of Health","keywords":"Displacement (psychology); Surface (topology); Body surface; Breathing; Finite element method; Spleen; Biomedical engineering; Materials science; Anatomy; Computer vision; Medicine; Physics; Computer science; Geometry; Mathematics; Internal medicine","score_opus":0.023642778887741744,"score_gpt":0.2771292070201039,"score_spread":0.25348642813236216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2537371119","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034202475,0.00001834286,0.9546229,0.000082607636,0.0000067149076,0.000080735925,1.2294942e-7,0.00017627938,0.010809817],"genre_scores_gemma":[0.6664018,0.0000041685657,0.33303878,0.00034922018,0.000002665589,4.7266465e-7,4.8871556e-7,0.0000013544757,0.00020106288],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941146,0.000014266212,0.00013844381,0.00011832336,0.00020770257,0.00010982093],"domain_scores_gemma":[0.9996079,0.000010238418,0.000041864147,0.0002162735,0.000072455696,0.000051252657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013802575,0.000054323526,0.00008800202,0.000028216557,0.00003472683,0.000029896652,0.0002975124,0.000019057727,0.0000524829],"category_scores_gemma":[0.000004233324,0.000038034515,0.000016125161,0.000115570234,0.000012415795,0.0005061565,0.00003102114,0.000049243412,0.000006045335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005182933,0.0008410666,0.0011707635,0.00011855641,0.00007524856,0.00010850629,0.005232852,0.019406125,0.47362396,0.30328923,0.006958764,0.1891231],"study_design_scores_gemma":[0.0002043511,0.00034600485,0.00011157826,0.000023541144,0.0000021532226,0.000004644411,0.000022822062,0.7344167,0.263749,0.0010361637,0.000005443771,0.00007757506],"about_ca_topic_score_codex":0.000109904424,"about_ca_topic_score_gemma":0.000004734614,"teacher_disagreement_score":0.7150106,"about_ca_system_score_codex":0.000014216681,"about_ca_system_score_gemma":0.00002151201,"threshold_uncertainty_score":0.15510026},"labels":[],"label_agreement":null},{"id":"W2537485100","doi":"10.1109/iembs.2004.1403529","title":"Modeling susceptibility difference artifacts produced by metallic implants in magnetic resonance imaging with point-based thin-plate spline image registration","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Imaging phantom; Spline (mechanical); Computer vision; Thin plate spline; Artificial intelligence; Image registration; Point (geometry); Compensation (psychology); Grid; Pixel; Magnetic resonance imaging; Computer science; Image (mathematics); Optics; Materials science; Mathematics; Physics; Spline interpolation; Geometry; Medicine","score_opus":0.015276962669769701,"score_gpt":0.2656012707258718,"score_spread":0.2503243080561021,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2537485100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2096998,0.00015854763,0.7867531,0.0023715955,0.000020416632,0.00046336508,0.0000035293388,0.00037527672,0.00015437964],"genre_scores_gemma":[0.6393364,0.000009473678,0.3596517,0.00076890516,0.000014670889,0.000035130433,0.000013930231,0.000010619546,0.0001591414],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974426,0.00013948756,0.00056993554,0.00084704807,0.00056605646,0.00043484737],"domain_scores_gemma":[0.9987004,0.00007413208,0.0001212972,0.0008061159,0.00014575834,0.00015232095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008564627,0.0002469126,0.00024657103,0.00013030483,0.000077124365,0.00024068654,0.0006146545,0.00004557955,0.0000867079],"category_scores_gemma":[0.0001447653,0.00019828691,0.000033041626,0.00039770213,0.00009642303,0.0012309798,0.000088717476,0.0002611299,0.0000231013],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008866402,0.00048029245,0.0015901902,0.000054040847,0.0000035129392,0.000050913764,0.00039232697,0.00074999995,0.7678265,0.00053748256,0.00056440727,0.2276617],"study_design_scores_gemma":[0.0005044264,0.000075021075,0.0008426602,0.000057060886,0.0000036772349,0.000009861782,0.000014888274,0.6752476,0.32257912,0.00045571904,0.0000069968596,0.00020293704],"about_ca_topic_score_codex":0.0002928589,"about_ca_topic_score_gemma":0.0003877458,"teacher_disagreement_score":0.6744976,"about_ca_system_score_codex":0.00013324607,"about_ca_system_score_gemma":0.00012428654,"threshold_uncertainty_score":0.8085906},"labels":[],"label_agreement":null},{"id":"W2538589246","doi":"10.1109/embc.2016.7590912","title":"Curvelet based residual complexity objective function for non-rigid registration of pre-operative MRI with intra-operative ultrasound images","year":2016,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Curvelet; Residual; Artificial intelligence; Computer science; Image registration; Computer vision; Feature (linguistics); Imaging phantom; Pattern recognition (psychology); Ultrasound; Probabilistic logic; Matching (statistics); Echogenicity; Image (mathematics); Mathematics; Algorithm; Radiology; Medicine; Wavelet transform","score_opus":0.021260829787189562,"score_gpt":0.3088479578323044,"score_spread":0.2875871280451148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2538589246","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036004714,0.0000040156337,0.9917187,0.0013682721,0.000074551885,0.0012427818,0.000067675326,0.00020488967,0.0017186115],"genre_scores_gemma":[0.50236386,0.000005415242,0.49608138,0.00044116267,0.000053025768,0.00022271677,0.000033067685,0.000011901507,0.0007874568],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981352,0.00016956883,0.00042438155,0.0005574871,0.00047218654,0.00024119338],"domain_scores_gemma":[0.99756354,0.00083297153,0.00027391093,0.00043715746,0.00079169846,0.00010074909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062866893,0.00021729416,0.00027069415,0.00011690245,0.00015749894,0.00011947461,0.00037019103,0.00006357653,0.00015046987],"category_scores_gemma":[0.00032323552,0.00013011413,0.00005179139,0.00027653232,0.00049837155,0.0013977734,0.00004053376,0.00009894463,0.000006012802],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013073373,0.0005865723,0.0013224963,0.000116491115,0.00020962946,0.0000054546185,0.0031185057,0.00005230026,0.889068,0.041395742,0.042663217,0.020154223],"study_design_scores_gemma":[0.0014365587,0.001779176,0.009175037,0.000095474425,0.000017044405,0.0000046935197,0.00017773613,0.0013507222,0.98282444,0.0028722931,0.00003434823,0.00023248729],"about_ca_topic_score_codex":0.00015296017,"about_ca_topic_score_gemma":0.00021263708,"teacher_disagreement_score":0.4987634,"about_ca_system_score_codex":0.00011512449,"about_ca_system_score_gemma":0.0003159494,"threshold_uncertainty_score":0.53059},"labels":[],"label_agreement":null},{"id":"W2540871663","doi":"10.1109/acssc.2007.4487200","title":"Locating Brain Tumors from MR Imagery Using Symmetry","year":2007,"lang":"en","type":"article","venue":"Conference record/Conference record - Asilomar Conference on Signals, Systems, & Computers","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Abnormality; Search engine indexing; Segmentation; Artificial intelligence; Bounding overwatch; Magnetic resonance imaging; Image segmentation; Computer vision; Minimum bounding box; Brain tumor; Symmetry (geometry); Exploit; Image (mathematics); Pattern recognition (psychology); Radiology; Mathematics; Medicine","score_opus":0.07365420974368848,"score_gpt":0.31997101672623984,"score_spread":0.24631680698255137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2540871663","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042145323,0.00021032989,0.94220805,0.0010870122,0.004587939,0.0020228468,0.00009123309,0.0019527647,0.005694497],"genre_scores_gemma":[0.71733046,0.0001421494,0.27927712,0.0015809328,0.00080753025,0.00013478275,0.00012615844,0.00014965306,0.00045122727],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.984518,0.0018737408,0.0040999665,0.003857429,0.0027238969,0.0029269936],"domain_scores_gemma":[0.9861268,0.0034001668,0.0029025474,0.003320392,0.0023497937,0.0019003204],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.004486086,0.0020395056,0.0027127913,0.0017731775,0.0008534167,0.0035976747,0.0059971884,0.00089530915,0.0007838947],"category_scores_gemma":[0.0009554523,0.0020925372,0.00058626727,0.0021801367,0.00081702543,0.0029356866,0.0014496736,0.0020898273,0.00065463415],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016786448,0.0004979271,0.0029748867,0.00039171247,0.0003081793,0.0007054197,0.0013207349,0.0000635224,0.035084974,0.027761031,0.0050289202,0.9256948],"study_design_scores_gemma":[0.0031380602,0.0019568938,0.00212842,0.009390049,0.00018477897,0.00035660222,0.004134365,0.92697173,0.028860994,0.012251572,0.004628262,0.0059982482],"about_ca_topic_score_codex":0.0055907452,"about_ca_topic_score_gemma":0.00027165658,"teacher_disagreement_score":0.92690825,"about_ca_system_score_codex":0.0010339759,"about_ca_system_score_gemma":0.0025475498,"threshold_uncertainty_score":0.9993808},"labels":[],"label_agreement":null},{"id":"W2544294341","doi":"10.1109/nssmic.2012.6551509","title":"Fully-automated segmentation of the striatum in the PET/MR images using data fusion","year":2012,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Modality (human–computer interaction); Artificial intelligence; Segmentation; Computer science; Pattern recognition (psychology); Computer vision; Region of interest; Image segmentation","score_opus":0.06520530720796046,"score_gpt":0.357381588506272,"score_spread":0.2921762812983115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2544294341","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.072453246,0.000055437933,0.92530894,0.0007744446,0.00026289033,0.00041055426,0.000008779697,0.00022202161,0.0005037084],"genre_scores_gemma":[0.66677207,0.000013445088,0.3323565,0.00075035024,0.000037180256,0.0000065741965,0.000018357094,0.0000046586715,0.000040859424],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99863696,0.00030644986,0.0002678684,0.00016231425,0.00045382415,0.00017260121],"domain_scores_gemma":[0.99872494,0.00013302796,0.00014323142,0.0009290746,0.0000385804,0.000031155272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012399758,0.000075246004,0.00008205425,0.00005873553,0.00006971231,0.00006720077,0.001636671,0.000020647336,0.000056105408],"category_scores_gemma":[0.00012819092,0.000039547464,0.000020830312,0.00049296144,0.00006129018,0.0011741776,0.00068700156,0.00009963441,0.000005171641],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011351049,0.00070214557,0.016574444,0.00008753416,0.000026977272,0.000008982472,0.0056258417,0.000031754982,0.8158218,0.0040493323,0.0566183,0.10044149],"study_design_scores_gemma":[0.0005804824,0.000039730152,0.032693774,0.000078868565,0.000021143616,0.00004918004,0.0010938729,0.2701035,0.6945733,0.00042843015,0.00013898054,0.00019876164],"about_ca_topic_score_codex":0.00017878131,"about_ca_topic_score_gemma":0.000008743623,"teacher_disagreement_score":0.5943188,"about_ca_system_score_codex":0.000028594666,"about_ca_system_score_gemma":0.00004246277,"threshold_uncertainty_score":0.3041369},"labels":[],"label_agreement":null},{"id":"W2546140527","doi":"10.1109/nnsp.1992.253676","title":"Unsupervised multi-level segmentation of multispectral images","year":2003,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Multispectral image; Artificial intelligence; Segmentation; Computer science; Cluster analysis; Pattern recognition (psychology); Image segmentation; Scale-space segmentation; Computer vision; Multispectral pattern recognition; Segmentation-based object categorization; Image texture; Representation (politics); Scale (ratio); Geography; Cartography","score_opus":0.051954098892123525,"score_gpt":0.31401641155571114,"score_spread":0.2620623126635876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2546140527","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024350446,0.000029885437,0.9948917,0.00010041024,0.00008757959,0.00020444655,0.00000228034,0.00021837426,0.0020302986],"genre_scores_gemma":[0.12614064,0.000015948453,0.8727483,0.00029123484,0.000004643811,0.000016074468,0.000002686627,0.0000052255523,0.0007752446],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989762,0.00009898641,0.00027034798,0.00022242924,0.00027194258,0.00016012746],"domain_scores_gemma":[0.9993987,0.000062020146,0.00007521389,0.00028827367,0.000094642906,0.00008112115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025640512,0.00009311954,0.00011613601,0.000094071635,0.000035232304,0.00003928916,0.00033760918,0.00003530323,0.00030466917],"category_scores_gemma":[0.00012241617,0.00008109543,0.000044963228,0.0002461892,0.000058943802,0.0004733784,0.000042595733,0.000061570165,0.000028143517],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000022791933,0.00027754172,0.001065762,0.000030260939,0.000018471233,0.000007853192,0.0008099667,0.00000594338,0.8821249,0.011590827,0.0021746461,0.10189158],"study_design_scores_gemma":[0.0006216322,0.000042543903,0.0033636389,0.000007442701,0.0000026107928,0.0000045100965,0.0001016476,0.002102493,0.99325836,0.00038324404,0.00001629797,0.00009558661],"about_ca_topic_score_codex":0.000035800687,"about_ca_topic_score_gemma":0.0000037339646,"teacher_disagreement_score":0.12370559,"about_ca_system_score_codex":0.000026724852,"about_ca_system_score_gemma":0.00004326777,"threshold_uncertainty_score":0.33359137},"labels":[],"label_agreement":null},{"id":"W2547282450","doi":"10.1109/igarss.2016.7729868","title":"Morphological interpolation for temporal changes","year":2016,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Space Agency; Indian Statistical Institute","keywords":"Interpolation (computer graphics); Focus (optics); Mathematical morphology; Computer science; Linear interpolation; Remote sensing; Artificial intelligence; Image (mathematics); Geology; Image processing; Pattern recognition (psychology)","score_opus":0.043349552358877365,"score_gpt":0.32002704884751276,"score_spread":0.2766774964886354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2547282450","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00036245954,0.000004045373,0.99014,0.008105144,0.000091410926,0.00015675061,0.0000011378315,0.00035832525,0.00078069005],"genre_scores_gemma":[0.18849805,0.00000369746,0.8080882,0.0017835704,0.000047480757,0.00006565124,0.0000013841411,0.0000024498506,0.0015095448],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995539,0.000018398016,0.00008267186,0.0001532448,0.00009337148,0.000098400385],"domain_scores_gemma":[0.99963295,0.00010452422,0.000030911593,0.00014689601,0.000038739734,0.00004597485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017262159,0.00004246362,0.00005040149,0.000038676757,0.0000213996,0.000027026184,0.0002618785,0.00003203978,0.00022039587],"category_scores_gemma":[0.000099941375,0.00002210077,0.000019233345,0.00005262372,0.000030369381,0.00021612097,0.0000739259,0.000015844,0.000032916683],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004679996,0.00002584761,0.0004118509,0.0000036500348,0.000003077808,0.00000297363,0.00005415184,5.9609877e-9,0.13329567,0.024401171,0.037610363,0.8041865],"study_design_scores_gemma":[0.0009735333,0.00075435365,0.0013424822,0.000044918626,0.0000032182397,0.000024399056,0.00002445178,0.008423556,0.9323991,0.041379195,0.014333692,0.00029712534],"about_ca_topic_score_codex":0.0000034004179,"about_ca_topic_score_gemma":0.0000032779885,"teacher_disagreement_score":0.80388945,"about_ca_system_score_codex":0.000013277686,"about_ca_system_score_gemma":0.000007892468,"threshold_uncertainty_score":0.24131803},"labels":[],"label_agreement":null},{"id":"W2552809514","doi":"10.15353/vsnl.v1i1.55","title":"Diagnosing Cardiac Deformations using 3d Optical Flow","year":2015,"lang":"en","type":"article","venue":"Vision Letters","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cardiac cycle; Optical flow; Regularization (linguistics); Point cloud; Artificial intelligence; Computer science; Computer vision; Match moving; Flow (mathematics); Motion (physics); Mathematics; Geometry; Cardiology; Medicine; Image (mathematics)","score_opus":0.03419996114248625,"score_gpt":0.31581113684606366,"score_spread":0.2816111757035774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2552809514","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0132563505,0.000026202768,0.9814694,0.004006674,0.00052963826,0.00012573661,0.0000012249001,0.00028630535,0.00029847227],"genre_scores_gemma":[0.030945165,0.0000039947377,0.96296614,0.0059532793,0.00010111879,0.000010230215,0.000005124653,0.000008035138,0.0000069344505],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988146,0.00008387509,0.00022252477,0.00020149391,0.00047243742,0.0002050959],"domain_scores_gemma":[0.9992362,0.00009442056,0.000054272314,0.00033399335,0.00006503077,0.0002160515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042596032,0.00009094108,0.000117886535,0.00012464337,0.00009213297,0.0002071402,0.00035311945,0.000038538277,0.000011761144],"category_scores_gemma":[0.0001713527,0.00008076566,0.000048961945,0.0002848146,0.00006305262,0.0010599914,0.00019119123,0.00011475118,0.00009362116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000443137,0.00010234545,0.00055567385,0.000020675408,0.00002415229,0.000060764076,0.0023701396,0.0034927847,0.08552077,0.00092705205,0.1475917,0.7593295],"study_design_scores_gemma":[0.0006304684,0.00012614139,0.00065569114,0.0001393275,0.000022903498,0.000035068384,0.00011263542,0.8602701,0.13069099,0.00041074175,0.006383241,0.0005226876],"about_ca_topic_score_codex":0.000014879074,"about_ca_topic_score_gemma":1.7185586e-7,"teacher_disagreement_score":0.8567773,"about_ca_system_score_codex":0.00011340088,"about_ca_system_score_gemma":0.00004431621,"threshold_uncertainty_score":0.3293528},"labels":[],"label_agreement":null},{"id":"W2555704095","doi":"10.1109/icbmi.2011.24","title":"Towards Model-Enhanced Real-Time Ultrasound Guided Cardiac Interventions","year":2011,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; General-purpose computing on graphics processing units; Graphics; Image registration; Computer vision; Artificial intelligence; Component (thermodynamics); Graph; Image (mathematics); Computer graphics (images)","score_opus":0.06788737169241216,"score_gpt":0.3300685022459524,"score_spread":0.26218113055354025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2555704095","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009127983,0.000010118009,0.819974,0.000070291615,0.000118536074,0.00017831528,0.0000028448417,0.00084189046,0.1778912],"genre_scores_gemma":[0.06593918,0.000076242955,0.92543596,0.0003068047,0.000022343405,0.000065546395,0.000007277069,0.000010568918,0.008136065],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99870265,0.000079720456,0.00036119443,0.00032997248,0.0002984803,0.00022801505],"domain_scores_gemma":[0.998961,0.000038988903,0.000083595296,0.00062092603,0.00013872779,0.00015679585],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00040479627,0.00012208018,0.00017663687,0.00009784134,0.00006360439,0.00007799683,0.00080088753,0.000056988283,0.0012524107],"category_scores_gemma":[0.00010266961,0.000106926826,0.00019347438,0.00024101212,0.000069266214,0.0006370673,0.00020566798,0.000083267034,0.0003254957],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008100857,0.0005052921,0.00004459709,0.0000914513,0.00015127413,0.000013241848,0.0060849655,0.000021921502,0.5187021,0.119787395,0.21317148,0.14141819],"study_design_scores_gemma":[0.00018336932,0.00009752115,0.00040324332,0.000052840158,0.000017088243,0.0000058268456,0.000037041846,0.015976548,0.9630342,0.01989068,0.000030684892,0.00027092025],"about_ca_topic_score_codex":0.00021462781,"about_ca_topic_score_gemma":0.0000024746253,"teacher_disagreement_score":0.44433215,"about_ca_system_score_codex":0.00004913077,"about_ca_system_score_gemma":0.000075039345,"threshold_uncertainty_score":0.99966055},"labels":[],"label_agreement":null},{"id":"W2557701977","doi":"10.1364/josaa.34.000027","title":"Implicit kernel sparse shape representation: a sparse-neighbors-based objection segmentation framework","year":2016,"lang":"en","type":"article","venue":"Journal of the Optical Society of America A","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Ontario Ministry of Citizenship and Multiculturalism","keywords":"Kernel (algebra); Computer science; Segmentation; Sparse approximation; Artificial intelligence; Representation (politics); Pattern recognition (psychology); Computer vision; Algorithm; Mathematics","score_opus":0.020802641464869733,"score_gpt":0.3026731553298402,"score_spread":0.2818705138649705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2557701977","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00848718,0.000033122782,0.97658265,0.014218657,0.00030275228,0.00018569447,0.0000020430457,0.00004856759,0.0001393355],"genre_scores_gemma":[0.21719109,0.00011571055,0.77946734,0.0029815896,0.00016536255,0.000008622068,3.6889023e-7,0.000011587719,0.000058329693],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976782,0.00017206959,0.0007232075,0.0002286837,0.00096683146,0.00023096945],"domain_scores_gemma":[0.9973263,0.0007148638,0.0009797565,0.00045049548,0.00035961697,0.00016894224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053838675,0.00014672296,0.0003076798,0.000051915384,0.00011268577,0.00006900265,0.00087049854,0.00010598279,0.0001961554],"category_scores_gemma":[0.00057387544,0.000083804116,0.00050820294,0.00065152085,0.00039211498,0.0005637349,0.00015998022,0.00030153102,0.000011489208],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014003286,0.0006475637,0.0030218656,0.000066410066,0.00028788648,0.0000098469,0.0021922765,0.00022781362,0.35838354,0.001578599,0.035565335,0.5978788],"study_design_scores_gemma":[0.0028535817,0.0016331415,0.019567467,0.0010533043,0.00019379453,0.00015356323,0.0012891458,0.05535173,0.89581174,0.0206019,0.0009153216,0.00057530444],"about_ca_topic_score_codex":0.000015563239,"about_ca_topic_score_gemma":1.263343e-7,"teacher_disagreement_score":0.5973035,"about_ca_system_score_codex":0.0001757566,"about_ca_system_score_gemma":0.00016866469,"threshold_uncertainty_score":0.3417433},"labels":[],"label_agreement":null},{"id":"W2558764425","doi":"10.1016/j.media.2016.11.008","title":"Direct and simultaneous estimation of cardiac four chamber volumes by multioutput sparse regression","year":2016,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"London Health Sciences Centre; St Joseph's Health Care; Western University","funders":"National Natural Science Foundation of China; Government of Ontario","keywords":"Discriminative model; Volume (thermodynamics); Artificial intelligence; Computer science; Pattern recognition (psychology); Mathematics; Computer vision","score_opus":0.008844177531851929,"score_gpt":0.27735978089106406,"score_spread":0.26851560335921215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2558764425","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011453066,0.0002777503,0.98634887,0.0012731344,0.000055435445,0.00011622295,0.000016149872,0.0001746698,0.0002847041],"genre_scores_gemma":[0.8454661,0.0006160936,0.15199555,0.00030177773,0.000039687566,0.000022953795,0.000019837882,0.000012053062,0.0015259531],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99759114,0.00019624528,0.00045545577,0.00045393943,0.0010730685,0.00023015097],"domain_scores_gemma":[0.99795115,0.0008379425,0.00021284758,0.0005013898,0.0001604051,0.00033627366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080414803,0.00016260534,0.00047219047,0.00024448673,0.000062929685,0.000053521573,0.00034307514,0.00010009332,0.0003148324],"category_scores_gemma":[0.0029670827,0.00009988521,0.00016888895,0.0007328902,0.00031417998,0.0004656484,0.00022704959,0.00009067839,0.000023141094],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004876887,0.00006283106,0.0007109588,0.00001889592,0.00023626698,0.000050093255,0.0001546013,0.000004443197,0.016596667,0.00001279714,0.007906402,0.97424114],"study_design_scores_gemma":[0.0006247308,0.00009761158,0.00119198,0.00022134346,0.0005669803,0.000010697437,0.000027594993,0.6771624,0.31851834,0.000189014,0.0009931512,0.0003961244],"about_ca_topic_score_codex":0.00015274575,"about_ca_topic_score_gemma":0.000008309325,"teacher_disagreement_score":0.97384506,"about_ca_system_score_codex":0.000035741636,"about_ca_system_score_gemma":0.00004234376,"threshold_uncertainty_score":0.40732008},"labels":[],"label_agreement":null},{"id":"W2562125360","doi":"10.1186/s12880-016-0170-8","title":"Skeleton-based cerebrovascular quantitative analysis","year":2016,"lang":"en","type":"article","venue":"BMC Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Ryerson University","keywords":"Skeleton (computer programming); Computer science; Artificial intelligence","score_opus":0.01732141839827223,"score_gpt":0.3113480598542735,"score_spread":0.29402664145600127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2562125360","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00091462705,0.00010873891,0.9934409,0.004152916,0.00015013019,0.0001221776,0.0000017856413,0.00058015034,0.0005285281],"genre_scores_gemma":[0.1712968,0.000025929596,0.8237839,0.004582516,0.00007221523,0.000049944603,0.000006137441,0.000016519907,0.00016606571],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99672705,0.0003589182,0.00042912766,0.0005818309,0.001503572,0.00039949446],"domain_scores_gemma":[0.9969618,0.001516996,0.00012869279,0.0007467256,0.00015227274,0.0004935685],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013897874,0.00017275149,0.00030598676,0.0004101789,0.00008763963,0.00010037069,0.0011989448,0.00006101546,0.0012503192],"category_scores_gemma":[0.0027496803,0.000111852,0.00028102324,0.0011965091,0.00029882995,0.0005283997,0.00021378689,0.0001382241,0.0001976201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001221874,0.00029492972,0.10072011,0.00010208235,0.00048390313,0.00018275714,0.00029415396,0.000022762824,0.006228526,0.022126677,0.011807709,0.8577242],"study_design_scores_gemma":[0.0035163937,0.000098354096,0.026232095,0.0005638372,0.0003745692,0.000025070283,0.000108362496,0.8851979,0.07536023,0.004542811,0.0028626397,0.001117772],"about_ca_topic_score_codex":0.000057176083,"about_ca_topic_score_gemma":0.00002830855,"teacher_disagreement_score":0.8851751,"about_ca_system_score_codex":0.00008125673,"about_ca_system_score_gemma":0.00041669674,"threshold_uncertainty_score":0.9996627},"labels":[],"label_agreement":null},{"id":"W2562549310","doi":"10.1117/1.jmi.3.4.044005","title":"Shape complexes: the intersection of label orderings and star convexity constraints in continuous max-flow medical image segmentation","year":2016,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Segmentation; Convexity; Geodesic; Image warping; Artificial intelligence; Image segmentation; Topology (electrical circuits); Computer vision; Computer science; Mathematics; Algorithm; Combinatorics; Geometry","score_opus":0.01509246697781281,"score_gpt":0.30533885768861574,"score_spread":0.29024639071080294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2562549310","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.081371926,0.00013262322,0.8987439,0.01921335,0.00029903816,0.00013035996,0.000002170998,0.000030177223,0.0000764236],"genre_scores_gemma":[0.8955202,0.0003553331,0.10129477,0.0026722618,0.0001288285,0.000005970669,8.7322206e-7,0.000012254647,0.000009479682],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965193,0.00030222943,0.00094219396,0.00019227668,0.0018150018,0.00022895416],"domain_scores_gemma":[0.9980279,0.000717625,0.00052712526,0.00016097908,0.00025893832,0.00030739006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039246366,0.00012934388,0.00033061177,0.00016298801,0.000048046033,0.000073163435,0.0008061022,0.00007839486,0.0007600478],"category_scores_gemma":[0.0024054516,0.00007412482,0.000055199795,0.00021757885,0.0011956528,0.0008034721,0.00026332066,0.0004555759,0.0000029269836],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002223654,0.000105212916,0.002261308,0.000037740636,0.000023452081,0.0002176038,0.0009354332,5.3736187e-8,0.024020549,0.00044514143,0.0013476324,0.9705836],"study_design_scores_gemma":[0.02930968,0.00094688253,0.03048085,0.010314336,0.00012198403,0.009167102,0.008850281,0.74342895,0.14650781,0.018773861,0.00096824695,0.0011300074],"about_ca_topic_score_codex":0.000044622124,"about_ca_topic_score_gemma":0.0000117446725,"teacher_disagreement_score":0.96945363,"about_ca_system_score_codex":0.00009706347,"about_ca_system_score_gemma":0.00028721403,"threshold_uncertainty_score":0.83219904},"labels":[],"label_agreement":null},{"id":"W2563050491","doi":"10.1109/crv.2016.31","title":"Dense Image Labeling Using Deep Convolutional Neural Networks","year":2016,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba; Nvidia","keywords":"Computer science; Pascal (unit); Convolutional neural network; Artificial intelligence; Pattern recognition (psychology); Classifier (UML); Segmentation; Contextual image classification; Machine learning; Image (mathematics)","score_opus":0.022207055467679957,"score_gpt":0.28796832370992126,"score_spread":0.2657612682422413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2563050491","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025296458,0.000048106253,0.9957037,0.00072298915,0.00021100334,0.0000796262,4.1280606e-7,0.00039995377,0.00030459338],"genre_scores_gemma":[0.16485146,0.000008934021,0.83324564,0.0015710181,0.000096793636,0.0000051703682,7.25855e-7,0.0000065951035,0.00021367187],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902123,0.00006242138,0.00019386839,0.00023904665,0.0002431096,0.00024033905],"domain_scores_gemma":[0.9993271,0.00014796101,0.00005494915,0.00024510498,0.00010828943,0.00011655701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023374528,0.000085188025,0.00008226137,0.00005370947,0.000083088664,0.000085247295,0.000389615,0.000040925912,0.00030460494],"category_scores_gemma":[0.000091694026,0.000056155346,0.00003571728,0.00015441987,0.00009542227,0.0007274525,0.00019564616,0.000063948784,0.00003493547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011090951,0.000092232476,0.0015140738,0.000011385169,0.00003184064,0.000109956076,0.00014692036,0.00037429348,0.3471667,0.026877414,0.0071359803,0.61652815],"study_design_scores_gemma":[0.00026229987,0.000019285373,0.00018974641,0.000016247744,0.0000029004764,0.00003757742,0.0000052935134,0.97125417,0.026818054,0.0012260905,0.000039742743,0.00012861617],"about_ca_topic_score_codex":0.000015599659,"about_ca_topic_score_gemma":0.000002445523,"teacher_disagreement_score":0.97087985,"about_ca_system_score_codex":0.00005598361,"about_ca_system_score_gemma":0.00002703408,"threshold_uncertainty_score":0.33352107},"labels":[],"label_agreement":null},{"id":"W2567592860","doi":"10.1016/j.neuroimage.2016.12.014","title":"Towards a Holistic Cortical Thickness Descriptor: Heat Kernel-Based Grey Matter Morphology Signatures","year":2016,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; University of California, San Diego; National Institutes of Health; Servier; U.S. Department of Defense; Eli Lilly and Company; Natural Science Foundation of Shandong Province; Eisai; National Natural Science Foundation of China; Northern California Institute for Research and Education; Pfizer; BioClinica; Novartis Pharmaceuticals Corporation; Takeda Pharmaceutical Company; Medpace; Genentech; Biogen Idec; Bristol-Myers Squibb; GE Healthcare; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; F. Hoffmann-La Roche; Synarc; Roche; Alzheimer's Drug Discovery Foundation; Merck; Fujirebio Europe; Alzheimer's Association; National Science Foundation","keywords":"Grey matter; Artificial intelligence; Pattern recognition (psychology); Voxel; Kernel (algebra); Brain morphometry; Computer science; Polygon mesh; White matter; Mathematics; Geometry; Magnetic resonance imaging; Pure mathematics","score_opus":0.03375723400928972,"score_gpt":0.2976733753786129,"score_spread":0.2639161413693232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2567592860","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016243909,0.000034942954,0.97527987,0.0058984905,0.00066700415,0.00023658207,0.000011826342,0.0005772383,0.0010501484],"genre_scores_gemma":[0.88412213,0.000007458131,0.08889963,0.026248261,0.00011498765,0.000066670036,0.000004082713,0.000034719866,0.00050206285],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976352,0.0003786789,0.00033673382,0.0006743255,0.000503761,0.00047132315],"domain_scores_gemma":[0.9983443,0.00035543874,0.000062878746,0.0008394438,0.00014012413,0.00025781666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030875008,0.00022996735,0.00025001122,0.00014487194,0.00009815865,0.00016869893,0.0010910128,0.00013405249,0.0009109136],"category_scores_gemma":[0.0005594732,0.0001575037,0.00009170964,0.00023192578,0.00038062493,0.0004903838,0.0002800777,0.00033351552,0.0006708692],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033958808,0.00017293128,0.0015061199,0.00003960909,0.000009258912,0.0007247222,0.00009396061,0.0000023458613,0.9084033,0.0016921174,0.06546239,0.021859294],"study_design_scores_gemma":[0.0030786593,0.0007679569,0.14963809,0.00019939136,0.000054434495,0.00038780834,0.0000116561305,0.007587459,0.82613367,0.007267733,0.0036755076,0.001197611],"about_ca_topic_score_codex":0.000027265598,"about_ca_topic_score_gemma":0.0000013235493,"teacher_disagreement_score":0.88638026,"about_ca_system_score_codex":0.000061691746,"about_ca_system_score_gemma":0.000116365314,"threshold_uncertainty_score":0.99738646},"labels":[],"label_agreement":null},{"id":"W2567599812","doi":"10.1016/j.neuroimage.2017.04.039","title":"3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study","year":2017,"lang":"en","type":"review","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":429,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Segmentation; Convolutional neural network; Artificial intelligence; Inference; Normalization (sociology); Context (archaeology); Atlas (anatomy); Pattern recognition (psychology); Process (computing); Machine learning","score_opus":0.09490925143572054,"score_gpt":0.41140573170163297,"score_spread":0.3164964802659124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2567599812","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000012805845,0.25458595,0.7417025,0.000039640167,0.00047309767,0.0028877268,0.000030125908,0.00017842634,0.000101269885],"genre_scores_gemma":[0.000021199698,0.8576088,0.13903657,0.00037854596,0.00032174037,0.0019887534,0.00020061524,0.00006906007,0.00037474887],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9964059,0.0004929048,0.00096606836,0.0010190727,0.0005646402,0.0005513901],"domain_scores_gemma":[0.9976173,0.00046227383,0.00053133356,0.0010985744,0.00011496629,0.00017554764],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00108119,0.00040548472,0.001095479,0.00027732828,0.00023339156,0.0004053809,0.0016263936,0.00021052317,0.00004267351],"category_scores_gemma":[0.0002923405,0.00037280252,0.00028440106,0.0002895693,0.000145128,0.0006066729,0.00051293673,0.0005939083,0.00004129265],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006011855,0.00055081706,0.000053586824,0.0009696971,0.000025211262,0.00011859526,0.00011109772,0.0000028272912,0.0000019145293,0.00016697646,0.0037951123,0.99419814],"study_design_scores_gemma":[0.0042914357,0.001647886,0.0010362238,0.0038436544,0.0006065897,0.00014204487,0.0000718924,0.08072897,0.000015488853,0.0003362774,0.90539354,0.0018860194],"about_ca_topic_score_codex":0.000007300692,"about_ca_topic_score_gemma":0.00002166695,"teacher_disagreement_score":0.99231213,"about_ca_system_score_codex":0.00015020768,"about_ca_system_score_gemma":0.00029176957,"threshold_uncertainty_score":0.9998724},"labels":[],"label_agreement":null},{"id":"W2567720931","doi":"","title":"Carotid Plaque Specimens: Semi-automatic Orientation Correction of micro-MR and micro-CT Images Driven by Axial Feature Segmentation","year":2015,"lang":"en","type":"dissertation","venue":"TSpace (University of Toronto)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Sunnybrook Research Institute","keywords":"Orientation (vector space); Feature (linguistics); Segmentation; Artificial intelligence; Computer vision; Materials science; Biomedical engineering; Pattern recognition (psychology); Nuclear medicine; Computer science; Medicine; Mathematics; Geometry","score_opus":0.006870238710154799,"score_gpt":0.2527299493650928,"score_spread":0.245859710654938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2567720931","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7057695,0.002871991,0.26381174,0.00082406856,0.0039303405,0.0035181942,0.0003342932,0.0008465346,0.018093335],"genre_scores_gemma":[0.41563943,0.0034352532,0.5306122,0.00013036579,0.00020585145,0.000015804713,0.0062644836,0.00012242919,0.043574214],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982466,0.00017604571,0.00024994317,0.0005224239,0.00059715606,0.00020784647],"domain_scores_gemma":[0.998127,0.00007831615,0.0008723402,0.00035815706,0.00041449265,0.0001497157],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023288604,0.00029065053,0.00047338527,0.00014381154,0.00013486325,0.000053536354,0.0005082328,0.0002129346,0.0005874705],"category_scores_gemma":[0.000043746546,0.00036453866,0.00010355989,0.00016674232,0.00012941699,0.0013111233,0.000106756,0.00022403295,0.000006413104],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009949801,0.00016174738,0.00035216796,0.00055101985,0.00016804328,0.000015984479,0.031603474,0.000007822889,0.76963,0.000025747295,0.086096615,0.11128788],"study_design_scores_gemma":[0.002822248,0.00079208944,0.030940194,0.0008293156,0.00037798184,0.000034453406,0.054700483,0.0035306192,0.9046833,0.000074688025,0.00029278593,0.0009218776],"about_ca_topic_score_codex":0.016050614,"about_ca_topic_score_gemma":0.0045870235,"teacher_disagreement_score":0.29013005,"about_ca_system_score_codex":0.00055605784,"about_ca_system_score_gemma":0.00019861813,"threshold_uncertainty_score":0.9998807},"labels":[],"label_agreement":null},{"id":"W2580265780","doi":"10.1049/iet-ipr.2016.0369","title":"Regularised differentiation for image derivatives","year":2017,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Image (mathematics); Computer science; Computer vision; Artificial intelligence","score_opus":0.0263171056947016,"score_gpt":0.3383444077653375,"score_spread":0.3120273020706359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2580265780","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00160797,0.00004708734,0.9944314,0.0022154173,0.00011802787,0.0003276661,0.0000029905643,0.00037319606,0.0008762538],"genre_scores_gemma":[0.20042823,0.0000048931556,0.7988366,0.0002496274,0.000076364384,0.00009434783,0.000008360296,0.000014797226,0.000286812],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988065,0.000034878303,0.00024461487,0.0003836625,0.00027241374,0.00025793028],"domain_scores_gemma":[0.99855494,0.00005431994,0.0003846478,0.0006295102,0.00028951484,0.000087051],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003257446,0.00014180882,0.00016002374,0.00007280543,0.00078876823,0.002046349,0.0011211452,0.00005473104,0.000017424565],"category_scores_gemma":[0.00068524363,0.00013007165,0.000059497557,0.0000706706,0.00019766229,0.0036733737,0.0002445846,0.0000992739,0.000007947229],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008677413,0.000064260894,0.00014116934,0.00018448377,0.000009391678,0.000006531458,0.00080209837,9.707255e-8,0.43764162,0.0006617631,0.0020712097,0.5584087],"study_design_scores_gemma":[0.0007031388,0.00004714531,0.0057566958,0.00010851218,0.000011901878,0.0000055004193,0.000051363222,0.04518692,0.93137807,0.016299209,0.0001926617,0.0002588862],"about_ca_topic_score_codex":0.0000040331806,"about_ca_topic_score_gemma":8.0021965e-7,"teacher_disagreement_score":0.5581498,"about_ca_system_score_codex":0.000032858494,"about_ca_system_score_gemma":0.000073186784,"threshold_uncertainty_score":0.99898964},"labels":[],"label_agreement":null},{"id":"W2580932265","doi":"10.1117/1.jmi.4.1.014001","title":"Deformable image registration for tissues with large displacements","year":2017,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; Hospital for Sick Children","keywords":"Medicine; Image registration; Computer vision; Artificial intelligence; Image (mathematics)","score_opus":0.016046479981313068,"score_gpt":0.36251565676395037,"score_spread":0.3464691767826373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2580932265","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000635947,0.00009062127,0.98386407,0.014217861,0.0002849442,0.00012574249,0.0000012837472,0.0000330143,0.0007465041],"genre_scores_gemma":[0.22499652,0.00017070265,0.77151394,0.002199469,0.0005647077,0.000017986407,0.0000041093895,0.000020587535,0.0005119542],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978284,0.000035946177,0.00042811505,0.0001380788,0.001321394,0.00024806577],"domain_scores_gemma":[0.99823844,0.000078961624,0.0007596025,0.00036778103,0.00028402897,0.0002712098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002278525,0.000095383315,0.00018623461,0.0000793904,0.00030276575,0.000582942,0.0014511618,0.000036992045,0.000063562824],"category_scores_gemma":[0.0011014229,0.00006497674,0.00005875426,0.000043295313,0.00013479567,0.0026221662,0.0001628357,0.00022806444,0.000004137141],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001151167,0.0003805187,0.0047763786,0.0001892267,0.000103967264,0.00083091436,0.0009112983,0.0000015643019,0.0037861995,0.0066823876,0.11167876,0.87054366],"study_design_scores_gemma":[0.018784987,0.0010128774,0.0052823164,0.0034113207,0.00017665738,0.0028357592,0.00091298117,0.71690387,0.15168889,0.013331279,0.084599234,0.001059837],"about_ca_topic_score_codex":0.00001254415,"about_ca_topic_score_gemma":0.0000049603395,"teacher_disagreement_score":0.8694838,"about_ca_system_score_codex":0.000045421028,"about_ca_system_score_gemma":0.00018797576,"threshold_uncertainty_score":0.5621323},"labels":[],"label_agreement":null},{"id":"W2586920272","doi":"","title":"Semi-automatic segmentation of prostate by directional search for edge boundaries","year":2015,"lang":"en","type":"article","venue":"Digital Library (University of West Bohemia)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sunnybrook Hospital","funders":"National Institutes of Health; Tekes","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Segmentation; Artificial intelligence; Image segmentation; Prostate; Computer vision; Medicine; Internal medicine","score_opus":0.01625503356986517,"score_gpt":0.22553957073452216,"score_spread":0.209284537164657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2586920272","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10933697,0.00008104282,0.8815393,0.0011114775,0.00010769131,0.00051717507,0.0006900185,0.0004128825,0.006203431],"genre_scores_gemma":[0.682425,0.000022048094,0.30932128,0.00017817943,0.000035622892,0.00000505439,0.0012301856,0.000024139155,0.006758464],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991666,0.000024150486,0.00014788694,0.00019447684,0.00033229002,0.00013459448],"domain_scores_gemma":[0.999367,0.00009626267,0.00012611414,0.00017232212,0.00009951331,0.00013877176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000091783266,0.000082599094,0.00014699045,0.000107827014,0.00009597998,0.00019544763,0.00045333346,0.000044174187,0.00004780352],"category_scores_gemma":[0.000026490592,0.000098336735,0.00005875177,0.00030047825,0.0003298155,0.0050191763,0.00024090247,0.000057398953,0.0000109023185],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020906082,0.00096694764,0.02602709,0.00084517786,0.00018583097,0.000022965332,0.013503401,0.000025513646,0.014299221,0.007889648,0.6601148,0.2759103],"study_design_scores_gemma":[0.0033483612,0.0011434149,0.0020401364,0.00022617148,0.0000336555,0.000020154663,0.005846729,0.019829886,0.9304137,0.008253827,0.028186472,0.0006574667],"about_ca_topic_score_codex":0.000013425315,"about_ca_topic_score_gemma":3.4346866e-7,"teacher_disagreement_score":0.9161145,"about_ca_system_score_codex":0.000030890893,"about_ca_system_score_gemma":0.00028601932,"threshold_uncertainty_score":0.40100557},"labels":[],"label_agreement":null},{"id":"W2589663521","doi":"10.1016/j.media.2017.02.007","title":"Longitudinal segmentation of age-related white matter hyperintensities","year":2017,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; Takeda Pharmaceutical Company; IXICO; H. Lundbeck A/S; Wolfson Foundation; National Institute on Aging; National Institute for Health and Care Research; Seventh Framework Programme; Northern California Institute for Research and Education; DoD Alzheimer's Disease Neuroimaging Initiative; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; National Institute on Handicapped Research; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Roche; Merck; Alzheimer's Drug Discovery Foundation; AbbVie; Fujirebio Europe; Alzheimer's Association; GE Healthcare; Alzheimer's Disease Neuroimaging Initiative; Medical Research Council; Johnson and Johnson; Meso Scale Diagnostics","keywords":"Hyperintensity; Segmentation; Lesion; Robustness (evolution); Computer science; Artificial intelligence; Longitudinal data; Longitudinal study; Pattern recognition (psychology); Medicine; Magnetic resonance imaging; Data mining; Radiology; Pathology","score_opus":0.016349241689653973,"score_gpt":0.3072204340925042,"score_spread":0.29087119240285025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2589663521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020201733,0.000026337895,0.9717162,0.0038913663,0.00011672389,0.00009178098,0.0000031750394,0.000113422306,0.0038392611],"genre_scores_gemma":[0.8330726,0.000040215,0.16380307,0.0010188586,0.00004099136,0.00002054675,0.0000312319,0.000010950436,0.0019615516],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975848,0.00011528733,0.00056836894,0.0004006463,0.0010900504,0.00024082027],"domain_scores_gemma":[0.9979313,0.00007317149,0.00039594393,0.001124091,0.00023928199,0.00023622779],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00074177905,0.0001520996,0.00043453733,0.00033598015,0.00019979246,0.00030103308,0.0014304838,0.0001023304,0.004302425],"category_scores_gemma":[0.0004884034,0.00012849539,0.00026792314,0.0004404817,0.0006949129,0.0009175306,0.00045923027,0.00020118833,0.00015455516],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003464821,0.0007127801,0.7086186,0.00023748048,0.00485872,0.0025916798,0.0042197164,0.000027284847,0.026037633,0.0012941904,0.03876963,0.21259768],"study_design_scores_gemma":[0.001163996,0.00012544259,0.8485288,0.00012538464,0.0012005977,0.000054179254,0.00029204608,0.059823014,0.08630972,0.0016901722,0.00008638678,0.000600251],"about_ca_topic_score_codex":0.0002448498,"about_ca_topic_score_gemma":0.000036225192,"teacher_disagreement_score":0.81287086,"about_ca_system_score_codex":0.000031766267,"about_ca_system_score_gemma":0.00004736334,"threshold_uncertainty_score":0.9966078},"labels":[],"label_agreement":null},{"id":"W2590760288","doi":"10.1016/j.knosys.2017.02.025","title":"Automatic computation of regions of interest by robust principal component analysis. Application to automatic dementia diagnosis","year":2017,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute on Aging; Canadian Institutes of Health Research; Genentech; National Institutes of Health; Pfizer; Novartis Pharmaceuticals Corporation; Ministerio de Ciencia e Innovación; F. Hoffmann-La Roche; GE Healthcare; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Takeda Pharmaceutical Company; Eli Lilly and Company; Bristol-Myers Squibb; Merck","keywords":"Computer science; Principal component analysis; Computation; Dementia; Component (thermodynamics); Artificial intelligence; Data mining; Pattern recognition (psychology); Algorithm; Medicine; Pathology","score_opus":0.05652803893087573,"score_gpt":0.3291563381050803,"score_spread":0.2726282991742046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2590760288","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05047636,0.00018286906,0.94750917,0.00020483938,0.00020988361,0.0010270145,0.000018486171,0.00023507129,0.00013629886],"genre_scores_gemma":[0.936044,0.0000020252778,0.063342236,0.000033002998,0.000019959565,0.0004939284,0.000037508253,0.0000123814625,0.00001499835],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99758923,0.0003170341,0.0010599406,0.000425054,0.00039486162,0.00021387433],"domain_scores_gemma":[0.99664384,0.00026939262,0.0012241626,0.0012827246,0.0003836174,0.00019624812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009433159,0.00019284623,0.00057688594,0.0004979574,0.00018826382,0.00017559371,0.0013941528,0.00008135008,0.000012755021],"category_scores_gemma":[0.00022293258,0.00018512679,0.00016481383,0.0006384035,0.00014551777,0.00024245234,0.0002372844,0.000086609056,0.000032650994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048058642,0.010901854,0.17491874,0.015564113,0.0062876055,0.000021934145,0.011089371,0.060034942,0.090635106,0.027996697,0.07372309,0.5287785],"study_design_scores_gemma":[0.00039263818,0.0001183752,0.009091009,0.0004736344,0.00023257136,8.6464723e-7,0.000041112336,0.9600415,0.029273251,0.00004641698,0.00011441415,0.00017417666],"about_ca_topic_score_codex":0.00034733856,"about_ca_topic_score_gemma":0.00012364262,"teacher_disagreement_score":0.9000066,"about_ca_system_score_codex":0.00012409406,"about_ca_system_score_gemma":0.00009975186,"threshold_uncertainty_score":0.75492513},"labels":[],"label_agreement":null},{"id":"W2591708961","doi":"10.1007/s13534-017-0020-9","title":"Automatic error correction using adaptive weighting for vessel-based deformable image registration","year":2017,"lang":"en","type":"article","venue":"Biomedical Engineering Letters","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Custom Security Industries (Canada); Ontario Tech University","funders":"","keywords":"Weighting; Image registration; Metric (unit); Artificial intelligence; Computer science; Computer vision; Adaptation (eye); Error detection and correction; Image (mathematics); Algorithm; Engineering; Radiology; Optics; Physics; Medicine","score_opus":0.025129942129246685,"score_gpt":0.28403622273370766,"score_spread":0.25890628060446097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2591708961","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010099516,0.000006085953,0.9859724,0.0018162825,0.0012556283,0.00028677774,0.0000024817984,0.0005414838,0.000019352707],"genre_scores_gemma":[0.16459243,5.3432615e-7,0.8345466,0.0005687037,0.00018629797,0.00006369813,0.00001235862,0.000017746466,0.00001161208],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872285,0.00001910363,0.00030086105,0.0002828184,0.00036552097,0.0003088402],"domain_scores_gemma":[0.99897194,0.00012753569,0.00023053729,0.00046008843,0.000054696484,0.00015522065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046966152,0.00014931751,0.000159674,0.00015333557,0.00030866094,0.0003013508,0.00060634786,0.00007792797,0.000009738629],"category_scores_gemma":[0.0006154158,0.00014389172,0.000070035705,0.0001191025,0.0001229139,0.0008650058,0.00007087252,0.00014238626,0.00000502209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011881994,0.00008974919,0.00002207308,0.0003106433,0.00004246869,0.00004408963,0.00023677957,0.00215963,0.87463194,0.00036882915,0.014945929,0.107135974],"study_design_scores_gemma":[0.00036164786,0.000057086963,0.0002150401,0.00015499424,0.000009693828,0.000009189367,0.0000055591554,0.9429332,0.055601183,0.000018009661,0.00047568127,0.00015869222],"about_ca_topic_score_codex":0.000040927705,"about_ca_topic_score_gemma":3.9863e-7,"teacher_disagreement_score":0.9407736,"about_ca_system_score_codex":0.00016322674,"about_ca_system_score_gemma":0.00007269719,"threshold_uncertainty_score":0.5867734},"labels":[],"label_agreement":null},{"id":"W2592472017","doi":"10.1111/j.1467-9469.2011.00753.x","title":"Confidence Regions for Means of Random Sets Using Oriented Distance Functions","year":2012,"lang":"en","type":"article","venue":"Scandinavian Journal of Statistics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Engineering and Physical Sciences Research Council; York University; University of South Carolina","keywords":"Mathematics; Set (abstract data type); Image (mathematics); Data set; Statistics; Algorithm; Artificial intelligence; Computer science","score_opus":0.03979938216736881,"score_gpt":0.32911004730023863,"score_spread":0.28931066513286985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592472017","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002869101,0.00019596971,0.99832493,0.000097302305,0.0006959979,0.00017465581,0.00016204722,0.000016735366,0.000045452478],"genre_scores_gemma":[0.25203857,0.000037494567,0.74771136,0.00005737539,0.00006952665,0.0000034278287,0.0000052974583,0.000007271126,0.00006966575],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986192,0.00008407663,0.0005855826,0.00009436065,0.0003853277,0.00023143624],"domain_scores_gemma":[0.99781513,0.00043282725,0.0006963794,0.0001984031,0.0006272346,0.00022999667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064684346,0.000097229975,0.0002541708,0.00013404811,0.000112468166,0.00003343238,0.00032269894,0.000034241293,0.000028913917],"category_scores_gemma":[0.00060386263,0.00008445191,0.000071193776,0.00026649938,0.00016398543,0.0005630989,0.000033391527,0.00012977482,0.000001091915],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036082257,0.00050473004,0.005298533,0.0003040523,0.00022537795,0.000048236867,0.005173417,0.00014354632,0.00786104,0.8980441,0.048148647,0.033887517],"study_design_scores_gemma":[0.03229846,0.0059275394,0.01335708,0.0065006763,0.0018412397,0.0035984907,0.0065742703,0.1709138,0.1528698,0.5885886,0.014639737,0.002890323],"about_ca_topic_score_codex":0.000007290656,"about_ca_topic_score_gemma":0.0000020828295,"teacher_disagreement_score":0.3094555,"about_ca_system_score_codex":0.00008259119,"about_ca_system_score_gemma":0.0001180851,"threshold_uncertainty_score":0.3443849},"labels":[],"label_agreement":null},{"id":"W2595012910","doi":"10.15353/vsnl.v2i1.90","title":"Evaluation of a Coherent Point Drift Algorithm for Breast Image Registration via Surface Markers","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; Sunnybrook Research Institute","keywords":"Affine transformation; Image registration; Artificial intelligence; Computer vision; Matching (statistics); Point set registration; Computer science; Point (geometry); Algorithm; Mathematics; Image (mathematics); Medicine; Pathology; Geometry","score_opus":0.014613050608372702,"score_gpt":0.31655649150161097,"score_spread":0.30194344089323827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2595012910","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002778421,0.0002643132,0.9944221,0.0016836606,0.00038023645,0.00037923554,0.000014542856,0.00002388885,0.000053640557],"genre_scores_gemma":[0.59309936,0.000013891608,0.40670654,0.00006007204,0.00007634812,0.000005938938,0.000003238997,0.000008069449,0.000026556054],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973664,0.00034028006,0.00080973393,0.0001559251,0.0012135815,0.000114030656],"domain_scores_gemma":[0.9956142,0.00040914185,0.0009850956,0.00012528621,0.002757874,0.00010843091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045169885,0.00011214675,0.00023322992,0.00016046659,0.000061908286,0.00014188698,0.00022117789,0.000028456841,0.000013618463],"category_scores_gemma":[0.00016606635,0.00007467769,0.00008466715,0.00012576423,0.00008449999,0.0011134116,0.00003798328,0.00005852486,0.0000013043162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021549682,0.00007650964,0.00007360426,0.00004349515,0.00004343991,0.0000023464477,0.0001416161,0.0005102122,0.020617606,0.00047071936,0.0048337774,0.9731651],"study_design_scores_gemma":[0.0019624059,0.00021889483,0.0033086957,0.0005531353,0.00004386063,0.0005248228,0.000109579065,0.982515,0.0023346131,0.00824222,0.00007022078,0.00011659726],"about_ca_topic_score_codex":0.000010521306,"about_ca_topic_score_gemma":2.2575519e-7,"teacher_disagreement_score":0.98200476,"about_ca_system_score_codex":0.00011722782,"about_ca_system_score_gemma":0.00021859795,"threshold_uncertainty_score":0.30452678},"labels":[],"label_agreement":null},{"id":"W2599622777","doi":"10.1016/j.compmedimag.2017.03.004","title":"Graph cut-based method for segmenting the left ventricle from MRI or echocardiographic images","year":2017,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Voxel; Cut; Artificial intelligence; Segmentation; Computer vision; Bézier curve; Computer science; Euclidean space; Coordinate system; Mathematics; Image segmentation; Geometry; Combinatorics","score_opus":0.021461030411814904,"score_gpt":0.33601303611788275,"score_spread":0.31455200570606784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2599622777","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00059114135,0.0005376468,0.9811036,0.015944166,0.00081335846,0.00049145485,0.00002105289,0.00045838946,0.000039161936],"genre_scores_gemma":[0.03464253,0.0006550979,0.9540058,0.010148672,0.00036535182,0.000094596486,0.000027917384,0.000035163066,0.00002490072],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99697065,0.00032935318,0.000507858,0.0007757425,0.0008799179,0.00053645275],"domain_scores_gemma":[0.99595976,0.0017120467,0.0003653702,0.001339826,0.00018291478,0.00044006447],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002401356,0.0003043501,0.0004469723,0.00025407324,0.0014196923,0.0012790755,0.002438035,0.00012380951,0.000024206569],"category_scores_gemma":[0.0009368845,0.00020428232,0.00028814757,0.00029366338,0.0008126436,0.00056665094,0.0007413698,0.00046584825,0.0000017880482],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007384113,0.00017425873,0.0057752198,0.00013253182,0.00022284787,0.00021034057,0.00040851053,0.000005328532,0.0019273355,0.0020657645,0.02023114,0.9687729],"study_design_scores_gemma":[0.0046764943,0.0001203943,0.008310433,0.0004950024,0.00014332384,0.0000885543,0.000062084335,0.9374132,0.015806194,0.024841031,0.007358946,0.0006843536],"about_ca_topic_score_codex":0.00016021614,"about_ca_topic_score_gemma":0.000009423697,"teacher_disagreement_score":0.9680885,"about_ca_system_score_codex":0.000013331096,"about_ca_system_score_gemma":0.00015122595,"threshold_uncertainty_score":0.9998803},"labels":[],"label_agreement":null},{"id":"W2600720017","doi":"10.15353/vsnl.v2i1.113","title":"A Local ROI-specific Atlas-based Segmentation of Prostate Gland and Transitional Zone in Diffusion MRI","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Segmentation; Atlas (anatomy); Computer science; Prostate; Prostate cancer; Sørensen–Dice coefficient; Magnetic resonance imaging; Artificial intelligence; Effective diffusion coefficient; Prostate gland; Region of interest; Computer vision; Image segmentation; Region growing; Medicine; Anatomy; Radiology; Scale-space segmentation; Cancer","score_opus":0.007047932377798291,"score_gpt":0.26023730487546815,"score_spread":0.2531893724976699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2600720017","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10627753,0.00043866163,0.8912926,0.0016861096,0.00012269724,0.00015335139,0.000005273481,0.000014123311,0.0000096824115],"genre_scores_gemma":[0.93208575,0.00009786607,0.06766101,0.000103886254,0.000027459695,0.0000032234227,0.0000039331253,0.0000056606273,0.000011195928],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982472,0.00016865213,0.0007023185,0.00015455038,0.0006194296,0.00010785731],"domain_scores_gemma":[0.99876416,0.00034358975,0.00041545313,0.000070077775,0.00030189136,0.00010485088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007170333,0.000107249754,0.00023421743,0.00035904092,0.000046580524,0.00009393587,0.0001280871,0.00002728101,0.0000070109127],"category_scores_gemma":[0.000018662608,0.00007087802,0.0000392503,0.00017588244,0.00013425012,0.0006844455,0.000031594638,0.00008385335,8.8940834e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000484292,0.000634002,0.030984325,0.00042479965,0.00004650585,0.0001431522,0.0027143757,0.0106134955,0.13421446,0.00443492,0.0030170623,0.8122886],"study_design_scores_gemma":[0.012481331,0.0008486232,0.14411928,0.0038442977,0.000018880293,0.0008970278,0.00057054224,0.8164886,0.013704787,0.0059530907,0.00062601996,0.00044748952],"about_ca_topic_score_codex":0.000010836009,"about_ca_topic_score_gemma":6.883796e-7,"teacher_disagreement_score":0.8258082,"about_ca_system_score_codex":0.000059993563,"about_ca_system_score_gemma":0.00008097457,"threshold_uncertainty_score":0.2890322},"labels":[],"label_agreement":null},{"id":"W2603342637","doi":"10.1007/s10278-017-9964-7","title":"Accuracy Validation of an Automated Method for Prostate Segmentation in Magnetic Resonance Imaging","year":2017,"lang":"en","type":"article","venue":"Journal of Digital Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"Prostate Cancer Canada; Cancer Care Ontario; Ontario Institute for Cancer Research; Movember Foundation","keywords":"Magnetic resonance imaging; Segmentation; Computer science; Prostate; Artificial intelligence; Observer (physics); Sørensen–Dice coefficient; Precision and recall; Pattern recognition (psychology); Image segmentation; Similarity (geometry); Context (archaeology); Computer vision; Medicine; Image (mathematics); Radiology; Cancer","score_opus":0.021322034687467583,"score_gpt":0.3774140373106404,"score_spread":0.3560920026231728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2603342637","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03153259,0.00026446942,0.9667529,0.0007822209,0.00015529653,0.0002863922,0.00000683044,0.000076801756,0.0001425353],"genre_scores_gemma":[0.44990838,0.000011101508,0.5499331,0.000082564206,0.000028561934,0.000007710227,0.0000042298607,0.000009427732,0.000014929761],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985291,0.00006667218,0.0006858424,0.00017881251,0.00035931135,0.00018029218],"domain_scores_gemma":[0.9978813,0.000190759,0.0011432575,0.00032478524,0.00037890408,0.00008099822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010044557,0.00010565274,0.00020429818,0.00025312413,0.00008759732,0.00092531194,0.0007490453,0.000015474376,0.0000028173924],"category_scores_gemma":[0.0009561105,0.0000980463,0.000064293105,0.00012140316,0.000064157706,0.00909241,0.00011163,0.000105454455,6.8649973e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002102472,0.00007383125,0.0064019496,0.000027606678,0.0000024812873,0.000027854889,0.0007136591,0.000028756462,0.03473102,0.000043454904,0.00016342993,0.9577649],"study_design_scores_gemma":[0.0019362649,0.00020624111,0.017485846,0.000344834,0.000014138184,0.00015871084,0.00024010452,0.459027,0.5120675,0.008200744,0.00011744967,0.00020120382],"about_ca_topic_score_codex":0.000014676034,"about_ca_topic_score_gemma":5.642384e-7,"teacher_disagreement_score":0.9575637,"about_ca_system_score_codex":0.000068917245,"about_ca_system_score_gemma":0.00009963561,"threshold_uncertainty_score":0.8922804},"labels":[],"label_agreement":null},{"id":"W2604419855","doi":"10.15353/vsnl.v1i1.56","title":"Multi-Neighborhood Convolutional Networks","year":2015,"lang":"en","type":"article","venue":"Vision Letters","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Scale (ratio); Feature (linguistics); Image (mathematics); Convolutional neural network; Pattern recognition (psychology); Space (punctuation); Artificial intelligence; Computer science; Mathematics; Algorithm; Geography; Cartography","score_opus":0.02929442153991434,"score_gpt":0.3017065741466955,"score_spread":0.27241215260678114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604419855","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005066208,0.00005646548,0.99143267,0.0068540894,0.0004806466,0.00010797446,6.1126883e-7,0.00038407132,0.00017685459],"genre_scores_gemma":[0.18787271,0.000006376636,0.7858749,0.025924984,0.00015634485,0.000020147192,0.000010530181,0.000010310523,0.00012371788],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988531,0.00008340962,0.00018566061,0.00026570584,0.00041075234,0.00020136913],"domain_scores_gemma":[0.99926513,0.000056583485,0.00006248741,0.0003197595,0.000072201685,0.00022382071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033618324,0.00009241732,0.00009087536,0.00008361744,0.000051542153,0.00009841368,0.0005381233,0.000044384167,0.000038187347],"category_scores_gemma":[0.00008218756,0.000081287006,0.000038031634,0.00023579816,0.00007302887,0.00048600652,0.00018600553,0.0001304038,0.0001546675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000120145705,0.0002097573,0.0013355573,0.0000070405026,0.00002257252,0.00009111047,0.00050471205,0.0011045968,0.029763363,0.0038460076,0.83528805,0.1278152],"study_design_scores_gemma":[0.0016108475,0.0001346889,0.0041580866,0.000037794052,0.00000485734,0.00002726476,0.000023552131,0.9818682,0.005436532,0.0004221634,0.005929367,0.0003466231],"about_ca_topic_score_codex":0.000008826717,"about_ca_topic_score_gemma":4.016353e-7,"teacher_disagreement_score":0.9807636,"about_ca_system_score_codex":0.000060823113,"about_ca_system_score_gemma":0.000036617323,"threshold_uncertainty_score":0.33147877},"labels":[],"label_agreement":null},{"id":"W2605505424","doi":"10.1117/12.2254364","title":"Volume calculation of CT lung lesions based on Halton low-discrepancy sequences","year":2017,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Robustness (evolution); Volume (thermodynamics); Lung; Algorithm; Lung volumes; Tomography; Computer science; Sagittal plane; Dimension (graph theory); Computed tomography; Mathematics; Radiology; Medicine; Physics","score_opus":0.014851977174504314,"score_gpt":0.2708444900550021,"score_spread":0.2559925128804978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605505424","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97468895,0.000028218607,0.017548364,0.00458938,0.00024826918,0.0005701446,0.000024500498,0.00013985319,0.0021623487],"genre_scores_gemma":[0.57052505,0.000030322899,0.42882037,0.00017079953,0.00014581281,0.00010538978,0.0000061622404,0.000027686236,0.00016838385],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99743694,3.8665736e-8,0.0007251561,0.00044275032,0.0010797229,0.00031537557],"domain_scores_gemma":[0.9973112,0.00014833953,0.0008341782,0.0001811267,0.0013857775,0.00013942434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008343994,0.0002618181,0.0003752848,0.00013375048,0.00019769222,0.00023871384,0.002298124,0.00011371569,0.000014093329],"category_scores_gemma":[0.0013468106,0.00021623785,0.00050036015,0.000209766,0.00037679213,0.0011980527,0.00027798192,0.00026465507,0.0000014037653],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004984839,0.0002573939,0.0016022713,0.0007860559,0.0001932572,4.8032354e-7,0.00022434953,0.00026423496,0.45354068,0.5342522,0.005193652,0.0036355346],"study_design_scores_gemma":[0.00074702164,0.00029654137,0.0038808517,0.00077587075,0.000060686525,0.0000058025585,0.00012613289,0.5080189,0.4837698,0.0017710335,0.00025875084,0.0002885801],"about_ca_topic_score_codex":0.000038740774,"about_ca_topic_score_gemma":1.6084807e-7,"teacher_disagreement_score":0.5324812,"about_ca_system_score_codex":0.00014727334,"about_ca_system_score_gemma":0.00007031961,"threshold_uncertainty_score":0.88179237},"labels":[],"label_agreement":null},{"id":"W2607586613","doi":"","title":"BOUNDARY POINT DETECTION FOR ULTRASOUND IMAGE SEGMENTATION USING GUMBEL DISTRIBUTIONS","year":2018,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Context (archaeology); Gumbel distribution; Boundary (topology); Artificial intelligence; Segmentation; Image segmentation; Noise (video); Computer vision; Computer science; Intensity (physics); Contrast (vision); Ultrasound; Point (geometry); Image (mathematics); Mathematics; Pattern recognition (psychology); Physics; Acoustics; Geometry; Optics; Geology; Statistics; Mathematical analysis","score_opus":0.021742967481904938,"score_gpt":0.32661376636630646,"score_spread":0.3048707988844015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2607586613","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007117824,0.0000070212154,0.99092436,0.00019816616,0.00034112003,0.00044301795,0.00001558375,0.00045649032,0.0004963921],"genre_scores_gemma":[0.14502525,0.000003184956,0.8541089,0.00048193158,0.00014623319,0.000058248705,0.000028813487,0.000007958868,0.00013944223],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99897695,0.000046852216,0.00024794866,0.00029234277,0.00021523047,0.00022069388],"domain_scores_gemma":[0.99914175,0.00011835569,0.000092267495,0.00028398182,0.00027172826,0.00009191882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003408469,0.00010177874,0.000086514134,0.00008142098,0.00042016845,0.00030470133,0.00026728972,0.000047394788,0.00013300126],"category_scores_gemma":[0.0002001678,0.00009691384,0.000055396224,0.0002926293,0.00017874528,0.0011720298,0.00006915979,0.000059360475,0.000037008605],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000050468366,0.00004253958,0.000022833714,0.0000102040585,0.000009464562,6.0186153e-7,0.00018444953,2.247181e-7,0.92516255,0.0012996823,0.0026087936,0.070653595],"study_design_scores_gemma":[0.00028032312,0.0001537579,0.00042368006,0.000009100955,0.0000100463685,0.000028864762,0.00007475992,0.016789641,0.97346264,0.008277352,0.00035439595,0.00013541356],"about_ca_topic_score_codex":0.000061976825,"about_ca_topic_score_gemma":0.000022653487,"teacher_disagreement_score":0.13790743,"about_ca_system_score_codex":0.00017566953,"about_ca_system_score_gemma":0.00006537547,"threshold_uncertainty_score":0.39520317},"labels":[],"label_agreement":null},{"id":"W2608324152","doi":"10.1090/crmp/044/08","title":"Variational versus pde-based approaches in mathematical image processing","year":2008,"lang":"en","type":"book-chapter","venue":"CRM proceedings & lecture notes","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Applied mathematics; Image (mathematics); Computer science; Image processing; Mathematics; Artificial intelligence; Computer vision","score_opus":0.057823373220532374,"score_gpt":0.2753572358026196,"score_spread":0.21753386258208723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2608324152","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000010554599,0.00027676433,0.7899922,0.0010848683,0.00009021594,0.00047184556,0.0000033461235,0.0004952901,0.20757492],"genre_scores_gemma":[0.101877436,0.000072898896,0.8942106,0.0016274214,0.00087370456,0.00029450178,0.00010905858,0.00021886731,0.00071551255],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.996936,0.000010114797,0.000644261,0.00093626836,0.0010371876,0.00043615417],"domain_scores_gemma":[0.99846,0.00033130115,0.00044510115,0.00025612555,0.00033125008,0.00017623437],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038079673,0.0005595695,0.00056099304,0.00048839336,0.00015988685,0.00040093143,0.0010130904,0.0005903666,0.0001769092],"category_scores_gemma":[0.000726672,0.000513554,0.00015356731,0.00023536898,0.00026334487,0.00065073965,0.00020039859,0.0009590452,0.00006929415],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015961035,0.00031561826,0.000026433565,0.001737065,0.00009499712,0.00016426187,0.0040584635,0.00003979811,0.002047285,0.7374593,0.00133084,0.2525663],"study_design_scores_gemma":[0.00231605,0.00039161582,0.00005991486,0.001726281,0.00010256033,0.0001422014,0.000018937595,0.08127233,0.053327765,0.85654014,0.0021453209,0.0019568927],"about_ca_topic_score_codex":0.000002106336,"about_ca_topic_score_gemma":0.0000010353673,"teacher_disagreement_score":0.25060943,"about_ca_system_score_codex":0.00025121597,"about_ca_system_score_gemma":0.00044393254,"threshold_uncertainty_score":0.9997316},"labels":[],"label_agreement":null},{"id":"W2608630160","doi":"10.1109/tip.2017.2699481","title":"A Multi-Objective Decision Making Approach for Solving the Image Segmentation Fusion Problem","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Image segmentation; Artificial intelligence; Image fusion; Computer vision; Computer science; Image processing; Segmentation; Image (mathematics); Scale-space segmentation; Fusion; Pattern recognition (psychology)","score_opus":0.03165457143483457,"score_gpt":0.34095523763401336,"score_spread":0.3093006661991788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2608630160","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00026503744,0.00004198447,0.9971124,0.00029081744,0.00022211293,0.0013432265,0.000010867972,0.00039519896,0.00031834163],"genre_scores_gemma":[0.24849033,0.000014175208,0.7506164,0.00021696067,0.000040439216,0.0005097902,0.0000019246927,0.000026921862,0.000083112456],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99794406,0.00006868522,0.0004189783,0.0006706673,0.00051604066,0.00038156836],"domain_scores_gemma":[0.99811375,0.00023842411,0.0004448614,0.00075037545,0.00036859044,0.00008402595],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008608294,0.00025654238,0.00020480888,0.00018869094,0.0032577321,0.0021171202,0.0012317569,0.000093758,0.00001305586],"category_scores_gemma":[0.00010130666,0.00019625347,0.00013789965,0.0002200622,0.00024669626,0.0034150686,0.000022674763,0.00035331465,0.000007653922],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039851388,0.00019574419,0.000002464536,0.00011229382,0.000013248415,0.0000028300342,0.001999171,0.0001932771,0.14824304,0.000007451004,0.00010630288,0.8490843],"study_design_scores_gemma":[0.0009370541,0.00009275053,0.00009437295,0.00033751718,0.00003652637,0.00002129541,0.0005144347,0.58022624,0.41665325,0.0008027454,0.0000084128205,0.00027537878],"about_ca_topic_score_codex":0.000019338586,"about_ca_topic_score_gemma":0.0000090490685,"teacher_disagreement_score":0.84880894,"about_ca_system_score_codex":0.00015581769,"about_ca_system_score_gemma":0.00012656701,"threshold_uncertainty_score":0.9989188},"labels":[],"label_agreement":null},{"id":"W2608682763","doi":"10.1109/icpr.2016.7900296","title":"A multi-objective approach based on TOPSIS to solve the image segmentation combination problem","year":2016,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"TOPSIS; Segmentation; Image segmentation; Computer science; Scale-space segmentation; Segmentation-based object categorization; Artificial intelligence; Consistency (knowledge bases); Image fusion; Image (mathematics); Pattern recognition (psychology); Fusion; Enhanced Data Rates for GSM Evolution; Mathematical optimization; Data mining; Mathematics; Operations research","score_opus":0.01900689717070563,"score_gpt":0.28456621280282834,"score_spread":0.2655593156321227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2608682763","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019661154,0.0000010238888,0.985712,0.0075854072,0.000059479436,0.0010584898,0.0000023048106,0.00036562036,0.005019098],"genre_scores_gemma":[0.040767025,0.0000011424123,0.9527102,0.005462135,0.000015132433,0.00043324058,0.000003971912,0.0000086669725,0.00059849897],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99862784,0.00011006421,0.00021384696,0.00038663702,0.00046809937,0.00019350692],"domain_scores_gemma":[0.9989551,0.00021770802,0.0000816194,0.0004305002,0.00021945286,0.00009566233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005701582,0.00012433551,0.000093610855,0.00013132722,0.00012036948,0.0001422631,0.0005515787,0.000038156362,0.00007124377],"category_scores_gemma":[0.0001751527,0.0000638832,0.000045063247,0.00037842887,0.00005651376,0.00053086213,0.00010879127,0.000067812,0.000119475495],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031556538,0.0010126596,0.00010293698,0.000027298722,0.000025168027,0.0000038129074,0.003099411,0.000040780982,0.28387484,0.016490607,0.021474177,0.67381674],"study_design_scores_gemma":[0.0017711926,0.0005046926,0.0021817333,0.00005714704,0.000008251835,0.0000023826922,0.00033433817,0.11919164,0.87374645,0.0017907021,0.0001164406,0.00029504774],"about_ca_topic_score_codex":0.00003080113,"about_ca_topic_score_gemma":0.0000037687976,"teacher_disagreement_score":0.6735217,"about_ca_system_score_codex":0.00015261375,"about_ca_system_score_gemma":0.000049413393,"threshold_uncertainty_score":0.26050815},"labels":[],"label_agreement":null},{"id":"W2609824936","doi":"10.1109/icpr.2016.7899956","title":"Spatially constrained sparse regression for the data-driven discovery of Neuroimaging biomarkers","year":2016,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Lasso (programming language); Elastic net regularization; Neuroimaging; Regression; Voxel; Computer science; Artificial intelligence; Regularization (linguistics); Multivariate statistics; Pattern recognition (psychology); Machine learning; Mathematics; Statistics; Feature selection; Psychology; Neuroscience","score_opus":0.055992748918120505,"score_gpt":0.3253803174776034,"score_spread":0.26938756855948287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2609824936","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009773669,0.000014177194,0.991923,0.0061840005,0.00014268498,0.00032059586,0.000028851646,0.00011901428,0.00029035806],"genre_scores_gemma":[0.3667116,0.000048971517,0.63187355,0.000837589,0.000041021696,0.000022385084,0.000009514418,0.000008656822,0.00044675134],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990456,0.000056104003,0.00023575332,0.00028707486,0.00023653585,0.00013890587],"domain_scores_gemma":[0.99798435,0.00083246233,0.00013999452,0.0009397262,0.00006174189,0.00004175239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040934054,0.00007990215,0.00010161281,0.000047113623,0.000054561697,0.00006540023,0.0014059398,0.000019229079,0.000026124735],"category_scores_gemma":[0.0003458036,0.00003463956,0.000035434045,0.0001077069,0.00023490303,0.0010214986,0.0005115653,0.00002867755,0.000002932656],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002084712,0.000039457384,0.00035173894,0.000019577701,0.00003465842,0.0000048917395,0.00007503042,0.0000020013474,0.25936565,0.0063051274,0.01651696,0.71726406],"study_design_scores_gemma":[0.0021776839,0.00024146413,0.0034851378,0.00042395166,0.000046571335,0.000022984137,0.000074801996,0.18066433,0.80646616,0.003367894,0.0026344869,0.00039455475],"about_ca_topic_score_codex":0.000030088533,"about_ca_topic_score_gemma":0.000006314082,"teacher_disagreement_score":0.7168695,"about_ca_system_score_codex":0.000009839565,"about_ca_system_score_gemma":0.0000816751,"threshold_uncertainty_score":0.26126093},"labels":[],"label_agreement":null},{"id":"W2610770792","doi":"10.1049/iet-ipr.2016.0489","title":"Convergent heterogeneous particle swarm optimisation algorithm for multilevel image thresholding segmentation","year":2017,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Thresholding; Image segmentation; Particle swarm optimization; Computer science; Image (mathematics); Segmentation; Artificial intelligence; Algorithm; Computer vision; Pattern recognition (psychology); Scale-space segmentation","score_opus":0.04206755606532445,"score_gpt":0.3486936152869593,"score_spread":0.30662605922163483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2610770792","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051407493,0.000097808734,0.99276733,0.0005757559,0.0002729956,0.00063224725,0.00001086141,0.00040229954,0.000099968995],"genre_scores_gemma":[0.14187655,0.000020357042,0.8571936,0.00042669813,0.00010933736,0.00021718364,0.000015965861,0.000025284487,0.00011501705],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981882,0.0000410518,0.00041072044,0.0005385668,0.00041422585,0.00040724664],"domain_scores_gemma":[0.9984069,0.00005992744,0.0004507275,0.00057887676,0.00033681036,0.00016675425],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00054384914,0.00020567648,0.0001923898,0.00006301034,0.00093400065,0.0017704873,0.0008701,0.000066951725,0.000030327947],"category_scores_gemma":[0.00022465836,0.00020572134,0.000083069426,0.00007136509,0.00016908286,0.0034486358,0.0002474648,0.00011139998,0.000025015088],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053006393,0.00006644893,0.000037279482,0.000072177,0.000010321969,0.000013579609,0.0007043606,0.000017722243,0.17441376,0.000021729142,0.00029915996,0.82433814],"study_design_scores_gemma":[0.0004983559,0.00003545331,0.00007950804,0.0000424125,0.000009903128,0.000010147213,0.000043733693,0.45083755,0.54768896,0.0005769914,0.000028890156,0.00014811532],"about_ca_topic_score_codex":0.00002008071,"about_ca_topic_score_gemma":0.000001045159,"teacher_disagreement_score":0.8241901,"about_ca_system_score_codex":0.000107948625,"about_ca_system_score_gemma":0.00009032696,"threshold_uncertainty_score":0.9992658},"labels":[],"label_agreement":null},{"id":"W2612003046","doi":"10.1007/s11548-017-1603-8","title":"BEM-based simulation of lung respiratory deformation for CT-guided biopsy","year":2017,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Key Research and Development Program of China; Fujian Provincial Department of Science and Technology; Fonds National de la Recherche Luxembourg; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Image registration; Boundary (topology); Lung; Computer vision; Radiology; Medicine; Mathematics; Image (mathematics)","score_opus":0.05210379252886271,"score_gpt":0.35626475482429576,"score_spread":0.30416096229543305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612003046","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.086575,0.00008570705,0.91097355,0.0008575762,0.0013851236,0.00007919077,0.0000036435367,0.000018958539,0.000021258005],"genre_scores_gemma":[0.8965736,0.000012980136,0.10240729,0.00073049526,0.00025685152,0.000003834574,0.00000670014,0.0000044652816,0.0000037845057],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861497,0.00013564277,0.0007388902,0.00012542652,0.0002818359,0.00010322044],"domain_scores_gemma":[0.99652505,0.0011091108,0.0013293596,0.00020787623,0.0007611486,0.0000674265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012639064,0.000091325455,0.0002786451,0.00038172942,0.00011252312,0.0001125169,0.00064030185,0.000059359583,0.000005543887],"category_scores_gemma":[0.00043405796,0.00007812384,0.0001548831,0.00003920293,0.00014248418,0.00077574997,0.000070821414,0.000104207524,3.3857802e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031972432,0.00034117154,0.13152051,0.00016562446,0.0008187655,0.00030201187,0.00040275286,0.009918914,0.0062773796,0.0027917381,0.02061864,0.82652277],"study_design_scores_gemma":[0.0017159779,0.0001573691,0.20814697,0.00028927467,0.00003462705,0.0007641281,0.0000054267134,0.77255785,0.013499312,0.00143194,0.0011781038,0.00021901731],"about_ca_topic_score_codex":0.000002531718,"about_ca_topic_score_gemma":2.2722314e-7,"teacher_disagreement_score":0.8263037,"about_ca_system_score_codex":0.000046456473,"about_ca_system_score_gemma":0.00014683274,"threshold_uncertainty_score":0.3185798},"labels":[],"label_agreement":null},{"id":"W2613363804","doi":"10.21474/ijar01/3955","title":"A REVIEW ON IMAGE REGISTRATION ON PRINCIPLE AXES METHOD AND MUTUAL INFORMATION METHOD.","year":2017,"lang":"en","type":"review","venue":"International Journal of Advanced Research","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Heritage College","funders":"","keywords":"Image registration; Mutual information; Computer image; DEPT; Artificial intelligence; Computer science; Image (mathematics); Computer vision; Mathematics; Medicine","score_opus":0.2441369108083163,"score_gpt":0.6214256578003426,"score_spread":0.37728874699202636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2613363804","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[6.0700216e-8,0.4638212,0.53298324,0.0010053345,0.0003379831,0.00048041315,0.000012392595,0.00001997344,0.0013394044],"genre_scores_gemma":[1.909778e-7,0.65269727,0.34650362,0.00035674684,0.00016945733,0.00004034884,0.000017168544,0.000009858471,0.00020531955],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9940481,0.0013717324,0.0013680188,0.00032183167,0.0026331975,0.00025708237],"domain_scores_gemma":[0.99286914,0.0017982073,0.0020939251,0.0006380093,0.0023942892,0.00020643196],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.009111662,0.00026100926,0.0008852157,0.0011449788,0.00016239067,0.0007225331,0.002798598,0.00014691438,0.00003331984],"category_scores_gemma":[0.008455316,0.00018915776,0.00027722184,0.00025923896,0.00012874953,0.0027424046,0.00045479005,0.0015109485,0.00006120652],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021086218,0.0000410402,4.6233716e-8,0.0025484478,0.00007579835,0.000060738497,0.00004791691,0.0000023053472,0.000008761644,0.0031087156,0.005365434,0.9887197],"study_design_scores_gemma":[0.00027350165,0.00044170208,0.0000014413984,0.04693642,0.000028198525,0.00041081625,0.00000632105,0.00011427945,0.00027444292,0.0016060753,0.9497519,0.00015490192],"about_ca_topic_score_codex":0.0000046162368,"about_ca_topic_score_gemma":6.3218937e-7,"teacher_disagreement_score":0.9885648,"about_ca_system_score_codex":0.00042065736,"about_ca_system_score_gemma":0.00066369615,"threshold_uncertainty_score":0.9998969},"labels":[],"label_agreement":null},{"id":"W2617022775","doi":"","title":"Assessing accuracy of automated segmentation methods for brain lateral ventricles in MRI data","year":2015,"lang":"en","type":"article","venue":"Undergraduate Research Journal","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Segmentation; Artificial intelligence; Computer science; Lateral ventricles; Computer vision; Pattern recognition (psychology); Magnetic resonance imaging; Hausdorff distance; Sørensen–Dice coefficient; Image segmentation; Anatomy; Radiology; Medicine","score_opus":0.34382303431021743,"score_gpt":0.5873624278722211,"score_spread":0.24353939356200371,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2617022775","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021204643,0.00016931834,0.9818685,0.015116909,0.00015153871,0.0003846911,0.0000036857248,0.000121810685,0.0000630679],"genre_scores_gemma":[0.0607097,0.000113198665,0.9389028,0.00012151243,0.00006317308,0.000016485645,0.00002436581,0.000012171049,0.000036599402],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952526,0.0023388916,0.0006141565,0.00032272615,0.000998835,0.00047277554],"domain_scores_gemma":[0.9957728,0.0024298616,0.0002829964,0.0005333999,0.00068515824,0.00029582717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017803332,0.00010339391,0.0002078438,0.00073831377,0.0001597533,0.00081336376,0.0016943952,0.000059230075,0.000006658589],"category_scores_gemma":[0.006665744,0.000089486355,0.000039078353,0.0010363634,0.00013130522,0.0035432475,0.00062459416,0.00047608206,0.0000036716895],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044797314,0.00026059715,0.00059215375,0.00008483563,0.000048921338,0.000075375705,0.0010513903,0.00013147508,0.0606815,0.0019237717,0.06587451,0.8692307],"study_design_scores_gemma":[0.0021874316,0.00029542416,0.00056353275,0.00022495807,0.000005870675,0.00014758899,0.00052134425,0.7551472,0.12398205,0.11591332,0.0008340716,0.00017719912],"about_ca_topic_score_codex":0.000049114296,"about_ca_topic_score_gemma":0.0000049214873,"teacher_disagreement_score":0.8690535,"about_ca_system_score_codex":0.00027351803,"about_ca_system_score_gemma":0.00081957655,"threshold_uncertainty_score":0.798},"labels":[],"label_agreement":null},{"id":"W2618692958","doi":"","title":"Méthodes de Monte Carlo en Vision Stéréoscopique","year":2002,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre de Géomatique du Québec","funders":"","keywords":"Humanities; Philosophy; Physics","score_opus":0.028465667437451048,"score_gpt":0.29341751306260994,"score_spread":0.2649518456251589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618692958","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008030014,0.002701992,0.9342525,0.029818391,0.00055144425,0.0009269344,0.000056848163,0.00087627285,0.022785608],"genre_scores_gemma":[0.07782779,0.004438183,0.86385834,0.00076376105,0.000086041386,0.00021759374,0.000084202635,0.000091570524,0.052632533],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.96899456,0.02581142,0.0012563121,0.0017636566,0.0011919877,0.0009820912],"domain_scores_gemma":[0.98640835,0.0046446696,0.00095657236,0.0041352957,0.0030872088,0.0007679012],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01482588,0.0007533389,0.0007644699,0.0003637503,0.000595914,0.0016042524,0.0045105354,0.00083830405,0.001020414],"category_scores_gemma":[0.0051395814,0.00085140043,0.00041889818,0.00083379797,0.0007385268,0.0008990798,0.00391256,0.0017590941,0.0002944373],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001461783,0.002049342,0.0012446172,0.000735591,0.00017449756,0.00011076734,0.054530453,0.0003082305,0.016741341,0.29103437,0.01594315,0.617113],"study_design_scores_gemma":[0.0012335476,0.0000049096575,0.0055662035,0.009558825,0.00011697617,0.00014103578,0.00027795517,0.5294327,0.38799572,0.018080365,0.045853645,0.0017381167],"about_ca_topic_score_codex":0.004480932,"about_ca_topic_score_gemma":0.0010426207,"teacher_disagreement_score":0.6153749,"about_ca_system_score_codex":0.00072728295,"about_ca_system_score_gemma":0.0006816808,"threshold_uncertainty_score":0.9998928},"labels":[],"label_agreement":null},{"id":"W2619398955","doi":"10.1007/978-3-319-59876-5_47","title":"Mesh-Based Active Model Initialization for Multiple Organ Segmentation in MR Images","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Initialization; Computer science; Segmentation; Active contour model; Artificial intelligence; Image segmentation; Computer vision; Scale-space segmentation; Convergence (economics); Pattern recognition (psychology)","score_opus":0.03212577520410622,"score_gpt":0.31323481752314225,"score_spread":0.28110904231903605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2619398955","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000020076302,0.00003427812,0.99694127,0.0005286109,0.00052126386,0.0012646221,0.000028931832,0.0001951921,0.0004657515],"genre_scores_gemma":[0.057046067,0.000021899506,0.94079375,0.001657045,0.00013657614,0.00010493732,0.00006758367,0.000036651083,0.0001354664],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99682635,0.000046271794,0.0005455646,0.001253866,0.0008581398,0.0004698022],"domain_scores_gemma":[0.9973517,0.0005802113,0.0005143855,0.0010245468,0.0003946964,0.0001344657],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00081767526,0.0004156472,0.00043024632,0.0009483726,0.0002827918,0.0005943543,0.002351507,0.00028618152,0.000013040365],"category_scores_gemma":[0.0005238185,0.00041206597,0.00009188348,0.00023795727,0.00058140553,0.0014265352,0.0004723144,0.00042705965,0.0000061505093],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002700851,0.00006808518,0.00004918268,0.00009463399,0.000007678607,0.000026606303,0.0012222627,0.048440263,0.0063306056,0.0017516941,0.00007556674,0.9419064],"study_design_scores_gemma":[0.00065352884,0.00009945404,0.000032491425,0.00026127556,0.000005221423,0.0000029295043,2.9241124e-7,0.7761034,0.17277388,0.049692016,0.000018340354,0.00035719032],"about_ca_topic_score_codex":0.000033198026,"about_ca_topic_score_gemma":0.00012623932,"teacher_disagreement_score":0.94154924,"about_ca_system_score_codex":0.00049056887,"about_ca_system_score_gemma":0.00083200657,"threshold_uncertainty_score":0.9998331},"labels":[],"label_agreement":null},{"id":"W2727053570","doi":"10.1016/j.media.2017.06.009","title":"Modelling and extraction of pulsatile radial distension and compression motion for automatic vessel segmentation from video","year":2017,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Pulsatile flow; Computer vision; Segmentation; Computer science; Artificial intelligence; Motion estimation; Orientation (vector space); Optical flow; Motion (physics); Computation; Mathematics; Image (mathematics); Algorithm","score_opus":0.023570089986048075,"score_gpt":0.3294118092825127,"score_spread":0.3058417192964646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2727053570","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09484768,0.00007830449,0.904152,0.0005758422,0.00006827232,0.0001945142,0.000011174852,0.000054396914,0.000017857074],"genre_scores_gemma":[0.694068,0.0001290678,0.30554315,0.00008438424,0.00003374561,0.000037761594,0.00008496957,0.0000055086684,0.000013380067],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839944,0.000118374184,0.00038872112,0.00035417188,0.0006165872,0.00012269494],"domain_scores_gemma":[0.998595,0.0003308247,0.00040925032,0.00041112356,0.00009103575,0.00016279367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065041916,0.00010438638,0.0002917526,0.00017462688,0.00037287368,0.00019077581,0.00031821482,0.00007178413,0.0001799661],"category_scores_gemma":[0.00045968202,0.00009225634,0.000079542115,0.00013560442,0.00017413244,0.0010041135,0.00020010362,0.000109101646,6.3054e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026957396,0.00018819663,0.002294827,0.00009055124,0.00028997994,0.000014816852,0.0010050628,0.0003843825,0.056434985,0.00008846642,0.00059655757,0.9385852],"study_design_scores_gemma":[0.0004906727,0.00003941951,0.004638965,0.000036447625,0.00026455912,0.0000019792558,0.0000604824,0.965147,0.028111344,0.0011118313,0.000011317194,0.00008595881],"about_ca_topic_score_codex":0.00057074934,"about_ca_topic_score_gemma":0.000010827613,"teacher_disagreement_score":0.9647626,"about_ca_system_score_codex":0.00002681863,"about_ca_system_score_gemma":0.00002079555,"threshold_uncertainty_score":0.37621045},"labels":[],"label_agreement":null},{"id":"W2742503988","doi":"10.1109/tmi.2017.2735239","title":"Significant Anatomy Detection Through Sparse Classification: A Comparative Study","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Canadian Statistical Sciences Institute; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Multiple Sclerosis Society of Canada","keywords":"Interpretability; Discriminative model; Artificial intelligence; Computer science; Regularization (linguistics); Pattern recognition (psychology); Univariate; Elastic net regularization; Ground truth; Image quality; Feature selection; Mathematics; Image (mathematics); Multivariate statistics; Machine learning","score_opus":0.07142750047562196,"score_gpt":0.38340453024460847,"score_spread":0.3119770297689865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2742503988","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033941178,0.000013042635,0.9897758,0.002999069,0.0009946246,0.0006075506,0.0000027590065,0.0005616808,0.0016513887],"genre_scores_gemma":[0.9717991,0.000025148143,0.026956432,0.0007964899,0.00008080706,0.0002201063,7.774204e-7,0.000014348116,0.00010684307],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969254,0.00026826566,0.00048585562,0.00068242603,0.0012970096,0.00034100618],"domain_scores_gemma":[0.99774724,0.00021245536,0.00025387012,0.0012912228,0.00016344815,0.00033176213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007355104,0.00023577522,0.00029313788,0.0001586597,0.0011455081,0.0005173143,0.0015655217,0.00007904688,0.00022426048],"category_scores_gemma":[0.00008455334,0.00021410233,0.000103792816,0.0002537003,0.00045396373,0.0015692115,0.000014663143,0.00068041764,0.00015159599],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002750192,0.0014715198,0.00018541644,0.000015139524,0.00007980734,0.0001968168,0.0035704954,0.000025958681,0.004720321,0.00034245406,0.0006965419,0.988668],"study_design_scores_gemma":[0.0044910726,0.0006329485,0.0072366884,0.00028281967,0.00013385243,0.00013862051,0.0045532905,0.52957666,0.44823906,0.0017821385,0.0017357442,0.0011971297],"about_ca_topic_score_codex":0.00019673079,"about_ca_topic_score_gemma":0.00006426698,"teacher_disagreement_score":0.9874709,"about_ca_system_score_codex":0.00012429515,"about_ca_system_score_gemma":0.00014424422,"threshold_uncertainty_score":0.88104385},"labels":[],"label_agreement":null},{"id":"W2750812135","doi":"10.1109/iccvw.2017.143","title":"Texture and Structure Incorporated ScatterNet Hybrid Deep Learning Network (TS-SHDL) for Brain Matter Segmentation","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Scatternet; Artificial intelligence; Segmentation; Conditional random field; Computer science; Deep learning; Pattern recognition (psychology); Computer vision","score_opus":0.013879850075616337,"score_gpt":0.28657331249921253,"score_spread":0.2726934624235962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2750812135","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022213245,0.0001611811,0.99163866,0.0032293554,0.0005667169,0.0012668931,0.000024589863,0.00046247413,0.00042877824],"genre_scores_gemma":[0.098403044,0.00005297639,0.8894673,0.007546099,0.00052821334,0.00023053342,0.00090114673,0.000057640096,0.0028130289],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975169,0.0002099275,0.00046807367,0.0010008404,0.0004056554,0.00039860792],"domain_scores_gemma":[0.99780625,0.00019282929,0.00076615874,0.0008465143,0.0002014958,0.00018672914],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0004750643,0.00040823093,0.0004172142,0.000130351,0.000331467,0.0012983156,0.0012481142,0.000304808,0.0002969731],"category_scores_gemma":[0.0001221244,0.00036618713,0.000086010674,0.00007738941,0.00013587519,0.00054314634,0.0015499669,0.00079859875,0.000019405632],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032350854,0.00004100074,0.012185605,0.0007435965,0.00019099595,0.000036613892,0.00097132847,0.0025064573,0.003759674,0.00062731956,0.27635905,0.702546],"study_design_scores_gemma":[0.0030229758,0.00045793457,0.020810628,0.0011091246,0.00017622438,0.00020452215,0.00011469968,0.66320515,0.064030245,0.23730282,0.006299381,0.0032662745],"about_ca_topic_score_codex":0.00006784473,"about_ca_topic_score_gemma":0.000028649625,"teacher_disagreement_score":0.6992797,"about_ca_system_score_codex":0.00008493002,"about_ca_system_score_gemma":0.00009012157,"threshold_uncertainty_score":0.999879},"labels":[],"label_agreement":null},{"id":"W2751388822","doi":"10.1007/978-3-319-66182-7_41","title":"Selecting the Optimal Sequence for Deformable Registration of Microscopy Image Sequences Using Two-Stage MST-based Clustering Algorithm","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Image registration; Computer science; Artificial intelligence; Similarity (geometry); Minimum spanning tree; Cluster analysis; Computer vision; Pattern recognition (psychology); Algorithm; Image (mathematics); Sequence (biology); Entropy (arrow of time)","score_opus":0.04695458422143553,"score_gpt":0.350202337896891,"score_spread":0.3032477536754554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2751388822","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008839577,0.00009929959,0.9975062,0.0002572387,0.00065599004,0.00092811865,0.000024180257,0.0001371214,0.00030342746],"genre_scores_gemma":[0.008775891,0.000010381281,0.99028414,0.00052187673,0.00019693469,0.000025641173,0.000009521731,0.000027973476,0.00014764495],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99645,0.000058093974,0.000783432,0.0010923696,0.000982878,0.00063322985],"domain_scores_gemma":[0.9960878,0.0006127909,0.0012464338,0.0013768884,0.00056039623,0.00011571855],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0025214197,0.00044458517,0.00048469554,0.0004036299,0.00082007656,0.0010789526,0.0038203858,0.00019677638,0.000007760571],"category_scores_gemma":[0.00031623984,0.0003552354,0.00014900998,0.00026971695,0.0016516125,0.001504459,0.00067320187,0.0005834164,0.0000015179202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017598279,0.000029322697,0.000013963516,0.0003182912,0.000023114995,0.000052563544,0.001019636,0.1394299,0.10885096,0.00088927906,0.000015927493,0.74933946],"study_design_scores_gemma":[0.00022386982,0.0001318773,0.0000015826076,0.000475408,0.000009941159,0.00003780522,7.429105e-7,0.76842207,0.22623368,0.004126951,0.00004196668,0.00029410384],"about_ca_topic_score_codex":0.00027992704,"about_ca_topic_score_gemma":0.00009444723,"teacher_disagreement_score":0.7490453,"about_ca_system_score_codex":0.0003959649,"about_ca_system_score_gemma":0.001342669,"threshold_uncertainty_score":0.99995804},"labels":[],"label_agreement":null},{"id":"W2751806885","doi":"10.1002/mp.12560","title":"Comparison of vessel enhancement algorithms applied to time‐of‐flight MRA images for cerebrovascular segmentation","year":2017,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Segmentation; Algorithm; Computer science; Medical imaging; Computer vision; Image segmentation; Image enhancement; Artificial intelligence; Image (mathematics)","score_opus":0.027247667745445115,"score_gpt":0.3581398000006968,"score_spread":0.33089213225525166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2751806885","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015400595,0.000019476853,0.996375,0.00067449396,0.00017417541,0.0006169362,0.000007285468,0.000058802176,0.0005337678],"genre_scores_gemma":[0.41855225,0.000019188725,0.5802123,0.00055736286,0.00022094944,0.0002392135,0.00004064851,0.00001924566,0.00013885103],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978705,0.000030882493,0.00048282297,0.00032262856,0.0010814582,0.0002117088],"domain_scores_gemma":[0.9983573,0.0001348669,0.00038449318,0.0007497297,0.00017218635,0.00020141162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005392701,0.00013659809,0.00039218133,0.00004161483,0.00013139565,0.000057265763,0.0012555186,0.00006829006,0.000071819595],"category_scores_gemma":[0.00022442326,0.00012279315,0.000095500516,0.00010732139,0.0001940282,0.000249626,0.00031985724,0.00010212814,0.000028515158],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012091217,0.0005575041,0.0001632574,0.00018727797,0.00007058296,7.742354e-7,0.0010997804,0.0000075320536,0.1134207,0.001232002,0.018288339,0.86496013],"study_design_scores_gemma":[0.00057715963,0.0001731681,0.00026957205,0.00007363755,0.00001853117,1.9982308e-7,0.000022205626,0.0046994,0.991738,0.0020390425,0.00026563124,0.00012345306],"about_ca_topic_score_codex":0.000014967922,"about_ca_topic_score_gemma":2.6182025e-7,"teacher_disagreement_score":0.8783173,"about_ca_system_score_codex":0.000034325134,"about_ca_system_score_gemma":0.00008989338,"threshold_uncertainty_score":0.50073594},"labels":[],"label_agreement":null},{"id":"W2751865213","doi":"10.1007/978-3-319-67534-3_3","title":"Robust Automatic Graph-Based Skeletonization of Hepatic Vascular Trees","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Skeletonization; Computer science; Binary tree; Algorithm; Segmentation; Context (archaeology); Artificial intelligence; Network topology; Image segmentation; Tree (set theory); Pattern recognition (psychology); Computer vision; Mathematics","score_opus":0.01985515983384144,"score_gpt":0.257547274109903,"score_spread":0.23769211427606157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2751865213","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000061644954,0.00020557457,0.99748015,0.0002605978,0.00060369226,0.0005177922,0.0000030500098,0.00028719354,0.00058029837],"genre_scores_gemma":[0.04705149,0.000042563748,0.9520055,0.00065615436,0.0000999227,0.000016361222,0.00001146786,0.000030399147,0.00008614042],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99621266,0.00006773669,0.0007065847,0.0010699145,0.0015219295,0.00042117943],"domain_scores_gemma":[0.9960897,0.00039890647,0.0007674885,0.002201305,0.0003783468,0.00016427177],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011237423,0.0004300166,0.00062239985,0.001085955,0.00023618701,0.0004143227,0.0040609306,0.00027276532,0.00005256146],"category_scores_gemma":[0.00037147157,0.00038937188,0.00019190783,0.00038903,0.0012068137,0.0007044467,0.0006177074,0.00043802895,0.000013471163],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001163933,0.000051828283,0.000100454854,0.0001914615,0.00001740667,0.00003667229,0.00029845664,0.02191806,0.0008610359,0.002049954,0.000026232718,0.97444725],"study_design_scores_gemma":[0.00030687923,0.00018405328,0.00065580336,0.0010434036,0.000019312623,0.000007634115,6.5605846e-8,0.9296105,0.016323352,0.051346127,0.000034454857,0.00046843864],"about_ca_topic_score_codex":0.000042383163,"about_ca_topic_score_gemma":0.000037241676,"teacher_disagreement_score":0.9739788,"about_ca_system_score_codex":0.00017810942,"about_ca_system_score_gemma":0.0006701349,"threshold_uncertainty_score":0.9998558},"labels":[],"label_agreement":null},{"id":"W2752573065","doi":"10.1007/s11517-018-1793-4","title":"Vessel segmentation and catheter detection in X-ray angiograms using superpixels","year":2018,"lang":"en","type":"article","venue":"Medical & Biological Engineering & Computing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Universitat de Barcelona","keywords":"Human physiology; Catheter; Computer vision; Segmentation; Artificial intelligence; Biomedical engineering; Radiology; Computer graphics (images); Medicine; Computer science; Nuclear medicine; Internal medicine","score_opus":0.02073233211075646,"score_gpt":0.2801988911749107,"score_spread":0.2594665590641542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2752573065","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35668895,0.0000721918,0.64263105,0.000066344146,0.00019488297,0.00010632903,2.0015086e-7,0.00022614797,0.0000139082],"genre_scores_gemma":[0.8239783,0.000013257141,0.17548591,0.0003376242,0.000167811,0.000005976373,0.0000021961782,0.0000075727125,0.0000013301513],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998375,0.000099513316,0.0003811599,0.0004341788,0.00033857813,0.0003715859],"domain_scores_gemma":[0.999283,0.00022545327,0.00005760263,0.00016988514,0.00004530971,0.00021874208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008496586,0.00016839945,0.00020960643,0.00016651083,0.000087355205,0.000083453124,0.0003579687,0.00018451596,0.000044008983],"category_scores_gemma":[0.00046224552,0.00013682876,0.000045555294,0.0004976777,0.0001538266,0.00019145032,0.00030223597,0.0002808823,0.000011585638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007790114,0.0001565222,0.0075744563,0.000039154726,0.000021380549,0.000053616528,0.0009461529,0.0003510312,0.13016897,0.0003706262,0.000024123927,0.8602862],"study_design_scores_gemma":[0.00036462836,0.0002687248,0.015042015,0.00014081594,0.0000033795482,0.000049883027,0.000027306947,0.9474831,0.03619577,0.00008793062,0.000088385765,0.0002480501],"about_ca_topic_score_codex":0.000054919656,"about_ca_topic_score_gemma":0.000002015248,"teacher_disagreement_score":0.94713205,"about_ca_system_score_codex":0.00007090586,"about_ca_system_score_gemma":0.000020756044,"threshold_uncertainty_score":0.5579715},"labels":[],"label_agreement":null},{"id":"W2753119246","doi":"10.1007/978-3-319-66182-7_90","title":"Combining Spatial and Non-spatial Dictionary Learning for Automated Labeling of Intra-ventricular Hemorrhage in Neonatal Brain MRI","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children","funders":"","keywords":"Computer science; Artificial intelligence; Natural language processing; Pattern recognition (psychology)","score_opus":0.009544921901032342,"score_gpt":0.2640576360688589,"score_spread":0.25451271416782656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2753119246","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003698529,0.00020471065,0.99727947,0.00045168627,0.00069596054,0.00064694096,0.0000055985724,0.00024814627,0.00009762152],"genre_scores_gemma":[0.32156643,0.000023785933,0.6777345,0.00038305952,0.00017097202,0.000023061202,0.000023398314,0.000026910442,0.00004791996],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970384,0.000057391157,0.0006557429,0.0010355124,0.0007584582,0.00045444298],"domain_scores_gemma":[0.9976793,0.0008257706,0.00055678125,0.0005987012,0.00020341654,0.00013606508],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013476419,0.00036210733,0.0005542594,0.0007863581,0.00029000675,0.00030712242,0.0016051426,0.00028573495,0.000008479359],"category_scores_gemma":[0.00049973244,0.0003616956,0.000082451,0.00020180072,0.00064236304,0.0005941959,0.0010154188,0.0007364883,0.0000016167652],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013122807,0.00002314818,0.00032495696,0.00013360743,0.000009613656,0.00021748489,0.0010962334,0.00884907,0.0011907184,0.00023487126,0.00001549218,0.9878917],"study_design_scores_gemma":[0.0007348354,0.00028274584,0.00030429292,0.000610323,0.000006498654,0.000070229704,6.308217e-7,0.98042953,0.010896244,0.0062579657,0.000051995696,0.00035468023],"about_ca_topic_score_codex":0.00021887649,"about_ca_topic_score_gemma":0.000092927476,"teacher_disagreement_score":0.987537,"about_ca_system_score_codex":0.00015184055,"about_ca_system_score_gemma":0.0003220243,"threshold_uncertainty_score":0.9998835},"labels":[],"label_agreement":null},{"id":"W2755962443","doi":"10.1109/icip.2017.8296526","title":"A two-stage minimum spanning tree (MST) based clustering algorithm for 2D deformable registration of time sequenced images","year":2017,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Minimum spanning tree; Spanning tree; Artificial intelligence; Image registration; Similarity (geometry); Computer vision; Distortion (music); Cluster analysis; Computer science; Sequence (biology); Image (mathematics); Tree (set theory); Pattern recognition (psychology); Algorithm; Mathematics; Combinatorics","score_opus":0.031407931889848556,"score_gpt":0.3253633132593516,"score_spread":0.29395538136950305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2755962443","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025561088,0.000010030838,0.9917513,0.00039691947,0.000095818774,0.00042299356,0.000010914121,0.00023442204,0.0068219695],"genre_scores_gemma":[0.014777236,0.0000027546723,0.9805713,0.0002608794,0.000046014233,0.00006323034,0.000016923026,0.000010489175,0.004251143],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870676,0.000033951084,0.00038276037,0.00030661703,0.00031877257,0.00025111286],"domain_scores_gemma":[0.9984188,0.00010758188,0.00043442956,0.00078466313,0.00016067835,0.000093843046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007224076,0.00013193046,0.00019651564,0.00009519979,0.0002482825,0.00033284002,0.0009401983,0.000050246024,0.000059656508],"category_scores_gemma":[0.0002034503,0.000118538555,0.00007708016,0.00006913444,0.00012840914,0.0013823615,0.00017731152,0.0000687004,0.000012429592],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020941814,0.00008002177,0.000045152352,0.00014662594,0.000030336765,0.000021825224,0.00027578708,0.00025199648,0.41563925,0.0005868381,0.00426686,0.5786344],"study_design_scores_gemma":[0.00050751,0.00008742169,0.000034206118,0.000040894913,0.0000050589033,0.0000016145425,0.000015766369,0.5804273,0.41853946,0.00019494681,0.0000540303,0.000091779715],"about_ca_topic_score_codex":0.00020221782,"about_ca_topic_score_gemma":0.000017401175,"teacher_disagreement_score":0.5801753,"about_ca_system_score_codex":0.000049673497,"about_ca_system_score_gemma":0.00011810388,"threshold_uncertainty_score":0.48338622},"labels":[],"label_agreement":null},{"id":"W2756976920","doi":"10.1109/access.2017.2755863","title":"A GPU-Accelerated Deformable Image Registration Algorithm With Applications to Right Ventricular Segmentation","year":2017,"lang":"en","type":"article","venue":"IEEE Access","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian VIGOUR Centre; University of Alberta","funders":"Nvidia","keywords":"Computer science; Graphics processing unit; Image registration; CUDA; Artificial intelligence; Segmentation; Image segmentation; Computer vision; Central processing unit; Algorithm; Image processing; Image (mathematics); Parallel computing","score_opus":0.03130902382242176,"score_gpt":0.3480757179311242,"score_spread":0.3167666941087024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2756976920","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018100617,0.000009337613,0.9942831,0.0007764417,0.00011903308,0.0009745989,0.000006817046,0.00029776557,0.0017228422],"genre_scores_gemma":[0.069352455,0.000016023296,0.9279474,0.0009534452,0.00014954733,0.001070841,0.000045945315,0.000018536099,0.00044579565],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99859875,0.000036295154,0.00026349642,0.00040823952,0.00044996117,0.00024327855],"domain_scores_gemma":[0.9982223,0.000019705873,0.00031085202,0.0009962205,0.0002766481,0.00017430459],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00024055071,0.000145016,0.00013485618,0.00012162259,0.0005946925,0.00203103,0.0017532653,0.000049397746,0.00004568937],"category_scores_gemma":[0.00002325368,0.00011995957,0.000028500312,0.00034430018,0.000075061005,0.004155835,0.0001585495,0.00010446455,0.000086111424],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032247346,0.00046635707,0.0009417124,0.00011068256,0.000100250465,0.0003957639,0.00048502177,0.00022741516,0.064581394,0.002324659,0.026614564,0.90371996],"study_design_scores_gemma":[0.0006226408,0.00011533644,0.0019312435,0.000045687197,0.000018205257,0.00004772248,0.000016135584,0.018833306,0.9748316,0.0012823062,0.0019351209,0.00032074042],"about_ca_topic_score_codex":0.0001909061,"about_ca_topic_score_gemma":0.000024299574,"teacher_disagreement_score":0.9102502,"about_ca_system_score_codex":0.000092913775,"about_ca_system_score_gemma":0.00008677352,"threshold_uncertainty_score":0.99900496},"labels":[],"label_agreement":null},{"id":"W2763147268","doi":"10.1109/tbme.2017.2759730","title":"Automatic Temporal Segmentation of Vessels of the Brain Using 4D ASL MRA Images","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Hotchkiss Brain Institute","keywords":"Segmentation; Artificial intelligence; Scale-space segmentation; Computer vision; Computer science; Image segmentation; Pattern recognition (psychology); Frame (networking)","score_opus":0.01853787358857887,"score_gpt":0.2880497906114136,"score_spread":0.2695119170228347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2763147268","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029878797,0.000009178961,0.9686208,0.0005976362,0.00055961736,0.0001844747,0.0000130470435,0.00012574415,0.000010717434],"genre_scores_gemma":[0.77762014,0.000004725852,0.2222673,0.00005201797,0.000017297802,0.000012233097,6.143006e-7,0.000009145068,0.000016511482],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99870336,0.000040646562,0.00038803997,0.00018073487,0.0005274211,0.00015982111],"domain_scores_gemma":[0.99889094,0.00013530065,0.00022191335,0.00060720876,0.000049535458,0.00009510283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033021325,0.000118951764,0.00018456044,0.00016134519,0.00017370524,0.00006983111,0.00081328384,0.00007463472,0.000038833285],"category_scores_gemma":[0.00007695036,0.000093156275,0.00009936265,0.0002383778,0.0002760756,0.00041742946,0.0000114343975,0.00017796921,0.0000013715152],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029727448,0.00018939137,0.000019426809,0.00020923806,0.000055580596,0.000004744463,0.0003962876,0.0030427298,0.8248866,0.000113126676,0.00012216465,0.17095773],"study_design_scores_gemma":[0.0002395358,0.000048262078,0.0003326729,0.00017864964,0.000011431054,0.0000062305876,0.000013790076,0.3331196,0.66592157,0.000041170053,0.0000121951125,0.000074892414],"about_ca_topic_score_codex":0.000068999216,"about_ca_topic_score_gemma":9.158594e-7,"teacher_disagreement_score":0.74774134,"about_ca_system_score_codex":0.000050148814,"about_ca_system_score_gemma":0.00008552649,"threshold_uncertainty_score":0.37988028},"labels":[],"label_agreement":null},{"id":"W2764668828","doi":"10.1117/12.877888","title":"Automatic 3D segmentation of ultrasound images using atlas registration and statistical texture prior","year":2011,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials","funders":"National Center for Research Resources; National Cancer Institute","keywords":"Artificial intelligence; Computer science; Computer vision; Segmentation; Image segmentation; Gabor filter; Atlas (anatomy); 3D ultrasound; Prostate biopsy; Pattern recognition (psychology); Ultrasound; Support vector machine; Image texture; Prostate cancer; Feature extraction; Medicine; Radiology; Cancer","score_opus":0.016400472593537583,"score_gpt":0.257053932646166,"score_spread":0.24065346005262842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2764668828","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90445495,0.000047145353,0.0939932,0.00023284431,0.00009596823,0.0005177803,0.000028759156,0.00010299162,0.00052634015],"genre_scores_gemma":[0.15704705,0.000048519414,0.8426849,0.000051089497,0.00006468817,0.00004701724,0.000005893074,0.000021109367,0.000029732297],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979181,5.8089316e-8,0.00076129136,0.0003539989,0.00071899587,0.00024750628],"domain_scores_gemma":[0.9979593,0.00022309164,0.0005801274,0.00007164436,0.0010521777,0.000113670896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006652003,0.00022958238,0.00032355124,0.00010555045,0.00007121038,0.00011654665,0.0007593404,0.00013473317,0.00002248218],"category_scores_gemma":[0.000768158,0.0001940373,0.00017419076,0.00027292408,0.00035560312,0.0010700414,0.00015047609,0.00019930191,4.4159663e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019886289,0.000104835104,0.00031646545,0.00054998446,0.00014010731,1.4740547e-7,0.0006781121,0.000003834751,0.7779364,0.21579021,0.00093027146,0.0035297559],"study_design_scores_gemma":[0.0008081388,0.00041963544,0.0033597553,0.0003691239,0.0001480681,0.000048819926,0.0008835475,0.08515094,0.9032559,0.0052002245,0.00003573424,0.00032010174],"about_ca_topic_score_codex":0.000023639273,"about_ca_topic_score_gemma":9.9644794e-8,"teacher_disagreement_score":0.7486917,"about_ca_system_score_codex":0.000097518365,"about_ca_system_score_gemma":0.000046650028,"threshold_uncertainty_score":0.7912612},"labels":[],"label_agreement":null},{"id":"W2766440712","doi":"10.1080/07038992.2017.1393329","title":"Similarity Ratio Based Adaptive Mahalanobis Distance Algorithm to Generate SAR Superpixels","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mahalanobis distance; Similarity (geometry); Pattern recognition (psychology); Artificial intelligence; Computer science; Algorithm; Mathematics; Geography; Image (mathematics)","score_opus":0.029898103299081005,"score_gpt":0.274809640733731,"score_spread":0.24491153743465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766440712","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022270374,0.000056996585,0.9921734,0.0044080615,0.0006209874,0.00012290991,0.000010556997,0.000022054399,0.00035801472],"genre_scores_gemma":[0.106219694,0.000004300329,0.8909606,0.0025138585,0.00019721287,1.8860437e-8,9.882032e-7,0.000011976512,0.00009135403],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985846,0.00011744798,0.00036592028,0.00023136631,0.00033702742,0.00036358964],"domain_scores_gemma":[0.9973922,0.00004970311,0.00032908242,0.0006243251,0.0005224364,0.001082276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063784875,0.0001485242,0.00024724763,0.0002452903,0.000562912,0.00067764265,0.0008544353,0.00006787203,0.000016664999],"category_scores_gemma":[0.0004848294,0.0001453779,0.000086690095,0.0001412424,0.00013059261,0.00067523256,0.000043691613,0.0002820104,0.00000925273],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030931765,0.0000021035955,0.000029218938,0.0000033427768,0.00001205413,0.0009446448,0.00032490786,0.000035046996,0.0013485018,0.00004964428,0.0026046794,0.99464273],"study_design_scores_gemma":[0.00069529255,0.00026267793,0.0015684007,0.00048143588,0.000025102423,0.00040999564,0.0000873064,0.7985128,0.1802392,0.0016851468,0.015515742,0.0005168814],"about_ca_topic_score_codex":0.0040632407,"about_ca_topic_score_gemma":0.008363948,"teacher_disagreement_score":0.9941259,"about_ca_system_score_codex":0.0003039872,"about_ca_system_score_gemma":0.0013247029,"threshold_uncertainty_score":0.65345234},"labels":[],"label_agreement":null},{"id":"W2770792541","doi":"10.1186/s12859-017-1903-6","title":"Brain medical image diagnosis based on corners with importance-values","year":2017,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Servier; H. Lundbeck A/S; IXICO; Natural Science Foundation of Heilongjiang Province; National Natural Science Foundation of China; Eisai; Genentech; Northern California Institute for Research and Education; Eli Lilly and Company; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; F. Hoffmann-La Roche; University of Southern California; Pfizer; Biogen; BioClinica; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; Foundation for the National Institutes of Health","keywords":"Artificial intelligence; Computer science; Classifier (UML); Pattern recognition (psychology); Matching (statistics); Similarity (geometry); Image registration; Image (mathematics); Medical imaging; Computer vision; Medicine; Pathology","score_opus":0.023194025837499698,"score_gpt":0.3068828850040999,"score_spread":0.2836888591666002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770792541","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004512963,0.0000048250263,0.98385596,0.0034228845,0.00012623289,0.00027460852,0.0000058286664,0.0003421761,0.011516191],"genre_scores_gemma":[0.005331072,0.000016305596,0.9857203,0.008667736,0.00004815177,0.00008182886,0.000012479265,0.000012139026,0.000109990666],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787664,0.000042690255,0.00042250886,0.00020076356,0.0011604652,0.00029693503],"domain_scores_gemma":[0.9974378,0.0003768038,0.0004056587,0.0013752519,0.0000907421,0.00031375175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008169064,0.00018163878,0.00020225852,0.0001046271,0.0003070248,0.0005510976,0.0018091016,0.00010323986,0.0002015777],"category_scores_gemma":[0.0017921209,0.00012960257,0.000060259765,0.000111064684,0.00039348626,0.0012368831,0.00021657693,0.00020724548,0.00011999276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008583112,0.00059397524,0.08997329,0.0007319989,0.000062464176,0.00026631475,0.002221581,0.000075112555,0.000029402001,0.010113723,0.42216858,0.4736777],"study_design_scores_gemma":[0.001113169,0.0003507111,0.0073780655,0.0002576993,0.000008257528,0.0000131229035,0.00008726366,0.984864,0.0042676413,0.0003908352,0.00095042796,0.000318827],"about_ca_topic_score_codex":0.000013002043,"about_ca_topic_score_gemma":0.00002056323,"teacher_disagreement_score":0.9847889,"about_ca_system_score_codex":0.000051618885,"about_ca_system_score_gemma":0.00031276655,"threshold_uncertainty_score":0.5314247},"labels":[],"label_agreement":null},{"id":"W2771193347","doi":"10.1016/j.neuroimage.2017.10.040","title":"A spatio-temporal reference model of the aging brain","year":2017,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ministerie van Economische Zaken; National Institute on Aging; Horizon 2020; Canadian Institutes of Health Research; National Institute of Biomedical Imaging and Bioengineering; European Research Council; National Institutes of Health; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Stichting voor de Technische Wetenschappen; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Seventh Framework Programme","keywords":"Percentile; Atrophy; Brain morphometry; Brain aging; Magnetic resonance imaging; Healthy aging; Psychology; Pattern recognition (psychology); Neuroscience; Artificial intelligence; Cognition; Medicine; Pathology; Computer science; Mathematics; Statistics; Gerontology; Radiology","score_opus":0.0697577841789151,"score_gpt":0.3267888131403655,"score_spread":0.2570310289614504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771193347","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023961117,0.0000044137314,0.96325254,0.0058149416,0.00011564762,0.0001445899,0.000003974881,0.00011678448,0.006585993],"genre_scores_gemma":[0.89610654,0.00000288164,0.10138899,0.0016415135,0.000013888593,0.0000066308826,7.154517e-7,0.000005777481,0.0008330361],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99910593,0.000068267895,0.00016668953,0.00022141091,0.0003113221,0.00012639041],"domain_scores_gemma":[0.9982819,0.000050091236,0.0002293367,0.001333492,0.000058778325,0.00004642924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024881688,0.00007202499,0.00008918522,0.000029118079,0.0002018217,0.00016624744,0.0019747156,0.000023575058,0.000012363463],"category_scores_gemma":[0.00043349207,0.000052605985,0.00003957137,0.000054931003,0.0001608903,0.0005804506,0.00065416726,0.00014712203,0.000007858208],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014327834,0.00024247699,0.021219134,0.00015519615,0.000017558044,0.000060888466,0.0023406218,0.00019646251,0.5321271,0.04520481,0.060132857,0.33828858],"study_design_scores_gemma":[0.00046386194,0.000063822205,0.036991507,0.000082740255,0.000005927504,0.00000903461,0.000007862386,0.4710525,0.4770665,0.013179517,0.0008399201,0.00023680834],"about_ca_topic_score_codex":0.00006340453,"about_ca_topic_score_gemma":0.000012502821,"teacher_disagreement_score":0.8721455,"about_ca_system_score_codex":0.000009242098,"about_ca_system_score_gemma":0.000060779224,"threshold_uncertainty_score":0.3669546},"labels":[],"label_agreement":null},{"id":"W2774501236","doi":"10.1109/bibm.2017.8217746","title":"Left ventricle segmentation by combining convolution neural network with active contour model and tensor voting in short-axis MRI","year":2017,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Hospital for Sick Children; National Natural Science Foundation of China","keywords":"Endocardium; Segmentation; Artificial intelligence; Computer science; Active contour model; Convolution (computer science); Pattern recognition (psychology); Convolutional neural network; Ellipse; Artificial neural network; Computer vision; Image segmentation; Mathematics; Medicine; Geometry; Cardiology","score_opus":0.020850252918177686,"score_gpt":0.2865587086552779,"score_spread":0.2657084557371002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2774501236","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1988703,0.000027773587,0.7999577,0.00039943084,0.000036832127,0.00027213988,0.0000013743194,0.00011444541,0.00032000052],"genre_scores_gemma":[0.885131,0.000010306738,0.11440816,0.00031908255,0.000016262118,0.000016066386,0.0000055594555,0.0000065640243,0.00008701098],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989502,0.000057113044,0.00019833582,0.0003111297,0.00023799269,0.00024522794],"domain_scores_gemma":[0.9994428,0.00006655593,0.00010457052,0.00023195757,0.00006326079,0.00009081293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002624757,0.00011176477,0.00014228375,0.000043864533,0.00027331527,0.00026750102,0.00031091372,0.00004381838,0.000008361846],"category_scores_gemma":[0.000044672488,0.00009664439,0.000014056988,0.000062067695,0.0000983168,0.0013550674,0.00015291283,0.0001355118,0.0000012882673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019301775,0.00032003017,0.27914143,0.000058376616,0.00008315505,0.00006572402,0.00550794,0.016106993,0.06552413,0.006706957,0.009090214,0.61720204],"study_design_scores_gemma":[0.000601707,0.00009416234,0.023005273,0.0000348864,0.000004678547,0.000008010856,0.00013722116,0.9550118,0.020694599,0.00027127698,0.0000029125808,0.00013349582],"about_ca_topic_score_codex":0.0001791279,"about_ca_topic_score_gemma":0.00009034985,"teacher_disagreement_score":0.93890476,"about_ca_system_score_codex":0.00006345294,"about_ca_system_score_gemma":0.000025301788,"threshold_uncertainty_score":0.3941044},"labels":[],"label_agreement":null},{"id":"W2776074204","doi":"10.1016/j.media.2017.12.006","title":"Fast elastic registration of soft tissues under large deformations","year":2017,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vancouver General Hospital; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Iterative closest point; Supine position; Computer science; Artificial intelligence; Focus (optics); Position (finance); Computer vision; Image registration; Segmentation; Medicine; Image (mathematics); Surgery; Point cloud","score_opus":0.015268306260974413,"score_gpt":0.33222229964755084,"score_spread":0.3169539933865764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2776074204","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00091681496,0.000032298925,0.99291307,0.003051985,0.00006169577,0.00007622402,0.000006791623,0.00012745986,0.002813667],"genre_scores_gemma":[0.8423963,0.00005030822,0.15577157,0.0007221002,0.00007238461,0.000021973594,0.000055951954,0.000007888222,0.0009014793],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997713,0.000091259106,0.0005327467,0.0002723653,0.0011605521,0.00023002754],"domain_scores_gemma":[0.99786896,0.00013354392,0.00044122708,0.0010887785,0.00022452242,0.00024299849],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009549281,0.00011476278,0.00030154118,0.00025999267,0.00030837802,0.00027744996,0.0014086025,0.00009524708,0.0010038487],"category_scores_gemma":[0.0019149806,0.000095813084,0.00017670981,0.00046401608,0.00031257732,0.0012583038,0.00030411914,0.0001658551,0.000071487964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026268921,0.0024528913,0.018485203,0.00042734444,0.004423626,0.00044347043,0.005361471,0.00030730604,0.010965745,0.06624937,0.05256363,0.8382937],"study_design_scores_gemma":[0.0010882952,0.00013742252,0.025010908,0.00012701815,0.0010413572,0.000018637373,0.00037126688,0.9226884,0.040263113,0.007775109,0.000942374,0.0005360945],"about_ca_topic_score_codex":0.00020860024,"about_ca_topic_score_gemma":0.00023981504,"teacher_disagreement_score":0.9223811,"about_ca_system_score_codex":0.000029020126,"about_ca_system_score_gemma":0.00010257042,"threshold_uncertainty_score":0.99990934},"labels":[],"label_agreement":null},{"id":"W2779175107","doi":"10.1007/s11042-017-5484-1","title":"A target-oriented segmentation method for specific tissues in MRI images of the brain","year":2017,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Central City Brewers and Distillers (Canada)","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Artificial intelligence; Segmentation; Computer vision; Image registration; Pattern recognition (psychology); Image segmentation; Image (mathematics)","score_opus":0.029820901893795795,"score_gpt":0.35428996755789005,"score_spread":0.3244690656640943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2779175107","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020402693,0.00008977392,0.99433887,0.0038170337,0.000050748098,0.001223154,0.0000502298,0.000038203183,0.00018797781],"genre_scores_gemma":[0.005326012,0.000056435078,0.99315894,0.00016842944,0.000047926147,0.0010326104,0.00001944381,0.0000057361262,0.00018444516],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992249,0.00004548301,0.00023100368,0.00024296563,0.00014174014,0.000113892616],"domain_scores_gemma":[0.9987788,0.0003400848,0.00020204327,0.0005619078,0.000071649534,0.000045516565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003706883,0.00007540383,0.00011821096,0.000043842632,0.00023611734,0.00012909624,0.00056327344,0.00003610968,0.00001158479],"category_scores_gemma":[0.00016423289,0.0000577549,0.000034008022,0.0001060284,0.00014300593,0.00036321112,0.00015352771,0.00006295133,0.0000023454363],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033113088,0.00008371219,0.0011063888,0.000028283912,0.000006289071,2.2657508e-7,0.0007716576,0.000004729797,0.08473776,0.0066939048,0.0037765305,0.9027872],"study_design_scores_gemma":[0.0011528847,0.00004216253,0.03631629,0.000048585352,0.000008081095,0.0000020997657,0.00019335456,0.035069413,0.8878328,0.00919404,0.029944498,0.00019574321],"about_ca_topic_score_codex":0.00003738196,"about_ca_topic_score_gemma":0.0000068569125,"teacher_disagreement_score":0.90259147,"about_ca_system_score_codex":0.000016008542,"about_ca_system_score_gemma":0.000021256694,"threshold_uncertainty_score":0.23551764},"labels":[],"label_agreement":null},{"id":"W2779656097","doi":"","title":"A Novel Method to Visualize Quantitative T2 MRI Data: qT2-View","year":2017,"lang":"en","type":"article","venue":"CMBES Proceedings","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision","score_opus":0.14838358688443204,"score_gpt":0.4713388355696764,"score_spread":0.32295524868524433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2779656097","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021050796,0.00006927169,0.98896885,0.0041856873,0.00020047724,0.00044379666,0.000012128669,0.00040618906,0.0055030757],"genre_scores_gemma":[0.0028408924,0.00003964563,0.9937597,0.00252663,0.000094824645,0.00007771188,0.00000522792,0.000018874347,0.0006365114],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979376,0.000016376536,0.0003301859,0.0008442292,0.00052417227,0.0003474426],"domain_scores_gemma":[0.9979743,0.00010879795,0.00028673024,0.0009793242,0.0003749188,0.0002759279],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0015232512,0.00019888661,0.00029094072,0.0001251276,0.0004097355,0.0011844173,0.0045878,0.00007163026,0.000037038426],"category_scores_gemma":[0.0015572183,0.00017743965,0.00004395765,0.00024932323,0.00012621185,0.0031839323,0.0024690502,0.00017819315,0.00017218656],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021911726,0.0002548599,0.0003429857,0.00027081146,0.000069706366,0.000015676153,0.005477069,3.5793676e-7,0.2823541,0.19613506,0.17561844,0.33943903],"study_design_scores_gemma":[0.0020704616,0.0012560595,0.0072939075,0.0013138534,0.00010683563,0.00021061381,0.0012633632,0.06930002,0.6298318,0.014996498,0.2702262,0.0021303608],"about_ca_topic_score_codex":0.00010381263,"about_ca_topic_score_gemma":0.000004478139,"teacher_disagreement_score":0.34747773,"about_ca_system_score_codex":0.00004476728,"about_ca_system_score_gemma":0.000069500464,"threshold_uncertainty_score":0.9998525},"labels":[],"label_agreement":null},{"id":"W2781817298","doi":"10.1117/12.467158","title":"Difficulties of T1 brain MRI segmentation techniques","year":2002,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Science Council","keywords":"Segmentation; Artificial intelligence; Computer science; Histogram; Image segmentation; Pattern recognition (psychology); Similarity (geometry); Data set; Scale-space segmentation; Intensity (physics); Set (abstract data type); Magnetic resonance imaging; Gaussian; Computer vision; Image (mathematics); Physics; Optics; Medicine","score_opus":0.012803127960105921,"score_gpt":0.24374618493256492,"score_spread":0.230943056972459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2781817298","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.966407,0.00011087977,0.02417914,0.005979895,0.0001744833,0.0008119523,0.000025046855,0.00034871174,0.0019629137],"genre_scores_gemma":[0.063313484,0.00019971201,0.9353296,0.00032976485,0.00018633645,0.00022243809,0.0000060765765,0.00003833366,0.0003742584],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99746656,3.3689272e-8,0.00084728486,0.00039314633,0.000976603,0.0003163756],"domain_scores_gemma":[0.9975483,0.0002105402,0.0005715558,0.000092573995,0.0014639564,0.000113089285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066889386,0.0002738082,0.00038414192,0.0001611466,0.00006789624,0.00012686916,0.001594218,0.00016512809,0.00002889364],"category_scores_gemma":[0.00059356744,0.0002278885,0.0004517269,0.0004629282,0.00029929462,0.0010929831,0.00027882017,0.00024718407,0.0000016114179],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012014687,0.0001587975,0.00009830454,0.00034811674,0.00014919978,8.200277e-8,0.00057978777,0.0000088091265,0.76110804,0.21277195,0.019844195,0.004920668],"study_design_scores_gemma":[0.0004971985,0.0003384312,0.00023639221,0.00025687128,0.000042106592,0.000010484625,0.0006404504,0.026222354,0.9693376,0.0013239087,0.00083763,0.00025656554],"about_ca_topic_score_codex":0.000008745453,"about_ca_topic_score_gemma":6.9316506e-8,"teacher_disagreement_score":0.91115046,"about_ca_system_score_codex":0.00012985694,"about_ca_system_score_gemma":0.000016602718,"threshold_uncertainty_score":0.92930233},"labels":[],"label_agreement":null},{"id":"W2782462533","doi":"","title":"Comparison of Gradient, Gradient Vector Flow and Pressure Force for Image Segmentation Using Active Contours","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Vector flow; Pressure gradient; Boundary (topology); Range (aeronautics); Computer vision; Image gradient; Balanced flow; Artificial intelligence; Flow (mathematics); Segmentation; Image segmentation; Pressure-gradient force; Image (mathematics); Computer science; Mathematics; Physics; Mathematical analysis; Geometry; Optics; Mechanics; Materials science; Image texture","score_opus":0.052190168679941536,"score_gpt":0.3461653889631144,"score_spread":0.29397522028317286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782462533","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017702065,0.00014828434,0.9807808,0.00016250217,0.000118626005,0.00068510044,0.000014966753,0.000113810915,0.00027387333],"genre_scores_gemma":[0.36360988,0.000012147968,0.63599163,0.000108124106,0.000020909027,0.000040233248,0.0000061700816,0.0000077249415,0.00020316921],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989465,0.000052987725,0.00028829,0.00028363254,0.00023833507,0.00019026897],"domain_scores_gemma":[0.9992383,0.0001325486,0.00018858169,0.00020347661,0.00013420616,0.00010288054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013792554,0.00011753187,0.00021506898,0.000088706554,0.00008665555,0.000060543927,0.00022798342,0.00004266839,0.000061748135],"category_scores_gemma":[0.00006240535,0.00010585309,0.000047843103,0.00014009529,0.00007991075,0.00064576487,0.00007383874,0.00006139088,0.0000011463488],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005506696,0.00070357794,0.0013537146,0.0004015339,0.0001961536,0.0000058614505,0.017026547,0.00014663933,0.620277,0.008478888,0.014157062,0.337198],"study_design_scores_gemma":[0.0004964318,0.00020507767,0.00031511413,0.000022970036,0.000025861389,0.0000041235894,0.00023863673,0.54728913,0.45091507,0.00032972766,0.000057017867,0.000100854006],"about_ca_topic_score_codex":0.000031582003,"about_ca_topic_score_gemma":0.00000586274,"teacher_disagreement_score":0.5471425,"about_ca_system_score_codex":0.000037214446,"about_ca_system_score_gemma":0.000009249781,"threshold_uncertainty_score":0.43165636},"labels":[],"label_agreement":null},{"id":"W2783637515","doi":"10.1016/j.compmedimag.2018.01.007","title":"A novel contour-based registration of lateral cephalogram and profile photograph","year":2018,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Peking University","keywords":"Artificial intelligence; Computer science; Computer vision; Cephalogram; Nasion; Iterative closest point; Landmark; Forehead; Robustness (evolution); Mathematics; Point cloud; Orthodontics; Malocclusion; Anatomy; Medicine","score_opus":0.015446419713489166,"score_gpt":0.28664991785636973,"score_spread":0.2712034981428806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783637515","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025191927,0.00013682302,0.9723122,0.0016248771,0.0001978871,0.00020447004,0.000004251897,0.00024571765,0.00008185645],"genre_scores_gemma":[0.5661475,0.000095182404,0.42985943,0.0037275008,0.00011018788,0.000022452803,0.000016075224,0.000010620539,0.000011023799],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982805,0.00010337078,0.00038864548,0.00038761148,0.0006111576,0.0002286639],"domain_scores_gemma":[0.9988316,0.0002099283,0.00016480188,0.00029792974,0.00018434587,0.00031134783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009468482,0.00015240118,0.00024595176,0.0002119885,0.00010892978,0.00015100175,0.00043133795,0.00008766995,0.000016781172],"category_scores_gemma":[0.00019881477,0.00013256578,0.00005043234,0.00039248288,0.0012190689,0.0002421372,0.00019174367,0.0001897292,5.649903e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012243792,0.0006958375,0.011674955,0.0004452754,0.000083421146,0.00008477998,0.001127261,1.2577955e-7,0.07976077,0.01230146,0.006402136,0.88730156],"study_design_scores_gemma":[0.007433529,0.0006978432,0.027900357,0.0010482514,0.000048783426,0.00030428014,0.000035410067,0.90574163,0.04801704,0.0053399303,0.0026739573,0.0007590136],"about_ca_topic_score_codex":0.000079774596,"about_ca_topic_score_gemma":0.000005603191,"teacher_disagreement_score":0.90574145,"about_ca_system_score_codex":0.0000050612484,"about_ca_system_score_gemma":0.000086230524,"threshold_uncertainty_score":0.54058754},"labels":[],"label_agreement":null},{"id":"W2783775739","doi":"10.1016/j.media.2018.09.002","title":"Joint registration and synthesis using a probabilistic model for alignment of MRI and histological sections","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; Engineering and Physical Sciences Research Council; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; University of California, San Diego; National Institutes of Health; Genentech; National Institute of Neurological Disorders and Stroke; IXICO; Servier; Eisai; DoD Alzheimer's Disease Neuroimaging Initiative; Pfizer; Biogen; BioClinica; National Institute of Mental Health; Neurosciences Research Foundation; National Center for Research Resources; F. Hoffmann-La Roche; University of Southern California; Wellcome Trust; National Institute of Diabetes and Digestive and Kidney Diseases; Synarc; Medpace; European Research Council; Northern California Institute for Research and Education; Massachusetts General Hospital; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Novartis Pharmaceuticals Corporation; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Artificial intelligence; Image registration; Computer science; Probabilistic logic; Computer vision; Rigid transformation; Robustness (evolution); Mutual information; Pattern recognition (psychology); Metric (unit); Inference; Affine transformation; Bayesian inference; Real-time MRI; Bayesian probability; Image (mathematics); Magnetic resonance imaging; Mathematics","score_opus":0.04816367954821354,"score_gpt":0.3234920483452383,"score_spread":0.27532836879702477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783775739","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011338588,0.000038858718,0.98710287,0.0011796459,0.000017349303,0.00018424338,0.0000048406228,0.00005749889,0.00007611708],"genre_scores_gemma":[0.40835628,0.00002761127,0.5912814,0.00021644324,0.000026093967,0.000047471574,0.0000025985523,0.0000033879635,0.0000386871],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985542,0.00008830945,0.00040262184,0.00036219714,0.00044592467,0.00014672961],"domain_scores_gemma":[0.9990189,0.00018444886,0.00016086425,0.0002667342,0.00017956187,0.00018946124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008583143,0.00009582009,0.00029451126,0.00019236145,0.0001148801,0.000053155913,0.00017947925,0.00008127541,0.000053450065],"category_scores_gemma":[0.0017587756,0.000077152916,0.00008588875,0.0004220397,0.0006118488,0.00019953592,0.00011998654,0.00006433065,4.469036e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002056513,0.0043495307,0.0025825999,0.0018943681,0.0058063706,0.00015949474,0.0126018645,0.0012514793,0.25387087,0.04694279,0.017026372,0.65330863],"study_design_scores_gemma":[0.00012637228,0.00007458223,0.00016578249,0.000022095277,0.0003890656,0.000009801151,0.00002499703,0.9828198,0.012983058,0.0032962514,0.000007959025,0.00008025944],"about_ca_topic_score_codex":0.000089791814,"about_ca_topic_score_gemma":0.00004492567,"teacher_disagreement_score":0.9815683,"about_ca_system_score_codex":0.000048068046,"about_ca_system_score_gemma":0.00007593886,"threshold_uncertainty_score":0.31462047},"labels":[],"label_agreement":null},{"id":"W2787868188","doi":"10.1109/crv.2017.15","title":"Leveraging Tree Statistics for Extracting Anatomical Trees from 3D Medical Images","year":2017,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Prior probability; Tree (set theory); Computer science; Artificial intelligence; Pattern recognition (psychology); Segmentation; Ground truth; Noise (video); Tree structure; Bayesian probability; Computer vision; Image (mathematics); Mathematics; Binary tree; Algorithm","score_opus":0.03916233929137122,"score_gpt":0.3539422692585025,"score_spread":0.3147799299671312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2787868188","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00088150066,0.000022327273,0.99360496,0.002557949,0.0002903974,0.00016627484,0.000021742044,0.00032273677,0.002132123],"genre_scores_gemma":[0.082715675,0.000017875109,0.91571075,0.00092503935,0.00017159605,0.000028166882,0.000015484746,0.000011247212,0.00040414583],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983289,0.000049046175,0.0003268277,0.00041105575,0.00060338306,0.00028075633],"domain_scores_gemma":[0.997772,0.00097196194,0.00019389112,0.0007139065,0.000094868556,0.00025339602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056255655,0.00013232467,0.00019628005,0.000054061165,0.00038094513,0.00063269446,0.001634758,0.00008854948,0.00045168505],"category_scores_gemma":[0.0026570854,0.00011318346,0.00004845851,0.00003081335,0.0001647882,0.00082054955,0.00038542427,0.00018489973,0.000022731932],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004580325,0.00004803358,0.0007823974,0.000007263205,0.000017474495,0.000048062666,0.00017295343,1.7352107e-7,0.0033348892,0.003072691,0.026828606,0.96568286],"study_design_scores_gemma":[0.0029095225,0.0001690304,0.036697652,0.00017818237,0.000043224518,0.000029198733,0.00022049964,0.5559442,0.3525602,0.047072444,0.0032678293,0.0009080024],"about_ca_topic_score_codex":0.00027970294,"about_ca_topic_score_gemma":0.00006479759,"teacher_disagreement_score":0.96477485,"about_ca_system_score_codex":0.000036489448,"about_ca_system_score_gemma":0.000115974435,"threshold_uncertainty_score":0.61010873},"labels":[],"label_agreement":null},{"id":"W2789826769","doi":"10.1109/cisp-bmei.2017.8301983","title":"Segmentation of GBM in MRI images using an efficient speed function based on level set method","year":2017,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Segmentation; Computer science; Image segmentation; Sørensen–Dice coefficient; Artificial intelligence; Pattern recognition (psychology); Level set (data structures); Filter (signal processing); Level set method; Dice; Set (abstract data type); Boundary (topology); Function (biology); Scale-space segmentation; Region of interest; Gold standard (test); Computer vision; Mathematics; Statistics","score_opus":0.11846055717961015,"score_gpt":0.4089342280549757,"score_spread":0.2904736708753656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789826769","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014876939,0.0000016677632,0.98362535,0.00018511145,0.00014792284,0.00025867406,0.000004266136,0.00007876119,0.0008213176],"genre_scores_gemma":[0.3231307,5.268457e-7,0.67649776,0.00030086073,0.000013479638,0.0000036859844,0.00000611511,0.000004818719,0.00004204585],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986763,0.00019108158,0.00026664705,0.00031355853,0.0004123232,0.00014009289],"domain_scores_gemma":[0.9988773,0.000075603115,0.00022876912,0.0006636586,0.000087617955,0.00006705166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010656175,0.00009993617,0.00013152024,0.00021170384,0.00012062581,0.00014647633,0.0004839937,0.00004482715,0.00006183814],"category_scores_gemma":[0.00011738974,0.00008937949,0.0000325231,0.00012682565,0.00005900866,0.00043409655,0.000091638976,0.00007810475,0.000004585947],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008208235,0.00067831425,0.002118687,0.000062240244,0.000012109713,0.000014890182,0.0007384098,0.039624188,0.7284351,0.0013072434,0.0005553756,0.22637136],"study_design_scores_gemma":[0.00037037223,0.00011784768,0.007968907,0.000023565606,0.0000031017055,8.9044437e-7,0.000040489627,0.5443269,0.44693753,0.0001446297,0.0000013026533,0.000064468055],"about_ca_topic_score_codex":0.00033321307,"about_ca_topic_score_gemma":0.000011478788,"teacher_disagreement_score":0.5047027,"about_ca_system_score_codex":0.000068421105,"about_ca_system_score_gemma":0.000059102254,"threshold_uncertainty_score":0.364479},"labels":[],"label_agreement":null},{"id":"W2789928581","doi":"10.1109/access.2018.2807698","title":"Glioma Segmentation Using a Novel Unified Algorithm in Multimodal MRI Images","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Hausdorff distance; Computer science; Segmentation; Robustness (evolution); Artificial intelligence; Sørensen–Dice coefficient; Dice; Image segmentation; Cluster analysis; Euclidean distance; Pattern recognition (psychology); Algorithm; Mathematics; Statistics","score_opus":0.0470482957096771,"score_gpt":0.37309017122658206,"score_spread":0.32604187551690494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789928581","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044500828,0.000009682576,0.9541206,0.00014517,0.0005064344,0.00028245107,0.000004936627,0.00022320643,0.00020670703],"genre_scores_gemma":[0.18127051,0.0000040579357,0.81796366,0.0005332117,0.00016152496,0.000025747655,0.000003627777,0.000011035977,0.000026620866],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985914,0.0000659144,0.00031079486,0.00039131375,0.0003724797,0.00026807102],"domain_scores_gemma":[0.9992088,0.00006347851,0.00013005042,0.00035312644,0.00015361328,0.00009092908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032633464,0.00013427738,0.00013976311,0.00030643956,0.000088978966,0.0003700829,0.0010424538,0.00006498407,0.00005699562],"category_scores_gemma":[0.000038863545,0.00013210089,0.000029404553,0.00084267755,0.00012776825,0.0022024242,0.00022261856,0.00011302273,0.000022573458],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008930285,0.00021131012,0.00063736766,0.000019834739,0.00001334509,0.000051450064,0.00090630335,0.00006281975,0.71959037,0.000105177736,0.0009407461,0.27745232],"study_design_scores_gemma":[0.0006044361,0.000042217285,0.0014701344,0.000033754994,0.0000032412881,0.000016941261,0.000027045082,0.21078259,0.78633606,0.0005104739,0.000016088683,0.00015700083],"about_ca_topic_score_codex":0.0006173187,"about_ca_topic_score_gemma":0.000024427693,"teacher_disagreement_score":0.27729532,"about_ca_system_score_codex":0.000106860156,"about_ca_system_score_gemma":0.00007283646,"threshold_uncertainty_score":0.53869176},"labels":[],"label_agreement":null},{"id":"W2790200737","doi":"10.21917/ijivp.2017.0220","title":"AUTOMATED CORPUS CALLOSUM SEGMENTATION IN MIDSAGITTAL BRAIN MR IMAGES","year":2017,"lang":"en","type":"article","venue":"ICTACT Journal on Image and Video Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Initialization; Segmentation; Artificial intelligence; Computer science; Corpus callosum; Computer vision; Image segmentation; Pattern recognition (psychology); Active contour model; Magnetic resonance imaging; Boundary (topology); Mathematics; Anatomy; Medicine","score_opus":0.01920980337350721,"score_gpt":0.3390254949088623,"score_spread":0.3198156915353551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790200737","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07472204,0.0003857357,0.91461676,0.007847396,0.0002835284,0.00023869623,0.000002873537,0.00047749322,0.0014254515],"genre_scores_gemma":[0.821643,0.00017135435,0.17511772,0.0026381267,0.0001535942,0.000014068711,0.000003021666,0.000023940678,0.00023515665],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983047,0.00012186803,0.0004568205,0.00032688683,0.0004588286,0.00033086646],"domain_scores_gemma":[0.99868125,0.00010373571,0.0005533913,0.0002985588,0.0001538303,0.00020925589],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00083694136,0.00019335227,0.0002252838,0.0002664679,0.0007269687,0.0032815041,0.00069522666,0.00007174364,0.000019730369],"category_scores_gemma":[0.00054076785,0.00016481707,0.00004730721,0.00012901422,0.00015620375,0.0044965306,0.00016210749,0.00043450165,0.00001174638],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003348048,0.00012545247,0.0030474032,0.00008308412,0.000012959765,0.00071888306,0.0010008296,0.00000252038,0.22337042,0.000047073645,0.0047589517,0.7667989],"study_design_scores_gemma":[0.0043232073,0.0006682798,0.08204489,0.0019021668,0.000026875872,0.001358761,0.00029315046,0.061503343,0.8425304,0.0038417724,0.0005924421,0.0009146959],"about_ca_topic_score_codex":0.000027752232,"about_ca_topic_score_gemma":0.000004309117,"teacher_disagreement_score":0.7658842,"about_ca_system_score_codex":0.00009534657,"about_ca_system_score_gemma":0.00013344953,"threshold_uncertainty_score":0.9977532},"labels":[],"label_agreement":null},{"id":"W2790348354","doi":"10.1016/j.media.2018.03.001","title":"Rapid fully automatic segmentation of subcortical brain structures by shape-constrained surface adaptation","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; Canadian Institutes of Health Research; European Commission","keywords":"Segmentation; Artificial intelligence; Adaptation (eye); Computer science; Computer vision; Surface (topology); Pattern recognition (psychology); Brain morphometry; Neuroscience; Mathematics; Psychology; Geometry; Magnetic resonance imaging; Medicine","score_opus":0.012098587567837887,"score_gpt":0.2926726285358938,"score_spread":0.28057404096805594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790348354","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05565547,0.00007548107,0.9407386,0.002713066,0.00007761084,0.00019877916,0.00001274622,0.0002893768,0.0002388543],"genre_scores_gemma":[0.5520454,0.000028415778,0.44570243,0.0019345316,0.00006268285,0.000013854615,0.00011533694,0.000012591494,0.000084734485],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959719,0.0004241347,0.0009196461,0.0005390149,0.0017926125,0.0003526918],"domain_scores_gemma":[0.99781436,0.0005169261,0.0003439903,0.0005548545,0.00036411523,0.00040578248],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012504867,0.0002186893,0.0004963006,0.00031661493,0.00011026646,0.00012994824,0.0009087409,0.00015806724,0.006909135],"category_scores_gemma":[0.0016780033,0.00018884914,0.00022540113,0.002056629,0.00083205453,0.000663831,0.00016549166,0.00020552891,0.0000462064],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016479398,0.00025100057,0.00050160085,0.00007099822,0.0008682862,0.000043995613,0.0016985958,0.000009591903,0.11733787,0.00067428994,0.024601793,0.8539255],"study_design_scores_gemma":[0.0006220159,0.00023986094,0.0014428579,0.000022932498,0.00032424048,0.000008280705,0.0002265729,0.83856744,0.15753616,0.0007192859,0.000056406,0.00023391886],"about_ca_topic_score_codex":0.00012679519,"about_ca_topic_score_gemma":0.000028920665,"teacher_disagreement_score":0.8536916,"about_ca_system_score_codex":0.000058488098,"about_ca_system_score_gemma":0.00019020094,"threshold_uncertainty_score":0.9939987},"labels":[],"label_agreement":null},{"id":"W2790437002","doi":"10.1117/12.2294545","title":"Tissue segmentation by fuzzy clustering technique: case study on Alzheimer's disease","year":2018,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec","funders":"","keywords":"Segmentation; Neuroimaging; White matter; Partial volume; Cluster analysis; Image segmentation; Artificial intelligence; Cerebrospinal fluid; Computer science; Fuzzy logic; Fuzzy clustering; Noise (video); Pattern recognition (psychology); Neuroscience; Medicine; Psychology; Radiology; Magnetic resonance imaging; Image (mathematics)","score_opus":0.03280737460103846,"score_gpt":0.3530660213205175,"score_spread":0.32025864671947907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790437002","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033726925,0.00003088087,0.9916141,0.00030973763,0.00016195132,0.0013192539,0.0000037167422,0.00089753245,0.002290155],"genre_scores_gemma":[0.7368122,0.0000021707992,0.26059863,0.0017232599,0.00009489504,0.00038096702,0.000008356246,0.000018669716,0.00036088296],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983749,0.00015299491,0.00027567413,0.00046254863,0.0005181554,0.00021573996],"domain_scores_gemma":[0.9988158,0.000045493474,0.00008072337,0.0005954622,0.00013121756,0.00033130325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046266828,0.00016244453,0.00011841094,0.00012117177,0.00014488303,0.00016939285,0.00044926023,0.000036138965,0.00010539715],"category_scores_gemma":[0.000034669167,0.00014233687,0.00002090459,0.0003235298,0.00007601902,0.0005404354,0.00026354202,0.000095828254,0.00013487211],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036079695,0.0012512418,0.0003385501,0.000012786692,0.000062159466,0.0032609596,0.0032820331,0.000002733472,0.028472096,0.00024482512,0.12955728,0.8334793],"study_design_scores_gemma":[0.0011146695,0.0031818387,0.00013504767,0.00003830222,0.000065216205,0.00042417808,0.0021949622,0.005345738,0.9849618,0.0007263097,0.0011646671,0.0006472697],"about_ca_topic_score_codex":0.00019597044,"about_ca_topic_score_gemma":0.000020330945,"teacher_disagreement_score":0.9564897,"about_ca_system_score_codex":0.00006524257,"about_ca_system_score_gemma":0.00004675269,"threshold_uncertainty_score":0.5804329},"labels":[],"label_agreement":null},{"id":"W2790478351","doi":"10.1109/tcsvt.2018.2818072","title":"Adaptive Polar Active Contour for Segmentation and Tracking in Ultrasound Videos","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Segmentation; Active contour model; Image segmentation; Tracking (education); Pattern recognition (psychology)","score_opus":0.03060426646676792,"score_gpt":0.2997541293162064,"score_spread":0.2691498628494384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790478351","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015041252,0.00016510217,0.9820269,0.00026065102,0.00037896342,0.001749303,0.0000716652,0.00027755258,0.000028621225],"genre_scores_gemma":[0.985588,0.000054711916,0.013175793,0.00016931203,0.000039201695,0.000909515,0.000002009602,0.000016535629,0.000044900073],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986507,0.000058166162,0.00036397445,0.0004973626,0.00014168993,0.00028808333],"domain_scores_gemma":[0.99885684,0.00049102225,0.00014626358,0.00022872545,0.00020394214,0.00007317556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037684577,0.00016908075,0.00028066325,0.00053903315,0.0002594598,0.00010668936,0.00021853883,0.00019677302,0.0000027570873],"category_scores_gemma":[0.00005715221,0.00016564726,0.000041832067,0.00033956612,0.00022828576,0.0005256025,0.0000026854782,0.00016460121,0.0000015581267],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007607107,0.00017183305,0.00009712813,0.00015959161,0.0001119424,0.0000050214294,0.0022951039,0.000021152826,0.14635259,0.009312125,0.00020534772,0.84119207],"study_design_scores_gemma":[0.003994675,0.0033287017,0.00031434358,0.00038329975,0.00006622207,0.00022419772,0.004039389,0.025730597,0.9503305,0.010314908,0.00065854896,0.00061460276],"about_ca_topic_score_codex":0.00011278879,"about_ca_topic_score_gemma":0.00015041366,"teacher_disagreement_score":0.9705468,"about_ca_system_score_codex":0.00010561269,"about_ca_system_score_gemma":0.0000452873,"threshold_uncertainty_score":0.67548996},"labels":[],"label_agreement":null},{"id":"W2792455544","doi":"10.1109/icip.2017.8296849","title":"Semantic image segmentation using the ICM algorithm","year":2017,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Image segmentation; Artificial intelligence; Image (mathematics); Scale-space segmentation; Segmentation; Segmentation-based object categorization; Pattern recognition (psychology); Computer vision; Algorithm","score_opus":0.034744069740008085,"score_gpt":0.3493946780092329,"score_spread":0.3146506082692248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792455544","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006964028,0.0000089682235,0.99484056,0.001592183,0.0002136069,0.00018286123,5.2334786e-7,0.00019077973,0.0022741118],"genre_scores_gemma":[0.012241494,0.000009993462,0.9859487,0.0011299307,0.000060324186,0.000010217005,9.866102e-7,0.000005242239,0.000593128],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991692,0.00005053733,0.00014555531,0.00019322171,0.00029295514,0.00014858047],"domain_scores_gemma":[0.9988471,0.00004104018,0.00013325669,0.0008613395,0.00006424596,0.0000530002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036138727,0.00007475197,0.00006829761,0.0000324565,0.0005530238,0.0008102658,0.001207763,0.000023623506,0.00009318199],"category_scores_gemma":[0.00007448015,0.000048098216,0.000032907814,0.00005486834,0.00013258841,0.0012237057,0.00034521383,0.00007675004,0.00005210846],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.710819e-7,0.00003385355,0.00015859678,0.000008998047,0.000013730433,0.000023628274,0.00047731856,0.0000013374045,0.08408522,0.0025514518,0.0047043967,0.9079409],"study_design_scores_gemma":[0.0002770169,0.000029428224,0.0013248293,0.000020334703,0.000010456904,0.00003087242,0.00010623209,0.45256463,0.540775,0.004533715,0.0001569073,0.00017055726],"about_ca_topic_score_codex":0.00019491347,"about_ca_topic_score_gemma":0.0000047238022,"teacher_disagreement_score":0.90777034,"about_ca_system_score_codex":0.000028672617,"about_ca_system_score_gemma":0.000030166098,"threshold_uncertainty_score":0.78134114},"labels":[],"label_agreement":null},{"id":"W2792722532","doi":"10.1117/12.2292925","title":"Automated registration and stitching of multiple 3D ultrasound images for monitoring neonatal intraventricular hemorrhage","year":2018,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Image stitching; Intraventricular hemorrhage; Artificial intelligence; Ultrasound; Image registration; Computer science; Medicine; Computer vision; Radiology; Image (mathematics)","score_opus":0.013228622620513237,"score_gpt":0.28804467847503734,"score_spread":0.2748160558545241,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792722532","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04360235,0.000041730567,0.95532346,0.000056203302,0.00012714382,0.000242787,0.0000033568972,0.000497916,0.0001050325],"genre_scores_gemma":[0.4736936,0.0000039039633,0.5261851,0.000020493484,0.00004791212,0.000009102063,0.0000030794208,0.0000030639096,0.00003370461],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99921536,0.00002701994,0.00022494447,0.00021427797,0.00018591757,0.00013244935],"domain_scores_gemma":[0.99926287,0.0002376986,0.00010586283,0.00019477951,0.00013903764,0.000059751987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029035972,0.00007899401,0.000098713375,0.00006559289,0.000083917315,0.00009945686,0.00021430511,0.000037994894,0.000009574576],"category_scores_gemma":[0.0004242534,0.000070035756,0.00002091644,0.00013687555,0.00009106345,0.0005160701,0.000059050857,0.000042527045,0.0000014840741],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011806691,0.00005002057,0.00220375,0.00009493188,0.000019212068,0.000013008919,0.0008151853,0.000005086223,0.7092386,0.00074982067,0.0011733862,0.2856252],"study_design_scores_gemma":[0.00027851312,0.00009870495,0.0016688443,0.000024825402,0.000004640272,0.000023008595,0.00006030432,0.086689584,0.9108688,0.00018483598,0.000018340126,0.000079591126],"about_ca_topic_score_codex":0.000060796847,"about_ca_topic_score_gemma":0.0000021026449,"teacher_disagreement_score":0.43009126,"about_ca_system_score_codex":0.000018583605,"about_ca_system_score_gemma":0.000021296346,"threshold_uncertainty_score":0.2855975},"labels":[],"label_agreement":null},{"id":"W2793364177","doi":"10.48550/arxiv.1803.07682","title":"A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Feature (linguistics); Context (archaeology); Image registration; Visualization; Computer vision; Image (mathematics); Grid; Computation; Pattern recognition (psychology); Process (computing); Compensation (psychology); Algorithm; Mathematics; Geography","score_opus":0.06191323794460714,"score_gpt":0.24468716427380074,"score_spread":0.1827739263291936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793364177","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007756394,0.0000064454175,0.9880274,0.001620121,0.00054489163,0.0010458526,0.00006407118,0.0006673402,0.000267519],"genre_scores_gemma":[0.60803837,0.000008737869,0.38998333,0.001415027,0.00015053048,0.000011158559,0.0001208299,0.000023272105,0.00024876988],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978421,0.00024150265,0.00017973199,0.001190916,0.0001801401,0.00036561507],"domain_scores_gemma":[0.99647444,0.0014054448,0.00042257534,0.0011765215,0.00030399623,0.00021701289],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031934865,0.0003397176,0.0003519115,0.00029214896,0.00020840736,0.00020312927,0.0019060492,0.000552979,0.000065636035],"category_scores_gemma":[0.0005487801,0.0003928451,0.0002535984,0.00048292914,0.00026329525,0.000408127,0.0006066662,0.00069037924,0.000050598966],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005932187,0.00089719455,0.0028011918,0.0008296655,0.0006797307,0.00027148187,0.0036305783,0.026126876,0.0018806041,0.8855842,0.06543805,0.011267222],"study_design_scores_gemma":[0.001395229,0.00041886023,0.0027039929,0.00059793843,0.00013361617,0.0000033880945,0.00010787598,0.42948774,0.028601723,0.5341406,0.0011992141,0.0012098446],"about_ca_topic_score_codex":0.000034277913,"about_ca_topic_score_gemma":0.000022026226,"teacher_disagreement_score":0.60028195,"about_ca_system_score_codex":0.0003570147,"about_ca_system_score_gemma":0.00033742175,"threshold_uncertainty_score":0.99985236},"labels":[],"label_agreement":null},{"id":"W2794388444","doi":"10.1016/j.compbiomed.2018.02.005","title":"Atlas selection for hippocampus segmentation: Relevance evaluation of three meta-information parameters","year":2018,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; Takeda Pharmaceutical Company; IXICO; Eisai; Servier; U.S. Department of Defense; Eli Lilly and Company; Lundbeckfonden; Elan; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Pfizer; BioClinica; Biogen; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; F. Hoffmann-La Roche; Merck; Alzheimer's Drug Discovery Foundation; GE Healthcare; Alzheimer's Disease Neuroimaging Initiative; Johnson and Johnson; Meso Scale Diagnostics; AbbVie; Fujirebio Europe; Alzheimer's Association","keywords":"Atlas (anatomy); Computer science; Segmentation; Brain atlas; Artificial intelligence; Neuroimaging; Pattern recognition (psychology); Image registration; Data mining; Image (mathematics); Medicine","score_opus":0.07074991430749918,"score_gpt":0.3823490516222275,"score_spread":0.3115991373147283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794388444","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022031074,0.00028135008,0.9754007,0.0009993427,0.0005555505,0.00058899657,0.0000012043532,0.00005202444,0.000089749716],"genre_scores_gemma":[0.3468987,0.00007860789,0.6515324,0.0012066205,0.000091227485,0.00015165981,0.00003508257,0.000002950283,0.0000027623546],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989576,0.00014278095,0.00038480072,0.00020210052,0.00018247576,0.00013029132],"domain_scores_gemma":[0.9988726,0.00037199157,0.0002146539,0.00014953862,0.00034782258,0.00004338873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016946427,0.00009669753,0.00023860663,0.00020506483,0.00005856908,0.000008536165,0.00019119256,0.00007705799,0.000016084821],"category_scores_gemma":[0.00039171142,0.00007370834,0.000027561458,0.0002711631,0.00032876237,0.00040247865,0.000045804005,0.00006503708,0.0000014801064],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003693251,0.000022835551,0.0015761462,0.000046621684,0.000117056145,1.0958116e-7,0.0010186968,0.000027576554,0.003139189,0.0075034383,0.0021247661,0.9843866],"study_design_scores_gemma":[0.005548529,0.0036080903,0.0073967655,0.00018512765,0.0004942647,0.000032035106,0.00013137134,0.6296709,0.063438654,0.28846744,0.0007003015,0.00032647085],"about_ca_topic_score_codex":0.000037427948,"about_ca_topic_score_gemma":0.000031725984,"teacher_disagreement_score":0.98406017,"about_ca_system_score_codex":0.000050084196,"about_ca_system_score_gemma":0.00004396124,"threshold_uncertainty_score":0.3005739},"labels":[],"label_agreement":null},{"id":"W2794642825","doi":"10.3389/fnins.2020.592352","title":"DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes","year":2020,"lang":"en","type":"preprint","venue":"Frontiers in Neuroscience","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Overfitting; Computer science; Segmentation; Convolutional neural network; Artificial intelligence; Context (archaeology); Deep learning; Memory footprint; Cross entropy; Voxel; Pattern recognition (psychology); Artificial neural network; Algorithm","score_opus":0.014212475666517675,"score_gpt":0.25157449461793824,"score_spread":0.23736201895142056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794642825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013302428,0.00042753448,0.98117906,0.0011205449,0.0028434615,0.00077507255,0.000015155007,0.00031237522,0.000024375404],"genre_scores_gemma":[0.76222813,0.0018062762,0.23396637,0.0015210893,0.0000833171,0.00030927392,0.000032641026,0.00002573968,0.000027148806],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99689394,0.00025799157,0.0006432973,0.0012880423,0.00057868037,0.00033805074],"domain_scores_gemma":[0.99891704,0.000035199144,0.00029757465,0.0004934594,0.00008128445,0.00017542296],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005326354,0.0002753522,0.00030777964,0.0011303583,0.000105709834,0.00034536613,0.0010595645,0.000176831,0.0000016894862],"category_scores_gemma":[0.00030097956,0.00031131075,0.000056930763,0.002005133,0.00028808435,0.000987979,0.0006930318,0.0006262967,9.233674e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057852656,0.0005555643,0.3224665,0.0006490184,0.000018842644,0.00013705257,0.0067589716,0.0015560806,0.095755205,0.00027265854,0.017029656,0.55474263],"study_design_scores_gemma":[0.00075206236,0.00020319194,0.26449454,0.00025381718,0.000013995108,0.000017138953,0.0001830714,0.68507165,0.038836874,0.009283205,0.00036936576,0.00052112644],"about_ca_topic_score_codex":0.00007325251,"about_ca_topic_score_gemma":0.000025411053,"teacher_disagreement_score":0.74892575,"about_ca_system_score_codex":0.00014957103,"about_ca_system_score_gemma":0.00012223056,"threshold_uncertainty_score":0.9999339},"labels":[],"label_agreement":null},{"id":"W2795981973","doi":"10.1016/j.neuroimage.2018.04.001","title":"Supervoxel based method for multi-atlas segmentation of brain MR images","year":2018,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Voxel; Markov random field; Pairwise comparison; Atlas (anatomy); Inference; Pattern recognition (psychology); Consistency (knowledge bases); Grid; Image segmentation; Grid cell; Computer vision; Mathematics","score_opus":0.05499210990206533,"score_gpt":0.38198654145856104,"score_spread":0.3269944315564957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795981973","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010104072,0.000012255098,0.99627733,0.001433713,0.00020508577,0.0005547732,0.000024565621,0.0002722447,0.00020959336],"genre_scores_gemma":[0.0072097443,0.000002302319,0.9884584,0.0038093484,0.000067220346,0.00007531162,0.000014412202,0.00001889129,0.0003443515],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99841267,0.00023700701,0.00034993258,0.0004417646,0.0003215482,0.00023705594],"domain_scores_gemma":[0.99843884,0.00052390737,0.00015352019,0.0005106184,0.0002749134,0.000098176264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006822814,0.0001440406,0.00018673365,0.00015847638,0.00008722836,0.00008357386,0.00064206665,0.00004829178,0.00008330391],"category_scores_gemma":[0.0006448824,0.00013720809,0.00009298321,0.00030583906,0.00015351822,0.0005457263,0.00011742445,0.00008700519,0.000022968938],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010861539,0.00010366226,0.0000867387,0.000049674167,0.0000051661805,0.000004154067,0.00020648468,0.0000017492731,0.9099907,0.00023169328,0.017694611,0.071614504],"study_design_scores_gemma":[0.00082251645,0.00032229346,0.0009253336,0.000013867806,0.0000070879987,0.0000031748798,0.000014883277,0.114626944,0.88237196,0.00019950309,0.00057827414,0.00011413464],"about_ca_topic_score_codex":0.000030003512,"about_ca_topic_score_gemma":0.0000037134014,"teacher_disagreement_score":0.11462519,"about_ca_system_score_codex":0.000021318325,"about_ca_system_score_gemma":0.00006766786,"threshold_uncertainty_score":0.55951834},"labels":[],"label_agreement":null},{"id":"W2797893281","doi":"10.1007/s40846-018-0402-1","title":"Development, Implementation and Validation of an Automatic Centerline Extraction Algorithm for Complex 3D Objects","year":2018,"lang":"en","type":"article","venue":"Journal of Medical and Biological Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; Health Sciences Centre; Canadian Institute for Advanced Research","funders":"University of Manitoba; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada; Health Sciences Centre Foundation","keywords":"Extraction (chemistry); Computer science; Algorithm; Artificial intelligence; Chromatography; Chemistry","score_opus":0.04232061595967974,"score_gpt":0.35047770096281883,"score_spread":0.3081570850031391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2797893281","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18386385,0.000025346682,0.8157863,0.00013518223,0.00009038819,0.00007178076,8.8185726e-7,0.000024499637,0.0000017889082],"genre_scores_gemma":[0.2128985,0.00005152717,0.78682476,0.00009004148,0.00012316283,0.0000030816282,0.000006277164,0.0000020154846,6.205512e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907386,0.00002931699,0.00041516972,0.0000939133,0.0002913198,0.00009642916],"domain_scores_gemma":[0.9994163,0.000109920555,0.00018218422,0.000039171853,0.00010445965,0.00014795794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009065745,0.000064413885,0.00014751268,0.00007781791,0.00003407311,0.000027017248,0.0001459727,0.000065095395,0.00004131907],"category_scores_gemma":[0.00013887223,0.00004287641,0.000017920514,0.000073289026,0.00004922626,0.00024850218,0.000049337577,0.00008007661,1.4421435e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000028753118,0.000036827405,0.00003698594,0.000026071542,0.000011377819,0.0000033013355,0.00015858491,6.44358e-7,0.0223048,0.000065107095,0.000019681309,0.9773337],"study_design_scores_gemma":[0.0016802653,0.0019219833,0.012132982,0.00019306195,0.000015309632,0.00037165702,0.0002329273,0.7407192,0.24134542,0.00022308767,0.0009846914,0.00017944131],"about_ca_topic_score_codex":0.0000016158375,"about_ca_topic_score_gemma":5.2882376e-7,"teacher_disagreement_score":0.9771543,"about_ca_system_score_codex":0.000014601516,"about_ca_system_score_gemma":0.000030326853,"threshold_uncertainty_score":0.17484494},"labels":[],"label_agreement":null},{"id":"W2799247387","doi":"10.1101/306811","title":"A New Approach to Symmetric Registration of Longitudinal Structural MRI of the Human Brain","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; BioClinica; F. Hoffmann-La Roche; University of Southern California; Biogen; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Landmark; Interpolation (computer graphics); Affine transformation; Neuroimaging; Artificial intelligence; Computer science; Transformation (genetics); Image registration; Computer vision; Permutation (music); Rigid transformation; Point (geometry); Gold standard (test); Algorithm; Mathematics; Motion (physics); Image (mathematics); Psychology; Geometry; Statistics","score_opus":0.02655879501651727,"score_gpt":0.2728654789713962,"score_spread":0.24630668395487892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799247387","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0812296,0.00006718384,0.9166031,0.00039993148,0.0005005223,0.0008907787,0.000024729372,0.00020412545,0.000080012454],"genre_scores_gemma":[0.6363571,0.000003346966,0.36326832,0.00013818651,0.00016105916,0.000034775636,1.3525992e-7,0.000021750262,0.00001533705],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99703926,0.00020154522,0.00075558765,0.0008335468,0.00088011305,0.0002899226],"domain_scores_gemma":[0.99607146,0.00006952813,0.0009065548,0.0021403006,0.00058584934,0.00022628799],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009526966,0.00031215057,0.00044021205,0.00040139345,0.000104713305,0.00015757915,0.0024650104,0.00025873267,0.000022928687],"category_scores_gemma":[0.0005636947,0.00026333454,0.00015013866,0.0015881313,0.00018455175,0.00023098184,0.0012438621,0.00040307723,0.000006042188],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014007899,0.00018312431,0.0068334285,0.0009869903,0.00018026032,0.000005627537,0.00013769619,0.00008793835,0.9244939,0.04222361,0.02477509,0.00007829425],"study_design_scores_gemma":[0.00027642024,0.000094669085,0.16798893,0.0003390566,0.000041140524,3.615788e-8,0.0000014965692,0.0017676455,0.8288688,0.000085329986,0.00013266761,0.00040382604],"about_ca_topic_score_codex":0.00022056195,"about_ca_topic_score_gemma":0.0000020509178,"teacher_disagreement_score":0.5551275,"about_ca_system_score_codex":0.00015364865,"about_ca_system_score_gemma":0.0006269165,"threshold_uncertainty_score":0.9999819},"labels":[],"label_agreement":null},{"id":"W2799274606","doi":"10.1002/mrm.27219","title":"A discrete polar Stockwell transform for enhanced characterization of tissue structure using MRI","year":2018,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hotchkiss Brain Institute; Ontario Brain Institute; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Multiple Sclerosis Society of Canada; Alberta Innovates; Multiple Sclerosis Society; Alberta Innovates - Health Solutions","keywords":"Artificial intelligence; Pattern recognition (psychology); Noise reduction; Segmentation; Pixel; Wavelet transform; Computer science; Wavelet; Polar coordinate system; Computer vision; Discrete wavelet transform; Mathematics; Geometry","score_opus":0.013585753994981235,"score_gpt":0.31035776429813805,"score_spread":0.2967720103031568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799274606","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055562295,0.00037789048,0.9418316,0.0009847534,0.00021404467,0.00082777813,0.000016991477,0.000048131456,0.00013653707],"genre_scores_gemma":[0.65902805,0.00014960337,0.33931276,0.0007638047,0.00033687626,0.000056929992,0.000033328666,0.00002118645,0.00029747473],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985273,0.000052464056,0.0004860253,0.00032290193,0.00036732838,0.00024399054],"domain_scores_gemma":[0.9992203,0.0000656493,0.00014438551,0.00034796822,0.0001515919,0.00007010644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031434235,0.00013859413,0.00029093717,0.00016282093,0.000044551227,0.000013616393,0.00047270767,0.00007775631,0.00021084676],"category_scores_gemma":[0.00012753396,0.000112742964,0.000020798003,0.00049042766,0.00030188792,0.00024797325,0.0000385082,0.00009811301,0.0000011263704],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002120954,0.0000105880135,0.000038087193,0.00005071447,9.3416463e-7,0.0000014090384,0.0014289411,3.9662436e-7,0.7057586,0.00015710693,0.000066851884,0.29246515],"study_design_scores_gemma":[0.0011258167,0.0013739252,0.0042242263,0.00038917916,0.000009545711,0.0000056880685,0.000039609287,0.016591288,0.97035426,0.0019438121,0.0037943162,0.00014835913],"about_ca_topic_score_codex":0.00007519578,"about_ca_topic_score_gemma":0.0000311642,"teacher_disagreement_score":0.60346574,"about_ca_system_score_codex":0.000040141673,"about_ca_system_score_gemma":0.00004932776,"threshold_uncertainty_score":0.45975247},"labels":[],"label_agreement":null},{"id":"W2803431205","doi":"10.4103/digm.digm_44_17","title":"A variational level set method image segmentation model with application to intensity inhomogene magnetic resonance imaging","year":2018,"lang":"en","type":"article","venue":"Digital Medicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Segmentation; Image segmentation; Artificial intelligence; Regularization (linguistics); Computer science; Level set (data structures); Level set method; Scale-space segmentation; Smoothing; Computer vision; Mathematics; Pattern recognition (psychology); Algorithm","score_opus":0.025646053573819362,"score_gpt":0.31998410171699276,"score_spread":0.2943380481431734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803431205","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006152964,0.000048748487,0.9930156,0.0038864694,0.00007506512,0.0004927495,0.00002593705,0.00022896282,0.0016111884],"genre_scores_gemma":[0.13162746,0.0000025939785,0.86271083,0.0049705585,0.00018797112,0.00011343898,0.00008599853,0.000016686407,0.0002844411],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981915,0.00002953719,0.00033076335,0.0005362743,0.000672485,0.00023948216],"domain_scores_gemma":[0.99856853,0.00006824926,0.000117137555,0.00046289974,0.000576068,0.00020713138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042284746,0.00017619843,0.00020055607,0.00017891983,0.0000952112,0.000121849334,0.00046289415,0.000030783867,0.000025037292],"category_scores_gemma":[0.00022764038,0.00014255445,0.000019473428,0.0006132632,0.00019923525,0.00083648314,0.00019319147,0.00009566954,0.00005693071],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009766205,0.0000725256,0.0006272785,0.000016603282,0.000008372741,0.000012013523,0.0024258518,0.00004141664,0.06263918,0.0023611195,0.009842698,0.9218553],"study_design_scores_gemma":[0.0017044974,0.0011191131,0.011342752,0.00020153685,0.00002710306,0.00017284437,0.00026288626,0.87739205,0.09445157,0.01150981,0.001280912,0.0005349191],"about_ca_topic_score_codex":0.00003464237,"about_ca_topic_score_gemma":0.000004646571,"teacher_disagreement_score":0.9213204,"about_ca_system_score_codex":0.00009601115,"about_ca_system_score_gemma":0.00008334338,"threshold_uncertainty_score":0.58132017},"labels":[],"label_agreement":null},{"id":"W2803522971","doi":"10.1007/s11548-018-1785-8","title":"Prostate segmentation in MRI using a convolutional neural network architecture and training strategy based on statistical shape models","year":2018,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":96,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Cancer Agency; University of British Columbia; University of British Columbia Hospital","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Segmentation; Artificial intelligence; Convolutional neural network; Active shape model; Pattern recognition (psychology); Regularization (linguistics); Image segmentation; Computer vision; Artificial neural network","score_opus":0.04544343148695823,"score_gpt":0.3097326037735641,"score_spread":0.26428917228660587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803522971","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11865139,0.00007595782,0.87974954,0.0007999831,0.0006167567,0.000067913454,0.0000048720162,0.000017956223,0.000015633135],"genre_scores_gemma":[0.71391284,0.000018176906,0.2841492,0.001540063,0.00036219432,0.0000021255523,0.000009601458,0.0000047675394,0.0000010270825],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834466,0.0003706516,0.0005454115,0.00021858788,0.0003264718,0.00019422393],"domain_scores_gemma":[0.99827397,0.0010412144,0.00028675006,0.000067189816,0.00022251823,0.00010837034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008338879,0.00012606254,0.0002684202,0.00036734913,0.00007058034,0.00010499115,0.00021217893,0.000075016884,0.00001573209],"category_scores_gemma":[0.00004717303,0.00010834741,0.000046005112,0.00012563656,0.0002793822,0.00036374902,0.000053009207,0.00026967662,2.462243e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006354599,0.00016198927,0.012937329,0.000020456437,0.00018636331,0.00084074354,0.0012516134,0.13083708,0.0007547592,0.0038631896,0.0018361716,0.84667486],"study_design_scores_gemma":[0.00056327204,0.00023073288,0.030616168,0.0001120758,0.0000059347417,0.0012158168,0.000014217296,0.96229,0.000050027025,0.0047870036,0.000013611793,0.00010117259],"about_ca_topic_score_codex":0.000004306407,"about_ca_topic_score_gemma":0.0000021382684,"teacher_disagreement_score":0.84657365,"about_ca_system_score_codex":0.000057916528,"about_ca_system_score_gemma":0.00018590546,"threshold_uncertainty_score":0.44182792},"labels":[],"label_agreement":null},{"id":"W2804106515","doi":"10.1002/mp.13000","title":"Technical Note: Harmonic analysis applied to <scp>MR</scp> image distortion fields specific to arbitrarily shaped volumes","year":2018,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"Princess Margaret Cancer Foundation","keywords":"Cuboid; Imaging phantom; Mathematical analysis; Cylinder; Laplace's equation; Finite element method; Boundary value problem; Harmonic; Method of mean weighted residuals; Linearization; Mathematics; Spherical harmonics; Scanner; Distortion (music); Quadratic equation; Ellipsoid; Physics; Geometry; Optics; Acoustics","score_opus":0.01630163193930066,"score_gpt":0.2880069241796368,"score_spread":0.2717052922403361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804106515","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026678008,0.000014963388,0.99067044,0.001883646,0.00030645574,0.00045053064,0.0000057690863,0.0006920581,0.0033083353],"genre_scores_gemma":[0.68638515,0.000012347683,0.30191988,0.010060079,0.0010599176,0.00016172271,0.000032342763,0.00002825335,0.00034028993],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99616235,0.0000739218,0.00057692133,0.0008802751,0.0017277298,0.00057876745],"domain_scores_gemma":[0.99737376,0.00029833164,0.00012629,0.0010309812,0.00019512308,0.000975541],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00065985025,0.00027384487,0.00046842903,0.00022911938,0.00017341149,0.00021047644,0.0017053778,0.00023053355,0.00025259444],"category_scores_gemma":[0.0006794882,0.0002546765,0.00020194196,0.0026534463,0.00030895343,0.00031086334,0.00064714893,0.0005176482,0.00090007175],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015579662,0.0005385505,0.00011485959,0.00003067835,0.000093534836,0.000052886466,0.0020322027,0.000006948482,0.04392268,0.002971702,0.21635427,0.7338661],"study_design_scores_gemma":[0.0013393151,0.0013867627,0.008508126,0.00013979,0.00034594766,0.000015271304,0.00009562749,0.032928888,0.9033504,0.02399793,0.026924985,0.0009669694],"about_ca_topic_score_codex":0.00003181777,"about_ca_topic_score_gemma":0.000027231104,"teacher_disagreement_score":0.8594277,"about_ca_system_score_codex":0.00016839054,"about_ca_system_score_gemma":0.00015011863,"threshold_uncertainty_score":0.9999905},"labels":[],"label_agreement":null},{"id":"W2804713302","doi":"10.1007/s11548-018-1788-5","title":"Fast and automatic bone segmentation and registration of 3D ultrasound to CT for the full pelvic anatomy: a comparative study","year":2018,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Segmentation; Artificial intelligence; Computer science; Computer vision; Image registration; 3D ultrasound; Pelvis; Medicine; Ultrasound; Radiology; Image (mathematics)","score_opus":0.03016027885799622,"score_gpt":0.3386226974225833,"score_spread":0.30846241856458706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804713302","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40625277,0.00012902667,0.5922833,0.0008011431,0.00034684743,0.00016954477,0.0000021398203,0.000009833726,0.0000053564636],"genre_scores_gemma":[0.89623904,0.000043782482,0.10310496,0.0004233235,0.00016638878,0.0000105295,0.0000027207125,0.0000029977177,0.0000062751524],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9986927,0.00021937636,0.00057997863,0.00016346306,0.00025338912,0.00009111245],"domain_scores_gemma":[0.9967067,0.0021696433,0.00049368455,0.000099234036,0.00046035368,0.000070358416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011481358,0.00009829326,0.0002916553,0.00025744693,0.00008930155,0.000106015366,0.00022078838,0.000026478943,0.000005025134],"category_scores_gemma":[0.00012956443,0.00006999822,0.00004053713,0.00011174615,0.00022855212,0.0003177941,0.0000637602,0.00008809619,2.730181e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043858544,0.00050335284,0.029935036,0.000060289905,0.0013306319,0.00011410096,0.014148301,0.000055200326,0.010070545,0.0010058248,0.012381186,0.929957],"study_design_scores_gemma":[0.0038207483,0.0044323783,0.8216641,0.00040022066,0.0002052635,0.011764415,0.0022627828,0.14412099,0.008324431,0.0020604911,0.00044261094,0.00050155754],"about_ca_topic_score_codex":0.000007696879,"about_ca_topic_score_gemma":0.000010874625,"teacher_disagreement_score":0.9294554,"about_ca_system_score_codex":0.000026253416,"about_ca_system_score_gemma":0.000062454455,"threshold_uncertainty_score":0.28544447},"labels":[],"label_agreement":null},{"id":"W2805445921","doi":"10.1007/s11548-018-1786-7","title":"Non-rigid registration of 3D ultrasound for neurosurgery using automatic feature detection and matching","year":2018,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke; Natural Sciences and Engineering Research Council of Canada; Fundação para a Ciência e a Tecnologia; Foundation for the National Institutes of Health","keywords":"Artificial intelligence; Image registration; Feature (linguistics); Computer science; Affine transformation; Computer vision; 3D ultrasound; Orientation (vector space); Hough transform; Pattern recognition (psychology); Matching (statistics); Ultrasound; Image (mathematics); Radiology; Mathematics; Medicine","score_opus":0.021829112972413352,"score_gpt":0.2977527841716612,"score_spread":0.2759236711992478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805445921","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3427511,0.000051274,0.6556398,0.0002589088,0.0012283046,0.00004819437,0.0000012038728,0.000015369107,0.0000058175433],"genre_scores_gemma":[0.7735149,0.00004354572,0.22556578,0.0004184932,0.00044282404,0.0000016095835,0.00000217156,0.000005131885,0.0000055253754],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882007,0.00012131831,0.00056167605,0.0001573274,0.0002312359,0.00010839053],"domain_scores_gemma":[0.99738044,0.0012700192,0.00071902014,0.00009573047,0.0004720995,0.00006268051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097628916,0.00009987537,0.00028385594,0.00035245882,0.00007631653,0.000093207746,0.00021315916,0.00009424805,0.0000028732113],"category_scores_gemma":[0.00017774882,0.000086849,0.00008399651,0.0001001128,0.00017886066,0.00050844986,0.000042837575,0.00013348255,1.2248123e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024740136,0.00017041118,0.013767798,0.00015561082,0.0005895336,0.00014164261,0.0012180078,0.00008268015,0.20963457,0.00038604083,0.005750514,0.76785576],"study_design_scores_gemma":[0.0021314914,0.0011648977,0.37173885,0.0011914298,0.0001453846,0.034094136,0.00007537147,0.48628163,0.094264746,0.0074518207,0.00075233425,0.0007078971],"about_ca_topic_score_codex":0.000005192088,"about_ca_topic_score_gemma":0.0000013103515,"teacher_disagreement_score":0.7671479,"about_ca_system_score_codex":0.000030898907,"about_ca_system_score_gemma":0.00008451927,"threshold_uncertainty_score":0.35415995},"labels":[],"label_agreement":null},{"id":"W2805783629","doi":"10.1109/isbi.2018.8363821","title":"Robust cerebrovascular segmentation in 4D ASL MRA images","year":2018,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Segmentation; Arterial spin labeling; Magnetic resonance angiography; Magnetic resonance imaging; Artificial intelligence; Sørensen–Dice coefficient; Computer science; Modality (human–computer interaction); Image segmentation; Similarity (geometry); Contrast (vision); Pattern recognition (psychology); Medicine; Radiology; Computer vision; Image (mathematics)","score_opus":0.02441461640173169,"score_gpt":0.27710396925285796,"score_spread":0.25268935285112626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805783629","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028228473,0.000016057942,0.99023503,0.0006003467,0.00014085315,0.00020317563,4.6767838e-7,0.00036604947,0.0056151487],"genre_scores_gemma":[0.053339027,0.000014740583,0.94416535,0.0017177323,0.00007648015,0.000032141248,0.000004237644,0.0000075097273,0.0006427858],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99875647,0.000086344844,0.000239528,0.0003234245,0.00034569114,0.00024852323],"domain_scores_gemma":[0.999329,0.00004020193,0.00005145395,0.00039347346,0.0000905834,0.0000953076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050373905,0.000096802214,0.00010346198,0.00015292724,0.00006561673,0.0001942497,0.0005092501,0.000044627355,0.0006212986],"category_scores_gemma":[0.00007368025,0.0000854693,0.000036318317,0.0004324493,0.00012776266,0.0008528389,0.00015950149,0.000084544816,0.00018739398],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010816425,0.0005144058,0.012489657,0.00007671665,0.00006034769,0.00005672818,0.0033262726,0.00006208402,0.16381136,0.020354398,0.084153704,0.71508354],"study_design_scores_gemma":[0.0005229886,0.00012613203,0.0062242975,0.00002428155,0.0000032532976,0.000009193083,0.00011628301,0.012434334,0.9782408,0.0018602781,0.00023603845,0.00020210474],"about_ca_topic_score_codex":0.00017581989,"about_ca_topic_score_gemma":0.00004250809,"teacher_disagreement_score":0.81442946,"about_ca_system_score_codex":0.00007189984,"about_ca_system_score_gemma":0.000053797365,"threshold_uncertainty_score":0.68027836},"labels":[],"label_agreement":null},{"id":"W2809931234","doi":"10.3390/rs10071039","title":"A Level Set Method for Infrared Image Segmentation Using Global and Local Information","year":2018,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"State Key Laboratory of Networking and Switching Technology; China Scholarship Council; Beijing University of Posts and Telecommunications; National Natural Science Foundation of China","keywords":"Level set (data structures); Computer science; Level set method; Artificial intelligence; Initialization; Computer vision; Signed distance function; Robustness (evolution); Image segmentation; Active contour model; Pixel; Segmentation; Pattern recognition (psychology)","score_opus":0.048039849801304255,"score_gpt":0.36813053973612914,"score_spread":0.32009068993482487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809931234","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035557772,0.0000061133505,0.99542326,0.00013455881,0.00014387348,0.00031068505,0.0000089550485,0.00015572616,0.0002610339],"genre_scores_gemma":[0.006600574,0.000002120337,0.9921139,0.0011821561,0.0000706384,5.521968e-8,0.000015366897,0.0000048751385,0.000010325801],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907845,0.00008109795,0.00025490395,0.00018330166,0.00021801126,0.00018423011],"domain_scores_gemma":[0.9992844,0.000061310384,0.00013365543,0.00018787444,0.00025028654,0.00008247217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047749397,0.00010201866,0.00010919099,0.000076489574,0.00015606784,0.00022884963,0.00010993214,0.000059696715,0.0000018057508],"category_scores_gemma":[0.00016523899,0.000104556784,0.000026947859,0.00024214512,0.00009709701,0.0011174787,0.00011588081,0.00004726857,0.0000054540114],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008181389,0.0000015765742,0.0000017845052,0.000022636286,0.000006427145,0.0000010640366,0.00068684656,0.0000074701047,0.022438116,0.000080147314,0.0004946162,0.9762511],"study_design_scores_gemma":[0.00034388292,0.00005972564,0.000057581783,0.000041110172,0.000008547295,0.000083634965,0.00016675347,0.8939587,0.10115734,0.0038427548,0.00017080911,0.00010917128],"about_ca_topic_score_codex":0.00010659299,"about_ca_topic_score_gemma":0.000007737298,"teacher_disagreement_score":0.976142,"about_ca_system_score_codex":0.00013099684,"about_ca_system_score_gemma":0.00007057694,"threshold_uncertainty_score":0.4263702},"labels":[],"label_agreement":null},{"id":"W2810978645","doi":"10.1002/hbm.24243","title":"An efficient and accurate method for robust inter‐dataset brain extraction and comparisons with 9 other methods","year":2018,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Fonds de Recherche du Québec - Santé","keywords":"Sørensen–Dice coefficient; Computer science; Segmentation; Artificial intelligence; Pattern recognition (psychology); Neuroimaging; Scanner; Dice; Image segmentation; Statistics; Mathematics","score_opus":0.09879626837937266,"score_gpt":0.4500226241151853,"score_spread":0.3512263557358126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810978645","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012054604,0.000017745115,0.99653345,0.0014903469,0.00005093978,0.0004224505,0.000027098058,0.00018594622,0.00006658538],"genre_scores_gemma":[0.0044786157,5.143682e-7,0.992037,0.0032324605,0.00008064699,0.000052573712,0.000047054808,0.0000146300945,0.00005651495],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984466,0.00045530548,0.00024164884,0.0004963813,0.00013521855,0.0002248816],"domain_scores_gemma":[0.9985845,0.00066633977,0.0001587489,0.00038132363,0.00007312492,0.00013597457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023641172,0.00014729337,0.00018650136,0.00016314293,0.0003830181,0.0003279915,0.00030271432,0.00005217214,0.000034177057],"category_scores_gemma":[0.00015530342,0.00012585487,0.000016301357,0.00016364844,0.00019896189,0.0004231003,0.00012685271,0.00012226825,0.0000016763814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047055295,0.00021151219,0.00018101232,0.00017676014,0.00008723362,0.0000065051445,0.010308594,0.000116796225,0.5574154,0.010672393,0.0966471,0.32412964],"study_design_scores_gemma":[0.0011899773,0.00083090056,0.0037276119,0.00018417732,0.000020371976,0.000071929244,0.0010706603,0.9315236,0.022110416,0.0016507544,0.03710925,0.00051031716],"about_ca_topic_score_codex":0.000048556376,"about_ca_topic_score_gemma":0.00004093123,"teacher_disagreement_score":0.93140686,"about_ca_system_score_codex":0.000025118345,"about_ca_system_score_gemma":0.000017277367,"threshold_uncertainty_score":0.5132213},"labels":[],"label_agreement":null},{"id":"W2842498157","doi":"10.3389/fninf.2018.00039","title":"Multi-Template Mesiotemporal Lobe Segmentation: Effects of Surface and Volume Feature Modeling","year":2018,"lang":"en","type":"article","venue":"Frontiers in Neuroinformatics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke; Canadian Institutes of Health Research; National Institute on Aging; National Institutes of Health","keywords":"Segmentation; Computer science; Feature (linguistics); Volume (thermodynamics); Artificial intelligence; Pattern recognition (psychology); Lobe; Anatomy; Medicine; Physics","score_opus":0.01305727405471223,"score_gpt":0.2622013681818623,"score_spread":0.24914409412715005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2842498157","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046521977,0.00011982267,0.95210904,0.00012691392,0.0006052504,0.00034529864,0.0000018038545,0.00010096974,0.00006889812],"genre_scores_gemma":[0.048110433,0.00009283517,0.95130676,0.00035842854,0.000016611728,0.00000412947,0.000003722933,0.0000083203895,0.000098758224],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886703,0.00006219609,0.00039666903,0.00016524078,0.0003062602,0.0002025896],"domain_scores_gemma":[0.99929833,0.000038880717,0.00017458922,0.00031247165,0.000081588274,0.000094153744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002453529,0.00013732746,0.00022016313,0.00013869317,0.00006140389,0.0000765317,0.00039303242,0.00007633544,0.0000018299799],"category_scores_gemma":[0.000100060846,0.00013207516,0.000028173894,0.0003153243,0.00012320477,0.001037289,0.00019026354,0.00016794412,0.000004010854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016606577,0.00070633175,0.07711899,0.005778403,0.00021527635,0.0002502994,0.07058698,0.008007536,0.042981155,0.00044351307,0.2813843,0.51236117],"study_design_scores_gemma":[0.00056673854,0.0001291633,0.0004386257,0.00008082924,0.0000051705715,0.0000067382366,0.0001074513,0.9554226,0.042875934,0.00019328883,0.000051060346,0.00012242798],"about_ca_topic_score_codex":0.000009821117,"about_ca_topic_score_gemma":0.0000013221088,"teacher_disagreement_score":0.94741505,"about_ca_system_score_codex":0.000031649222,"about_ca_system_score_gemma":0.000043489177,"threshold_uncertainty_score":0.5385869},"labels":[],"label_agreement":null},{"id":"W28462678","doi":"10.5555/1140752.1140756","title":"Multi-scale morphological modeling of a class of structural texture","year":2005,"lang":"en","type":"article","venue":"Machine Graphics & Vision International Journal archive","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Texture compression; Texture filtering; Texture (cosmology); Texture synthesis; Artificial intelligence; Computer science; Projective texture mapping; Segmentation; Image texture; Computer vision; Pattern recognition (psychology); Scale (ratio); Piecewise; Texture atlas; Texture mapping; Bidirectional texture function; Computer graphics; Representation (politics); Image segmentation; Image (mathematics); Mathematics; Geography","score_opus":0.016394393749520803,"score_gpt":0.32843662504707755,"score_spread":0.31204223129755676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W28462678","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037054237,0.00012570272,0.9604785,0.0017923968,0.00024826828,0.000086153515,0.000037192905,0.000035229783,0.0001423326],"genre_scores_gemma":[0.58189225,0.00007898201,0.41755632,0.00036566463,0.00007694261,0.0000017386631,0.000008761863,0.0000050879066,0.000014219002],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977395,0.00014138367,0.00072432915,0.00023666974,0.0009948205,0.00016331827],"domain_scores_gemma":[0.9986401,0.00012411723,0.000426997,0.00021136484,0.00045217192,0.00014521263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042305913,0.00014682756,0.00022351618,0.00047055422,0.00007629408,0.00009503579,0.0014903902,0.00006147684,0.000068859794],"category_scores_gemma":[0.00013655236,0.0001083592,0.00021084656,0.00019453645,0.00015891368,0.00041257203,0.00040589657,0.00057653914,0.0000020552568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035059336,0.0010675027,0.008067726,0.000044482298,0.00039967033,0.00022291619,0.0046711783,0.021168517,0.35895348,0.039375104,0.0010272223,0.5646516],"study_design_scores_gemma":[0.00068219716,0.00014551637,0.0023578876,0.000071240094,0.000006736667,0.00033430877,0.000024752855,0.9705716,0.0058958144,0.019679066,0.00012237325,0.00010850718],"about_ca_topic_score_codex":0.00003784831,"about_ca_topic_score_gemma":0.000018214454,"teacher_disagreement_score":0.9494031,"about_ca_system_score_codex":0.000030971434,"about_ca_system_score_gemma":0.000044139328,"threshold_uncertainty_score":0.441876},"labels":[],"label_agreement":null},{"id":"W2885149986","doi":"10.11159/icbes18.135","title":"Segmentation of the Heart Ventricle and Atrium in Handheld Ultrasound Images","year":2018,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Ventricle; Ultrasound; Cardiology; Computer science; Cardiac Ultrasound; Ultrasonic imaging; Mobile device; Internal medicine; Medicine; Computer vision; Radiology","score_opus":0.005812932890742315,"score_gpt":0.22641044898499385,"score_spread":0.22059751609425154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885149986","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95039684,0.0005510082,0.046710014,0.00064333575,0.0009910993,0.00052460615,0.0000012051057,0.000069402755,0.00011250273],"genre_scores_gemma":[0.99549216,0.000018012859,0.0043050875,0.00007063936,0.00004191345,0.000007272338,1.6379797e-8,0.0000027410824,0.00006216685],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990712,0.000009613215,0.00020415813,0.00023325281,0.00031750227,0.00016427178],"domain_scores_gemma":[0.99951637,0.00010903239,0.00010072923,0.00009709623,0.00011885509,0.000057896053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047836354,0.000082835104,0.00013910147,0.0001678028,0.00010401681,0.0001650606,0.00042397223,0.000018803796,2.2270005e-7],"category_scores_gemma":[0.00008097495,0.00005109992,0.000017144785,0.0010457471,0.0002942935,0.00026415908,0.00019631654,0.000103701765,7.887632e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021632995,0.00014031497,0.053170178,0.00047660636,0.00002495068,0.000001104541,0.0012376187,0.00021408973,0.85796005,0.042110477,0.0022491224,0.04239383],"study_design_scores_gemma":[0.000383984,0.0002557777,0.06449495,0.00045701407,0.0000061888354,0.00005123868,0.000017788414,0.3185989,0.61536056,0.00013178382,0.00008936948,0.00015245323],"about_ca_topic_score_codex":0.000023282682,"about_ca_topic_score_gemma":6.704805e-7,"teacher_disagreement_score":0.3183848,"about_ca_system_score_codex":0.000020686082,"about_ca_system_score_gemma":0.000019233043,"threshold_uncertainty_score":0.20837943},"labels":[],"label_agreement":null},{"id":"W2887311618","doi":"10.1007/s11548-018-1840-5","title":"Using the variogram for vector outlier screening: application to feature-based image registration","year":2018,"lang":"en","type":"review","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"National Institute of Biomedical Imaging and Bioengineering; International Research Center for Neurointelligence, University of Tokyo; National Institutes of Health; University of Tokyo","keywords":"Outlier; Variogram; Computer science; Matching (statistics); Point set registration; Image registration; Anomaly detection; Artificial intelligence; Feature vector; Feature (linguistics); Euclidean vector; Pattern recognition (psychology); Displacement (psychology); Data mining; Image (mathematics); Mathematics; Point (geometry); Statistics; Machine learning; Kriging","score_opus":0.08154513706720924,"score_gpt":0.3883939012649314,"score_spread":0.30684876419772217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887311618","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000011425038,0.11656558,0.8799897,0.0015867222,0.0013942141,0.0004094826,0.0000103602015,0.000035964,0.000006842116],"genre_scores_gemma":[0.000028330907,0.08452285,0.9115229,0.0017645648,0.0019830763,0.00006123546,0.00006917034,0.000022301963,0.000025533045],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99770594,0.0004260089,0.0009411335,0.00033861047,0.0004004815,0.00018779968],"domain_scores_gemma":[0.99532735,0.0020069708,0.001381016,0.00030812112,0.00084802904,0.00012853666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001797621,0.000248955,0.0008112347,0.0005911786,0.00012067349,0.00029724036,0.0011333709,0.00024334592,0.0000036560427],"category_scores_gemma":[0.00022971808,0.00016781194,0.0005150496,0.00026785335,0.00016172985,0.00027481435,0.00011166469,0.00033615105,0.0000013010491],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002912798,0.00004515556,0.000009180447,0.00016987913,0.00034466907,0.000028494667,0.00003282486,0.000012577909,0.00001559219,0.00024503574,0.038919374,0.9601481],"study_design_scores_gemma":[0.00033131609,0.00020816109,0.00018832636,0.0027355223,0.00026769278,0.0025360512,0.000002679155,0.02052121,0.000059738428,0.0002667987,0.9725122,0.00037034202],"about_ca_topic_score_codex":0.0000031812338,"about_ca_topic_score_gemma":9.2246574e-7,"teacher_disagreement_score":0.9597778,"about_ca_system_score_codex":0.0001094993,"about_ca_system_score_gemma":0.0003849431,"threshold_uncertainty_score":0.6843173},"labels":[],"label_agreement":null},{"id":"W2889829193","doi":"10.1371/journal.pone.0196945","title":"Improving the SIENA performance using BEaST brain extraction","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"NeuroRx Research (Canada); McGill University; Montreal Neurological Institute and Hospital","funders":"Genentech; National Institutes of Health; Mitacs; Eisai; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Eli Lilly and Company; U.S. Department of Defense; Compute Canada; Northern California Institute for Research and Education; Acorda Therapeutics; McGill University; Pfizer; Biogen; BioClinica; Sanofi Genzyme; F. Hoffmann-La Roche; University of Southern California; Sanofi; Alzheimer's Disease Neuroimaging Initiative; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Reproducibility; Brain size; Medicine; Segmentation; Nuclear medicine; Magnetic resonance imaging; Mathematics; Computer science; Artificial intelligence; Statistics; Radiology","score_opus":0.06385642346107932,"score_gpt":0.2833986253565527,"score_spread":0.21954220189547335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889829193","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2341411,0.000011040575,0.7643435,0.00068739284,0.00005757891,0.00013757798,2.762799e-7,0.0001883058,0.00043322574],"genre_scores_gemma":[0.59087735,0.0000050035665,0.40718248,0.001349341,0.00027259818,0.000013080117,6.4867896e-7,0.000007077528,0.00029242525],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99912024,0.000046249785,0.00013536825,0.00017861577,0.00035774705,0.00016178114],"domain_scores_gemma":[0.9993556,0.000069632515,0.00008787876,0.00033569193,0.000102793834,0.00004837292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003863888,0.00006204321,0.00006451862,0.00004331189,0.0002104548,0.00011237114,0.00042007858,0.000027799564,0.000043998687],"category_scores_gemma":[0.00015712446,0.00004639624,0.000014948867,0.00021116008,0.0001003799,0.00072874164,0.00013201036,0.00011872569,0.000055763976],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002577636,0.00015294118,0.0002320871,0.000026464057,0.00001284381,0.0000011539402,0.0004945106,5.903897e-7,0.87936294,0.00012662502,0.00026364913,0.119323626],"study_design_scores_gemma":[0.0000660719,0.00008043607,0.00057102955,0.000054858487,0.0000072454204,0.0000048696365,0.00001571488,0.2564803,0.7425612,0.00006196844,0.000030371988,0.00006592038],"about_ca_topic_score_codex":0.000026994612,"about_ca_topic_score_gemma":0.0000019563852,"teacher_disagreement_score":0.35716102,"about_ca_system_score_codex":0.000043834214,"about_ca_system_score_gemma":0.00003339418,"threshold_uncertainty_score":0.18919837},"labels":[],"label_agreement":null},{"id":"W2892378903","doi":"10.1007/978-3-030-00889-5_6","title":"TreeNet: Multi-loss Deep Learning Network to Predict Branch Direction for Extracting 3D Anatomical Trees","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Leverage (statistics); Artificial intelligence; Deep learning; Tree (set theory); Computation; Pattern recognition (psychology); Computer vision; Algorithm; Mathematics","score_opus":0.02019455017194562,"score_gpt":0.2937288437028671,"score_spread":0.2735342935309215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2892378903","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010890425,0.00020496095,0.99567086,0.00030872406,0.0018148529,0.0009647124,0.000002973061,0.00061991723,0.00030410296],"genre_scores_gemma":[0.017410407,0.000031001386,0.9788683,0.0015177327,0.0016212623,0.000067829045,0.000010426586,0.00005357239,0.00041945573],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955156,0.000088653665,0.0007147953,0.0018043885,0.0009811319,0.00089545286],"domain_scores_gemma":[0.9969346,0.0011166646,0.00039606335,0.00083515997,0.00036955884,0.00034794363],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017039501,0.0005320816,0.000568922,0.00065102533,0.00051527406,0.0006057822,0.0023213245,0.00039740375,0.00004979959],"category_scores_gemma":[0.0006030352,0.00050970813,0.00015645474,0.0007433959,0.0005278262,0.00073653593,0.0008954921,0.00086456165,0.00002808521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013673981,0.000025841156,0.00017134637,0.000024599512,0.000011981366,0.000014096744,0.00070229743,0.015002796,0.00041516882,0.00015046408,0.00012275891,0.983345],"study_design_scores_gemma":[0.00044266414,0.00051545416,0.00050404394,0.0005356905,0.000014026144,0.00004662411,3.748234e-7,0.97892916,0.0058768727,0.009487863,0.00293142,0.0007158212],"about_ca_topic_score_codex":0.000019016057,"about_ca_topic_score_gemma":0.0001993875,"teacher_disagreement_score":0.9826292,"about_ca_system_score_codex":0.00037495737,"about_ca_system_score_gemma":0.00022190991,"threshold_uncertainty_score":0.9997355},"labels":[],"label_agreement":null},{"id":"W2894275259","doi":"10.1109/globalsip.2018.8646668","title":"CYLINDRICAL TRANSFORM: 3D SEMANTIC SEGMENTATION OF KIDNEYS WITH LIMITED ANNOTATED IMAGES","year":2018,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Segmentation; Computer science; Convolutional neural network; Pattern recognition (psychology); Computer vision; Image segmentation; Scale-space segmentation; Dependency (UML)","score_opus":0.0126229921388814,"score_gpt":0.27369691644247673,"score_spread":0.26107392430359533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2894275259","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004691182,0.000007013041,0.9881196,0.0009561549,0.000056149092,0.00030865107,0.0000025602244,0.00039441828,0.0054642675],"genre_scores_gemma":[0.29887488,0.000013915673,0.6998018,0.0009241193,0.000027089101,0.000018189763,0.0000121048915,0.000009182872,0.0003187477],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99866927,0.00007007929,0.00032280738,0.00028501108,0.0004475662,0.000205296],"domain_scores_gemma":[0.99910754,0.00006148281,0.00010219547,0.00031120423,0.0002832184,0.00013435839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021620328,0.00012543448,0.00016572798,0.00015513388,0.000058433092,0.000058113146,0.00040533402,0.00004910176,0.0002362208],"category_scores_gemma":[0.000042689957,0.000090357644,0.000031089854,0.00067088153,0.00024741184,0.00063509605,0.000048483274,0.0000835126,0.000039943596],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010794554,0.0005862432,0.001164123,0.00014064746,0.00011840474,0.000044674496,0.0032466862,0.0000054361617,0.49814615,0.0018937406,0.016565828,0.47798014],"study_design_scores_gemma":[0.00062898174,0.0007058319,0.0012253061,0.00003870293,0.000013658588,0.000016359276,0.00006452876,0.01220735,0.98466325,0.00023700288,0.000056622768,0.00014242163],"about_ca_topic_score_codex":0.00005340872,"about_ca_topic_score_gemma":0.0000067727074,"teacher_disagreement_score":0.4865171,"about_ca_system_score_codex":0.00002315536,"about_ca_system_score_gemma":0.000071316,"threshold_uncertainty_score":0.36846778},"labels":[],"label_agreement":null},{"id":"W2898895044","doi":"10.1109/embc.2018.8512375","title":"Co-Sparse Analysis Model Based Image Registration to Compensate Brain Shift by Using Intra-Operative Ultrasound Imaging","year":2018,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Image registration; Artificial intelligence; Computer science; Computer vision; Medical imaging; Transformation (genetics); Modalities; Image (mathematics); Pattern recognition (psychology)","score_opus":0.023570404544139636,"score_gpt":0.34455624938263724,"score_spread":0.3209858448384976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898895044","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071874578,0.0000041927424,0.9861956,0.003381185,0.000036958038,0.00030280897,0.000017569762,0.00036308763,0.0025111013],"genre_scores_gemma":[0.36803603,4.9203044e-7,0.6227555,0.00892519,0.000025778,0.0000115240655,0.000035421293,0.000008972654,0.0002010665],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795294,0.00019318603,0.00041842746,0.00060791144,0.00050229655,0.00032525853],"domain_scores_gemma":[0.99856526,0.00019896509,0.0001362746,0.0006097595,0.00024259242,0.00024714012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075161824,0.0002016204,0.00024690965,0.00030425144,0.00023385644,0.0006288358,0.00062065135,0.000042412164,0.00026705023],"category_scores_gemma":[0.00019419116,0.00018860544,0.0000810556,0.0010608524,0.0002363561,0.0011634554,0.000056097877,0.00011777485,0.00005626845],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010942622,0.00008510063,0.0003225805,0.0000053566077,0.00005556318,0.000008613368,0.0015400006,0.0009331673,0.9575961,0.0014291022,0.032676898,0.0053365394],"study_design_scores_gemma":[0.00013275883,0.00003151278,0.000067668094,0.0000070784786,0.000022989367,0.000001534998,0.000035123598,0.6098459,0.38941103,0.00024009595,0.000043790595,0.00016053244],"about_ca_topic_score_codex":0.00021816495,"about_ca_topic_score_gemma":0.000091986716,"teacher_disagreement_score":0.6089127,"about_ca_system_score_codex":0.0001291096,"about_ca_system_score_gemma":0.0001293173,"threshold_uncertainty_score":0.7691106},"labels":[],"label_agreement":null},{"id":"W2899208051","doi":"10.1109/embc.2018.8513450","title":"Discrete Heat Kernel Smoothing in Irregular Image Domains","year":2018,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institutes of Health; University College Dublin; McGill University; National Institute of Biomedical Imaging and Bioengineering; University of Wisconsin-Madison","keywords":"Smoothing; Heat kernel; Kernel (algebra); Computer science; Artificial intelligence; Edge-preserving smoothing; Kernel smoother; Algorithm; Graph; Pattern recognition (psychology); Representation (politics); Filter (signal processing); Mathematics; Kernel method; Computer vision; Pixel; Bilateral filter; Theoretical computer science; Radial basis function kernel; Mathematical analysis; Discrete mathematics","score_opus":0.009972666750853483,"score_gpt":0.292409115688175,"score_spread":0.2824364489373215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899208051","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004864822,0.000011918192,0.9783894,0.0015570286,0.00012805211,0.00013799059,5.1464417e-7,0.00037949884,0.014530765],"genre_scores_gemma":[0.16932997,0.0000060390944,0.8270383,0.0025795163,0.0000800936,0.000015979733,0.0000014325657,0.000008422794,0.0009402683],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99883175,0.00006930039,0.00022484167,0.00031822178,0.00030055986,0.00025529924],"domain_scores_gemma":[0.9993004,0.00005002555,0.000025501484,0.00046945104,0.00004755342,0.000107060136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000428626,0.00009759744,0.00011136147,0.00011594907,0.000061303246,0.00015973067,0.00067699456,0.000042998818,0.00028826058],"category_scores_gemma":[0.00007555298,0.000080648046,0.000032397144,0.0003287654,0.00017378281,0.00085954496,0.0002912316,0.00010650462,0.0001660286],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022203178,0.00031974263,0.0034564387,0.00007533426,0.000035015975,0.00037931514,0.010265585,0.0000028898626,0.5141851,0.109256506,0.07593847,0.28606343],"study_design_scores_gemma":[0.0008677771,0.00021157574,0.006238809,0.00009414734,0.0000036495976,0.000025548708,0.00016613462,0.06390737,0.90184724,0.023852818,0.0023136435,0.00047127856],"about_ca_topic_score_codex":0.00020919094,"about_ca_topic_score_gemma":0.00005136634,"teacher_disagreement_score":0.38766217,"about_ca_system_score_codex":0.000052578718,"about_ca_system_score_gemma":0.000029685028,"threshold_uncertainty_score":0.3288732},"labels":[],"label_agreement":null},{"id":"W2899349100","doi":"10.4018/ijsi.2019010107","title":"Dealing With Noise and Partial Volume Effects in Alzheimer Disease Brain Tissue Classification by a Fuzzy-Possibilistic Modeling Based on Fuzzy-Genetic Initialization","year":2018,"lang":"en","type":"article","venue":"International Journal of Software Innovation","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Initialization; Artificial intelligence; Partial volume; Cluster analysis; Computer science; Fuzzy logic; Voxel; Noise (video); Segmentation; Pattern recognition (psychology); Fuzzy clustering; Neuroimaging; Data mining; Image (mathematics); Neuroscience","score_opus":0.023729773455865952,"score_gpt":0.3209921558983013,"score_spread":0.29726238244243536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899349100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07063796,0.000054110136,0.9268396,0.0018687824,0.000294341,0.00021901738,0.0000056420413,0.000057793397,0.000022782246],"genre_scores_gemma":[0.8856225,0.000005742366,0.112415984,0.0016885531,0.0001929654,0.000017282322,0.00003849155,0.000014305089,0.0000041731073],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979788,0.00010993564,0.0006941465,0.00026897783,0.00081155135,0.00013660955],"domain_scores_gemma":[0.9975832,0.00019726063,0.00051424577,0.00016955225,0.0014444359,0.000091297006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055482145,0.00014405206,0.00014391752,0.0005517775,0.000062938365,0.00020377255,0.00037757173,0.0000620349,0.000011553792],"category_scores_gemma":[0.0011653603,0.00013179697,0.000019084093,0.0005584578,0.00009035753,0.00075039937,0.000042256386,0.0001672169,0.000004224024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012745184,0.00078616774,0.03602323,0.00009493957,0.00015503244,0.00029607152,0.0012053464,0.020519096,0.022852764,0.021011308,0.0017598516,0.8940217],"study_design_scores_gemma":[0.0018517855,0.00072842755,0.015346491,0.0006662594,0.000033931134,0.0000309499,0.000016275228,0.9617381,0.011857953,0.007337455,0.000114169954,0.00027819385],"about_ca_topic_score_codex":0.000015602616,"about_ca_topic_score_gemma":0.0000027645717,"teacher_disagreement_score":0.94121903,"about_ca_system_score_codex":0.0001240288,"about_ca_system_score_gemma":0.0001757702,"threshold_uncertainty_score":0.53745246},"labels":[],"label_agreement":null},{"id":"W2899369863","doi":"10.1101/460675","title":"A framework for evaluating correspondence between brain images using anatomical fiducials","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Canadian Institutes of Health Research; Canada First Research Excellence Fund; Compute Canada; Royal College of Physicians and Surgeons of Canada; Fondation Brain Canada","keywords":"Fiducial marker; Computer science; Protocol (science); Artificial intelligence; Voxel; Neuroimaging; Spatial normalization; Set (abstract data type); Template; Image registration; Computer vision; Pattern recognition (psychology); Image (mathematics); Neuroscience; Medicine; Psychology","score_opus":0.06790429780880851,"score_gpt":0.3658935056299926,"score_spread":0.2979892078211841,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899369863","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10871932,0.00021818874,0.8865077,0.00056439784,0.0012874929,0.0015033575,0.0001748275,0.0010233315,0.000001383931],"genre_scores_gemma":[0.17837627,0.000014885356,0.8192059,0.00091438764,0.0010908337,0.0002969247,4.0485605e-7,0.00009721858,0.0000031985933],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99451715,0.00062965794,0.0010605021,0.0018379695,0.0010746819,0.0008800572],"domain_scores_gemma":[0.99334955,0.0017200903,0.0009983194,0.002247946,0.0012156728,0.0004684191],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004353802,0.00065402646,0.0008714276,0.00046507144,0.0003620368,0.0009400731,0.0027879388,0.0008743784,0.00006601625],"category_scores_gemma":[0.008132064,0.0007220829,0.00025506236,0.00083106844,0.00035218996,0.0005843518,0.0019335026,0.0010163613,0.00004715492],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041820476,0.00014000815,0.0025826765,0.000680942,0.00021897577,0.00004128223,0.00007775955,0.000016546077,0.98670805,0.003947398,0.005296897,0.00024763512],"study_design_scores_gemma":[0.00047221003,0.00020921156,0.0075601963,0.0016575705,0.00013130203,3.5791842e-8,0.0000021549472,0.031100074,0.95616966,0.0011375165,0.0002774944,0.0012825984],"about_ca_topic_score_codex":0.000028709668,"about_ca_topic_score_gemma":1.8754669e-7,"teacher_disagreement_score":0.06965695,"about_ca_system_score_codex":0.00048118332,"about_ca_system_score_gemma":0.001518471,"threshold_uncertainty_score":0.99952304},"labels":[],"label_agreement":null},{"id":"W2900489637","doi":"10.1016/j.neuroimage.2019.03.042","title":"Bayesian QuickNAT: Model uncertainty in deep whole-brain segmentation for structure-wise quality control","year":2019,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":130,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; BioClinica; Takeda Pharmaceuticals U.S.A.; Medpace; Bayerisches Staatsministerium für Bildung und Kultus, Wissenschaft und Kunst; Synarc; Janssen Research and Development; Nvidia; Takeda Pharmaceutical Company; Genentech; Biogen Idec; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Fujirebio Europe; Alzheimer's Association; Merck Company Foundation; F. Hoffmann-La Roche; Alzheimer's Drug Discovery Foundation; GE Healthcare; Alzheimer's Disease Neuroimaging Initiative; Johnson and Johnson; Meso Scale Diagnostics","keywords":"Computer science; Artificial intelligence; Segmentation; Bayesian probability; Posterior probability; Voxel; Pattern recognition (psychology); Convolutional neural network; Data mining; Machine learning","score_opus":0.018662985786254412,"score_gpt":0.3143268530772084,"score_spread":0.295663867290954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900489637","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023684474,0.000013856075,0.9706671,0.0036187777,0.0002122035,0.0013422243,0.00005440561,0.00023473975,0.00017221062],"genre_scores_gemma":[0.7963887,0.0000015170475,0.19419919,0.008890365,0.00003323398,0.00010214887,0.000042845837,0.000022008782,0.00031999045],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977416,0.00029929943,0.0005032778,0.0006359897,0.00044970712,0.00037017502],"domain_scores_gemma":[0.9984518,0.00048912986,0.0001936186,0.0006314101,0.000101098536,0.00013292764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006682782,0.00019929171,0.00028261714,0.00016551341,0.000058125395,0.00015635816,0.0006930386,0.000086844164,0.000052367315],"category_scores_gemma":[0.00034282138,0.00019406386,0.000083985375,0.0002718943,0.000056450077,0.000824822,0.000097594726,0.00022387881,0.000020252179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016052439,0.00019722096,0.0020335037,0.00026798708,0.000017596918,0.000020755717,0.0018195942,0.02264204,0.8189487,0.0061035613,0.0050071566,0.14278136],"study_design_scores_gemma":[0.0022677842,0.00013144736,0.0020239814,0.000018287958,0.0000045572415,0.0000026959674,0.00004336231,0.96329314,0.02184407,0.010018043,0.00011101163,0.00024162186],"about_ca_topic_score_codex":0.000053714943,"about_ca_topic_score_gemma":0.00005783988,"teacher_disagreement_score":0.9406511,"about_ca_system_score_codex":0.0001075098,"about_ca_system_score_gemma":0.00008958737,"threshold_uncertainty_score":0.79136944},"labels":[],"label_agreement":null},{"id":"W2901178560","doi":"10.1016/j.procir.2018.08.171","title":"A model retrieving based method for bolus shaping","year":2018,"lang":"en","type":"article","venue":"Procedia CIRP","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CancerCare Manitoba; University of Manitoba","funders":"U.S. Department of Agriculture","keywords":"Bolus (digestion); Computer science; Computer vision; Segmentation; Artificial intelligence; Medicine; Surgery","score_opus":0.0852788802681201,"score_gpt":0.37873186426247035,"score_spread":0.29345298399435027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901178560","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004883367,0.000038255064,0.9967432,0.0010507735,0.00013143333,0.00038288144,0.0000022416812,0.0005310426,0.0010713505],"genre_scores_gemma":[0.021798015,0.0000021014612,0.97278297,0.00495975,0.00016180308,0.00013949834,0.0000023141633,0.000013071582,0.00014045525],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883914,0.000027852682,0.00021421985,0.00037901098,0.00027088256,0.00026888814],"domain_scores_gemma":[0.9990085,0.00019553374,0.00009578311,0.00031327584,0.00026687275,0.00012003279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008020972,0.000102809296,0.00013077832,0.0001033011,0.00013074695,0.000119945966,0.00063379266,0.000061089166,0.00002118559],"category_scores_gemma":[0.0010197405,0.000099526325,0.000051688818,0.00029952385,0.00004760459,0.0004160296,0.00011637331,0.000085572676,0.000016472257],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003198891,0.00009508092,0.0001064878,0.0003030372,0.000020367883,0.0000037113039,0.0021975252,0.000054121803,0.07052861,0.014722747,0.038971435,0.87296486],"study_design_scores_gemma":[0.00021466223,0.00006241602,0.00001437437,0.000032791144,0.000004340628,0.0000018327569,0.000005151414,0.8176404,0.17313436,0.008338296,0.00044392346,0.00010740523],"about_ca_topic_score_codex":0.000003626032,"about_ca_topic_score_gemma":0.0000012817029,"teacher_disagreement_score":0.87285745,"about_ca_system_score_codex":0.00004613087,"about_ca_system_score_gemma":0.00019599797,"threshold_uncertainty_score":0.40585658},"labels":[],"label_agreement":null},{"id":"W2901240113","doi":"10.1109/cvpr.2019.01046","title":"Divergence Prior and Vessel-Tree Reconstruction","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; University of Waterloo; Western University","funders":"Robarts Research Institute","keywords":"Regularization (linguistics); Divergence (linguistics); Curvature; Artificial intelligence; Computer science; Mathematics; Vector field; Algorithm; Computer vision; Geometry","score_opus":0.0196072834531498,"score_gpt":0.2778272156785668,"score_spread":0.25821993222541695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901240113","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034170817,0.00011497702,0.9883679,0.000525969,0.0012603126,0.00034468135,0.0000017946621,0.0004646861,0.0055025574],"genre_scores_gemma":[0.03172107,0.00057977065,0.965105,0.0005376081,0.0000672948,0.000038385755,0.0000053133544,0.000008688468,0.0019368933],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987455,0.000056581022,0.00023461919,0.0005590998,0.00026616722,0.00013800435],"domain_scores_gemma":[0.99899673,0.00005617356,0.0001436625,0.00062707043,0.00008220688,0.000094135255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023087443,0.00014480737,0.00017574972,0.000101957085,0.00004352807,0.00019719506,0.000700335,0.00015655794,0.00016183872],"category_scores_gemma":[0.00006173553,0.00012935468,0.000037174577,0.000085876556,0.000076929486,0.0003313074,0.0018635791,0.0002991494,0.00006984245],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.828766e-7,0.000012194672,0.001289961,0.00009814626,0.000012293267,0.0000032895116,0.00015303762,0.0000032098046,0.00088512356,0.0018695333,0.0065162503,0.98915595],"study_design_scores_gemma":[0.0015884044,0.00044319217,0.05737639,0.0018122607,0.00010568331,0.00039879436,0.0002879326,0.24733637,0.51539123,0.16647321,0.0051627615,0.0036237414],"about_ca_topic_score_codex":0.000053000957,"about_ca_topic_score_gemma":0.0000022599684,"teacher_disagreement_score":0.9855322,"about_ca_system_score_codex":0.0000354948,"about_ca_system_score_gemma":0.00009549661,"threshold_uncertainty_score":0.52749306},"labels":[],"label_agreement":null},{"id":"W2904516142","doi":"10.1007/978-981-13-3044-5_32","title":"Multi-kernel Collaboration-Induced Fuzzy Local Information C-Means Algorithm for Image Segmentation","year":2018,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Kernel (algebra); Artificial intelligence; Pattern recognition (psychology); Fuzzy logic; Image segmentation; Computer science; Radial basis function kernel; Cluster analysis; Kernel method; Algorithm; Mathematics; Segmentation; Support vector machine","score_opus":0.03464853237173302,"score_gpt":0.3330817609094066,"score_spread":0.29843322853767357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904516142","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000005544879,0.000043124255,0.98462266,0.00064716104,0.00045508865,0.001509865,0.000086596534,0.0002533579,0.01237661],"genre_scores_gemma":[0.00038911024,0.00047364968,0.99605036,0.0018941902,0.00004829849,0.00022440396,0.0004930829,0.000011955348,0.00041495042],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971536,0.000055217464,0.0013054851,0.0003657021,0.00078516384,0.00033483325],"domain_scores_gemma":[0.9949583,0.0002538886,0.00079672236,0.0017704521,0.002026968,0.00019370025],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.0016667704,0.00033912843,0.00032273095,0.0013793793,0.0006900966,0.0015832088,0.0029075923,0.00023183321,0.000022550485],"category_scores_gemma":[0.00012878355,0.00035598027,0.00006691777,0.00072399864,0.0011652805,0.025767347,0.0013815265,0.00035982236,0.00016156885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030547103,0.000029632722,0.0000015585732,0.00005018581,0.000010029808,2.2258672e-7,0.004867497,0.000023886214,0.00008277965,0.030087225,0.0021377704,0.96270615],"study_design_scores_gemma":[0.0009644223,0.00018159558,0.00007082157,0.00016450588,0.000011640763,0.0000151642225,0.0002787443,0.96720314,0.0011130295,0.0042207907,0.02530044,0.00047570805],"about_ca_topic_score_codex":0.000018225972,"about_ca_topic_score_gemma":0.000016827875,"teacher_disagreement_score":0.96717924,"about_ca_system_score_codex":0.00044229135,"about_ca_system_score_gemma":0.0006794773,"threshold_uncertainty_score":0.9998892},"labels":[],"label_agreement":null},{"id":"W2905056485","doi":"10.1007/s11548-018-1897-1","title":"ARENA: Inter-modality affine registration using evolutionary strategy","year":2018,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Concordia University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Image registration; Affine transformation; Context (archaeology); Computer science; Wilcoxon signed-rank test; Artificial intelligence; Metric (unit); Magnetic resonance imaging; Image (mathematics); Medicine; Pattern recognition (psychology); Mathematics; Radiology; Statistics; Mann–Whitney U test","score_opus":0.04384541313363915,"score_gpt":0.32354778055774547,"score_spread":0.2797023674241063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905056485","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056423143,0.00014387797,0.93871003,0.0022442942,0.0022712662,0.000040034654,0.0000021009437,0.000043652064,0.00012157526],"genre_scores_gemma":[0.83049953,0.00005784178,0.167217,0.0008785529,0.0013122506,0.0000010885396,0.0000070716947,0.0000049462383,0.00002173462],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99825835,0.00030138646,0.00073265453,0.00021081553,0.0003445593,0.00015223178],"domain_scores_gemma":[0.99786866,0.0004468986,0.0005965673,0.00015989823,0.0008192246,0.00010875642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092604634,0.00012406374,0.00027824639,0.00037355168,0.000079863166,0.00011539614,0.00053975853,0.00010521606,0.000045448065],"category_scores_gemma":[0.00013122121,0.000107437336,0.00011834749,0.00014419654,0.0002984731,0.00077187724,0.00012615674,0.00021146296,0.00000250156],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002915267,0.0004054488,0.019783206,0.0000251222,0.0007753061,0.0009326691,0.00048327658,0.00013191215,0.00915233,0.0077924253,0.04636994,0.91385686],"study_design_scores_gemma":[0.0026919257,0.0016611031,0.5574099,0.00089403946,0.00010335507,0.049436595,0.00010204106,0.33829096,0.013162235,0.02930102,0.005765522,0.0011813005],"about_ca_topic_score_codex":0.000012664436,"about_ca_topic_score_gemma":0.0000021118797,"teacher_disagreement_score":0.91267556,"about_ca_system_score_codex":0.00010046158,"about_ca_system_score_gemma":0.00022055935,"threshold_uncertainty_score":0.43811676},"labels":[],"label_agreement":null},{"id":"W2908676718","doi":"10.3166/ts.35.121-136","title":"Automatic ranking of image thresholding techniques using consensus of ground truth","year":2018,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Thresholding; Ground truth; Ranking (information retrieval); Artificial intelligence; Image (mathematics); Pattern recognition (psychology); Computer science; Mathematics","score_opus":0.03314039487924562,"score_gpt":0.30869351623370583,"score_spread":0.2755531213544602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908676718","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24010786,0.000018709143,0.7587071,0.000043028864,0.000067359884,0.00025437493,0.0000031008326,0.00022881672,0.00056961126],"genre_scores_gemma":[0.5586017,0.0000014913858,0.44126317,0.00007576818,0.000041981704,0.000006258285,9.0624263e-7,0.000006777188,0.0000019574181],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981832,0.00011084155,0.00066332583,0.0002582953,0.0005568771,0.00022746784],"domain_scores_gemma":[0.99876946,0.0001955537,0.0003902849,0.00030349958,0.0002743814,0.00006682026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008920998,0.0001448612,0.00027871437,0.0002233001,0.00007903603,0.00005486378,0.00054246024,0.000049886632,0.00032898822],"category_scores_gemma":[0.000078911755,0.00013423151,0.000069921094,0.0004151041,0.00041198402,0.00030796678,0.00015168838,0.000081238424,0.0000023754756],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011053445,0.0001251327,0.00013645606,0.0001489883,0.000040313727,0.000010586166,0.0012105573,0.0000017008296,0.8947529,0.004782778,0.0003095592,0.09846994],"study_design_scores_gemma":[0.00033941973,0.00025993408,0.00034513144,0.0002509659,0.000021054102,0.000015797963,0.000073825366,0.07924103,0.916812,0.0024971243,0.000011037232,0.00013267888],"about_ca_topic_score_codex":0.00005345287,"about_ca_topic_score_gemma":0.0000012197896,"teacher_disagreement_score":0.3184938,"about_ca_system_score_codex":0.000053830223,"about_ca_system_score_gemma":0.000083987004,"threshold_uncertainty_score":0.5473802},"labels":[],"label_agreement":null},{"id":"W2908882356","doi":"10.1371/journal.pone.0210641","title":"FAst Segmentation Through SURface Fairing (FASTSURF): A novel semi-automatic hippocampus segmentation method","year":2019,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Genentech; National Institutes of Health; Servier; Eisai; Canadian Institutes of Health Research; Vrije Universiteit Amsterdam; ZonMw; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Segmentation; Artificial intelligence; Hippocampus; Computer science; Computer vision; Pattern recognition (psychology); Biology; Neuroscience","score_opus":0.04059367147001036,"score_gpt":0.29545808901178267,"score_spread":0.2548644175417723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908882356","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17736979,0.00004773763,0.81934947,0.00043903987,0.00013637343,0.0010302769,0.0000065018044,0.0008350965,0.0007857068],"genre_scores_gemma":[0.069765836,0.000030472518,0.928409,0.0010404215,0.00004320955,0.000082329614,0.000041274285,0.000033235305,0.0005542685],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99685836,0.0002256126,0.00059386075,0.00067861256,0.0012173796,0.00042618962],"domain_scores_gemma":[0.99829316,0.0003285566,0.0003528156,0.0006984699,0.00018644542,0.00014053352],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00070493185,0.00027730403,0.00039158727,0.00011703965,0.00011914917,0.00026894905,0.00071451033,0.00011032848,0.0003235359],"category_scores_gemma":[0.00009742134,0.00028477988,0.00007724376,0.00063291076,0.00004355542,0.0020556478,0.0002730726,0.0002552306,0.0005041359],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005202178,0.0007700066,0.0010185578,0.00030649625,0.00014598851,0.000003920885,0.0029786762,0.00029925915,0.9550138,0.0004092602,0.00018750501,0.03886133],"study_design_scores_gemma":[0.0009287509,0.00015833239,0.0003795831,0.00031042148,0.00005374432,0.000008002477,0.00042973764,0.191186,0.8050469,0.0011711386,0.0000042964875,0.00032307665],"about_ca_topic_score_codex":0.000059616465,"about_ca_topic_score_gemma":0.0000037849481,"teacher_disagreement_score":0.19088674,"about_ca_system_score_codex":0.00024750415,"about_ca_system_score_gemma":0.00008512513,"threshold_uncertainty_score":0.9999604},"labels":[],"label_agreement":null},{"id":"W2909569419","doi":"10.1016/b978-012431152-7/50012-4","title":"Structural Analysis Applied to Epilepsy","year":2005,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research","keywords":"Epilepsy; Psychology; Computer science; Neuroscience","score_opus":0.015121487702255102,"score_gpt":0.26617172951062806,"score_spread":0.251050241808373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909569419","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000031565114,0.000076156415,0.15238422,0.00013626959,0.00014906532,0.0005393913,0.000011495536,0.00041926408,0.846281],"genre_scores_gemma":[0.00033665495,0.000007852539,0.19442403,0.0033033036,0.00027057584,0.0000601825,0.00002519017,0.00003661007,0.8015356],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973885,0.000024285237,0.000603477,0.0008542035,0.0007785176,0.00035102313],"domain_scores_gemma":[0.9978705,0.000059831254,0.0002655162,0.0013202105,0.00010845542,0.00037549666],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026424782,0.0004341855,0.0006855927,0.00067547243,0.0000986266,0.00018951301,0.0015323308,0.00025422033,0.0008658279],"category_scores_gemma":[0.000014358256,0.0004019225,0.00033843715,0.00010969869,0.00009331077,0.000094409865,0.0005367871,0.0004214448,0.00054512324],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020464465,0.0000016857995,0.0000014760343,0.0000098822975,0.00021859004,0.000012956093,0.00021790277,0.000007348637,0.000043283777,0.017457394,0.00049058365,0.98153687],"study_design_scores_gemma":[0.000307143,0.00012220762,0.00010801415,0.000112774505,0.0006937336,0.00001760966,0.0000050542253,0.000707429,0.002151988,0.026537063,0.96774733,0.0014896232],"about_ca_topic_score_codex":5.4868065e-7,"about_ca_topic_score_gemma":0.000015222689,"teacher_disagreement_score":0.9800472,"about_ca_system_score_codex":0.00014793104,"about_ca_system_score_gemma":0.000085800646,"threshold_uncertainty_score":0.99984324},"labels":[],"label_agreement":null},{"id":"W2910795818","doi":"10.3166/ts.35.317-330","title":"A brain nuclear magnetic resonance image segmentation algorithm based on non-rigid registration","year":2018,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Segmentation; Computer vision; Artificial intelligence; Image registration; Magnetic resonance imaging; Image (mathematics); Computer science; Image segmentation; Algorithm; Nuclear magnetic resonance; Pattern recognition (psychology); Physics; Medicine; Radiology","score_opus":0.010752988788539359,"score_gpt":0.2624864547572693,"score_spread":0.2517334659687299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910795818","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011698448,0.000011874061,0.99238646,0.0024096514,0.00013876383,0.0005537588,0.000009706154,0.00034990977,0.0029700052],"genre_scores_gemma":[0.06970356,0.000005542602,0.91942745,0.010042586,0.00031760946,0.000095053525,0.000034606805,0.00002609884,0.00034750154],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978334,0.00014235082,0.0004052306,0.0005177411,0.0008002442,0.00030105034],"domain_scores_gemma":[0.9990378,0.00010331551,0.0001549399,0.00041319567,0.00014845599,0.00014232016],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006052492,0.00019708127,0.00014334991,0.00015795576,0.00019422862,0.00026976556,0.0005586688,0.000061477076,0.0011747419],"category_scores_gemma":[0.000041334883,0.00019341893,0.000060344813,0.00035639358,0.00018916845,0.00062205136,0.000054635042,0.00012926503,0.00025079263],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042260708,0.00027528248,0.00001785606,0.00001972184,0.0000049978535,0.00003320391,0.0007127733,0.0000059229287,0.09947829,0.00100103,0.070751,0.82765764],"study_design_scores_gemma":[0.0020897964,0.0031716018,0.0029371753,0.0001272643,0.0000121350495,0.000008870237,0.00006580692,0.7878227,0.19436394,0.0007126143,0.008255173,0.0004329024],"about_ca_topic_score_codex":0.000024105631,"about_ca_topic_score_gemma":0.000004341445,"teacher_disagreement_score":0.8272248,"about_ca_system_score_codex":0.00011802651,"about_ca_system_score_gemma":0.00007503452,"threshold_uncertainty_score":0.99973834},"labels":[],"label_agreement":null},{"id":"W2912156897","doi":"10.1109/bibm.2018.8621509","title":"Brain MRI Segmentation using efficient 3D Fully Convolutional Neural Networks","year":2018,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Segmentation; Convolutional neural network; Hyperparameter; Computer science; Artificial intelligence; White matter; Magnetic resonance imaging; Pattern recognition (psychology); Grey matter; Image segmentation; Computer vision; Medicine; Radiology","score_opus":0.021121930727263684,"score_gpt":0.30310738082726346,"score_spread":0.28198545009999976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912156897","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006323958,0.000019507288,0.9907981,0.0009533139,0.00052742363,0.00022217841,8.254311e-7,0.00039815716,0.0007565436],"genre_scores_gemma":[0.23263077,0.0000011972477,0.76092434,0.005882205,0.0002857444,0.0000116548545,0.0000090316025,0.000008175749,0.00024686885],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860513,0.000107300984,0.00026593328,0.0003298277,0.0004189901,0.00027280767],"domain_scores_gemma":[0.9992443,0.00010003122,0.0000940998,0.00026821575,0.00016852829,0.00012486643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039447137,0.0001137855,0.00009581235,0.000094175695,0.000182844,0.00014486448,0.0004037718,0.00005327198,0.0004131174],"category_scores_gemma":[0.000047359703,0.0001025272,0.00003750874,0.00035399717,0.00018345934,0.00033989764,0.00019213231,0.00009687258,0.00004463686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007644775,0.0007280186,0.0024229328,0.000052436983,0.00011960648,0.0000744749,0.002989559,0.102426626,0.13130508,0.0439645,0.15205649,0.5637838],"study_design_scores_gemma":[0.00023402351,0.00008598196,0.00038138902,0.000007658183,0.000003002363,0.000020071477,0.000020706113,0.986367,0.012533121,0.00012528787,0.000100188096,0.00012156439],"about_ca_topic_score_codex":0.000036612448,"about_ca_topic_score_gemma":0.0000042974502,"teacher_disagreement_score":0.8839404,"about_ca_system_score_codex":0.00009908802,"about_ca_system_score_gemma":0.000052993782,"threshold_uncertainty_score":0.45233458},"labels":[],"label_agreement":null},{"id":"W2912309144","doi":"","title":"Revised Selected Papers of the 4th International Workshop on Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges - Volume 8330","year":2013,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Volume (thermodynamics); Computer science; Data science","score_opus":0.02737002169707739,"score_gpt":0.26745485808186775,"score_spread":0.24008483638479036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912309144","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008378452,0.00013649419,0.98405004,0.006271874,0.000044093194,0.00023182135,0.000004292504,0.000038220413,0.000844711],"genre_scores_gemma":[0.80820733,0.00010793537,0.19108486,0.0005025321,0.000009136339,0.000010199391,0.0000017286562,0.0000042720817,0.00007199704],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989707,0.000103435304,0.00023522247,0.00020054495,0.00039457818,0.00009554547],"domain_scores_gemma":[0.99910647,0.00038935203,0.000095448166,0.00016969258,0.0001923346,0.000046729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015547496,0.0000811329,0.00011392459,0.00004019308,0.00005772358,0.00005515561,0.00030886062,0.00002183656,0.000049955983],"category_scores_gemma":[0.00010327404,0.000047923004,0.000022112537,0.00011888198,0.00021412823,0.00022007248,0.00019490761,0.00009909321,6.5924144e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003908545,0.00038313252,0.003978297,0.00026211052,0.00019092354,0.0000020496066,0.004322376,0.1161292,0.009374161,0.42091292,0.015172898,0.42923287],"study_design_scores_gemma":[0.00014877926,0.000013601168,0.004978286,0.00009407936,0.0000039576616,0.0000041508038,0.000058520043,0.9783588,0.0010743451,0.015197366,0.000014508607,0.000053613414],"about_ca_topic_score_codex":0.00004744437,"about_ca_topic_score_gemma":0.0000014691209,"teacher_disagreement_score":0.8622296,"about_ca_system_score_codex":0.000013831261,"about_ca_system_score_gemma":0.000036687594,"threshold_uncertainty_score":0.19542435},"labels":[],"label_agreement":null},{"id":"W2912696013","doi":"10.1016/j.media.2005.09.002","title":"United Snakes","year":2005,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":122,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"Turun Yliopisto","keywords":"Computer science; Artificial intelligence; Robustness (evolution); Segmentation; Computer vision; Image segmentation; Pattern recognition (psychology); Biology","score_opus":0.010482848239352076,"score_gpt":0.3051294323258833,"score_spread":0.2946465840865312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912696013","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010862215,0.000063620166,0.98204553,0.012840524,0.00003588022,0.00005792881,0.0000013083671,0.00054106844,0.0033279385],"genre_scores_gemma":[0.107773125,0.00012403392,0.86957246,0.020066733,0.00027193423,0.000035028886,0.00007042454,0.000013851376,0.0020724148],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974465,0.00015909132,0.00040697126,0.00039403222,0.0012911233,0.00030231022],"domain_scores_gemma":[0.99843067,0.00017525406,0.000088618435,0.0006663175,0.00014729239,0.00049183686],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00082703104,0.00013540857,0.00027477054,0.0007705534,0.00008504134,0.0001598743,0.0012820651,0.0000980287,0.0051257005],"category_scores_gemma":[0.00084150664,0.000110081935,0.00021397429,0.0038948648,0.00018119825,0.00064393145,0.00024951162,0.00024244693,0.00045060457],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002561711,0.0003028958,0.00086989097,0.000011110476,0.0007108242,0.0002269671,0.00047502836,0.000019820403,0.0017325934,0.002546798,0.055117447,0.93798405],"study_design_scores_gemma":[0.0007774418,0.000077090626,0.0022515068,0.000028346489,0.0006550457,0.000027135125,0.00007340756,0.87470526,0.068011,0.0008226946,0.05194847,0.00062259403],"about_ca_topic_score_codex":0.00007223414,"about_ca_topic_score_gemma":0.000038879625,"teacher_disagreement_score":0.9373615,"about_ca_system_score_codex":0.000045849014,"about_ca_system_score_gemma":0.000067295616,"threshold_uncertainty_score":0.99578375},"labels":[],"label_agreement":null},{"id":"W2912848025","doi":"10.1002/hbm.24803","title":"Accurate and robust segmentation of neuroanatomy in T1‐weighted MRI by combining spatial priors with deep convolutional neural networks","year":2019,"lang":"en","type":"preprint","venue":"Human Brain Mapping","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"National Institute of Biomedical Imaging and Bioengineering; Fonds de Recherche du Québec - Santé; University of California, San Diego; Genentech; University of California, Los Angeles; U.S. Food and Drug Administration; National Institutes of Health; Canada First Research Excellence Fund; Eisai; Northern California Institute for Research and Education; F. Hoffmann-La Roche; Elan; Novartis; Medpace; GlaxoSmithKline; AstraZeneca; Eli Lilly and Company; Bristol-Myers Squibb; Pfizer; Synarc; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Sørensen–Dice coefficient; Segmentation; Artificial intelligence; Computer science; Convolutional neural network; Pattern recognition (psychology); Population; Deep learning; Prior probability; Robustness (evolution); Image segmentation; Computer vision; Bayesian probability","score_opus":0.01972984430705215,"score_gpt":0.26444553970711426,"score_spread":0.2447156954000621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912848025","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055685405,0.00014493917,0.942376,0.00044967575,0.00019268844,0.0008837172,0.0000057380093,0.00015802734,0.000103805636],"genre_scores_gemma":[0.9156471,0.000024853169,0.08304347,0.00081083464,0.000054609194,0.00008097328,0.00026388743,0.000029769379,0.000044514745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99730724,0.0003593746,0.00069729844,0.0007729678,0.0005220509,0.00034109646],"domain_scores_gemma":[0.99832815,0.00029033862,0.00069121487,0.00044838284,0.00013393223,0.00010800352],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00060901145,0.00032091266,0.00045161138,0.00034859675,0.0001243063,0.00022017237,0.0006836264,0.00019391277,0.000027478449],"category_scores_gemma":[0.000045319146,0.0003245959,0.00004967669,0.00028981428,0.00020714632,0.0004413536,0.0008412868,0.00076313294,9.359519e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027592646,0.0011635587,0.12528722,0.004460272,0.0007972784,0.00041347652,0.02984801,0.6485478,0.049692143,0.015456688,0.018836629,0.105220996],"study_design_scores_gemma":[0.001073953,0.00009867551,0.028951112,0.0003996632,0.000009563201,0.000007778286,0.00012922406,0.9680721,0.00033370816,0.0005618039,0.00001201036,0.00035041937],"about_ca_topic_score_codex":0.00020856586,"about_ca_topic_score_gemma":0.00004625812,"teacher_disagreement_score":0.8599617,"about_ca_system_score_codex":0.00010896361,"about_ca_system_score_gemma":0.00008821353,"threshold_uncertainty_score":0.9999206},"labels":[],"label_agreement":null},{"id":"W2913051454","doi":"10.54294/fi9kgd","title":"Automatized Evaluation of the Left Ventricular Ejection Fraction from Echocardiographic Images Using Graph Cut","year":2014,"lang":"en","type":"preprint","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Cut; Voxel; Segmentation; Ejection fraction; Artificial intelligence; Graph; Computer science; Ventricle; Computer vision; Tracing; Pattern recognition (psychology); Mathematics; Image segmentation; Medicine; Cardiology; Heart failure","score_opus":0.03134100522776131,"score_gpt":0.3197310404142543,"score_spread":0.28839003518649303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913051454","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056284525,0.00016731593,0.94027907,0.0001118034,0.0013348628,0.0009689464,0.00000681938,0.00045632437,0.0003903126],"genre_scores_gemma":[0.7353795,0.000061456245,0.26417008,0.00014706636,0.0001259693,0.000054889148,0.000034588902,0.000016512047,0.000009906209],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954395,0.0013039097,0.0006035436,0.0006083185,0.0018736023,0.00017108631],"domain_scores_gemma":[0.99696106,0.0001455966,0.0008517227,0.0013090187,0.0006706342,0.0000619976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024134577,0.0002445132,0.000379303,0.00044237747,0.00013326808,0.00019533852,0.0010016051,0.0002931163,0.000112587826],"category_scores_gemma":[0.00034780888,0.00018390629,0.0004293608,0.000522825,0.000104059785,0.00039711595,0.0007193182,0.0004758166,0.000004933021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055082888,0.0012064761,0.053580023,0.0011423089,0.0033113703,0.000018945644,0.0015572011,0.07171452,0.26198927,0.0016293186,0.02160435,0.5821911],"study_design_scores_gemma":[0.00037602376,0.000016176287,0.012526766,0.00026004246,0.00032119808,0.000005158939,0.000020014066,0.6552244,0.28515807,0.045843165,0.0000147293495,0.00023427591],"about_ca_topic_score_codex":0.0008806523,"about_ca_topic_score_gemma":0.0000072223434,"teacher_disagreement_score":0.67909503,"about_ca_system_score_codex":0.00020424848,"about_ca_system_score_gemma":0.00019806689,"threshold_uncertainty_score":0.7499481},"labels":[],"label_agreement":null},{"id":"W2914410118","doi":"10.1109/jbhi.2018.2865450","title":"Convolutional Neural Network With Shape Prior Applied to Cardiac MRI Segmentation","year":2018,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":209,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Segmentation; Convolutional neural network; Sørensen–Dice coefficient; Artificial intelligence; Ventricle; Endocardium; Magnetic resonance imaging; Cardiac magnetic resonance imaging; Deep learning; Hausdorff distance; Pattern recognition (psychology); Image segmentation; Computer vision; Medicine; Radiology; Cardiology","score_opus":0.023785007031338647,"score_gpt":0.3144664781195526,"score_spread":0.29068147108821396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914410118","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072519234,0.000051551357,0.98447526,0.0072183697,0.00061601674,0.0002574279,0.0000034055317,0.000037409663,0.000088622844],"genre_scores_gemma":[0.04321811,0.00014048326,0.9383903,0.017178642,0.0010405267,0.000008297341,0.0000046629025,0.000006049299,0.000012930253],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978233,0.000039202398,0.00096542534,0.00007667943,0.00079981395,0.00029560406],"domain_scores_gemma":[0.99834865,0.00006620726,0.0005849809,0.00011490715,0.00022134249,0.0006638924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001375201,0.00010667516,0.00027166613,0.00015399525,0.00018506084,0.00008347753,0.00029653896,0.000054204374,0.000023426046],"category_scores_gemma":[0.00001802147,0.00007268497,0.000029492048,0.00037635613,0.00021756765,0.00042969547,0.00006493035,0.00020883638,0.000013007766],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008366134,0.000077844445,0.00031490644,0.00020406702,0.000056672743,0.000005473702,0.0059434753,0.000043316333,0.00022843943,0.001099618,0.12864847,0.86329406],"study_design_scores_gemma":[0.019682767,0.05499731,0.060827915,0.0037365055,0.00024205694,0.002758932,0.006539503,0.62524635,0.01858686,0.009395127,0.19470376,0.0032829307],"about_ca_topic_score_codex":0.000003882681,"about_ca_topic_score_gemma":0.0000010601743,"teacher_disagreement_score":0.8600111,"about_ca_system_score_codex":0.00008309844,"about_ca_system_score_gemma":0.00037923254,"threshold_uncertainty_score":0.2964007},"labels":[],"label_agreement":null},{"id":"W2915211339","doi":"","title":"Symmetric atlasing and model based segmentation: An application to the hippocampus in older adults","year":2006,"lang":"en","type":"article","venue":"QUT ePrints (Queensland University of Technology)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"","keywords":"Segmentation; Tracing; Computer science; Artificial intelligence; Computer vision; Pattern recognition (psychology)","score_opus":0.0050191255872556895,"score_gpt":0.2186931485700479,"score_spread":0.2136740229827922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2915211339","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3680682,0.000011984804,0.62957925,0.0018147319,0.000007801781,0.00025738936,0.000001370951,0.00012332705,0.00013593039],"genre_scores_gemma":[0.7412903,0.0000068711715,0.2585788,0.00009497639,0.0000032769037,0.000002530137,0.0000036204533,0.0000026888304,0.000016962907],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992219,0.00003421454,0.00013192087,0.00032320688,0.00015472443,0.00013399679],"domain_scores_gemma":[0.99937177,0.00003100481,0.00008895712,0.00039570153,0.00007057738,0.000042002575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019996775,0.00008216457,0.000109799184,0.00053663657,0.00008924052,0.000017073058,0.0005589246,0.000097871285,0.0000050482513],"category_scores_gemma":[0.000020699043,0.00007814324,0.000017321898,0.0008879116,0.00010518144,0.00020920741,0.00018229157,0.00011957098,0.0000059832223],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027279233,0.00020018172,0.022350034,0.000030604104,0.000005551547,0.000012859623,0.00048681063,0.0017183851,0.0022401526,0.0072839567,0.00026337383,0.9653808],"study_design_scores_gemma":[0.002015539,0.00013299723,0.13938619,0.00011710893,0.000015486094,0.000008419963,0.00070526084,0.80787313,0.026137445,0.023125283,0.00016034111,0.00032276308],"about_ca_topic_score_codex":0.0004091459,"about_ca_topic_score_gemma":0.00022911617,"teacher_disagreement_score":0.965058,"about_ca_system_score_codex":0.000058680882,"about_ca_system_score_gemma":0.00003269568,"threshold_uncertainty_score":0.3186589},"labels":[],"label_agreement":null},{"id":"W2915543933","doi":"10.1118/1.1586267","title":"Prostate boundary segmentation from 3D ultrasound images","year":2003,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"London Health Sciences Centre; Robarts Clinical Trials; Western University","funders":"","keywords":"Segmentation; Initialization; Computer science; Artificial intelligence; Boundary (topology); Image segmentation; Computer vision; Pattern recognition (psychology); Algorithm; Mathematics","score_opus":0.011186782005391693,"score_gpt":0.27677001620372194,"score_spread":0.26558323419833024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2915543933","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017562599,0.00012965933,0.9939481,0.00040690464,0.0003891046,0.00021240243,0.000007911151,0.000379295,0.0027703687],"genre_scores_gemma":[0.31975582,0.00023792872,0.6687622,0.009698646,0.000482557,0.00014092372,0.00017055184,0.00004589684,0.0007054472],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976744,0.00017271863,0.00030366905,0.0004000286,0.0011643299,0.00028485543],"domain_scores_gemma":[0.9987591,0.00033864297,0.00010469546,0.00042080774,0.000076390286,0.00030037624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042990333,0.00015898327,0.00017347658,0.000029629262,0.00012903208,0.00019695671,0.0005558464,0.00007795585,0.0005564723],"category_scores_gemma":[0.00058473053,0.00014143814,0.000053025542,0.00030616432,0.00023550582,0.000707468,0.00008626298,0.00027903143,0.00018701503],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053070044,0.00040198778,0.0018792086,0.000029392608,0.000051674502,0.00009214373,0.0015617424,0.000002046314,0.022009598,0.0019942014,0.034505453,0.9374672],"study_design_scores_gemma":[0.00059565296,0.000065666965,0.00053796713,0.000046476063,0.000010691504,0.000009947056,0.000037790272,0.00016435822,0.9400353,0.05662,0.0016237847,0.00025234965],"about_ca_topic_score_codex":0.0000379832,"about_ca_topic_score_gemma":0.000001923857,"teacher_disagreement_score":0.9372149,"about_ca_system_score_codex":0.00006302207,"about_ca_system_score_gemma":0.00023896828,"threshold_uncertainty_score":0.6092981},"labels":[],"label_agreement":null},{"id":"W2916721488","doi":"10.1101/561118","title":"Bridging micro and macro: accurate registration of the BigBrain dataset with the MNI PD25 and ICBM152 atlases","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital; Western University","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Segmentation; Artificial intelligence; Image registration; Atlas (anatomy); Brain atlas; Magnetic resonance imaging; Computer vision; Pattern recognition (psychology); Anatomy; Medicine; Radiology; Image (mathematics)","score_opus":0.013521852159081544,"score_gpt":0.23531714599822687,"score_spread":0.2217952938391453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2916721488","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45732126,0.0008245916,0.5339695,0.004708429,0.00037589332,0.0015635703,0.0009712596,0.00025968175,0.0000058042906],"genre_scores_gemma":[0.9677276,0.00022759942,0.03079957,0.0010555248,0.00007894163,0.00006860433,0.0000021096268,0.000031705586,0.000008340753],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99800164,0.00023563902,0.00036820755,0.0007131069,0.000428236,0.00025314844],"domain_scores_gemma":[0.99725264,0.00015387929,0.00063721015,0.0016715352,0.00018297642,0.00010174217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009089349,0.0003016858,0.00029414037,0.00009224075,0.00016694602,0.00049126835,0.0011184086,0.00014816457,0.0000038758467],"category_scores_gemma":[0.00019312528,0.00019078306,0.00003637344,0.0003476244,0.0003618432,0.00037656797,0.0012318785,0.0004551641,0.0000027628241],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002646829,0.000076032906,0.0041898396,0.0007741185,0.00013533515,0.000026939086,0.000089209454,0.00003309164,0.97328657,0.0011324382,0.020067105,0.00016287304],"study_design_scores_gemma":[0.00044180622,0.000070214235,0.053276226,0.0006967411,0.00007483573,2.8822902e-7,0.0000083822315,0.004013365,0.9389957,0.0000069139573,0.0019164322,0.000499098],"about_ca_topic_score_codex":0.00010854562,"about_ca_topic_score_gemma":0.0000073243123,"teacher_disagreement_score":0.5104064,"about_ca_system_score_codex":0.000045886467,"about_ca_system_score_gemma":0.00033458747,"threshold_uncertainty_score":0.77799076},"labels":[],"label_agreement":null},{"id":"W2922464199","doi":"10.1117/12.2512799","title":"Preliminary results comparing thin-plate splines with finite element methods for modeling brain deformation during neurosurgery using intraoperative ultrasound","year":2019,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke","keywords":"Thin plate spline; Finite element method; Spline (mechanical); Computer science; Deformation (meteorology); Computer vision; Preprocessor; Artificial intelligence; Algorithm; Structural engineering; Engineering; Spline interpolation; Geology","score_opus":0.04771102057871853,"score_gpt":0.34829181897911915,"score_spread":0.30058079840040064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922464199","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17226152,0.000010737024,0.82619905,0.00021273039,0.0001399771,0.00074501574,0.0000024733342,0.0002831478,0.00014532461],"genre_scores_gemma":[0.30685264,0.000005456442,0.6925588,0.0004008985,0.00002759413,0.00003392769,0.000018739098,0.00001313833,0.00008877033],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809337,0.00019459375,0.000639393,0.00046337262,0.0002813263,0.00032794126],"domain_scores_gemma":[0.998029,0.0010603121,0.00022298211,0.00039759654,0.00019501669,0.00009508297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014797524,0.00020293065,0.00026457018,0.00018296693,0.0002013221,0.00028213632,0.0003637166,0.000051336534,0.000009329294],"category_scores_gemma":[0.00034422125,0.00015893504,0.000052677336,0.0002750831,0.000027040942,0.0016585717,0.00016168805,0.00016319186,0.00000467099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007354081,0.00011195146,0.0003834189,0.00029147722,0.00007195697,0.00000729748,0.0053583398,0.8047174,0.17266567,0.0015375459,0.000099881916,0.014019632],"study_design_scores_gemma":[0.00064312504,0.00018354465,0.000065703985,0.00009890664,0.0000071407903,0.000023141278,0.00013888702,0.798698,0.19975607,0.00019876842,0.0000076224806,0.00017909674],"about_ca_topic_score_codex":0.000036430152,"about_ca_topic_score_gemma":0.0000038208264,"teacher_disagreement_score":0.13459112,"about_ca_system_score_codex":0.00006873305,"about_ca_system_score_gemma":0.00006401523,"threshold_uncertainty_score":0.6481183},"labels":[],"label_agreement":null},{"id":"W2922498131","doi":"10.1117/12.2512844","title":"Deformable MRI-TRUS surface registration from statistical deformation models of the prostate","year":2019,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Image registration; Deformation (meteorology); Computer science; Prostate; Artificial intelligence; Geology; Computer vision; Medicine; Internal medicine","score_opus":0.012095405423319888,"score_gpt":0.24340582907500322,"score_spread":0.23131042365168333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922498131","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039088212,0.000008224637,0.9538542,0.000364516,0.000121111945,0.0003868978,0.000013594969,0.000113907176,0.00604931],"genre_scores_gemma":[0.6015818,0.000008790862,0.39747384,0.00025533806,0.0000046398977,0.000005395572,0.000017100741,0.0000034693899,0.00064962293],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988648,0.00006890357,0.000323413,0.00016507512,0.00044630788,0.00013149387],"domain_scores_gemma":[0.9991433,0.00007922129,0.0001657905,0.00046803104,0.00009778439,0.000045852445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002865574,0.000074266136,0.00010047046,0.000019650413,0.00004036766,0.00007117698,0.0004563252,0.000039646933,0.000108796754],"category_scores_gemma":[0.000027389027,0.000047115926,0.000026555073,0.0001656247,0.00004627618,0.0013624687,0.00012766398,0.00008611348,0.000051981904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007660098,0.00031577906,0.005397509,0.00025195538,0.000071208975,0.0000035164796,0.007461747,0.036334038,0.08402689,0.7314948,0.026829094,0.10773685],"study_design_scores_gemma":[0.00020831062,0.000042299533,0.00094000506,0.000020094792,0.0000030306721,0.000001567837,0.000054212276,0.7878318,0.1719412,0.038830265,0.000053226868,0.000073972784],"about_ca_topic_score_codex":0.0003927232,"about_ca_topic_score_gemma":0.0000151847935,"teacher_disagreement_score":0.75149775,"about_ca_system_score_codex":0.00004056947,"about_ca_system_score_gemma":0.00008042251,"threshold_uncertainty_score":0.19213316},"labels":[],"label_agreement":null},{"id":"W2941217945","doi":"10.1007/s11548-019-01932-2","title":"Deformable multimodal registration for navigation in beating-heart cardiac surgery","year":2019,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Image registration; Ultrasound; Artificial intelligence; Medicine; Affine transformation; Computer vision; Computer science; Similarity (geometry); Radiology; Image (mathematics); Mathematics","score_opus":0.01694739577455843,"score_gpt":0.28573823288170846,"score_spread":0.26879083710715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2941217945","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2962539,0.0001386294,0.69956845,0.0013244338,0.0025377963,0.00012604386,0.000002936038,0.00002472538,0.000023115988],"genre_scores_gemma":[0.85546577,0.00007903622,0.14319421,0.00078824704,0.00038705257,0.000011347799,0.00004249939,0.0000072264893,0.00002459837],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9983637,0.00022305675,0.00076999894,0.00018676743,0.00029496712,0.000161531],"domain_scores_gemma":[0.99681646,0.0020962073,0.00048256264,0.00012041418,0.00041554339,0.00006883271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020169574,0.0001058664,0.00040601843,0.00043731424,0.000033026892,0.00009153637,0.00027352516,0.00010704658,0.000006465984],"category_scores_gemma":[0.00017372753,0.00009553961,0.00019749973,0.00012690906,0.00005115263,0.0008193429,0.000046977344,0.0001808877,0.000002189487],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033085913,0.0002512034,0.4583975,0.000105832005,0.00039291364,0.00014275078,0.00065314834,0.0007316558,0.0076811337,0.0016201846,0.035721947,0.49397087],"study_design_scores_gemma":[0.002057645,0.00047477134,0.7096059,0.0011932778,0.000037791986,0.0039677788,0.000088646164,0.25563306,0.014971243,0.004558118,0.006602106,0.00080969057],"about_ca_topic_score_codex":0.000011105407,"about_ca_topic_score_gemma":7.3493237e-7,"teacher_disagreement_score":0.5592119,"about_ca_system_score_codex":0.00008150402,"about_ca_system_score_gemma":0.0001701885,"threshold_uncertainty_score":0.38959923},"labels":[],"label_agreement":null},{"id":"W2943173407","doi":"10.1016/j.cmpb.2019.04.030","title":"Automated brain extraction from head CT and CTA images using convex optimization with shape propagation","year":2019,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"University of Calgary; Heart and Stroke Foundation of Canada","keywords":"Jaccard index; Artificial intelligence; Sørensen–Dice coefficient; Computer science; Segmentation; Computer vision; Pattern recognition (psychology); Active contour model; Image segmentation; Medicine","score_opus":0.046231111087655485,"score_gpt":0.39149002060751525,"score_spread":0.34525890951985977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943173407","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061194364,0.0002554125,0.9363375,0.00087381125,0.00020952233,0.00067641924,9.507555e-7,0.00044040557,0.000011664831],"genre_scores_gemma":[0.022371564,0.000044843906,0.9768835,0.00054702524,0.00006757811,0.0000193423,0.000045495,0.000011826201,0.000008871573],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983967,0.00036908145,0.00032570228,0.0004923878,0.00022844628,0.00018771594],"domain_scores_gemma":[0.9991494,0.00025489755,0.00016707148,0.00024010577,0.00008117248,0.000107354965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010700893,0.00016695262,0.00027756,0.0002202139,0.000051869447,0.00017504756,0.00016836055,0.00006444761,0.000017996428],"category_scores_gemma":[0.000029162022,0.0001257996,0.0000130622375,0.0005544087,0.00016747607,0.00058256695,0.0001275119,0.00018551832,7.3881614e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013178327,0.000053448548,0.003206764,0.000057573743,0.000014041613,0.000018786459,0.00031830234,0.00005369267,0.015042927,0.00003892226,0.000046445635,0.9811359],"study_design_scores_gemma":[0.0010101052,0.0004945943,0.0051192706,0.0003131112,0.000010342263,0.0000896047,0.000027911607,0.98718745,0.005172818,0.00023489427,0.00017781176,0.00016206686],"about_ca_topic_score_codex":0.00016135683,"about_ca_topic_score_gemma":0.0000018120753,"teacher_disagreement_score":0.9871338,"about_ca_system_score_codex":0.000034257388,"about_ca_system_score_gemma":0.000031403506,"threshold_uncertainty_score":0.5129959},"labels":[],"label_agreement":null},{"id":"W2944133343","doi":"","title":"Image registration, fusion, and segmentation","year":2010,"lang":"en","type":"book","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Artificial intelligence; Computer vision; Image registration; Segmentation; Image segmentation; Computer science; Image fusion; Image (mathematics)","score_opus":0.010247638139983578,"score_gpt":0.2694682774899982,"score_spread":0.2592206393500146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944133343","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000010972028,0.000035617126,0.68601483,0.00059798424,0.00015874731,0.00025203655,0.0000023774983,0.00035763576,0.31257966],"genre_scores_gemma":[0.0000016392207,0.00009693089,0.54293364,0.00090022874,0.00008459121,0.000018975088,0.000060711147,0.000010506149,0.45589274],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99856985,0.00003202626,0.0003446125,0.00046272858,0.00045296646,0.00013784225],"domain_scores_gemma":[0.9988359,0.00007627166,0.000231857,0.00053980603,0.00017764624,0.00013848672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031240104,0.00019460112,0.00017277471,0.00013684745,0.000092943716,0.00032777668,0.00048491123,0.0002634154,0.0005266574],"category_scores_gemma":[0.00006984831,0.00017495394,0.00003660908,0.00006696938,0.00015042945,0.0006500299,0.00021726688,0.00037537396,0.00009487118],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010474246,0.000016737371,0.000002137787,0.00006354533,0.000009973414,0.000017905579,0.00018261847,7.760944e-9,0.019134242,0.030947829,0.7849675,0.16465648],"study_design_scores_gemma":[0.0016915172,0.00053241325,0.0002588446,0.00040578295,0.00010584497,0.00031260352,0.000070165755,0.0049379105,0.19411635,0.34142515,0.45345846,0.0026849494],"about_ca_topic_score_codex":0.000012165843,"about_ca_topic_score_gemma":0.000018838828,"teacher_disagreement_score":0.331509,"about_ca_system_score_codex":0.000051937004,"about_ca_system_score_gemma":0.00025512575,"threshold_uncertainty_score":0.71344143},"labels":[],"label_agreement":null},{"id":"W2946713113","doi":"10.1016/j.mri.2019.05.001","title":"Pathology-preserving intensity standardization framework for multi-institutional FLAIR MRI datasets","year":2019,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto; University of Guelph","funders":"National Institute on Aging; Natural Sciences and Engineering Research Council of Canada; U.S. Department of Defense","keywords":"Fluid-attenuated inversion recovery; Standardization; Computer science; Scanner; Sørensen–Dice coefficient; Pattern recognition (psychology); Hyperintensity; Artificial intelligence; Magnetic resonance imaging; Medicine; Radiology; Image (mathematics); Image segmentation","score_opus":0.01923193137316755,"score_gpt":0.30834191373674674,"score_spread":0.2891099823635792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946713113","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007537401,0.0016595256,0.9945353,0.0012499829,0.00058438204,0.0006623403,0.00009413023,0.00031281656,0.00014773461],"genre_scores_gemma":[0.013680708,0.000064516236,0.9836875,0.0021287294,0.000067536115,0.000082129496,0.00010253034,0.000014121426,0.00017221815],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982306,0.000069683985,0.0003290091,0.0006098088,0.00039885,0.00036208003],"domain_scores_gemma":[0.9985846,0.0001764762,0.00011508891,0.00080805615,0.00022061667,0.00009512947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005957308,0.00017660834,0.00022134882,0.0001084679,0.00018051441,0.00020034671,0.000916528,0.00006263085,0.00013100586],"category_scores_gemma":[0.0007564515,0.00016486633,0.000061361214,0.00027599154,0.00014211203,0.0008327498,0.0005101677,0.00021033034,0.00005498475],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045763703,0.000117410884,0.014743223,0.00012873481,0.0000056479807,0.0000937653,0.0005689884,0.00011579401,0.0068794526,0.027725156,0.022205628,0.9273704],"study_design_scores_gemma":[0.0017958998,0.00016209822,0.03506725,0.0005005049,0.000015486086,0.00010869966,0.000096830525,0.84876966,0.023476452,0.019422006,0.06993244,0.0006526898],"about_ca_topic_score_codex":0.000015488415,"about_ca_topic_score_gemma":0.0000023016992,"teacher_disagreement_score":0.92671776,"about_ca_system_score_codex":0.0000885822,"about_ca_system_score_gemma":0.000097054064,"threshold_uncertainty_score":0.67230535},"labels":[],"label_agreement":null},{"id":"W2946997304","doi":"10.1101/599571","title":"Hippocampal subfields revealed through unfolding and unsupervised clustering of laminar and morphological features in 3D BigBrain","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Royal College of Physicians and Surgeons of Canada","keywords":"Hippocampal formation; Hippocampus; Cluster analysis; Neuroimaging; Neuroscience; Artificial intelligence; Computer science; Laminar flow; Pattern recognition (psychology); Folding (DSP implementation); Laminar organization; Human brain; Biology; Physics","score_opus":0.017003053367023478,"score_gpt":0.24854577764526986,"score_spread":0.23154272427824638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946997304","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65107536,0.0012952688,0.3459328,0.000368415,0.000327849,0.00069694093,0.000023421177,0.00026838097,0.000011558785],"genre_scores_gemma":[0.76039803,0.0005814359,0.23844604,0.00044096133,0.00004976057,0.000053263106,1.4365635e-7,0.00002762263,0.0000027669944],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972825,0.00028258478,0.00061158446,0.0010159332,0.0003933781,0.00041403357],"domain_scores_gemma":[0.99816805,0.00024053617,0.0003304034,0.00095451175,0.0001499621,0.00015651876],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00096945907,0.00040727368,0.00068665564,0.00028746118,0.000064291766,0.00020907728,0.0008029578,0.0005870587,0.000016432225],"category_scores_gemma":[0.0003460301,0.00040045113,0.00006146635,0.00044545173,0.00022135854,0.00038673868,0.0016561309,0.0007994376,0.000002364609],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005323099,0.0001815374,0.024103368,0.0020826466,0.00008508622,0.00038819888,0.00040748026,0.00008455759,0.9704638,0.0014491985,0.00036100065,0.00033988163],"study_design_scores_gemma":[0.002739919,0.00045163347,0.36052933,0.0032578602,0.00007815465,0.0000011511211,0.0000406554,0.022895314,0.60775995,0.00012522274,0.00014695576,0.0019738574],"about_ca_topic_score_codex":0.00010198117,"about_ca_topic_score_gemma":0.0000039232914,"teacher_disagreement_score":0.36270386,"about_ca_system_score_codex":0.00009073518,"about_ca_system_score_gemma":0.0001652036,"threshold_uncertainty_score":0.99984473},"labels":[],"label_agreement":null},{"id":"W2947066738","doi":"10.1146/annurev.bioeng.2.1.457","title":"Three-Dimensional Ultrasound Imaging","year":2000,"lang":"en","type":"review","venue":"Annual Review of Biomedical Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":138,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Ultrasound; Computer science; Volume rendering; Rendering (computer graphics); Computer vision; Artificial intelligence; Power doppler; Ultrasound imaging; Transducer; Ultrasonic imaging; Ultrasonic sensor; Radiology; Medicine; Acoustics","score_opus":0.012410263675897914,"score_gpt":0.30821464176904667,"score_spread":0.29580437809314875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947066738","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.2452535e-9,0.6570348,0.34198076,0.00006489174,0.00021523482,0.00037901514,0.000041473257,0.00021875922,0.00006503698],"genre_scores_gemma":[4.733713e-8,0.9150453,0.08403517,0.000495905,0.00014623023,0.000083520434,0.00013562004,0.00003799643,0.000020214926],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99631065,0.00007635795,0.0014789919,0.0005943588,0.0011015977,0.00043804772],"domain_scores_gemma":[0.99769276,0.0006531742,0.00037355942,0.0007976927,0.00010603589,0.0003767962],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009452522,0.0004893026,0.001934004,0.00035046894,0.000032552776,0.000033410553,0.0016113473,0.0001844382,0.00042273116],"category_scores_gemma":[0.00074603275,0.0003780936,0.00060405576,0.0011564093,0.00014714223,0.00033960099,0.00030600087,0.00056965015,0.00012230812],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.04003455e-7,0.00003125456,1.9497856e-8,0.06964339,0.000037410962,0.000035170975,0.000005874699,1.3323512e-7,0.0000011298781,0.000094042094,0.011918828,0.9182326],"study_design_scores_gemma":[0.00004611229,0.000023994255,2.1040412e-7,0.17429446,0.00011669866,0.00019491895,2.7989296e-7,0.00028203253,0.0000048762868,0.000017176093,0.82472575,0.0002935129],"about_ca_topic_score_codex":0.000009214617,"about_ca_topic_score_gemma":7.523291e-8,"teacher_disagreement_score":0.9179391,"about_ca_system_score_codex":0.00008308889,"about_ca_system_score_gemma":0.00032319548,"threshold_uncertainty_score":0.9998671},"labels":[],"label_agreement":null},{"id":"W2949046915","doi":"","title":"Post-reconstruction MRI-guided Enhancement of PET Images using Parallel Level Set Method with Bregman Iteration","year":2019,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Algorithm; Broyden–Fletcher–Goldfarb–Shanno algorithm; Set (abstract data type); Iterative reconstruction; Iterative method; Data set; Computer vision; Mathematics","score_opus":0.041930049266570846,"score_gpt":0.3406989723853079,"score_spread":0.29876892311873704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949046915","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032462116,0.0000062129193,0.9657704,0.00028416168,0.000116052855,0.000380981,0.0000049312175,0.00009518734,0.0008799047],"genre_scores_gemma":[0.046011776,0.000005317068,0.9525922,0.0004352984,0.00001554603,0.000011817596,0.000013094864,0.0000069140265,0.0009080389],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987059,0.00012143277,0.00033209854,0.00032101208,0.00036793118,0.00015162275],"domain_scores_gemma":[0.9990394,0.000056846857,0.00019767918,0.00037122655,0.0002753056,0.000059503094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004286092,0.00012359886,0.00017498534,0.0001311607,0.00004152486,0.00008726274,0.00027576773,0.000028279832,0.0003346702],"category_scores_gemma":[0.000022796132,0.00009673899,0.00003525294,0.00018752691,0.000042110547,0.00075177697,0.00008877233,0.000067941764,0.000017550668],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000228579,0.000068410765,0.0009701468,0.000079982136,0.000047540634,0.000005135595,0.0006730662,0.00021698792,0.9117919,0.0027404418,0.0012154866,0.08216803],"study_design_scores_gemma":[0.0004776036,0.00023892414,0.00048569991,0.00006464718,0.000008980793,0.00016752211,0.00011471045,0.063156925,0.9347592,0.0003496901,0.000016633632,0.00015946724],"about_ca_topic_score_codex":0.00018242684,"about_ca_topic_score_gemma":0.000009606415,"teacher_disagreement_score":0.082008556,"about_ca_system_score_codex":0.000050159397,"about_ca_system_score_gemma":0.00009262909,"threshold_uncertainty_score":0.39449015},"labels":[],"label_agreement":null},{"id":"W2949644957","doi":"10.48550/arxiv.1905.00469","title":"Fully Automatic Brain Tumor Segmentation using a Normalized Gaussian Bayesian Classifier and 3D Fluid Vector Flow","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Segmentation; Pattern recognition (psychology); Computer science; Bayesian probability; Gaussian; Mixture model; Image segmentation; Physics","score_opus":0.04767297298342429,"score_gpt":0.22375514862922413,"score_spread":0.17608217564579984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949644957","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.108452916,0.00002183277,0.88916993,0.0002618093,0.00036430778,0.00078852853,0.000015904141,0.0004961934,0.00042855012],"genre_scores_gemma":[0.68803847,0.000028411365,0.31007847,0.00095400034,0.000059326896,0.000005270306,0.000037486658,0.000036716,0.0007618491],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974318,0.0004129422,0.00038297902,0.0011262507,0.00023594119,0.00041010117],"domain_scores_gemma":[0.99797267,0.0002080509,0.00041692107,0.0009825741,0.00012448082,0.0002953329],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048083207,0.0004028789,0.00045638566,0.000435816,0.00016527412,0.00031860248,0.0010137246,0.00024058753,0.0003062647],"category_scores_gemma":[0.00010664481,0.0004489703,0.00013663422,0.00052115176,0.00015757796,0.0008564985,0.0012431964,0.0004703738,0.000054399687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00093951763,0.0032000286,0.028914202,0.015043678,0.0039995066,0.013058502,0.025805969,0.16658367,0.33385184,0.16323921,0.040328275,0.2050356],"study_design_scores_gemma":[0.0006696104,0.000074685435,0.00048785814,0.00028005659,0.00006336805,0.00001924628,0.00011729889,0.99198455,0.0034633426,0.0023347805,0.000024135226,0.00048109307],"about_ca_topic_score_codex":0.00012567104,"about_ca_topic_score_gemma":0.00001440299,"teacher_disagreement_score":0.8254008,"about_ca_system_score_codex":0.00039047014,"about_ca_system_score_gemma":0.00036222927,"threshold_uncertainty_score":0.9997962},"labels":[],"label_agreement":null},{"id":"W2951503827","doi":"10.1007/s11760-019-01513-5","title":"An adaptive active contour model driven by weighted local and global image fitting constraints for image segmentation","year":2019,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Constraint (computer-aided design); Image (mathematics); Active contour model; Energy (signal processing); Function (biology); Image segmentation; Mathematics; Energy functional; Artificial intelligence; Range (aeronautics); Segmentation; Monotone polygon; Computer science; Computer vision; Mathematical optimization; Algorithm; Geometry; Statistics","score_opus":0.012377353223315972,"score_gpt":0.29929107326472615,"score_spread":0.2869137200414102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951503827","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017031163,0.000118663855,0.98119205,0.00018533114,0.000025297193,0.00071343547,0.00008480889,0.00020794409,0.00044131782],"genre_scores_gemma":[0.5002166,0.000007974019,0.499161,0.0004834163,0.000021016334,0.000042240503,0.000030531068,0.000012470826,0.00002476474],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981284,0.00010806315,0.0003534034,0.0007088656,0.00032115832,0.00038009582],"domain_scores_gemma":[0.9988729,0.00013556468,0.00025633644,0.00016625157,0.00033022035,0.00023873069],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040490576,0.00025892293,0.00028826273,0.00006696418,0.0002501972,0.00067803403,0.00029064377,0.00009789879,0.000028292607],"category_scores_gemma":[0.000041774467,0.0002460009,0.00004242439,0.00017330683,0.00047990825,0.004437965,0.00012643631,0.0001676646,0.000004559443],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006857525,0.00005701192,0.00005243181,0.00010008596,0.000017857166,0.000008826903,0.00096564094,0.00000457617,0.52123475,0.0001506112,0.0003500113,0.4769896],"study_design_scores_gemma":[0.0010872909,0.00027177326,0.000046724017,0.00011919856,0.000022858661,0.000021530683,0.0012177659,0.6720198,0.32147524,0.0034575535,0.0000025629001,0.0002577561],"about_ca_topic_score_codex":0.000022821112,"about_ca_topic_score_gemma":0.0000022247134,"teacher_disagreement_score":0.6720152,"about_ca_system_score_codex":0.000096718664,"about_ca_system_score_gemma":0.00015449473,"threshold_uncertainty_score":0.9999992},"labels":[],"label_agreement":null},{"id":"W2952181926","doi":"","title":"Towards a Robust Automated Technique to Construct Aortic Finite Element Meshes Directly from Medical Images","year":2013,"lang":"en","type":"article","venue":"CMBES Proceedings","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Construct (python library); Finite element method; Polygon mesh; Computer vision; Computer science; Artificial intelligence; Computer graphics (images); Engineering; Structural engineering","score_opus":0.013030473565795059,"score_gpt":0.27385639230604625,"score_spread":0.2608259187402512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952181926","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022909341,0.00005561027,0.9622371,0.0046717324,0.00019545853,0.00140618,0.000009979766,0.0050303754,0.003484229],"genre_scores_gemma":[0.2584107,0.000035405952,0.7370424,0.0030813501,0.000102683225,0.0011724777,0.000009201845,0.000029148197,0.000116618816],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99683696,0.00003331689,0.00065038615,0.0007517184,0.0011719448,0.0005556928],"domain_scores_gemma":[0.99820286,0.00014790268,0.00017721832,0.00031858915,0.00049167086,0.0006617796],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00057709956,0.00031708909,0.0003800278,0.00025175058,0.00013686098,0.00058629364,0.0015768994,0.00018717011,0.0011972486],"category_scores_gemma":[0.0013825916,0.0002723479,0.00008185344,0.0007408602,0.0002209953,0.0013805457,0.000763598,0.0003061234,0.00030478704],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000117582995,0.00026284592,0.0017235654,0.00016408136,0.0001191361,0.00004981018,0.002187464,0.0000029291227,0.36785185,0.0028646404,0.2398582,0.3849037],"study_design_scores_gemma":[0.00037790378,0.00021302768,0.0031727117,0.00036220538,0.000018820938,0.00003195921,0.00018099564,0.049728505,0.9405015,0.004077281,0.00078991434,0.0005451928],"about_ca_topic_score_codex":0.00047877355,"about_ca_topic_score_gemma":0.0000028124825,"teacher_disagreement_score":0.5726496,"about_ca_system_score_codex":0.00012471688,"about_ca_system_score_gemma":0.00018882564,"threshold_uncertainty_score":0.9999729},"labels":[],"label_agreement":null},{"id":"W2952430792","doi":"10.48550/arxiv.1607.05194","title":"HeMIS: Hetero-Modal Image Segmentation","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Modalities; Segmentation; Artificial intelligence; Inference; Embedding; Computer science; Modality (human–computer interaction); Pattern recognition (psychology); Imputation (statistics); Missing data; Image (mathematics); Modal; Machine learning","score_opus":0.052062198809625815,"score_gpt":0.21535971308574875,"score_spread":0.16329751427612293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952430792","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013961157,0.00002164849,0.9804557,0.00025658458,0.00046501026,0.00038220335,0.000020552377,0.0008006199,0.0036365283],"genre_scores_gemma":[0.89991605,0.00018997594,0.096082985,0.0005493994,0.00015595146,0.0000072656744,0.00004165336,0.00003189592,0.0030248277],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99790084,0.00017444797,0.00026642735,0.0011172349,0.00018885524,0.0003521721],"domain_scores_gemma":[0.99797726,0.000095035306,0.00031113543,0.0011898351,0.00018364156,0.00024306675],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002651407,0.00031290622,0.0002772536,0.00024121325,0.00011036712,0.00018633487,0.0018629197,0.0002489622,0.00017974664],"category_scores_gemma":[0.00004937063,0.00031221862,0.00018341532,0.00029065224,0.0001771768,0.0009207493,0.0018912656,0.00038996496,0.00026628602],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035126187,0.002040246,0.0072712526,0.0022627823,0.0015338961,0.0074221217,0.00448587,0.004518476,0.34025496,0.26919898,0.09592988,0.26473027],"study_design_scores_gemma":[0.003228479,0.00028831404,0.00087546126,0.000846467,0.00018490925,0.000036069297,0.0002124978,0.15813224,0.5213724,0.31146282,0.00068882713,0.0026715803],"about_ca_topic_score_codex":0.00003356125,"about_ca_topic_score_gemma":0.0000031447742,"teacher_disagreement_score":0.8859549,"about_ca_system_score_codex":0.0003384843,"about_ca_system_score_gemma":0.00016195695,"threshold_uncertainty_score":0.999933},"labels":[],"label_agreement":null},{"id":"W2952735543","doi":"10.1109/tmi.2019.2905770","title":"Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":301,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; University of British Columbia; Montreal Neurological Institute and Hospital; Toronto Metropolitan University; University of Calgary; McGill University","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Allergy and Infectious Diseases; National Institutes of Health; Hotchkiss Brain Institute, University of Calgary; Universitair Medisch Centrum Utrecht; Institute for Basic Science; Daegu Gyeongbuk Institute of Science and Technology; University College London Hospitals NHS Foundation Trust; Télécom Paris; Multiple Sclerosis Society; Ministry of Advanced Education; Schweizerische Multiple Sklerose Gesellschaft; Ministerio de Ciencia y Tecnología; University of British Columbia; Huazhong University of Science and Technology; Natural Sciences and Engineering Research Council of Canada; Ministerio de Economía y Competitividad; Leids Universitair Medisch Centrum; National Natural Science Foundation of China; National Research Foundation of Korea; Ministerio de Educación, Cultura y Deporte; Sun Yat-sen University; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; ZonMw; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; European Regional Development Fund; King's College London; National Research Foundation; Alzheimer Society; National Institute for Health and Care Research; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Technische Universität München; Canadian Institutes of Health Research; Alzheimer's Society; Inselspital, Universitätsspital Bern; Hotchkiss Brain Institute; Skolkovo Institute of Science and Technology; Ministry of Advanced Education and Skills Development; Brigham and Women's Hospital; National University Health System; Nvidia; Ministry of Education; University of Bern; Université Paris-Saclay; Universiteit Utrecht; Universität Basel; University of Dundee; McGill University; Universitat Politècnica de Catalunya; Sungkyunkwan University; Universitat de Girona; University College London; Vrije Universiteit Amsterdam; National Science Foundation","keywords":"Segmentation; Hyperintensity; Artificial intelligence; Robustness (evolution); Scanner; Computer science; Fluid-attenuated inversion recovery; Percentile; Hausdorff distance; Pattern recognition (psychology); Image segmentation; Sørensen–Dice coefficient; Computer vision; Mathematics; Magnetic resonance imaging; Statistics; Medicine; Radiology","score_opus":0.010848220368478224,"score_gpt":0.29273774722350154,"score_spread":0.28188952685502333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952735543","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07959679,0.000019086054,0.9171916,0.0020406174,0.0003174359,0.00037731882,0.000020802594,0.000048381207,0.0003879198],"genre_scores_gemma":[0.9149702,0.00007038218,0.08450593,0.000346263,0.0000059839426,0.000021672902,0.000001897484,0.000008632234,0.000068996545],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976552,0.0002064085,0.0007215768,0.00023560181,0.0010487282,0.00013250126],"domain_scores_gemma":[0.9987863,0.00023992316,0.000355211,0.00037451222,0.00016898228,0.00007512353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007904036,0.00011971679,0.00027173499,0.00015485263,0.00006606469,0.000017723782,0.00030348546,0.000045254485,0.00023656142],"category_scores_gemma":[0.00002682972,0.00008982647,0.00008129203,0.00022706627,0.0002486816,0.00034698143,0.000016507285,0.00019272543,0.0000021048597],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015102218,0.0010215738,0.0075393463,0.0020687832,0.0002856816,0.000010351171,0.019074606,0.0008562421,0.25689223,0.0003649152,0.0008614198,0.71087384],"study_design_scores_gemma":[0.0035314797,0.00024086909,0.010032949,0.0010950816,0.000069080066,0.000026566135,0.0022375158,0.2053256,0.7769106,0.00030136842,0.0000111211275,0.0002177445],"about_ca_topic_score_codex":0.00004122214,"about_ca_topic_score_gemma":0.000004156789,"teacher_disagreement_score":0.83537346,"about_ca_system_score_codex":0.00005443872,"about_ca_system_score_gemma":0.00011603231,"threshold_uncertainty_score":0.36630172},"labels":[],"label_agreement":null},{"id":"W2952786159","doi":"10.48550/arxiv.1809.10245","title":"Cylindrical Transform: 3D Semantic Segmentation of Kidneys With Limited Annotated Images","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Segmentation; Convolutional neural network; Computer science; Pattern recognition (psychology); Computer vision; Image segmentation; Dependency (UML)","score_opus":0.042914358840044416,"score_gpt":0.21307338563428324,"score_spread":0.17015902679423883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952786159","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05233518,0.000014542081,0.9450338,0.0001504201,0.00015085281,0.00058927236,0.000025912956,0.0004297788,0.0012702587],"genre_scores_gemma":[0.8780692,0.00016739564,0.12101538,0.00018897289,0.000036707013,0.0000035038177,0.00008797766,0.00002364191,0.00040717592],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997944,0.00020652526,0.00036321988,0.0009153974,0.00025677122,0.00031407614],"domain_scores_gemma":[0.9979471,0.00009901507,0.00040283962,0.00086576625,0.0004502387,0.00023505538],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028068703,0.00032507168,0.00040896714,0.00041379055,0.00008969402,0.00008234708,0.0012875823,0.00024522736,0.00009072521],"category_scores_gemma":[0.000046255856,0.0003154211,0.00012589453,0.0009389991,0.0004000755,0.0005911769,0.0005057534,0.00042835754,0.000031889915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005766503,0.015894799,0.06945696,0.017029302,0.010537708,0.015499981,0.033526804,0.12499444,0.16508058,0.066907294,0.07923019,0.39607546],"study_design_scores_gemma":[0.0034949696,0.0013716595,0.0037273357,0.0010002638,0.0004981812,0.000035718855,0.00030234558,0.59806323,0.3786761,0.011167373,0.00009029017,0.0015725646],"about_ca_topic_score_codex":0.00011995527,"about_ca_topic_score_gemma":0.000009143134,"teacher_disagreement_score":0.8257341,"about_ca_system_score_codex":0.0001386826,"about_ca_system_score_gemma":0.00026656737,"threshold_uncertainty_score":0.9999298},"labels":[],"label_agreement":null},{"id":"W2953239640","doi":"10.1002/sam.11429","title":"Interactive volumetric segmentation for textile micro‐tomography data using wavelets and nonlocal means","year":2019,"lang":"en","type":"article","venue":"Statistical Analysis and Data Mining The ASA Data Science Journal","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Light Source (Canada)","funders":"Office of Science; National Aeronautics and Space Administration; U.S. Department of Energy","keywords":"Segmentation; Artificial intelligence; Voxel; Pattern recognition (psychology); Computer science; Wavelet; Discriminative model","score_opus":0.06895868629883593,"score_gpt":0.3872516588172325,"score_spread":0.31829297251839656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953239640","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009753337,0.00009878901,0.9861996,0.00028149103,0.00014410896,0.00020626141,0.0032836027,0.000019400775,0.000013416119],"genre_scores_gemma":[0.05466449,0.00011799677,0.94325125,0.00034457247,0.000059386715,0.0000017586926,0.0015468514,0.0000061620885,0.000007538991],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99690324,0.0001526823,0.00051082775,0.0011969921,0.0008503985,0.00038587378],"domain_scores_gemma":[0.9951995,0.0011603974,0.00035968778,0.0027911114,0.00020585315,0.00028345216],"candidate_categories":["scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.005901194,0.00016213735,0.00030644052,0.00068941724,0.0006931135,0.0019727114,0.006486909,0.000032153166,0.00006467013],"category_scores_gemma":[0.0017738638,0.00010903647,0.00002528695,0.0025498709,0.00071226666,0.0070594447,0.0063105896,0.00024917145,0.0000029304713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003690007,0.000116427495,0.010228169,0.000031577423,0.00049686787,0.00001733552,0.0006776615,0.000013800947,0.009388524,0.0004773606,0.009146909,0.96936846],"study_design_scores_gemma":[0.00026551314,0.000085231644,0.004993571,0.000026458369,0.00042037666,0.000092492715,0.0008235009,0.9923142,0.0003188684,0.00026105478,0.0002400181,0.0001586704],"about_ca_topic_score_codex":0.00011310022,"about_ca_topic_score_gemma":0.00003334611,"teacher_disagreement_score":0.99230045,"about_ca_system_score_codex":0.00003654813,"about_ca_system_score_gemma":0.00021537552,"threshold_uncertainty_score":0.9990633},"labels":[],"label_agreement":null},{"id":"W2953949094","doi":"10.3389/fnagi.2019.00150","title":"Dilated Saliency U-Net for White Matter Hyperintensities Segmentation Using Irregularity Age Map","year":2019,"lang":"en","type":"article","venue":"Frontiers in Aging Neuroscience","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Janssen Alzheimer Immunotherapy Research And Development; National Institute on Aging; University of Southern California; Biotechnology and Biological Sciences Research Council; Canadian Institutes of Health Research; Directorate for Biological Sciences; National Institutes of Health; Genentech; IXICO; Lembaga Pengelola Dana Pendidikan; H. Lundbeck A/S; Servier; Eisai; Johnson and Johnson Pharmaceutical Research and Development; Mrs Gladys Row Fogo Charitable Trust; Northern California Institute for Research and Education; BioClinica; Biogen; Pfizer; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Hyperintensity; Segmentation; White matter; Artificial intelligence; Pattern recognition (psychology); Psychology; Computer science; Medicine; Magnetic resonance imaging; Radiology","score_opus":0.02006016651693,"score_gpt":0.27491196393595796,"score_spread":0.25485179741902797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953949094","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06760941,0.000024854147,0.9280546,0.0005129992,0.0029875212,0.0005455125,0.000004844458,0.00016312161,0.00009716794],"genre_scores_gemma":[0.2295469,0.0000062836484,0.76487166,0.00440509,0.000026379324,0.000035286783,0.000007442201,0.000017707756,0.0010832243],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981204,0.00009444941,0.00031423633,0.00064952153,0.0004019603,0.00041943332],"domain_scores_gemma":[0.9992412,0.000034332297,0.00013167539,0.00044428548,0.00006182622,0.00008668875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045183452,0.00016291912,0.00020734358,0.00029639353,0.00011662406,0.00029984466,0.0008352798,0.000048068698,0.000016543132],"category_scores_gemma":[0.000054647662,0.00016607562,0.000050300692,0.0005340334,0.00018410623,0.001224672,0.0002249244,0.00015314684,0.000010053541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036886035,0.00017564828,0.49283633,0.00031522205,0.000006746783,0.00009780412,0.0044377283,0.0019023049,0.45903552,0.00047405274,0.027254576,0.013427188],"study_design_scores_gemma":[0.0011686521,0.00018863908,0.12388849,0.00023918448,0.000012525178,0.00004917852,0.00059859647,0.77865857,0.089408495,0.003582718,0.0013503074,0.0008546109],"about_ca_topic_score_codex":0.00002872947,"about_ca_topic_score_gemma":0.000001258071,"teacher_disagreement_score":0.7767563,"about_ca_system_score_codex":0.000120844714,"about_ca_system_score_gemma":0.000061126004,"threshold_uncertainty_score":0.67723674},"labels":[],"label_agreement":null},{"id":"W2959974913","doi":"10.1145/3340074.3340094","title":"Sketch-based Registration of 3D Cine MRI to 4D flow MRI","year":2019,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Image registration; Sketch; Computer vision; Real-time MRI; Artificial intelligence; Magnetic resonance imaging; Context (archaeology); Steady-state free precession imaging; Blood flow; Ventricle; Cardiac cycle; Medical imaging; Visualization; Medicine; Radiology; Image (mathematics); Algorithm; Geology","score_opus":0.011362591180823564,"score_gpt":0.27610426529355064,"score_spread":0.2647416741127271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2959974913","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018047125,0.000004490729,0.9862811,0.0039253286,0.00012655415,0.00030827135,0.0000010165375,0.00022489131,0.007323655],"genre_scores_gemma":[0.05954485,0.0000017529493,0.93560326,0.002872128,0.000020949046,0.000012301728,0.000005661927,0.000004509015,0.0019345735],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989522,0.000038101043,0.00025690778,0.00024432968,0.0003859176,0.00012251743],"domain_scores_gemma":[0.9991093,0.00006232272,0.00007960879,0.000544784,0.000112354246,0.00009159571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003327155,0.00007425295,0.00011827286,0.00009509738,0.000019193478,0.000051931125,0.00044095848,0.000037434656,0.00058608205],"category_scores_gemma":[0.000057648405,0.00006292266,0.000027948534,0.00029810463,0.00003213319,0.0002563172,0.00006537916,0.000052997133,0.00013473936],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005474791,0.0005017773,0.0019257284,0.00028169397,0.00003398129,0.000019869722,0.0011096379,0.003240427,0.25275254,0.04699282,0.23792195,0.45516482],"study_design_scores_gemma":[0.0004085138,0.0003413683,0.00064257317,0.00004577364,0.000002818974,0.0000014807815,0.000015009696,0.29626045,0.6999036,0.0005813052,0.0016532704,0.00014382308],"about_ca_topic_score_codex":0.000057670233,"about_ca_topic_score_gemma":0.0000134804,"teacher_disagreement_score":0.455021,"about_ca_system_score_codex":0.000024559153,"about_ca_system_score_gemma":0.00008497283,"threshold_uncertainty_score":0.64171875},"labels":[],"label_agreement":null},{"id":"W2962754235","doi":"10.1038/s41597-019-0217-0","title":"An accurate registration of the BigBrain dataset with the MNI PD25 and ICBM152 atlases","year":2019,"lang":"en","type":"article","venue":"Scientific Data","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital; Western University","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Segmentation; Brain atlas; Atlas (anatomy); Image registration; Magnetic resonance imaging; Artificial intelligence; Brain mapping; Pattern recognition (psychology); Neuroscience; Medicine; Biology; Anatomy; Radiology; Image (mathematics)","score_opus":0.037542854995450604,"score_gpt":0.3138715182451634,"score_spread":0.2763286632497128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962754235","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07382153,0.000105369145,0.89685076,0.012455628,0.0012045024,0.0015638696,0.013087067,0.00020482074,0.00070645986],"genre_scores_gemma":[0.89339906,0.000016856418,0.086347416,0.002268535,0.000071417395,0.000024465538,0.01469193,0.000017670065,0.0031626746],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99853754,0.0001449842,0.0001583458,0.0005235331,0.00051186926,0.00012372503],"domain_scores_gemma":[0.99555665,0.00009059513,0.00016941999,0.0040797247,0.000053561704,0.000050057417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001782886,0.000070996124,0.0000710275,0.00003947717,0.00017994136,0.0006238033,0.003547199,0.000018178976,0.000033259148],"category_scores_gemma":[0.00012670452,0.000034397854,0.0000073998,0.00047835233,0.0004596743,0.0017339688,0.00093976076,0.00007960734,0.000017426135],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000073805722,0.00007026123,0.0003187941,0.000032716394,0.000010426809,0.0000020475343,0.0004408503,0.000005993551,0.044903427,0.0025474823,0.906198,0.0454626],"study_design_scores_gemma":[0.0017137737,0.0006572783,0.024563378,0.0003814541,0.00009130696,0.00009864792,0.0020621563,0.16350037,0.44190848,0.0035847246,0.36045286,0.0009855803],"about_ca_topic_score_codex":0.000058230682,"about_ca_topic_score_gemma":0.00011857165,"teacher_disagreement_score":0.8195775,"about_ca_system_score_codex":0.0000058733913,"about_ca_system_score_gemma":0.00011059917,"threshold_uncertainty_score":0.6591637},"labels":[],"label_agreement":null},{"id":"W2963102096","doi":"10.1007/978-3-030-00934-2_83","title":"","year":2018,"lang":"en","type":"book-chapter","venue":"EUR Research Repository (Erasmus University Rotterdam)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Computer science; Artificial intelligence; Smoothing; Pattern recognition (psychology); Voxel; Bayesian network; Inference; Classifier (UML); Algorithm; Computer vision","score_opus":0.057751172919968084,"score_gpt":0.30429430448242634,"score_spread":0.24654313156245825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963102096","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012287586,0.00023451321,0.40591156,0.002371989,0.0012611275,0.0031001049,0.000037809972,0.0026128583,0.5832413],"genre_scores_gemma":[0.003021975,0.00036606356,0.04926399,0.00068806886,0.0012711867,0.000013752454,0.000051552575,0.00015789694,0.9451655],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98586017,0.0017666158,0.0010756972,0.0034060583,0.0059105656,0.0019809154],"domain_scores_gemma":[0.9884201,0.0011127788,0.0007644694,0.004798683,0.0031484526,0.0017555017],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","research_integrity"],"consensus_categories":["sts"],"category_scores_codex":[0.0036649993,0.0011122819,0.0011286255,0.0031507665,0.003071422,0.0014059271,0.0106043825,0.0011636692,0.00032668785],"category_scores_gemma":[0.00024058556,0.0012855536,0.00064036675,0.0007056219,0.0031045894,0.00305947,0.007174598,0.0042836103,0.0005686344],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013335153,0.0008560917,0.00028252575,0.0010088469,0.0015872658,0.07657519,0.011918832,0.0000064162746,0.049264606,0.19822378,0.6138152,0.045127705],"study_design_scores_gemma":[0.007104867,0.005308331,0.00042935388,0.0030675256,0.00018381557,0.0015204997,0.0007088635,0.0014651642,0.13160227,0.03092,0.81270576,0.0049835574],"about_ca_topic_score_codex":0.00038880008,"about_ca_topic_score_gemma":0.000058516034,"teacher_disagreement_score":0.36192426,"about_ca_system_score_codex":0.002368946,"about_ca_system_score_gemma":0.0015826836,"threshold_uncertainty_score":0.9996307},"labels":[],"label_agreement":null},{"id":"W2967750615","doi":"10.3389/fnins.2019.00909","title":"Linear Registration of Brain MRI Using Knowledge-Based Multiple Intermediator Libraries","year":2019,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Pfizer; Novartis Pharmaceuticals Corporation; Northern California Institute for Research and Education; F. Hoffmann-La Roche; Bristol-Myers Squibb; Eli Lilly and Company; Biogen; BioClinica; Eisai; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Image registration; Computer science; Similarity (geometry); Artificial intelligence; Transformation (genetics); Mutual information; Cluster analysis; Image (mathematics); Pattern recognition (psychology); Computation; Computer vision; Algorithm","score_opus":0.020625397623983938,"score_gpt":0.28176614552359963,"score_spread":0.2611407478996157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967750615","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030755375,0.000044390195,0.9665506,0.00039878898,0.0017020428,0.0002644583,0.0000022214813,0.00010149861,0.00018062655],"genre_scores_gemma":[0.3594431,0.000005111239,0.6393162,0.0010221595,0.000018412104,0.000007758002,0.0000013880594,0.0000074509285,0.00017842095],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855113,0.00012592052,0.00033983018,0.0004262791,0.00034154096,0.00021531378],"domain_scores_gemma":[0.9990986,0.00013513466,0.00018518457,0.0004516171,0.000049915416,0.00007957429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043491565,0.000104629544,0.00017065869,0.00028331098,0.00003996191,0.000079884754,0.0010288153,0.00004999788,0.0000050394165],"category_scores_gemma":[0.0007213644,0.000102963524,0.000038223017,0.00084749906,0.0003179233,0.00110348,0.00016655115,0.00013669772,0.0000031138648],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000677695,0.00060240173,0.20176497,0.0003140733,0.000003738266,0.000038796956,0.0027184759,0.0034089128,0.70678204,0.0028510634,0.040493783,0.04095395],"study_design_scores_gemma":[0.00030335374,0.000096944204,0.0020809954,0.00005710576,7.687686e-7,0.0000013788632,0.000025864152,0.81382436,0.18243411,0.00030850235,0.00076273136,0.00010390012],"about_ca_topic_score_codex":0.0000108413915,"about_ca_topic_score_gemma":0.000001794676,"teacher_disagreement_score":0.81041545,"about_ca_system_score_codex":0.00004555494,"about_ca_system_score_gemma":0.00026708716,"threshold_uncertainty_score":0.41987306},"labels":[],"label_agreement":null},{"id":"W2970459281","doi":"10.1016/j.mri.2019.08.022","title":"Whole volume brain extraction for multi-centre, multi-disease FLAIR MRI datasets","year":2019,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Guelph; Toronto Metropolitan University","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; U.S. Department of Defense","keywords":"Fluid-attenuated inversion recovery; Computer science; Artificial intelligence; Segmentation; Neuroimaging; Magnetic resonance imaging; Pattern recognition (psychology); Random forest; Scanner; Feature extraction; Real-time MRI; Medicine; Radiology","score_opus":0.017091261722296584,"score_gpt":0.3081190410271341,"score_spread":0.29102777930483753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970459281","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006716695,0.002859012,0.9891759,0.005000221,0.00051061803,0.001136873,0.00014775155,0.00043427048,0.00006368695],"genre_scores_gemma":[0.0052065533,0.000049847025,0.9781665,0.0038529697,0.000101245605,0.00016919414,0.00023261482,0.000040219904,0.012180822],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977763,0.000105923325,0.00038858247,0.0007977272,0.00041772018,0.0005137015],"domain_scores_gemma":[0.9983785,0.00014900388,0.0001463331,0.0009446545,0.0001161243,0.0002653934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046787653,0.00022716325,0.0002000313,0.00013356387,0.00012639283,0.00029597257,0.0008686267,0.000039679737,0.00022009302],"category_scores_gemma":[0.00033958125,0.00023637898,0.00008782999,0.00025827315,0.000089582616,0.001219056,0.00025125992,0.00017467001,0.00045364985],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026836467,0.00029690942,0.00893386,0.00010590417,0.000002962292,0.000057052006,0.0002401703,0.000017021635,0.018605461,0.00021184777,0.13908836,0.8324136],"study_design_scores_gemma":[0.001874422,0.000041367653,0.031299226,0.00011164578,0.000008342814,0.000008239108,0.000051625735,0.7188553,0.0039865496,0.0001154248,0.24331185,0.00033596237],"about_ca_topic_score_codex":0.00005060281,"about_ca_topic_score_gemma":0.000006773846,"teacher_disagreement_score":0.8320776,"about_ca_system_score_codex":0.00008213361,"about_ca_system_score_gemma":0.00009383584,"threshold_uncertainty_score":0.9639255},"labels":[],"label_agreement":null},{"id":"W2971032076","doi":"10.1101/747998","title":"BISON: Brain tISue segmentatiON pipeline using T1-weighted magnetic resonance images and a random forests classifier","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Government of Canada; Pfizer","keywords":"Random forest; Segmentation; Artificial intelligence; Generalizability theory; Computer science; Pattern recognition (psychology); Magnetic resonance imaging; Kappa; Image segmentation; Scanner; Medicine; Mathematics; Radiology; Statistics","score_opus":0.016137510303012123,"score_gpt":0.2563632265926506,"score_spread":0.24022571628963846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971032076","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.088376336,0.004830978,0.9028373,0.00064207567,0.0008383248,0.0016316508,0.00007553861,0.0007585382,0.000009278492],"genre_scores_gemma":[0.47869235,0.0007063452,0.51842815,0.0013217537,0.00035972998,0.00028829766,0.0000013131408,0.0001328766,0.00006916784],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9959096,0.00042502975,0.0008104967,0.0014896605,0.00076296105,0.00060227525],"domain_scores_gemma":[0.9968677,0.00027623618,0.0005602291,0.0014921759,0.0004791058,0.00032456464],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010858906,0.0006193872,0.0006892001,0.00045664763,0.00017616316,0.0007296356,0.0010383504,0.00047013292,0.000054754455],"category_scores_gemma":[0.00033869772,0.00063540164,0.0001169833,0.00071266893,0.00022769839,0.0007069709,0.0010879169,0.0007257984,0.000036888407],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000084412204,0.00018990485,0.008082345,0.0007647146,0.000057079364,0.00017290586,0.00007122466,0.000038640897,0.9820151,0.00030482918,0.0066665844,0.0015523022],"study_design_scores_gemma":[0.0035221507,0.00017295973,0.046323292,0.0011731128,0.000102260696,3.5255368e-7,0.0000041338385,0.112013765,0.8335692,0.000045256253,0.001633925,0.0014395707],"about_ca_topic_score_codex":0.00006615896,"about_ca_topic_score_gemma":0.0000034593181,"teacher_disagreement_score":0.390316,"about_ca_system_score_codex":0.00027010645,"about_ca_system_score_gemma":0.00051046646,"threshold_uncertainty_score":0.9996097},"labels":[],"label_agreement":null},{"id":"W2972409747","doi":"","title":"Concurrent Visualization of and Mapping between 2D and 3D Medical Images for Disease Pattern Analysis","year":2010,"lang":"en","type":"article","venue":"Pattern Recognition in Bioinformatics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Visualization; Computer science; Data visualization; Artificial intelligence","score_opus":0.03113938770387932,"score_gpt":0.325401527842826,"score_spread":0.2942621401389467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972409747","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06565734,0.000013869015,0.93360335,0.00020826406,0.00006713226,0.00028304808,0.00009844149,0.00005007479,0.000018471328],"genre_scores_gemma":[0.93140966,0.00011134758,0.06754593,0.00049101125,0.000039380757,0.0000610004,0.00033373092,0.000006147618,0.0000018073306],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998719,0.000040164494,0.00061557064,0.00015232488,0.00033340094,0.00013958833],"domain_scores_gemma":[0.9990207,0.0002703729,0.00025971144,0.00014350565,0.00011778452,0.00018793381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059046847,0.000104815656,0.00021901062,0.00039585034,0.00004046466,0.00008567653,0.00017507051,0.00007321193,0.000051453084],"category_scores_gemma":[0.00025129042,0.00009795823,0.000040580224,0.0002872385,0.00010985017,0.00048950646,0.00012362289,0.00011356501,0.0000023863865],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.287297e-7,0.000021743948,0.09437009,0.0002812291,0.000025414476,6.8759414e-7,0.00055768085,1.7909306e-7,0.000028377057,0.0000052081564,0.000048747876,0.9046601],"study_design_scores_gemma":[0.0010251673,0.0000693128,0.1279834,0.0002747454,0.00013662795,0.0000035702897,0.0001559254,0.8634416,0.005933086,0.00063821935,0.00004842183,0.00028993294],"about_ca_topic_score_codex":0.000019240513,"about_ca_topic_score_gemma":0.000013455993,"teacher_disagreement_score":0.9043702,"about_ca_system_score_codex":0.000009031917,"about_ca_system_score_gemma":0.000028240065,"threshold_uncertainty_score":0.39946207},"labels":[],"label_agreement":null},{"id":"W2975965984","doi":"10.4103/digm.digm_7_19","title":"Local Gauss multiplicative components method for brain magnetic resonance image segmentation","year":2019,"lang":"en","type":"article","venue":"Digital Medicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Segmentation; Artificial intelligence; Scale-space segmentation; Computer science; Image segmentation; Pattern recognition (psychology); Contrast (vision); Multiplicative function; Image (mathematics); Segmentation-based object categorization; Gaussian; Computer vision; Mathematics; Physics","score_opus":0.017570214429172188,"score_gpt":0.32719827294939785,"score_spread":0.3096280585202257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2975965984","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008043822,0.00020752804,0.9911304,0.0039394624,0.00020937702,0.0011387812,0.00002165035,0.00024536852,0.002303047],"genre_scores_gemma":[0.08441194,0.000013603973,0.90734,0.0052464963,0.0001031435,0.00027009254,0.00018303438,0.00002978597,0.0024018886],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983015,0.00005552503,0.00038144624,0.00050520233,0.0004806996,0.0002756409],"domain_scores_gemma":[0.99836355,0.00071592396,0.00013513597,0.00045537573,0.00016623276,0.00016379208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041832673,0.00017659918,0.00027956738,0.000113985334,0.000045419776,0.00008293693,0.0005809536,0.00005212159,0.0000823401],"category_scores_gemma":[0.0003226245,0.00014419644,0.00004900187,0.00029668954,0.00018612007,0.00087495375,0.00014391539,0.00010986325,0.0001300739],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040682306,0.00007942482,0.00016871947,0.00005515109,0.00000798451,0.0000062143445,0.0007749309,0.0000013675891,0.07265625,0.0018680837,0.011858475,0.91248274],"study_design_scores_gemma":[0.015170836,0.005809473,0.0147856595,0.00096960634,0.00004268078,0.000109742665,0.0016965304,0.38150877,0.49841198,0.02289724,0.05716896,0.0014285133],"about_ca_topic_score_codex":0.00002025593,"about_ca_topic_score_gemma":5.3027264e-7,"teacher_disagreement_score":0.9110542,"about_ca_system_score_codex":0.000076876546,"about_ca_system_score_gemma":0.000025986643,"threshold_uncertainty_score":0.58801603},"labels":[],"label_agreement":null},{"id":"W2978560616","doi":"10.1002/hbm.24693","title":"A framework for evaluating correspondence between brain images using anatomical fiducials","year":2019,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Canadian Institutes of Health Research; Western University; Compute Canada; Royal College of Physicians and Surgeons of Canada; Fondation Brain Canada; Canada First Research Excellence Fund; Health Research","keywords":"Fiducial marker; Computer science; Artificial intelligence; Voxel; Spatial normalization; Protocol (science); Neuroimaging; Set (abstract data type); Template; Computer vision; Neuroanatomy; Pattern recognition (psychology); Neuroscience; Medicine; Psychology","score_opus":0.13458642540492088,"score_gpt":0.43452676409636615,"score_spread":0.29994033869144526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978560616","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11825211,0.000030572086,0.87850344,0.001691954,0.00019181469,0.0008422634,0.000005618753,0.00035854505,0.00012369703],"genre_scores_gemma":[0.13599734,4.367442e-7,0.8597257,0.0035069298,0.00026238445,0.00004913869,0.000010521706,0.000026301072,0.0004212053],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99726,0.00038458573,0.00056384347,0.0006954534,0.00059384404,0.00050228415],"domain_scores_gemma":[0.9953019,0.0034123263,0.00030981516,0.0006745242,0.00015406245,0.00014734702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033392664,0.00021255892,0.00036382157,0.0002725403,0.00035828337,0.00036904358,0.0011201503,0.0001538863,0.00021620994],"category_scores_gemma":[0.0032359052,0.0002270259,0.00012328528,0.000445946,0.000099328885,0.0006447043,0.00038990224,0.00031585462,0.00005581925],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007627965,0.00003330662,0.0029847536,0.00016965269,0.00003887248,0.0000073439055,0.0024417634,0.000021189151,0.91143614,0.037277333,0.007917923,0.037664123],"study_design_scores_gemma":[0.0027555134,0.00081887667,0.046581663,0.003010246,0.000049755512,0.000031617805,0.00084422925,0.21533117,0.07943088,0.6464944,0.002389232,0.0022624517],"about_ca_topic_score_codex":0.000015800108,"about_ca_topic_score_gemma":4.3967304e-7,"teacher_disagreement_score":0.83200526,"about_ca_system_score_codex":0.00013627218,"about_ca_system_score_gemma":0.00013132226,"threshold_uncertainty_score":0.92578477},"labels":[],"label_agreement":null},{"id":"W2978868208","doi":"10.1016/j.swevo.2019.100591","title":"Multilevel thresholding by fuzzy type II sets using evolutionary algorithms","year":2019,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Histogram; Thresholding; Evolutionary algorithm; Algorithm; Image segmentation; Fitness function; Fuzzy logic; Particle swarm optimization; Benchmark (surveying); Entropy (arrow of time); Artificial intelligence; Segmentation; Pattern recognition (psychology); Mathematical optimization; Image (mathematics); Mathematics; Machine learning; Genetic algorithm","score_opus":0.024728815565025476,"score_gpt":0.29719712747352517,"score_spread":0.2724683119084997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978868208","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12260685,0.0009779162,0.8740759,0.0003618267,0.0006884495,0.0003621034,0.000013809773,0.00036698327,0.0005461701],"genre_scores_gemma":[0.57128274,0.000051144238,0.4278026,0.00045062084,0.00006195427,0.00000800974,0.0001121792,0.00001408987,0.0002166787],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831533,0.000088396366,0.0003246485,0.0005094474,0.00047497326,0.0002872184],"domain_scores_gemma":[0.9991936,0.00010045615,0.00013738337,0.00021696798,0.00021254648,0.00013905896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023056549,0.00018541876,0.00017667815,0.00017566876,0.0003688991,0.000067955836,0.00027114723,0.0001061158,0.000047675145],"category_scores_gemma":[0.00003332764,0.00019332311,0.000042711003,0.00037954978,0.00008355223,0.0011099902,0.00032558004,0.00016577568,0.000058490543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013506725,0.001221766,0.012214979,0.00039817663,0.00023939875,0.000057272697,0.004434384,0.048258908,0.09144337,0.0133607285,0.23199163,0.59624434],"study_design_scores_gemma":[0.00053229474,0.00017895352,0.0062080473,0.000058121957,0.000009063409,0.00007035784,0.00005624926,0.9798998,0.0012191227,0.010916186,0.0005618937,0.00028990922],"about_ca_topic_score_codex":0.000054072712,"about_ca_topic_score_gemma":2.0947081e-7,"teacher_disagreement_score":0.93164086,"about_ca_system_score_codex":0.00017301712,"about_ca_system_score_gemma":0.00012188159,"threshold_uncertainty_score":0.78834873},"labels":[],"label_agreement":null},{"id":"W2979977962","doi":"10.1109/embc.2019.8857218","title":"Ultrasound segmentation using U-Net: learning from simulated data and testing on real data","year":2019,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Segmentation; Deep learning; Test data; Imaging phantom; Task (project management); Image segmentation; Ultrasound; Envelope (radar)","score_opus":0.12947299250812158,"score_gpt":0.35764543537850013,"score_spread":0.22817244287037855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979977962","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24987829,0.000010609013,0.74851125,0.00003581339,0.000076840115,0.00017961048,0.000015417178,0.00031834302,0.0009738158],"genre_scores_gemma":[0.41169858,0.000015052685,0.5872583,0.00032172073,0.00003708265,4.2690868e-7,0.00057446794,0.000009587805,0.00008479106],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984977,0.00011282536,0.0002230469,0.00068344065,0.0003263426,0.00015659761],"domain_scores_gemma":[0.99741256,0.0010078339,0.000118310396,0.0013437666,0.000041444622,0.00007606638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047975854,0.0001067729,0.00011806112,0.00005965887,0.00009247451,0.00028663882,0.0012123632,0.000044243363,0.000097127864],"category_scores_gemma":[0.00053068943,0.000096983844,0.0000049123887,0.00022179741,0.000029879253,0.0016192226,0.001095207,0.00016112279,0.000039946495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001234839,0.00007878342,0.06491516,0.00002776816,0.000049133097,0.000017024904,0.00076681975,0.0018786493,0.5947244,0.00014696571,0.0012934835,0.33608946],"study_design_scores_gemma":[0.00028204845,0.00005648916,0.004447744,0.00004389791,0.0000071263967,0.0000054923703,0.00012853737,0.9822192,0.012517247,0.00012739738,0.000028667127,0.00013615527],"about_ca_topic_score_codex":0.0010359903,"about_ca_topic_score_gemma":0.000010211002,"teacher_disagreement_score":0.98034054,"about_ca_system_score_codex":0.000028581338,"about_ca_system_score_gemma":0.000034570832,"threshold_uncertainty_score":0.39548862},"labels":[],"label_agreement":null},{"id":"W2981265883","doi":"10.1049/el.2019.2123","title":"RSF model with SCE‐based global constraint for image segmentation","year":2019,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; University of Alberta","keywords":"Constraint (computer-aided design); Image segmentation; Segmentation; Image (mathematics); Artificial intelligence; Computer vision; Computer science; Pattern recognition (psychology); Algorithm; Engineering; Mechanical engineering","score_opus":0.007744714785534058,"score_gpt":0.26322185469037196,"score_spread":0.2554771399048379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981265883","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010930054,0.00002023312,0.98156446,0.0061619245,0.000053273565,0.00073739403,0.0000087145545,0.00027258246,0.00025134438],"genre_scores_gemma":[0.07814389,0.0000026254968,0.9081242,0.013524495,0.000018073975,0.00010439806,0.000030608353,0.000012525734,0.00003915104],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866956,0.000033999586,0.00017866812,0.00037293605,0.00034034843,0.00040448236],"domain_scores_gemma":[0.9993541,0.00005323594,0.000102723076,0.00033824277,0.000071367824,0.00008032882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002268067,0.00014377377,0.00013160844,0.00004969169,0.000056221612,0.00013499836,0.00043397583,0.00003719015,0.000020748168],"category_scores_gemma":[0.0000131021125,0.00012958095,0.000052955864,0.00019548644,0.00007004633,0.00043498672,0.000032376283,0.00011652168,0.000023075601],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000081340186,0.00011841532,0.00022635689,0.00009258922,0.00006438856,0.000012159516,0.00018650512,0.0053187446,0.90766823,0.026395053,0.01617158,0.043664623],"study_design_scores_gemma":[0.0015741428,0.00035088445,0.000020304311,0.000023654424,0.000013514763,0.000011911389,0.000013805147,0.6344622,0.36146325,0.0016282757,0.00015917387,0.00027894616],"about_ca_topic_score_codex":0.0000041812514,"about_ca_topic_score_gemma":0.0000039948673,"teacher_disagreement_score":0.6291434,"about_ca_system_score_codex":0.0003166427,"about_ca_system_score_gemma":0.00026718978,"threshold_uncertainty_score":0.5284158},"labels":[],"label_agreement":null},{"id":"W2982224576","doi":"10.1007/978-3-030-33843-5_8","title":"Modeling and Analysis Brain Development via Discriminative Dictionary Learning","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Discriminative model; Computer science; Artificial intelligence; Representation (politics); Machine learning; Rank (graph theory); Pattern recognition (psychology); Class (philosophy); Mathematics","score_opus":0.017411754969218343,"score_gpt":0.2700221907409863,"score_spread":0.252610435771768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982224576","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008662055,0.00018732558,0.99785197,0.0004634106,0.000272322,0.0002726776,9.3894937e-7,0.00019815694,0.00066657027],"genre_scores_gemma":[0.07601693,0.0000315265,0.9219713,0.0012165788,0.00007172197,0.000009762852,0.000017898932,0.000019987296,0.00064428814],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99673766,0.00005855949,0.0004890639,0.0013136135,0.0010117043,0.00038940192],"domain_scores_gemma":[0.9984662,0.000430254,0.00019248542,0.0005541896,0.00019696928,0.00015992108],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010117787,0.0003689565,0.0004690626,0.0013781142,0.00027233758,0.00034290785,0.0012989059,0.00018691014,0.000026083562],"category_scores_gemma":[0.00011228542,0.00033589677,0.00008959281,0.0007852527,0.00036274627,0.0006799327,0.0013266387,0.00074795727,0.000015606898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017605298,0.000012332248,0.00010765447,0.000025654195,0.00005362389,0.000021664355,0.0021688063,0.08516244,0.00013378725,0.0010883501,0.0000037487723,0.9112202],"study_design_scores_gemma":[0.000099036115,0.000067335706,0.00015482427,0.00013771567,0.00002390523,0.000013950554,6.209449e-7,0.9847885,0.0011929214,0.013064168,0.00006851268,0.00038853983],"about_ca_topic_score_codex":0.000018482602,"about_ca_topic_score_gemma":0.000024819597,"teacher_disagreement_score":0.91083163,"about_ca_system_score_codex":0.00028242156,"about_ca_system_score_gemma":0.00039283794,"threshold_uncertainty_score":0.9999093},"labels":[],"label_agreement":null},{"id":"W2988118004","doi":"10.1016/j.jacc.2019.08.1048","title":"A New Japanese CTO Algorithm","year":2019,"lang":"en","type":"letter","venue":"Journal of the American College of Cardiology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; McGill University Health Centre","funders":"","keywords":"Medicine; Algorithm; Computer science","score_opus":0.011755735693861286,"score_gpt":0.2686419173489799,"score_spread":0.2568861816551186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2988118004","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00005507717,0.00020082534,0.68878967,0.30793783,0.0020179672,0.00022760578,0.000041055187,0.000023377666,0.0007066126],"genre_scores_gemma":[0.00041002588,0.00022185882,0.47850057,0.5079156,0.0066292817,0.000005435234,0.000002632819,0.000045082164,0.006269513],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99684453,0.00085083884,0.00085021055,0.00026085292,0.00087671337,0.0003168337],"domain_scores_gemma":[0.995218,0.0006422511,0.0026454118,0.0010140412,0.00039270587,0.000087623645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063552137,0.00025011037,0.0015008593,0.00032140705,0.0000337714,0.000019362193,0.0035229693,0.00022423608,0.000018129418],"category_scores_gemma":[0.00027789702,0.00016067774,0.0007707377,0.0006199426,0.0004191276,0.00016826954,0.0005195942,0.0015493624,0.000012413727],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000085603015,0.0000033524627,0.000027516267,0.000016342336,0.00021930676,0.00059298944,0.000032096665,0.000009361145,0.00022435015,0.000008332588,0.978461,0.020396743],"study_design_scores_gemma":[0.0006734288,0.0012703004,0.0002999053,0.00017837813,0.00018050401,0.0069762813,0.000085469066,0.00018975828,0.0017676466,0.001028574,0.9869789,0.00037088085],"about_ca_topic_score_codex":0.000032046388,"about_ca_topic_score_gemma":2.4981085e-7,"teacher_disagreement_score":0.21028908,"about_ca_system_score_codex":0.000132984,"about_ca_system_score_gemma":0.00088081625,"threshold_uncertainty_score":0.6731295},"labels":[],"label_agreement":null},{"id":"W2989263680","doi":"10.1016/j.neuroimage.2019.116328","title":"Hippocampal subfields revealed through unfolding and unsupervised clustering of laminar and morphological features in 3D BigBrain","year":2019,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Canadian Institutes of Health Research; Western University; Natural Sciences and Engineering Research Council of Canada; Royal College of Physicians and Surgeons of Canada","keywords":"Hippocampal formation; Hippocampus; Neuroimaging; Neuroscience; Cluster analysis; Artificial intelligence; Computer science; Pattern recognition (psychology); Folding (DSP implementation); Biology","score_opus":0.016387391997202343,"score_gpt":0.26925520284316085,"score_spread":0.2528678108459585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2989263680","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8511614,0.00014121324,0.14685245,0.00062930584,0.00008056534,0.0002595206,0.0000018583873,0.000093832925,0.00077983434],"genre_scores_gemma":[0.8647792,0.00011130121,0.13371211,0.0012769053,0.00001142662,0.0000061180176,0.0000010063686,0.000007204423,0.000094726805],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9988947,0.00014516439,0.00022763431,0.0003631472,0.00018664691,0.00018273927],"domain_scores_gemma":[0.9993583,0.00022416897,0.00006457524,0.0002789946,0.000021035514,0.000052905852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002917515,0.0001139412,0.00021271223,0.000086887485,0.000026791364,0.00006129222,0.00027787584,0.00007868494,0.000034797857],"category_scores_gemma":[0.00013550672,0.000100289435,0.000020351741,0.00021115536,0.00009998771,0.00040384426,0.00035115163,0.00021172893,0.0000023696377],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000533884,0.00011079932,0.029164009,0.00035733078,0.000008537047,0.0005173872,0.0034191955,0.000014976368,0.8603301,0.0012469403,0.00096951117,0.10380783],"study_design_scores_gemma":[0.005795995,0.0019630725,0.6786,0.0005969675,0.000021609017,0.00079815445,0.0005528756,0.05876336,0.24607785,0.005410251,0.0003061577,0.0011137055],"about_ca_topic_score_codex":0.000039590293,"about_ca_topic_score_gemma":0.0000053749527,"teacher_disagreement_score":0.649436,"about_ca_system_score_codex":0.000009130566,"about_ca_system_score_gemma":0.000011965229,"threshold_uncertainty_score":0.40896845},"labels":[],"label_agreement":null},{"id":"W2990371092","doi":"10.48550/arxiv.1911.10352","title":"Shape Detection of Liver From 2D Ultrasound Images","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Ultrasound; Speckle noise; Computer science; Artificial intelligence; Computer vision; Ultrasound imaging; Noise (video); Radiology; Speckle pattern; Medicine; Image (mathematics)","score_opus":0.048597641338897844,"score_gpt":0.19678644855488395,"score_spread":0.1481888072159861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990371092","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1347726,0.00007865235,0.8634826,0.000011162991,0.00036214598,0.00026490985,0.000031501593,0.00027764987,0.0007187769],"genre_scores_gemma":[0.9802182,0.00038680257,0.018649938,0.00008917563,0.000048224087,8.9398094e-7,0.000021880918,0.000013334908,0.0005715599],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984027,0.00016074027,0.00023048224,0.00085207983,0.0001557783,0.00019820317],"domain_scores_gemma":[0.997944,0.0003020494,0.00036513343,0.0010852921,0.00019048588,0.00011306387],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018971512,0.00022779228,0.0003053154,0.00022979699,0.000050524475,0.000077221026,0.0015292694,0.00026477,0.0002280577],"category_scores_gemma":[0.000088480054,0.0002623891,0.00017216618,0.00033418488,0.0001483544,0.00049845496,0.0011288199,0.00044987188,0.0001362839],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028897892,0.0014823683,0.018901402,0.0015516133,0.001473344,0.001480302,0.00478007,0.02895455,0.76985854,0.017054655,0.0073546297,0.14681956],"study_design_scores_gemma":[0.0008108943,0.00017912031,0.016284563,0.0003112715,0.00017701653,0.000006930671,0.00013787912,0.28238505,0.675736,0.022921197,0.00012099101,0.000929055],"about_ca_topic_score_codex":0.0007889791,"about_ca_topic_score_gemma":0.000021319702,"teacher_disagreement_score":0.8454456,"about_ca_system_score_codex":0.00013947154,"about_ca_system_score_gemma":0.000103478036,"threshold_uncertainty_score":0.99998283},"labels":[],"label_agreement":null},{"id":"W2990848657","doi":"10.1016/j.neunet.2019.11.017","title":"Learning physical properties in complex visual scenes: An intelligent machine for perceiving blood flow dynamics from static CT angiography imaging","year":2019,"lang":"en","type":"article","venue":"Neural Networks","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":105,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Science and Technology Planning Project of Guangdong Province; Fundamental Research Funds for the Central Universities; Shenzhen Peacock Plan; National Natural Science Foundation of China; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Science Foundation of Guangdong Province","keywords":"Artificial intelligence; Computer science; Tree (set theory); Encoder; Representation (politics); Blood flow; Computer vision; Visualization; Pattern recognition (psychology); Machine learning; Medicine; Mathematics; Radiology","score_opus":0.015785249271737866,"score_gpt":0.27848929229948083,"score_spread":0.262704043027743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990848657","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40834156,0.00008489263,0.5907277,0.000102703925,0.00012816185,0.00037731614,0.0000026524224,0.00022583186,0.00000921118],"genre_scores_gemma":[0.9462219,0.000012098861,0.052956965,0.0005036671,0.00010279251,0.000035984285,0.00012838235,0.000027354768,0.00001085214],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826074,0.00020536337,0.0003174419,0.00051503064,0.00028545366,0.00041598213],"domain_scores_gemma":[0.99924326,0.00019941392,0.00010535349,0.00027156455,0.000057489186,0.0001229474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021127019,0.0002171921,0.00028398662,0.00015584323,0.000092728355,0.0002573325,0.0005542481,0.000027777958,0.000027425112],"category_scores_gemma":[0.000032192707,0.00018966036,0.00014854866,0.00035686675,0.00007175875,0.000668092,0.00019417105,0.0003622923,0.000003495877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031784144,0.00042109127,0.06768041,0.00005996529,0.000041803607,0.000029658648,0.0025096273,0.08009286,0.003685546,0.000051215564,0.000054485346,0.84534156],"study_design_scores_gemma":[0.00035758392,0.00026028047,0.0057866224,0.00007920398,0.000015631485,0.0000053622716,0.00021970831,0.99226534,0.0005788418,0.00020371226,0.0000028449138,0.00022484061],"about_ca_topic_score_codex":0.00038185925,"about_ca_topic_score_gemma":0.00010055641,"teacher_disagreement_score":0.9121725,"about_ca_system_score_codex":0.000044660366,"about_ca_system_score_gemma":0.00001399669,"threshold_uncertainty_score":0.7734125},"labels":[],"label_agreement":null},{"id":"W2991009002","doi":"10.1093/jmicro/dfz052","title":"SC-1 Introduction to practical AI image processing and analysis without programming","year":2019,"lang":"en","type":"article","venue":"Microscopy","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Object Research Systems (Canada)","funders":"","keywords":"Computer science; Image processing; Artificial intelligence; Image (mathematics); Computer graphics (images); Computer vision","score_opus":0.009136331120998245,"score_gpt":0.351262314405291,"score_spread":0.3421259832842927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991009002","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027361788,0.00002711634,0.96585625,0.006038785,0.00008636186,0.00032559535,5.0986955e-7,0.0002383427,0.000065237284],"genre_scores_gemma":[0.04728975,0.0000028580475,0.95101494,0.0012741282,0.0000885744,0.000025622912,0.0000050118265,0.000007936972,0.00029120213],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988199,0.000052961685,0.00019096542,0.00048512846,0.00023291344,0.00021815098],"domain_scores_gemma":[0.9993272,0.000020510743,0.00007433618,0.0003284158,0.00011984949,0.00012967568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039282488,0.00010304322,0.00017326571,0.00021341702,0.000076201424,0.00056976214,0.0001981507,0.00004117644,0.00006212358],"category_scores_gemma":[0.000105861705,0.00009376989,0.000030868938,0.00090453663,0.00005768689,0.0009715039,0.00016323231,0.0001463133,0.0000782111],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014092929,0.000078078374,0.009339533,0.00005702565,0.000043283057,0.000004970809,0.000872803,0.0000018592699,0.73096734,0.00013871847,0.003075078,0.2554072],"study_design_scores_gemma":[0.00033222217,0.00022984855,0.0031465772,0.000044540644,0.00010485531,0.00003426197,0.00009505601,0.013894249,0.9721958,0.00014197004,0.00944423,0.0003363613],"about_ca_topic_score_codex":0.0000155055,"about_ca_topic_score_gemma":0.0000022077847,"teacher_disagreement_score":0.25507087,"about_ca_system_score_codex":0.000041955205,"about_ca_system_score_gemma":0.000054596647,"threshold_uncertainty_score":0.5494229},"labels":[],"label_agreement":null},{"id":"W2993140656","doi":"10.1137/18m1202980","title":"Linkage Between Piecewise Constant Mumford--Shah Model and Rudin--Osher--Fatemi Model and Its Virtue in Image Segmentation","year":2019,"lang":"en","type":"article","venue":"SIAM Journal on Scientific Computing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Sorbonne Université; Chinese University of Hong Kong; Research Grants Council, University Grants Committee; National Natural Science Foundation of China; Engineering and Physical Sciences Research Council; Isaac Newton Trust; Leverhulme Trust; Alan Turing Institute; City University of Hong Kong","keywords":"Piecewise; Segmentation; Thresholding; Image segmentation; Artificial intelligence; Image (mathematics); Mathematics; Pattern recognition (psychology); Scale-space segmentation; Computer science; Segmentation-based object categorization; Computer vision; Mathematical analysis","score_opus":0.028912995081462264,"score_gpt":0.30705497164246254,"score_spread":0.27814197656100026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2993140656","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47797135,0.00009420693,0.52067363,0.000259666,0.0002496367,0.00027926272,0.00000555559,0.0000735729,0.00039314624],"genre_scores_gemma":[0.8541795,0.000028747114,0.1451647,0.0002760599,0.000048271046,0.0000022750403,0.0000050996764,0.000016208098,0.0002791231],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968629,0.00018106932,0.000821245,0.0007693376,0.000883084,0.00048237632],"domain_scores_gemma":[0.99836403,0.0002728467,0.00043591644,0.00035497968,0.00021549746,0.00035670222],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0033088084,0.00025859918,0.0003656148,0.00060646347,0.00039446744,0.0013301739,0.0006417634,0.0000960393,0.0000143661755],"category_scores_gemma":[0.0001337578,0.00023732625,0.00006225426,0.00061762,0.00019700275,0.0014401618,0.00049465144,0.00064238865,0.000029667559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057856636,0.00038348764,0.0064866133,0.00037875495,0.00008396154,0.00021126658,0.0143806115,0.065272704,0.589435,0.012737117,0.0021398948,0.30843276],"study_design_scores_gemma":[0.0009472515,0.000087496315,0.0003864706,0.00032740916,0.000007796818,0.00005271578,0.0001981244,0.97919875,0.013940322,0.004585726,0.000008393195,0.00025952293],"about_ca_topic_score_codex":0.0000023712096,"about_ca_topic_score_gemma":0.000002291521,"teacher_disagreement_score":0.91392606,"about_ca_system_score_codex":0.00016605786,"about_ca_system_score_gemma":0.00019561664,"threshold_uncertainty_score":0.99970657},"labels":[],"label_agreement":null},{"id":"W2996147833","doi":"10.1109/tmi.2020.3027500","title":"Laplacian Flow Dynamics on Geometric Graphs for Anatomical Modeling of Cerebrovascular Networks","year":2020,"lang":"en","type":"preprint","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Polygon mesh; Computer science; Laplace operator; Convergence (economics); Laplacian matrix; Graph; Algorithm; Theoretical computer science; Topology (electrical circuits); Mathematics; Computer graphics (images)","score_opus":0.019708018346453478,"score_gpt":0.28485114836345404,"score_spread":0.2651431300170006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996147833","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013877943,0.000098334436,0.99303615,0.003189082,0.0019595984,0.0008200589,0.000061666484,0.00059986254,0.00009647593],"genre_scores_gemma":[0.6737746,0.00036544105,0.322461,0.0028766962,0.00015178323,0.00021430885,0.00006888366,0.00007570386,0.0000115397215],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953486,0.00020883745,0.0010046165,0.0011512917,0.0017403418,0.000546313],"domain_scores_gemma":[0.997248,0.0006034573,0.00024928272,0.0009721297,0.00025030645,0.0006768568],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010042972,0.00046881224,0.0007663953,0.001008802,0.00016133243,0.00015296256,0.0018567045,0.00046699884,0.00010128379],"category_scores_gemma":[0.0002369692,0.00047070166,0.0007056709,0.0011231902,0.0002144244,0.0002254229,0.000050331208,0.0022932827,0.000008491326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003634762,0.0003420224,0.000006349525,0.00030006748,0.00019368163,0.00002964774,0.00012489667,0.3251805,0.000020603673,0.00045618432,0.00067989883,0.67262983],"study_design_scores_gemma":[0.00070233666,0.00008452593,0.000002513303,0.0005234589,0.00009026644,0.000010393394,0.000027496379,0.99253035,0.0026064694,0.0030262354,0.0000131244105,0.00038285047],"about_ca_topic_score_codex":0.00007934137,"about_ca_topic_score_gemma":0.000014525189,"teacher_disagreement_score":0.67363584,"about_ca_system_score_codex":0.00027041667,"about_ca_system_score_gemma":0.00034118266,"threshold_uncertainty_score":0.99977446},"labels":[],"label_agreement":null},{"id":"W2997712445","doi":"10.1007/s12021-019-09448-5","title":"FCN Based Label Correction for Multi-Atlas Guided Organ Segmentation","year":2020,"lang":"en","type":"article","venue":"Neuroinformatics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Zhejiang Province; Canadian Institutes of Health Research; National Institute on Aging; National Institutes of Health; National Natural Science Foundation of China; Foundation for the National Institutes of Health","keywords":"Artificial intelligence; Computer science; Segmentation; Atlas (anatomy); Pattern recognition (psychology); Image registration; Robustness (evolution); Convolutional neural network; Computer vision; Image segmentation; Image (mathematics)","score_opus":0.09332659954151756,"score_gpt":0.3348719228073064,"score_spread":0.2415453232657888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997712445","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007077546,0.0000023967036,0.99583113,0.0015179423,0.00048353005,0.00063292164,0.0000058880146,0.0006003947,0.00021805291],"genre_scores_gemma":[0.0044169314,0.0000048312695,0.97967166,0.015641445,0.000050660903,0.000051301082,0.000046509827,0.000014015931,0.00010266974],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883986,0.000035397683,0.00045054092,0.00016650704,0.0003216527,0.00018606572],"domain_scores_gemma":[0.99910915,0.00012623036,0.0002063976,0.00024258633,0.00015065467,0.00016498532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015662111,0.00012696603,0.00012944583,0.0000705125,0.00010885935,0.000172061,0.00042900277,0.00004786645,0.00002418764],"category_scores_gemma":[0.0005829459,0.00012350857,0.000043513835,0.000345835,0.000026426404,0.00090552674,0.00007826652,0.00011604445,0.00008071815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044779616,0.00033558288,0.00031639976,0.0007193123,0.000031859334,0.000012489905,0.009520875,0.001179018,0.08378812,0.0009964396,0.485637,0.41741812],"study_design_scores_gemma":[0.0008908842,0.0001857947,0.00004933048,0.00000971858,0.0000059882177,0.0000038761036,0.00005290416,0.7993379,0.198216,0.000017233437,0.0011275903,0.0001027685],"about_ca_topic_score_codex":0.0000028071013,"about_ca_topic_score_gemma":8.9520415e-7,"teacher_disagreement_score":0.7981589,"about_ca_system_score_codex":0.00003886497,"about_ca_system_score_gemma":0.000089529596,"threshold_uncertainty_score":0.5036533},"labels":[],"label_agreement":null},{"id":"W2997834033","doi":"10.1063/1.5095557","title":"Hybrid image segmentation method based on anisotropic Gaussian kernels and adjacent graph region merging","year":2020,"lang":"en","type":"article","venue":"Review of Scientific Instruments","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Science and Technology Major Project; National Natural Science Foundation of China; University of Guelph","keywords":"Artificial intelligence; Computer science; Image segmentation; Segmentation; Pattern recognition (psychology); Scale-space segmentation; Gaussian function; Segmentation-based object categorization; Robustness (evolution); Edge detection; Computer vision; Range segmentation; Kernel (algebra); Algorithm; Gaussian; Image processing; Mathematics; Image (mathematics)","score_opus":0.02772090775851768,"score_gpt":0.3115290853295266,"score_spread":0.28380817757100896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997834033","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0069929664,0.0006336317,0.98642355,0.004228181,0.00032467966,0.00084974774,0.00000869843,0.00014110157,0.00039741627],"genre_scores_gemma":[0.13236335,0.003281388,0.85603863,0.008092671,0.000031267755,0.00006113686,0.000054033637,0.00001900068,0.000058517646],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974169,0.00029438827,0.00055303046,0.0006631161,0.0008366893,0.0002358911],"domain_scores_gemma":[0.99874437,0.00004316387,0.00038447857,0.00047254586,0.0001204635,0.00023495319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085932884,0.00018085359,0.0003170473,0.00016963245,0.00016134787,0.00018983558,0.0005678333,0.000024591249,0.00007440061],"category_scores_gemma":[0.00015935866,0.00015651717,0.000095795884,0.0006898876,0.00016579739,0.00063731865,0.00017077662,0.0001203095,0.000022213044],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006135732,0.00008083212,0.00021533978,0.0029591187,0.00001476171,0.000015497459,0.00016991366,0.0000013824094,0.020314477,0.00073281076,0.0048881746,0.97060156],"study_design_scores_gemma":[0.0012917082,0.00043032633,0.00051898946,0.008413699,0.0000733642,0.00002218664,0.00005248374,0.11086479,0.8739009,0.0008748232,0.0030735964,0.0004831653],"about_ca_topic_score_codex":0.000004318451,"about_ca_topic_score_gemma":8.0093194e-8,"teacher_disagreement_score":0.9701184,"about_ca_system_score_codex":0.000056637517,"about_ca_system_score_gemma":0.00007364643,"threshold_uncertainty_score":0.63825846},"labels":[],"label_agreement":null},{"id":"W3002763863","doi":"10.3389/fnins.2020.00015","title":"Shape Information Improves the Cross-Cohort Performance of Deep Learning-Based Segmentation of the Hippocampus","year":2020,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; IXICO; Servier; Karolinska Institutet; Vetenskapsrådet; Eisai; H. Lundbeck A/S; Genentech; Stiftelsen för Strategisk Forskning; Northern California Institute for Research and Education; Stockholms Läns Landsting; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; European Commission; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Segmentation; Context (archaeology); Computer science; Artificial intelligence; Cohort; Deep learning; Neuroimaging; Artificial neural network; Pattern recognition (psychology); Machine learning; Medicine; Psychology; Neuroscience; Pathology","score_opus":0.008249968021111533,"score_gpt":0.24177687204532983,"score_spread":0.2335269040242183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3002763863","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.318036,0.000017546261,0.68060577,0.00047393003,0.0004583169,0.00031254988,8.5308915e-7,0.000040737577,0.00005426955],"genre_scores_gemma":[0.97031945,0.000020438516,0.027262889,0.002357037,0.0000074139416,0.000023645129,8.916499e-7,0.0000029670216,0.000005293486],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854636,0.00011294657,0.00039059398,0.00019721125,0.0005940602,0.00015884479],"domain_scores_gemma":[0.99917823,0.000048067377,0.00035795724,0.00028158198,0.000088868685,0.00004529183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000482716,0.00008644736,0.000115853036,0.0000745378,0.00012348512,0.000083797444,0.0014133984,0.000030865976,0.0000031795428],"category_scores_gemma":[0.0005736584,0.000056175893,0.000042327,0.0010414411,0.0005128204,0.0013223931,0.00019441513,0.0002033,9.355928e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003174603,0.000044974324,0.45322308,0.00015317096,0.0000025434051,7.084131e-7,0.0029099246,0.013157741,0.07761026,0.000073418494,0.00049237977,0.45230004],"study_design_scores_gemma":[0.00014707021,0.000121193974,0.15719116,0.000011667278,0.0000019042777,5.640468e-7,0.000047766818,0.70478475,0.13753507,0.00006605643,0.000043933836,0.000048871516],"about_ca_topic_score_codex":0.0000056600707,"about_ca_topic_score_gemma":2.803716e-7,"teacher_disagreement_score":0.691627,"about_ca_system_score_codex":0.000028601402,"about_ca_system_score_gemma":0.00008256669,"threshold_uncertainty_score":0.26264694},"labels":[],"label_agreement":null},{"id":"W3004310692","doi":"10.1101/2020.01.28.922971","title":"Reliability Assessment of Tissue Classification Algorithms for Multi-Center and Multi-Scanner Data","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Consortium canadien en neurodégénérescence associée au vieillissement","keywords":"Scanner; Segmentation; Robustness (evolution); Artificial intelligence; Sample size determination; Computer science; Pattern recognition (psychology); Brain size; Statistics; Mathematics; Nuclear medicine; Medicine; Magnetic resonance imaging; Biology; Radiology","score_opus":0.11299603627700448,"score_gpt":0.36265910453804745,"score_spread":0.24966306826104295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004310692","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023565844,0.0001303099,0.99316585,0.0006990432,0.0005851946,0.0017761326,0.0008365,0.000449284,0.0000011066659],"genre_scores_gemma":[0.20632912,0.00009854311,0.79290557,0.00023018259,0.00009755561,0.0002895265,0.0000064236388,0.000040757615,0.0000023054324],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965065,0.00020582054,0.0007948441,0.0016917187,0.00048055878,0.00032056557],"domain_scores_gemma":[0.99533886,0.00012124129,0.0006551146,0.0029108548,0.0006694605,0.00030449772],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012859609,0.0003793964,0.00054098596,0.00014710103,0.00009136615,0.00023643512,0.00214933,0.00032180757,0.0000072334437],"category_scores_gemma":[0.0006712413,0.00038549645,0.00006498992,0.00029750532,0.00019481318,0.00055194047,0.002465581,0.0004888966,0.00000362905],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013942525,0.0014536036,0.004299058,0.0021369031,0.00016189527,0.000013830888,0.000054473803,0.0000079045,0.98735297,0.0011965894,0.0025002186,0.00080864073],"study_design_scores_gemma":[0.0013991764,0.000093457194,0.092616126,0.00023849602,0.0000619984,1.2879656e-8,0.0000032401608,0.6857457,0.21747778,0.0000049873674,0.0017981713,0.00056084234],"about_ca_topic_score_codex":0.000040188694,"about_ca_topic_score_gemma":0.0000013557592,"teacher_disagreement_score":0.76987517,"about_ca_system_score_codex":0.00016856268,"about_ca_system_score_gemma":0.00052119454,"threshold_uncertainty_score":0.9998597},"labels":[],"label_agreement":null},{"id":"W3006404851","doi":"10.1038/s41597-020-0379-9","title":"High-resolution T2-FLAIR and non-contrast CT brain atlas of the elderly","year":2020,"lang":"en","type":"article","venue":"Scientific Data","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Foothills Medical Centre; University of Calgary","funders":"Canadian Open Neuroscience Platform; University of Calgary; Canada Research Chairs; Heart and Stroke Foundation of Canada","keywords":"Fluid-attenuated inversion recovery; Spatial normalization; Neuroimaging; Contrast (vision); Computer science; Modalities; Normalization (sociology); Artificial intelligence; Modality (human–computer interaction); Voxel; Brain atlas; Brain mapping; Pattern recognition (psychology); Medicine; Neuroscience; Magnetic resonance imaging; Radiology; Psychology","score_opus":0.030584969278722597,"score_gpt":0.27253003643218593,"score_spread":0.24194506715346334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006404851","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009204991,0.00005483042,0.97654015,0.012598374,0.0007309949,0.00028037603,0.00030604991,0.00010535172,0.00017889871],"genre_scores_gemma":[0.80559033,0.000006821452,0.19138823,0.0018388965,0.00010268433,0.000009556886,0.00030634043,0.00001024426,0.00074687967],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984125,0.00008606046,0.00023561077,0.0006029156,0.0004917115,0.0001712173],"domain_scores_gemma":[0.99812025,0.000083718514,0.00012091287,0.0014956864,0.000057968216,0.00012145238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008843914,0.000080423015,0.000113404516,0.00004310068,0.00017492483,0.00031258358,0.0028125695,0.000019011373,0.00003884719],"category_scores_gemma":[0.00048554802,0.000057483896,0.000021463848,0.00061798625,0.00042832296,0.00083658437,0.0018123601,0.0001068999,0.000028762097],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030080857,0.000035580884,0.000075040254,0.000039361348,0.000009673962,0.0000053195654,0.0004950963,0.000002419373,0.11966547,0.0013920398,0.7030622,0.1752148],"study_design_scores_gemma":[0.0013966036,0.00031683804,0.008115712,0.00018388477,0.000035944406,0.000023789793,0.00019292453,0.14125842,0.79354167,0.0040245135,0.050408404,0.00050127634],"about_ca_topic_score_codex":0.0000611925,"about_ca_topic_score_gemma":0.000022549762,"teacher_disagreement_score":0.79638535,"about_ca_system_score_codex":0.00001035934,"about_ca_system_score_gemma":0.00009661008,"threshold_uncertainty_score":0.52265006},"labels":[],"label_agreement":null},{"id":"W3006530269","doi":"10.1007/s12021-019-09439-6","title":"Automated White Matter Hyperintensity Segmentation Using Bayesian Model Selection: Assessment and Correlations with Cognitive Change","year":2020,"lang":"en","type":"article","venue":"Neuroinformatics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; IXICO; H. Lundbeck A/S; Centre For Medical Engineering, King’s College London; Servier; Eisai; University of Southern California; Wellcome Trust; University College London; NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research; National Institute on Aging; National Institute for Health and Care Research; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; Alzheimer's Society; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association; Wolfson Foundation; Brain Research Trust; Pennington Biomedical Research Foundation; Foundation for the National Institutes of Health","keywords":"Hyperintensity; Artificial intelligence; Bayesian probability; Computer science; Segmentation; Cognition; Feature selection; Selection (genetic algorithm); Pattern recognition (psychology); Machine learning; Psychology; Medicine; Magnetic resonance imaging; Neuroscience","score_opus":0.055219246915741205,"score_gpt":0.3116836159447391,"score_spread":0.2564643690289979,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006530269","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02808624,0.0000012987789,0.9693244,0.0010846921,0.000032189335,0.00043047793,0.000006181822,0.0005978774,0.00043661208],"genre_scores_gemma":[0.44187146,0.0000032112496,0.55052984,0.007534125,0.0000150771,0.000017674236,0.000012105524,0.000008207518,0.000008313203],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911153,0.000036527243,0.00025358758,0.00016315973,0.00028808627,0.00014710246],"domain_scores_gemma":[0.9994139,0.00003347995,0.00015323715,0.00010027458,0.00015389387,0.00014518523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000067584755,0.00012571306,0.00012451952,0.000069445356,0.00015075419,0.00018192334,0.00011579313,0.000035306417,0.000021189991],"category_scores_gemma":[0.000015498628,0.00011361881,0.000015846397,0.00031607863,0.000048107304,0.001558118,0.00010438723,0.00016215377,0.000011818111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021939883,0.0006472879,0.663611,0.0019995922,0.00035723936,0.00016026302,0.15653023,0.052890796,0.01466359,0.0026035758,0.020231428,0.086085595],"study_design_scores_gemma":[0.00030066626,0.00011908909,0.016695261,0.00002820119,0.000019716419,0.000075321695,0.00021122347,0.9817212,0.00068838574,0.000017862301,0.0000019904096,0.00012106317],"about_ca_topic_score_codex":0.000004074736,"about_ca_topic_score_gemma":0.0000010740443,"teacher_disagreement_score":0.92883044,"about_ca_system_score_codex":0.00003957742,"about_ca_system_score_gemma":0.00006311526,"threshold_uncertainty_score":0.46332407},"labels":[],"label_agreement":null},{"id":"W3010431109","doi":"10.3390/s20051392","title":"Semantically Guided Large Deformation Estimation with Deep Networks","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Deutsche Forschungsgemeinschaft","keywords":"Computer science; Artificial intelligence; Segmentation; Deep learning; Parameterized complexity; Inference; Regularization (linguistics); Computer vision; Face (sociological concept); Network architecture; Pattern recognition (psychology); Algorithm","score_opus":0.013925458248526052,"score_gpt":0.2571882668844139,"score_spread":0.2432628086358878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010431109","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070066387,0.000007615043,0.9886285,0.0028213465,0.000040512055,0.00019344634,4.467595e-7,0.0005616689,0.0007398244],"genre_scores_gemma":[0.42104882,0.0000051981283,0.575128,0.003729199,0.000040997085,0.0000075445087,0.000013097975,0.000007631146,0.000019497571],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990022,0.00006135273,0.00022150784,0.00020264534,0.00031335535,0.00019890613],"domain_scores_gemma":[0.9994262,0.000041612242,0.00008703963,0.00020398943,0.00008816881,0.00015301719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018024814,0.000093716204,0.000103555416,0.000036996786,0.000071512906,0.000103961844,0.0002712026,0.000046131987,0.000035460733],"category_scores_gemma":[0.00012756305,0.00007464627,0.000023371242,0.00033764477,0.000028993918,0.00046358118,0.0000735229,0.000111468886,0.00008307704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009034319,0.00027416524,0.0017070412,0.00028118337,0.00012792839,0.0002716389,0.015638754,0.21330228,0.0021111565,0.055043172,0.026149716,0.6850026],"study_design_scores_gemma":[0.00023662868,0.000076499375,0.00034280668,0.00001392156,0.0000050484587,0.000012859569,0.000034983455,0.9942038,0.0046418095,0.00020908748,0.000121507124,0.00010101665],"about_ca_topic_score_codex":0.000003194869,"about_ca_topic_score_gemma":0.0000020504353,"teacher_disagreement_score":0.78090155,"about_ca_system_score_codex":0.000021653897,"about_ca_system_score_gemma":0.000019943875,"threshold_uncertainty_score":0.30439866},"labels":[],"label_agreement":null},{"id":"W3010958984","doi":"10.1016/j.compbiomed.2020.103708","title":"A diffeomorphic unsupervised method for deformable soft tissue image registration","year":2020,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Artificial intelligence; Jacobian matrix and determinant; Image registration; Voxel; Computer science; Computer vision; Field (mathematics); Vector field; Pattern recognition (psychology); Image (mathematics); Mathematics; Geometry","score_opus":0.03531143681376778,"score_gpt":0.3710795633139373,"score_spread":0.3357681265001695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010958984","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00042516072,0.00024704085,0.9776371,0.020895448,0.0001995666,0.00035439758,0.0000016965538,0.00012982204,0.0001097713],"genre_scores_gemma":[0.01933061,0.00009379045,0.96787083,0.012437175,0.0001697442,0.000044169305,0.000029304296,0.0000052131327,0.000019150311],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903554,0.000108668166,0.0002759164,0.00032966462,0.00006968577,0.00018053848],"domain_scores_gemma":[0.99925715,0.00033246097,0.000076652475,0.00015672258,0.000044508386,0.00013249049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004959978,0.00010814233,0.00024821569,0.00007573271,0.000055771012,0.000014432011,0.0003212342,0.000083265506,0.000011776828],"category_scores_gemma":[0.00024990208,0.00008333876,0.000015771595,0.00019077768,0.00018180515,0.00016277313,0.00010296534,0.000118850374,0.0000018965591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008676442,0.00004923336,0.00044945435,0.00022197847,0.00002529083,0.00003250071,0.0034244626,0.000005817156,0.14232102,0.031331547,0.02032493,0.801727],"study_design_scores_gemma":[0.0062974617,0.0041880445,0.0011191362,0.00023229272,0.000029480392,0.0000888527,0.00015886067,0.8661161,0.05523826,0.05314914,0.012912503,0.0004698818],"about_ca_topic_score_codex":0.000022573933,"about_ca_topic_score_gemma":0.0000029851246,"teacher_disagreement_score":0.86611027,"about_ca_system_score_codex":0.000014912326,"about_ca_system_score_gemma":0.00002553139,"threshold_uncertainty_score":0.3398456},"labels":[],"label_agreement":null},{"id":"W3012075782","doi":"10.1002/mp.14127","title":"Creation of an anthropomorphic CT head phantom for verification of image segmentation","year":2020,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; University of Liverpool; National Institute on Aging; National Institute for Health and Care Research; Northern California Institute for Research and Education; Alzheimer's Disease Neuroimaging Initiative; GE Healthcare; Pfizer; Biogen; BioClinica; Roche; University of Southern California; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Merck; Alzheimer's Drug Discovery Foundation; Takeda Pharmaceutical Company; AbbVie; Fujirebio Europe; Alzheimer's Association","keywords":"Imaging phantom; Segmentation; Hounsfield scale; Computer science; Artificial intelligence; Voxel; Contouring; Image segmentation; Biomedical engineering; Computer vision; Nuclear medicine; Medicine; Computed tomography; Radiology; Computer graphics (images)","score_opus":0.040154937067320234,"score_gpt":0.360070164925745,"score_spread":0.31991522785842474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012075782","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0137470355,0.0000152330995,0.9842497,0.0013661064,0.00006933596,0.0003383411,0.000014470992,0.00010313573,0.00009662637],"genre_scores_gemma":[0.7911761,0.000027325033,0.20778519,0.00069324876,0.00013394647,0.000040497776,0.00012938525,0.000010113596,0.000004207931],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985187,0.000072550705,0.00037873388,0.00023773956,0.00068294036,0.00010933911],"domain_scores_gemma":[0.9990388,0.00012743339,0.00024182448,0.0002322081,0.00018761486,0.00017207942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030212637,0.00008482189,0.00019150121,0.000026532975,0.000034750257,0.000017214246,0.0004261464,0.000033980712,0.00006127847],"category_scores_gemma":[0.0003875599,0.000080619626,0.00005289223,0.00030264579,0.00021843611,0.0005745236,0.000048246435,0.00007571588,0.0000043585887],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027009924,0.00037226034,0.000085149346,0.0002399116,0.000017408565,0.00000295145,0.0013441986,0.000004599043,0.2273429,0.0024672116,0.0009842872,0.76711214],"study_design_scores_gemma":[0.00057048135,0.00037659088,0.00019118053,0.000035078425,0.000010922961,0.0000010038783,0.00005256821,0.04746135,0.9486671,0.0025240239,0.00003691951,0.000072769704],"about_ca_topic_score_codex":0.000030891573,"about_ca_topic_score_gemma":9.374166e-7,"teacher_disagreement_score":0.77742904,"about_ca_system_score_codex":0.000019257139,"about_ca_system_score_gemma":0.00011223079,"threshold_uncertainty_score":0.3287573},"labels":[],"label_agreement":null},{"id":"W3012431403","doi":"10.5220/0008912101160122","title":"Exploiting Bilateral Symmetry in Brain Lesion Segmentation with Reflective Registration","year":2020,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Segmentation; Image segmentation; Artificial intelligence; Computer science; Image registration; Bilateral symmetry; Computer vision; Lesion; Symmetry (geometry); Medicine; Mathematics; Geometry; Image (mathematics); Pathology; Engineering","score_opus":0.04794187359080684,"score_gpt":0.31994658118883795,"score_spread":0.27200470759803114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012431403","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015631339,0.0000036731158,0.9729312,0.007951383,0.000019482579,0.00023875991,2.501502e-7,0.00031827064,0.0029056068],"genre_scores_gemma":[0.44793165,0.0000035309156,0.54616296,0.0057596415,0.000027529972,0.000026935099,0.0000074240475,0.000005961108,0.000074380696],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989369,0.00009826835,0.00022450961,0.0003141949,0.00029258418,0.00013357686],"domain_scores_gemma":[0.99958074,0.00007287319,0.000091227,0.00012819395,0.000044638047,0.000082314036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022385333,0.000086534594,0.00009017647,0.00008866466,0.000040438237,0.000109570596,0.00020650483,0.00003398717,0.000022448572],"category_scores_gemma":[0.000102371065,0.00007030219,0.000014076701,0.0005806188,0.000023892015,0.0010431878,0.00005612472,0.00011448254,0.000012549702],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008593517,0.000119330376,0.0042186175,0.00008210746,0.00001562888,0.00013015069,0.010921733,0.000057219906,0.5695736,0.016474573,0.005633816,0.39268732],"study_design_scores_gemma":[0.0008693034,0.0005823769,0.0025297776,0.0000765804,0.0000024593153,0.000011693643,0.000924365,0.034552056,0.95843655,0.0017346481,0.000038777747,0.00024138295],"about_ca_topic_score_codex":0.000053247033,"about_ca_topic_score_gemma":0.000021436668,"teacher_disagreement_score":0.43230033,"about_ca_system_score_codex":0.0000699708,"about_ca_system_score_gemma":0.000042329877,"threshold_uncertainty_score":0.286684},"labels":[],"label_agreement":null},{"id":"W3012525336","doi":"10.1007/s00500-020-04842-7","title":"Improving image thresholding by the type II fuzzy entropy and a hybrid optimization algorithm","year":2020,"lang":"en","type":"article","venue":"Soft Computing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Thresholding; Computer science; Image segmentation; Algorithm; Entropy (arrow of time); Benchmark (surveying); Artificial intelligence; Fuzzy logic; Segmentation; Pattern recognition (psychology); Image (mathematics)","score_opus":0.011203683376685387,"score_gpt":0.24812610405731028,"score_spread":0.23692242068062488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012525336","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001429165,0.0002289558,0.9951223,0.0022892088,0.00013507405,0.00019154958,0.0000015691788,0.0004805304,0.00012162839],"genre_scores_gemma":[0.089063026,0.000014711279,0.90712255,0.00360574,0.00015659715,0.0000026318282,0.000007704946,0.000013753098,0.000013263272],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988432,0.00006903945,0.00023228159,0.00035980833,0.00026071118,0.00023494677],"domain_scores_gemma":[0.9993287,0.00013215235,0.00013769857,0.00019412837,0.00008619936,0.00012117278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032760997,0.00012351459,0.00012329315,0.000028506714,0.00040701614,0.00031941218,0.0004956797,0.000025765785,0.000014406747],"category_scores_gemma":[0.00026521002,0.00009951046,0.0000265697,0.00028625884,0.00007360168,0.0003989491,0.0007643838,0.00019778228,0.000004989132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000026103687,0.000019505987,0.00004690154,0.000026812322,0.000013440114,0.000014483858,0.001739344,0.0007086163,0.022221282,0.00029848158,0.0075993864,0.9673091],"study_design_scores_gemma":[0.00017592618,0.000077562596,0.0000072909093,0.000016526099,0.0000051728325,0.000015361456,0.000059013364,0.97913206,0.02010706,0.00015371837,0.00013396016,0.00011638],"about_ca_topic_score_codex":0.000017550841,"about_ca_topic_score_gemma":4.4369045e-8,"teacher_disagreement_score":0.9784234,"about_ca_system_score_codex":0.000024466039,"about_ca_system_score_gemma":0.00003368397,"threshold_uncertainty_score":0.40579185},"labels":[],"label_agreement":null},{"id":"W301273030","doi":"","title":"Sur la résolution efficace d'équations aux dérivées partielles en mécanique des fluides multiphasique et imagerie médicale","year":2014,"lang":"fr","type":"dissertation","venue":"theses.fr (ABES)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Humanities; Physics; Philosophy","score_opus":0.03730898489184529,"score_gpt":0.34359501946435006,"score_spread":0.30628603457250475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W301273030","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043246187,0.0013227203,0.9332542,0.0017518474,0.0007286802,0.00148459,0.00006925647,0.0011848033,0.016957756],"genre_scores_gemma":[0.76946104,0.0038399745,0.21834332,0.0012434552,0.00042126622,0.0009409235,0.0008476101,0.00020389538,0.0046985233],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9908085,0.003748016,0.0015770864,0.0014495094,0.0013353282,0.0010815789],"domain_scores_gemma":[0.99055237,0.005410864,0.00082680135,0.0013528178,0.0011903197,0.00066680484],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0036228872,0.0010271546,0.0010480082,0.00046552866,0.0007476978,0.00095761096,0.0019963323,0.0010453922,0.0008170953],"category_scores_gemma":[0.006984297,0.0010319547,0.00045375965,0.0008574501,0.0016269532,0.002116866,0.00040728945,0.0013519948,0.00051788136],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015085647,0.002222476,0.0010287912,0.0016011536,0.0004263772,0.00016941706,0.080327936,0.0005710928,0.15752879,0.31011763,0.0072614616,0.438594],"study_design_scores_gemma":[0.0023265684,0.0008154916,0.010988371,0.0046001016,0.00048637978,0.00015349533,0.008724957,0.08976065,0.7955302,0.022343608,0.06107998,0.0031901887],"about_ca_topic_score_codex":0.0074284566,"about_ca_topic_score_gemma":0.002074365,"teacher_disagreement_score":0.7262148,"about_ca_system_score_codex":0.0003596235,"about_ca_system_score_gemma":0.0013448402,"threshold_uncertainty_score":0.9992131},"labels":[],"label_agreement":null},{"id":"W3014584416","doi":"10.7488/era/239","title":"Development of machine learning schemes for segmentation, characterisation, and evolution prediction of white matter hyperintensities in structural brain MRI","year":2020,"lang":"en","type":"dissertation","venue":"ERA","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; UK Dementia Research Institute; Lembaga Pengelola Dana Pendidikan; Eisai; Fondation Leducq; Mrs Gladys Row Fogo Charitable Trust; BioClinica; European Commission; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; University of Southern California; Wellcome Trust; University of Edinburgh; Northern California Institute for Research and Education; Nvidia; Bristol-Myers Squibb; Eli Lilly and Company; Biogen; Alzheimer's Association","keywords":"Hyperintensity; White matter; Artificial intelligence; Segmentation; Pattern recognition (psychology); Computer science; Magnetic resonance imaging; Medicine; Radiology","score_opus":0.010533611478765786,"score_gpt":0.2575088281232527,"score_spread":0.24697521664448693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014584416","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3910109,0.00008483837,0.6076549,0.00038365475,0.0002131281,0.0005225906,0.000019710033,0.00006452097,0.000045732773],"genre_scores_gemma":[0.39706606,0.000019853009,0.5997292,0.00017299816,0.000041253024,0.00013764417,0.002153823,0.000022467235,0.00065670576],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9986983,0.000053847536,0.0006114694,0.00027256488,0.00025804443,0.00010576004],"domain_scores_gemma":[0.9991475,0.000048472506,0.00044046942,0.00009873783,0.00022811144,0.000036733705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021190957,0.00014707935,0.00025448503,0.00021950027,0.0000821799,0.000027934268,0.00014542561,0.00009268763,0.000032580832],"category_scores_gemma":[0.00009137825,0.00015144525,0.000032332624,0.00015865896,0.000029146504,0.00036893124,0.000050874965,0.00016078039,8.3461725e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003264667,0.00008559845,0.11693169,0.004605827,0.00019355051,0.000002608143,0.18191597,0.00003063392,0.5857108,0.0018451409,0.0011853861,0.1071663],"study_design_scores_gemma":[0.00093734544,0.00015776568,0.65723896,0.0004772208,0.000021606424,0.000004690821,0.0026210605,0.033812743,0.3035385,0.0004180196,0.0004709341,0.00030115407],"about_ca_topic_score_codex":0.000012664183,"about_ca_topic_score_gemma":0.00002017314,"teacher_disagreement_score":0.5403073,"about_ca_system_score_codex":0.000072515824,"about_ca_system_score_gemma":0.000115415714,"threshold_uncertainty_score":0.6175758},"labels":[],"label_agreement":null},{"id":"W3015963463","doi":"10.21013/jas.v14.n2.p1","title":"Algorithm Selection in Multimodal Medical Image Registration","year":2020,"lang":"en","type":"article","venue":"IRA-International Journal of Applied Sciences (ISSN 2455-4499)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robustness (evolution); Image registration; Computer science; Artificial intelligence; Computer vision; Medical imaging; Process (computing); Pattern recognition (psychology); Image (mathematics)","score_opus":0.02067961568496332,"score_gpt":0.31719054044290557,"score_spread":0.2965109247579423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015963463","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004999475,0.000027213893,0.9797124,0.010978791,0.0005513074,0.00016900794,0.0000023930675,0.000094059564,0.0034653377],"genre_scores_gemma":[0.40978658,0.00006279822,0.5857821,0.0036023648,0.0007186009,0.00001052696,0.0000034291372,0.000009919687,0.000023677061],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950137,0.00009826707,0.0009930254,0.00045137285,0.003145007,0.00029862073],"domain_scores_gemma":[0.9982822,0.0001798073,0.00066118856,0.00010666919,0.00039268797,0.00037746003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019879108,0.00018388916,0.00027382022,0.00040765095,0.000110918845,0.00043901833,0.002622196,0.00012111335,0.00024242462],"category_scores_gemma":[0.0003906893,0.00016149548,0.000106244486,0.0009228614,0.0003332373,0.0015377156,0.00022265514,0.00054392585,0.000036718262],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013533761,0.00043083035,0.0006666048,0.000020129195,0.000073189956,0.0005120338,0.0033018938,0.00074089033,0.064038105,0.014546095,0.006512429,0.90902245],"study_design_scores_gemma":[0.00414504,0.0009913858,0.0030003965,0.00023925719,0.000020336258,0.0006661416,0.000861104,0.69618464,0.27650553,0.013845435,0.0027674467,0.00077331904],"about_ca_topic_score_codex":0.000031677828,"about_ca_topic_score_gemma":0.000011843102,"teacher_disagreement_score":0.90824914,"about_ca_system_score_codex":0.00017987915,"about_ca_system_score_gemma":0.000576394,"threshold_uncertainty_score":0.65855944},"labels":[],"label_agreement":null},{"id":"W3017638456","doi":"10.1109/jtehm.2020.2989390","title":"3D Motion Estimation of Left Ventricular Dynamics Using MRI and Track-to-Track Fusion","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Translational Engineering in Health and Medicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; St Joseph's Health Care; Canadian VIGOUR Centre; École de Technologie Supérieure; University of Alberta","funders":"Servier; Natural Sciences and Engineering Research Council of Canada; Heart and Stroke Foundation of Canada; Servier Canada","keywords":"Track (disk drive); Computer science; Computer vision; Motion (physics); Artificial intelligence; Dynamics (music); Fusion; Physics; Acoustics","score_opus":0.021541026129866327,"score_gpt":0.30316711056053536,"score_spread":0.28162608443066905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3017638456","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06151216,0.0006767575,0.93063253,0.0069323205,0.00010848034,0.00012253076,0.0000010882779,0.000013004653,0.0000010997281],"genre_scores_gemma":[0.5982669,0.0002545269,0.40091062,0.00046913725,0.000090254885,4.641559e-7,0.0000028649492,0.000004744711,4.795516e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887294,0.000030085967,0.0005741947,0.00009416273,0.00033218614,0.000096446085],"domain_scores_gemma":[0.999331,0.0000971706,0.00017698955,0.000040086696,0.00007344943,0.0002812929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054310553,0.0000700889,0.00020992137,0.00022252074,0.000020859508,0.0000065751683,0.00007787874,0.000033218268,0.0000030701372],"category_scores_gemma":[0.00015043642,0.0000612897,0.000015955982,0.00021406596,0.000019697374,0.00023986661,0.0000056301747,0.00014959756,6.6744434e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053949887,0.000049237846,0.0013026396,0.0012055122,0.000015049198,0.000034833774,0.004560002,0.75198215,0.006435975,0.0006060984,0.0000661273,0.23368841],"study_design_scores_gemma":[0.00066941924,0.0003528203,0.0028891128,0.00051660906,0.000005391316,0.00009877299,0.000018391764,0.99450594,0.00078963145,0.00008665052,0.00002265086,0.000044633194],"about_ca_topic_score_codex":0.000014165937,"about_ca_topic_score_gemma":8.9531727e-7,"teacher_disagreement_score":0.5367547,"about_ca_system_score_codex":0.000037647154,"about_ca_system_score_gemma":0.000077835146,"threshold_uncertainty_score":0.24993214},"labels":[],"label_agreement":null},{"id":"W3024581418","doi":"10.1016/j.media.2020.101723","title":"Dynamically constructed network with error correction for accurate ventricle volume estimation","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Volume (thermodynamics); Residual; Algorithm; Ventricle; Constraint (computer-aided design); Artificial intelligence; Mathematics; Medicine","score_opus":0.010639167569552811,"score_gpt":0.28153158846780824,"score_spread":0.27089242089825544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3024581418","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00085139344,0.000017037855,0.98987323,0.008225525,0.0001199641,0.00030131443,0.00000566755,0.000500781,0.00010511119],"genre_scores_gemma":[0.17596136,0.000008242522,0.81734234,0.0061185714,0.00015652795,0.00009464946,0.00018888124,0.000015935397,0.00011349303],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99771976,0.00013659433,0.00045801298,0.0005036073,0.0008540692,0.00032796542],"domain_scores_gemma":[0.99835235,0.00024587827,0.00022510195,0.0003066575,0.00036405344,0.00050598854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045888772,0.00016233286,0.00036868238,0.00013156827,0.00014276513,0.00019333008,0.0006234731,0.00010103331,0.0006990213],"category_scores_gemma":[0.001898493,0.00013319033,0.00016255758,0.0027965475,0.00018627507,0.00058474735,0.000121723286,0.00021906134,0.000044360302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019248256,0.0002488656,0.003498369,0.000115599214,0.0017096953,0.00025901728,0.00071960106,0.008417823,0.0009601797,0.00046774335,0.1296766,0.853734],"study_design_scores_gemma":[0.00040018995,0.00016152086,0.0008369461,0.000019050012,0.0003450521,0.000008837113,0.000030160343,0.99653935,0.0011751376,0.00015011421,0.00017168981,0.00016197236],"about_ca_topic_score_codex":0.000030131301,"about_ca_topic_score_gemma":0.000016647675,"teacher_disagreement_score":0.9881215,"about_ca_system_score_codex":0.000054045042,"about_ca_system_score_gemma":0.00014382895,"threshold_uncertainty_score":0.76537925},"labels":[],"label_agreement":null},{"id":"W3028312999","doi":"10.48550/arxiv.2005.09871","title":"Local semi-supervised approach to brain tissue classification in child brain MRI","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Brain tissue; Psychology; Medicine; Artificial intelligence; Computer science; Neuroscience","score_opus":0.07988444207565104,"score_gpt":0.23443262877958998,"score_spread":0.15454818670393894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3028312999","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018207696,0.000015572163,0.98072416,0.008483086,0.00015227174,0.0009426483,0.000011561221,0.0005775518,0.007272399],"genre_scores_gemma":[0.9215281,0.000031850755,0.07264341,0.004810054,0.00007382766,0.0000131138595,0.00010130855,0.000030024994,0.00076833356],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99696743,0.00038749218,0.0003634108,0.0016735073,0.0002284963,0.00037966025],"domain_scores_gemma":[0.99791336,0.00015719843,0.0001788477,0.0012215817,0.00009933427,0.00042966346],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004930819,0.00034637275,0.0004021215,0.00046551638,0.00008887454,0.0001477793,0.0025555831,0.00036001948,0.00004419743],"category_scores_gemma":[0.00018075483,0.0004237207,0.00010991255,0.001442191,0.0001359961,0.00041264726,0.0019645537,0.00091752765,0.000187011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023284774,0.0022152793,0.0016601383,0.0014837465,0.00027587175,0.0019731007,0.01271899,0.17867808,0.011065041,0.50433683,0.15592378,0.1294363],"study_design_scores_gemma":[0.0006755265,0.0000996164,0.0015712166,0.00017172417,0.000016829319,0.000008355383,0.0003369753,0.9805913,0.004898698,0.0092900125,0.0016438419,0.0006958856],"about_ca_topic_score_codex":0.00016873777,"about_ca_topic_score_gemma":0.000023550008,"teacher_disagreement_score":0.9197073,"about_ca_system_score_codex":0.00040358753,"about_ca_system_score_gemma":0.00019446856,"threshold_uncertainty_score":0.9998215},"labels":[],"label_agreement":null},{"id":"W3033051258","doi":"10.1016/j.jneumeth.2020.108789","title":"Detector of 3-D salient points based on the dual-tree complex wavelet transform for the positioning of hippocampi meshes in magnetic resonance images","year":2020,"lang":"en","type":"article","venue":"Journal of Neuroscience Methods","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; Canadian Institutes of Health Research; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Artificial intelligence; Complex wavelet transform; Computer science; Hausdorff distance; Computer vision; Segmentation; Polygon mesh; Pattern recognition (psychology); Similarity (geometry); Wavelet; Wavelet transform; Mathematics; Discrete wavelet transform; Image (mathematics)","score_opus":0.08158911913647764,"score_gpt":0.3700781988635437,"score_spread":0.28848907972706606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033051258","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029123193,0.000114947514,0.976589,0.019815736,0.00014749786,0.0003419517,0.000006851958,0.000011982545,0.000059700542],"genre_scores_gemma":[0.2210756,0.000036666534,0.7730915,0.0057489863,0.00002361432,0.000011977777,8.641618e-8,0.000006762793,0.0000047995927],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99747103,0.0007136789,0.0007077942,0.000210117,0.0007016321,0.00019576318],"domain_scores_gemma":[0.9961859,0.002785638,0.00047995997,0.0002707727,0.00018745632,0.000090272944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035461332,0.000115018855,0.00027113833,0.00013327945,0.0000979895,0.00006416681,0.0012145861,0.000025580977,0.000015847269],"category_scores_gemma":[0.0026215177,0.00006489106,0.00014139789,0.00079645053,0.0003710801,0.0002722539,0.00006990956,0.00024097842,1.6691774e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005830418,0.0000611699,0.00002380062,0.000026906397,0.0000011122095,0.000008929065,0.00050059916,0.000118888325,0.6854953,0.00026246964,0.00024271378,0.31319976],"study_design_scores_gemma":[0.00046377993,0.0015273775,0.012605179,0.00009911,0.00001033976,0.00001646377,0.00008456212,0.20615105,0.7768492,0.0018181998,0.0002991877,0.00007551023],"about_ca_topic_score_codex":0.000003868876,"about_ca_topic_score_gemma":7.13477e-7,"teacher_disagreement_score":0.31312424,"about_ca_system_score_codex":0.000025357707,"about_ca_system_score_gemma":0.00012102786,"threshold_uncertainty_score":0.31383908},"labels":[],"label_agreement":null},{"id":"W3034833495","doi":"10.1007/978-3-030-50120-4_15","title":"An Unsupervised Learning Approach to Discontinuity-Preserving Image Registration","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Classification of discontinuities; Computer science; Smoothness; Artificial intelligence; Discontinuity (linguistics); Image (mathematics); Image registration; Computer vision; Domain (mathematical analysis); Function (biology); Pattern recognition (psychology); Mathematics","score_opus":0.02248690502111718,"score_gpt":0.27910977858262226,"score_spread":0.2566228735615051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034833495","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000025236815,0.000044638244,0.9878015,0.0012458017,0.0003201496,0.000663618,0.0000024009826,0.000616938,0.0092797475],"genre_scores_gemma":[0.031193918,0.000009154638,0.9653944,0.0026861678,0.00037133697,0.00002522322,0.000024090708,0.000039017294,0.0002566936],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99533325,0.0001219712,0.0006155144,0.0019751461,0.0014080912,0.0005460055],"domain_scores_gemma":[0.9972948,0.00019939434,0.0002815383,0.0014745952,0.00025842656,0.0004912812],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0012058336,0.00048332108,0.00049325125,0.0005922767,0.00027972285,0.0015042961,0.0051086317,0.00023418169,0.00002588377],"category_scores_gemma":[0.00043015697,0.00046515305,0.00009753713,0.0008127528,0.00041441567,0.0018274694,0.0016188375,0.0011458297,0.000038491744],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009033425,0.00007292178,0.000025426536,0.00011002162,0.000010012335,0.00008323471,0.0042964886,0.011735445,0.016884763,0.009357405,0.00018097399,0.95723426],"study_design_scores_gemma":[0.00023093227,0.00040151068,0.000072032344,0.00029018222,0.00000793697,0.000027421534,0.0000016638407,0.94589925,0.019263549,0.032586966,0.00037371423,0.0008448144],"about_ca_topic_score_codex":0.000038565515,"about_ca_topic_score_gemma":0.000020505742,"teacher_disagreement_score":0.9563895,"about_ca_system_score_codex":0.0002265112,"about_ca_system_score_gemma":0.00035543882,"threshold_uncertainty_score":0.99978},"labels":[],"label_agreement":null},{"id":"W3035229831","doi":"10.1049/iet-ipr.2019.1428","title":"Eigenstructure involving the histogram for image thresholding","year":2020,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Humber Polytechnic","funders":"","keywords":"Thresholding; Balanced histogram thresholding; Histogram; Image histogram; Artificial intelligence; Computer vision; Image (mathematics); Computer science; Histogram matching; Pattern recognition (psychology); Image segmentation; Image texture","score_opus":0.029766948938383716,"score_gpt":0.2988947107943282,"score_spread":0.2691277618559445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035229831","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0003558691,0.0008086997,0.989006,0.007958726,0.000121554964,0.00042579585,0.000003223372,0.0007316522,0.00058851705],"genre_scores_gemma":[0.086704075,0.000015644226,0.90386295,0.009020281,0.00025300687,0.00007859272,0.00000529291,0.00002712009,0.000033035398],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984468,0.000045980694,0.00031287418,0.0004672456,0.00036727745,0.00035978973],"domain_scores_gemma":[0.9990032,0.00010807465,0.0001886966,0.00031241088,0.0002297193,0.0001579549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036333292,0.00018541804,0.00017866014,0.00004776374,0.00045673831,0.0009579345,0.0011983018,0.000060091148,0.000026676615],"category_scores_gemma":[0.0004838834,0.00013529992,0.00008932439,0.00047074797,0.00018988614,0.0015117865,0.000284849,0.00027401454,0.000010356352],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008726573,0.000019233667,0.00003978398,0.00034594553,0.000011451783,0.00002303619,0.0056537483,0.0000034294446,0.33299458,0.0007056752,0.01804692,0.6421475],"study_design_scores_gemma":[0.00080927607,0.00015101889,0.00009420554,0.0002043674,0.00004290777,0.000059275448,0.0010093249,0.2998574,0.6739832,0.0150838,0.007996595,0.00070861337],"about_ca_topic_score_codex":0.0000047155895,"about_ca_topic_score_gemma":6.4433925e-7,"teacher_disagreement_score":0.64143884,"about_ca_system_score_codex":0.00004748852,"about_ca_system_score_gemma":0.00012152206,"threshold_uncertainty_score":0.9237384},"labels":[],"label_agreement":null},{"id":"W3035596171","doi":"10.3934/dcdss.2020389","title":"Segmentation of color images using mean curvature flow and parametric curves","year":2020,"lang":"en","type":"article","venue":"Discrete and Continuous Dynamical Systems - S","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Parametric statistics; Segmentation; Computer vision; Image segmentation; Parametric equation; Curvature; Intersection (aeronautics); Mathematics; Mean curvature flow; Scale-space segmentation; Computer science; Pattern recognition (psychology); Mean curvature; Geometry; Statistics; Geography","score_opus":0.015256154679259573,"score_gpt":0.26315132338860653,"score_spread":0.24789516870934697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035596171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0870426,0.0030656068,0.908501,0.0005701988,0.00008367734,0.00050146197,0.000031180774,0.0001297766,0.0000744545],"genre_scores_gemma":[0.93465257,0.00027197608,0.06438178,0.0005660356,0.000040873365,0.00002148351,0.000022617101,0.000011259135,0.00003140644],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986139,0.0001620061,0.0003788308,0.00035350883,0.0003181473,0.00017361548],"domain_scores_gemma":[0.9992573,0.0001160855,0.00019431635,0.00015808185,0.00008611989,0.00018808847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002244156,0.00014623863,0.00034913426,0.000063768595,0.00005942395,0.00014030818,0.00022996194,0.00007582037,0.0000036683314],"category_scores_gemma":[0.00013036854,0.00012050139,0.00004106708,0.00036498104,0.00012236775,0.00038190614,0.00015576555,0.00012670852,6.3102345e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021548932,0.0004007788,0.03285747,0.01111534,0.0007883132,0.0002477647,0.010726193,0.00033648362,0.5151285,0.016798312,0.008540896,0.40284446],"study_design_scores_gemma":[0.00071863114,0.00037225435,0.001578146,0.0005156484,0.00007721513,0.000036652422,0.00045387915,0.9872656,0.008403223,0.0001691174,0.00006260538,0.00034705398],"about_ca_topic_score_codex":0.00013893929,"about_ca_topic_score_gemma":0.0000020475736,"teacher_disagreement_score":0.9869291,"about_ca_system_score_codex":0.000017418042,"about_ca_system_score_gemma":0.000017875209,"threshold_uncertainty_score":0.4913904},"labels":[],"label_agreement":null},{"id":"W3038044614","doi":"10.1016/j.patcog.2020.107520","title":"Active contour model for inhomogenous image segmentation based on Jeffreys divergence","year":2020,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Segmentation; Robustness (evolution); Divergence (linguistics); Active contour model; Artificial intelligence; Image segmentation; Computer science; Pattern recognition (psychology); Curve fitting; Scale-space segmentation; Computer vision; Euclidean geometry; Euclidean distance; Algorithm; Mathematics; Geometry; Machine learning","score_opus":0.06130237279996337,"score_gpt":0.29457821689050184,"score_spread":0.23327584409053848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3038044614","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003651909,0.0000029371993,0.99311066,0.0016158746,0.000103136284,0.0008224163,0.00015242978,0.0003306619,0.00020998268],"genre_scores_gemma":[0.5802546,0.000009939326,0.40272743,0.016082607,0.000108714914,0.0004530241,0.00032746847,0.000020860693,0.000015362288],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998721,0.00008254097,0.0002368828,0.0004233791,0.0003334771,0.00020270339],"domain_scores_gemma":[0.99920326,0.0001329465,0.00015140708,0.0001686571,0.00019179437,0.00015194048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015125003,0.00014645813,0.00013343897,0.00007094821,0.00010604121,0.00009682781,0.0003118458,0.00005542421,0.000106269465],"category_scores_gemma":[0.000148462,0.00014946783,0.00007303897,0.00014924102,0.000029209632,0.0005874873,0.000053867287,0.00010917322,0.00013519004],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050421862,0.000107668464,0.000057878166,0.00006751472,0.000011172883,0.0000073244096,0.0013950394,0.00016448605,0.040106766,0.000005727257,0.0019041599,0.95612186],"study_design_scores_gemma":[0.00058395235,0.00022744964,0.00008230863,0.000027513586,0.000009682908,5.8433506e-7,0.00003776337,0.68629164,0.31185237,0.00074316555,0.0000045988218,0.00013899575],"about_ca_topic_score_codex":0.000014978237,"about_ca_topic_score_gemma":0.000005152797,"teacher_disagreement_score":0.95598286,"about_ca_system_score_codex":0.00007119139,"about_ca_system_score_gemma":0.00004686892,"threshold_uncertainty_score":0.6095121},"labels":[],"label_agreement":null},{"id":"W3046361972","doi":"10.1007/978-3-030-54407-2_33","title":"Level Sets Driven by Adaptive Hybrid Region-Based Energy for Medical Image Segmentation","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Image segmentation; Segmentation; Energy (signal processing); Artificial intelligence; Level set (data structures); Image (mathematics); Region growing; Computer vision; Set (abstract data type); Intensity (physics); Pattern recognition (psychology); Scale-space segmentation; Mathematics; Optics; Statistics; Physics","score_opus":0.028193920657566256,"score_gpt":0.28086967898808535,"score_spread":0.2526757583305191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046361972","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000012879062,0.00011818074,0.992799,0.004969927,0.0006865154,0.0006545072,0.000057275724,0.00036534964,0.0003479451],"genre_scores_gemma":[0.006992198,0.000033772463,0.9762789,0.016017845,0.00027438777,0.000095064584,0.00011707435,0.000050110553,0.000140636],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947915,0.00008222011,0.00072420674,0.0017745357,0.0020483485,0.00057918497],"domain_scores_gemma":[0.996768,0.0010477096,0.00044712675,0.0008411571,0.00038849268,0.0005075376],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00063507055,0.00055794814,0.00056947983,0.00048501266,0.00022997719,0.00040194162,0.003336069,0.00030354722,0.00004919557],"category_scores_gemma":[0.00037739327,0.0005326259,0.00018086427,0.00041730498,0.0009900351,0.0006926751,0.0007393687,0.0006214443,0.000015045194],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018090916,0.000044377037,0.0000029237171,0.00004760332,0.000016894546,0.00018900794,0.0001846272,0.00042210432,0.0010159082,0.003450878,0.005510286,0.9890973],"study_design_scores_gemma":[0.0006764056,0.00041562144,0.0000027973304,0.00033264828,0.000011274803,0.0000406795,2.7494244e-7,0.8759836,0.080260105,0.041022446,0.00063189445,0.00062225957],"about_ca_topic_score_codex":0.00003177788,"about_ca_topic_score_gemma":0.000021609465,"teacher_disagreement_score":0.988475,"about_ca_system_score_codex":0.00042851965,"about_ca_system_score_gemma":0.0012942962,"threshold_uncertainty_score":0.9997125},"labels":[],"label_agreement":null},{"id":"W3046895563","doi":"","title":"A Survey on Shape Correspondence","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Correspondence problem; Shape analysis (program analysis); Representation (politics); Computer science; Polygon mesh; Correspondence analysis; Point (geometry); Pipeline (software); Space (punctuation); Artificial intelligence; Algorithm; Mathematics; Theoretical computer science; Machine learning; Geometry; Static analysis","score_opus":0.030257396851451936,"score_gpt":0.31911544890015936,"score_spread":0.2888580520487074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046895563","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014191094,0.0000013045538,0.9767329,0.0003569835,0.0004014991,0.00007883437,0.0000010242758,0.0004257402,0.0078106085],"genre_scores_gemma":[0.5875854,0.0000023287491,0.39976645,0.0070030265,0.000040241415,0.000014872108,0.0000037217333,0.0000062983045,0.0055776676],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99925023,0.00006020257,0.00010022888,0.0002028355,0.00026893802,0.0001175465],"domain_scores_gemma":[0.99912095,0.00028944568,0.000026818634,0.00040742086,0.000058602505,0.000096785654],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005883895,0.000055607914,0.000054098327,0.00005806907,0.000034358418,0.000090618414,0.00072046305,0.0000360224,0.0013919309],"category_scores_gemma":[0.00048040008,0.000043548236,0.000015182853,0.00021181013,0.00004213943,0.00021613354,0.00010899438,0.00017417182,0.0006342765],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008835775,0.000114818286,0.0034776188,0.000002370718,0.0000032701355,0.0000225292,0.00014406434,2.5796507e-7,0.059209008,0.026478583,0.126221,0.7843176],"study_design_scores_gemma":[0.0003712832,0.00032288584,0.39501718,0.0000146365965,0.0000011978307,0.000013033379,0.0000061728333,0.07521213,0.5227598,0.0027003365,0.0031802398,0.00040112983],"about_ca_topic_score_codex":0.00007380954,"about_ca_topic_score_gemma":0.00007496982,"teacher_disagreement_score":0.78391653,"about_ca_system_score_codex":0.000005496002,"about_ca_system_score_gemma":0.000037104604,"threshold_uncertainty_score":0.99952096},"labels":[],"label_agreement":null},{"id":"W3046930396","doi":"10.18280/ria.340302","title":"State-of-the Art Optimal Multilevel Thresholding Methods for Brain MR Image Analysis","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Thresholding; Image (mathematics); Artificial intelligence; State (computer science); Computer science; Computer vision; Pattern recognition (psychology); Psychology; Algorithm","score_opus":0.07492645802293127,"score_gpt":0.3839715316763195,"score_spread":0.3090450736533882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046930396","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00071356737,0.00006197804,0.992975,0.0052875793,0.00012676483,0.00051145395,0.000010709653,0.00014393836,0.00016902495],"genre_scores_gemma":[0.042054,0.0000123829095,0.95567447,0.001567981,0.0000326052,0.000062194864,0.0000049642626,0.00001375056,0.000577649],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99811035,0.00019356808,0.000645407,0.0005181586,0.00023042999,0.00030209764],"domain_scores_gemma":[0.99786687,0.0008335815,0.00026258026,0.00067245564,0.00020633037,0.00015821302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011490395,0.00015967514,0.00032441097,0.00012694106,0.00013377013,0.000113202805,0.0013912102,0.000047694437,0.000112852314],"category_scores_gemma":[0.0013228235,0.00013119729,0.00034123313,0.0014706277,0.00016247132,0.00034195947,0.0003722288,0.00017616602,0.00004456734],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017753497,0.00010134097,0.00007748654,0.00011845431,0.00015729365,0.0000037257573,0.005046449,0.026296621,0.2987884,0.0026255697,0.0061723585,0.6605945],"study_design_scores_gemma":[0.000018866918,0.000040611427,0.000011196361,0.0000120960885,0.000024080407,7.836185e-7,0.00005953398,0.5128145,0.4851885,0.0006469821,0.0011033218,0.00007946816],"about_ca_topic_score_codex":0.000008015959,"about_ca_topic_score_gemma":0.0000014001613,"teacher_disagreement_score":0.66051507,"about_ca_system_score_codex":0.000031224852,"about_ca_system_score_gemma":0.000053874017,"threshold_uncertainty_score":0.535007},"labels":[],"label_agreement":null},{"id":"W3049136820","doi":"10.1038/s41598-020-69163-z","title":"A multimodal computational pipeline for 3D histology of the human brain","year":2020,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"European Research Council; University College London; National Institute for Health and Care Research; Koning Boudewijnstichting; Multiple System Atrophy Coalition; Reta Lila Weston Institute of Neurological Studies, UCL Queen Square Institute of Neurology,University College London; Multiple System Atrophy Trust; Wellcome Trust; Medical Research Council; Wellcome","keywords":"Histology; Human brain; Computer science; Ex vivo; Pipeline (software); Magnetic resonance imaging; Neuroimaging; 3d model; Pathology; Biomedical engineering; Anatomy; In vivo; Medicine; Artificial intelligence; Biology; Neuroscience; Radiology","score_opus":0.029233105677387326,"score_gpt":0.3099053153335131,"score_spread":0.28067220965612577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3049136820","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036763418,0.000014787388,0.98803216,0.0062242863,0.0013549487,0.0003951927,0.000002514534,0.00010679464,0.0001929708],"genre_scores_gemma":[0.42018506,5.233487e-8,0.5767395,0.0020373778,0.000053076532,0.0000356687,0.00002892682,0.0000068326326,0.000913544],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983861,0.000060844937,0.000483647,0.00047349778,0.00045259867,0.00014330953],"domain_scores_gemma":[0.99873817,0.000105762825,0.00035837066,0.00046774227,0.0002408362,0.00008912006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008265044,0.00007175279,0.00013323686,0.00005393249,0.00020605362,0.00007370483,0.00049941713,0.00003421542,0.000045640885],"category_scores_gemma":[0.00068398856,0.000053863165,0.0000849817,0.00037438102,0.00034986442,0.00015281583,0.00020924455,0.00006953146,0.0000028394954],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000056367567,0.00019337259,0.0011146439,0.000116526084,0.000018888843,0.000071195325,0.0034687372,0.00083033025,0.19751841,0.006231653,0.71317846,0.07725214],"study_design_scores_gemma":[0.0006902722,0.00012272307,0.0012592042,0.000043715536,0.000016917254,0.00015012061,0.000044823788,0.6286016,0.23885076,0.08021627,0.049683172,0.000320393],"about_ca_topic_score_codex":0.000008348871,"about_ca_topic_score_gemma":0.0000024736855,"teacher_disagreement_score":0.6634953,"about_ca_system_score_codex":0.000022733711,"about_ca_system_score_gemma":0.00015230455,"threshold_uncertainty_score":0.21964762},"labels":[],"label_agreement":null},{"id":"W3081927078","doi":"10.1109/embc44109.2020.9176059","title":"Validation of a diffeomorphic registration algorithm using true deformation computed from thin plate spline interpolation","year":2020,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research","keywords":"Image registration; Diffeomorphism; Artificial intelligence; Spline (mechanical); Algorithm; Computer science; Interpolation (computer graphics); Thin plate spline; Computer vision; Mathematics; Spline interpolation; Image (mathematics); Mathematical analysis; Physics","score_opus":0.04175146224498659,"score_gpt":0.2834749003851779,"score_spread":0.24172343814019132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081927078","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033646647,0.000007935404,0.96484244,0.0007318472,0.00010299406,0.00025821305,0.000010136157,0.0003187333,0.000081064085],"genre_scores_gemma":[0.337781,0.000002554715,0.6615544,0.00043573475,0.000052148698,0.0000029425441,0.0001622637,0.0000048670877,0.00000409253],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985904,0.00010731245,0.0005669059,0.00024433667,0.00039019846,0.00010083259],"domain_scores_gemma":[0.9990734,0.00007484272,0.00039815455,0.00021799543,0.00014629718,0.00008935082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022991677,0.00011208259,0.0001673548,0.000089395624,0.000050462913,0.00010075531,0.000313774,0.00007150553,0.000071382274],"category_scores_gemma":[0.00008790417,0.00010428828,0.000042407955,0.00037894154,0.00003451595,0.0013072003,0.00010076487,0.000107189066,0.00001094772],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047237423,0.00017719896,0.00046515363,0.00010084291,0.00007294656,0.000008891524,0.00795816,0.0024140896,0.71991277,0.0041285,0.0012455202,0.2634687],"study_design_scores_gemma":[0.00024621544,0.000056496516,0.0002675913,0.000026842572,0.0000067006804,0.0000019082597,0.000034785648,0.70299166,0.2951545,0.0011370506,0.000003641804,0.00007259705],"about_ca_topic_score_codex":0.00020294709,"about_ca_topic_score_gemma":0.00000203027,"teacher_disagreement_score":0.70057756,"about_ca_system_score_codex":0.00004300755,"about_ca_system_score_gemma":0.0000450951,"threshold_uncertainty_score":0.42527527},"labels":[],"label_agreement":null},{"id":"W3082747761","doi":"10.1109/embc44109.2020.9176322","title":"Automated Analysis of Brain Microvasculature: From Segmentation to Anatomical Modeling","year":2020,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Computer vision; Pipeline (software); Convolutional neural network; Graph; Image segmentation; Pattern recognition (psychology); Skeletonization; Theoretical computer science","score_opus":0.021948465984900866,"score_gpt":0.30169781010986757,"score_spread":0.2797493441249667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082747761","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08168154,0.000013444967,0.9133395,0.003968381,0.00002115669,0.00013226674,0.000006881495,0.0007911222,0.000045729],"genre_scores_gemma":[0.4199357,0.0000013523419,0.5726857,0.0073185912,0.000009332333,0.0000047397707,0.000036742957,0.0000034595432,0.0000043689265],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988223,0.00007119828,0.0003217998,0.0003373011,0.00033497793,0.000112461224],"domain_scores_gemma":[0.9993459,0.000073868025,0.000056613037,0.00025892825,0.00008007638,0.00018459687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014237323,0.00008474152,0.00022383439,0.0001966875,0.00002044605,0.000057080724,0.00046042248,0.000048295027,0.0001472302],"category_scores_gemma":[0.00011728143,0.00007734789,0.00011257779,0.0018480725,0.000012910955,0.00023940163,0.00015940267,0.00006454902,0.000017148508],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014048848,0.00006752907,0.00049169944,0.000012940633,0.000900576,0.000007258337,0.0039110463,0.02611666,0.9134945,0.00078236137,0.014354941,0.039846394],"study_design_scores_gemma":[0.00010957916,0.00001940914,0.0003191649,0.0000030656904,0.000059568385,7.946259e-8,0.00003968477,0.79506,0.20425227,0.000063026615,0.0000064450974,0.0000677479],"about_ca_topic_score_codex":0.00020178726,"about_ca_topic_score_gemma":0.000010499993,"teacher_disagreement_score":0.7689433,"about_ca_system_score_codex":0.000026512285,"about_ca_system_score_gemma":0.000025852722,"threshold_uncertainty_score":0.31541553},"labels":[],"label_agreement":null},{"id":"W3087714138","doi":"10.1016/j.mri.2020.09.013","title":"Balanced multi-image demons for non-rigid registration of magnetic resonance images","year":2020,"lang":"en","type":"article","venue":"Magnetic Resonance Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ontario Ministry of Research, Innovation and Science","keywords":"Artificial intelligence; Image registration; Computer science; Histogram; Algorithm; Computer vision; Image processing; Mathematics; Pattern recognition (psychology); Image (mathematics)","score_opus":0.018495913211896958,"score_gpt":0.2844581556116756,"score_spread":0.2659622423997786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087714138","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007384627,0.008594162,0.9829881,0.0049339454,0.00014660895,0.001069013,0.00004345164,0.00036910165,0.0011171277],"genre_scores_gemma":[0.06565281,0.00038179892,0.9298584,0.0026338312,0.00012312914,0.0002842057,0.0000147182545,0.000045405166,0.0010057201],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99698985,0.00010310147,0.00084543816,0.00089073146,0.0006022395,0.000568614],"domain_scores_gemma":[0.9980668,0.00025721404,0.0003227186,0.00077273516,0.00034010175,0.00024046897],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047080533,0.00032019842,0.00043098262,0.00011521501,0.00014408659,0.00021788159,0.0013091611,0.000067357854,0.000077982026],"category_scores_gemma":[0.0008337463,0.00033138084,0.00014309773,0.00064917695,0.00036919347,0.00086793,0.00025169042,0.00022796738,0.000026035988],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041622905,0.00009226293,0.0021974475,0.00017178484,0.00000192598,0.00003851135,0.0006423532,0.0000027181527,0.1664114,0.00052887306,0.023045467,0.80682564],"study_design_scores_gemma":[0.004784476,0.001043602,0.07792355,0.0004931654,0.00004360967,0.00003719799,0.00017976253,0.48037758,0.39496446,0.0015171121,0.03751549,0.001119993],"about_ca_topic_score_codex":0.000056668257,"about_ca_topic_score_gemma":0.000004064515,"teacher_disagreement_score":0.80570567,"about_ca_system_score_codex":0.000048323345,"about_ca_system_score_gemma":0.00014424913,"threshold_uncertainty_score":0.9999138},"labels":[],"label_agreement":null},{"id":"W3092442943","doi":"10.1002/mrm.28547","title":"BISON: Brain tissue segmentation pipeline using T <sub>1</sub> ‐weighted magnetic resonance images and a random forest classifier","year":2020,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Canadian Institutes of Health Research","keywords":"Segmentation; Random forest; Magnetic resonance imaging; Artificial intelligence; Hyperintensity; White matter; Computer science; Image segmentation; Pattern recognition (psychology); Kappa; Nuclear medicine; Medicine; Radiology; Mathematics","score_opus":0.023469087844184753,"score_gpt":0.29607475906204794,"score_spread":0.27260567121786317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092442943","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06668939,0.057007287,0.845046,0.028570246,0.00027166988,0.001618119,0.000010453527,0.00035573682,0.00043112214],"genre_scores_gemma":[0.32802373,0.013480141,0.6154329,0.03950111,0.0015112645,0.0005855603,0.000077012184,0.00018299687,0.0012052594],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99613523,0.00038469298,0.0009954118,0.0009845347,0.00093018747,0.00056994753],"domain_scores_gemma":[0.9981919,0.0005442258,0.0002214627,0.00052231527,0.00015028019,0.0003698198],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009044909,0.0003971981,0.0006505208,0.00027596508,0.00011954339,0.00011031868,0.0006896175,0.00015107753,0.00015434013],"category_scores_gemma":[0.0009981222,0.00034656053,0.00004066502,0.0013857909,0.00059436716,0.0005139909,0.00027400992,0.0004294639,0.000019837351],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102390506,0.000043463664,0.0014999587,0.00008912904,0.0000014483385,0.00021519166,0.0012078749,0.000005233998,0.17669189,0.000107196916,0.013045427,0.8069908],"study_design_scores_gemma":[0.025056107,0.0044595106,0.055155363,0.0021189116,0.00009039274,0.00024889284,0.00062801765,0.56878483,0.2963926,0.004143647,0.041320294,0.0016014407],"about_ca_topic_score_codex":0.00011497902,"about_ca_topic_score_gemma":0.00004261591,"teacher_disagreement_score":0.80538934,"about_ca_system_score_codex":0.00008566771,"about_ca_system_score_gemma":0.00009378111,"threshold_uncertainty_score":0.9998986},"labels":[],"label_agreement":null},{"id":"W3099227255","doi":"10.3389/fninf.2020.601829","title":"Semi-Supervised Learning in Medical Images Through Graph-Embedded Random Forest","year":2020,"lang":"en","type":"article","venue":"Frontiers in Neuroinformatics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"ACT-X; National Natural Science Foundation of China","keywords":"Random forest; Computer science; Bottleneck; Artificial intelligence; Machine learning; Graph; Robustness (evolution); Entropy (arrow of time); Medical imaging; Random graph; Data mining; Theoretical computer science","score_opus":0.014374720544343479,"score_gpt":0.25773240143457266,"score_spread":0.2433576808902292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3099227255","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031688786,0.00010226838,0.99080086,0.0027755,0.00036166888,0.00038028258,0.0000013208377,0.00039350914,0.0020157322],"genre_scores_gemma":[0.06596197,0.0009365835,0.9182259,0.014659237,0.00006966312,0.00005282764,0.000021713917,0.000028656876,0.00004345097],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974396,0.00018870978,0.0008429534,0.00026091092,0.0008612305,0.00040656177],"domain_scores_gemma":[0.9990644,0.00018546365,0.00015952294,0.0003063788,0.000038869664,0.00024540513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005319173,0.00020171668,0.0003952612,0.00020871223,0.000059080045,0.00014281271,0.0011937699,0.000121821715,0.00003053041],"category_scores_gemma":[0.0015742733,0.00019137588,0.00007918034,0.0010028366,0.00013763778,0.0016360978,0.00034443315,0.00078654836,0.000019293198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003285083,0.00042618945,0.08787452,0.0011833905,0.00007785464,0.001761454,0.08726945,0.011168985,0.00053469953,0.0018567635,0.38208273,0.42543548],"study_design_scores_gemma":[0.0033364585,0.00013200956,0.00069036696,0.00010353939,0.000004237432,0.0000135261735,0.00088585605,0.98920393,0.0023854366,0.0016978459,0.0012784597,0.00026835938],"about_ca_topic_score_codex":0.000010275617,"about_ca_topic_score_gemma":0.000003052679,"teacher_disagreement_score":0.9780349,"about_ca_system_score_codex":0.000039836745,"about_ca_system_score_gemma":0.00013101834,"threshold_uncertainty_score":0.78040814},"labels":[],"label_agreement":null},{"id":"W3106325597","doi":"","title":"Adaptive Gradient Quantization for Data-Parallel SGD","year":2020,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Hyperparameter; Quantization (signal processing); Computer science; Parametric statistics; Artificial intelligence; Algorithm; Heuristic; Machine learning; Theoretical computer science; Mathematics; Statistics","score_opus":0.11280027183829801,"score_gpt":0.3202749403614523,"score_spread":0.20747466852315433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106325597","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006561817,0.00009479273,0.99633896,0.0015949928,0.00028572336,0.0007250825,0.000018875971,0.00061562064,0.00026035515],"genre_scores_gemma":[0.55562323,0.000012732031,0.43559664,0.0071057915,0.0002639601,0.0002819446,0.0010428142,0.000017331113,0.0000555428],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857277,0.000046437104,0.00057355285,0.00022583822,0.00039757055,0.00018385341],"domain_scores_gemma":[0.9987974,0.00004418728,0.0004115949,0.00030212454,0.00031176006,0.00013294244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030624692,0.000122714,0.00015609134,0.000080756836,0.0001691688,0.00070109393,0.00090204895,0.000052428935,0.0000023392904],"category_scores_gemma":[0.00021451304,0.00010771616,0.00002457446,0.00036634057,0.000030008418,0.0083498685,0.00016377271,0.00008928727,0.000034032393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000086100794,0.000057708774,0.000089282155,0.0019835606,0.000033747172,0.0000028611444,0.013944652,0.007781766,0.0014025164,0.02776122,0.063617826,0.88323873],"study_design_scores_gemma":[0.00029650863,0.00010778471,0.000015841002,0.00004484655,0.000004945857,0.000006994908,0.00025805584,0.99119216,0.0006673572,0.00006068883,0.0072190603,0.0001257824],"about_ca_topic_score_codex":0.000016115167,"about_ca_topic_score_gemma":3.683867e-7,"teacher_disagreement_score":0.98341036,"about_ca_system_score_codex":0.00003398054,"about_ca_system_score_gemma":0.00008133937,"threshold_uncertainty_score":0.6760665},"labels":[],"label_agreement":null},{"id":"W3107405636","doi":"10.18280/ria.340502","title":"Intensity Profiles in Active Shape Model","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Active shape model; Computer science; Landmark; Intensity (physics); Active appearance model; Segmentation; Matching (statistics); Simplicity; Artificial intelligence; Process (computing); Computer vision; Image (mathematics); Mathematics; Physics","score_opus":0.07865083618006251,"score_gpt":0.30755031183262427,"score_spread":0.22889947565256175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107405636","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008686566,0.00003088742,0.98413557,0.0044607488,0.00006512847,0.0002973053,0.000002323727,0.00027395826,0.0020475038],"genre_scores_gemma":[0.8770194,0.00003648278,0.11993321,0.0028061746,0.00003894625,0.000039303122,0.0000033079257,0.000009734521,0.000113409274],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986036,0.000047743306,0.0003769628,0.0004937205,0.00020991598,0.00026810207],"domain_scores_gemma":[0.999203,0.00007705499,0.000087526096,0.00035360406,0.00010714588,0.00017161941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021677912,0.00013313336,0.00019998022,0.00008570193,0.00005368311,0.0000702706,0.0008308053,0.00006414465,0.00014264826],"category_scores_gemma":[0.00031833324,0.00013265402,0.00005717606,0.0006448059,0.00008547611,0.0004933641,0.00030881545,0.00027412435,0.00033774332],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007792633,0.00034444442,0.00049145886,0.00013289884,0.00001689454,0.00013753513,0.020664234,0.041415818,0.08776893,0.022487663,0.003861613,0.8226006],"study_design_scores_gemma":[0.000016149856,0.00004204588,0.00003333811,0.000024376233,9.952531e-7,0.000002780388,0.00025108946,0.65430933,0.3423518,0.002828978,0.000042426007,0.000096694464],"about_ca_topic_score_codex":0.000019468804,"about_ca_topic_score_gemma":0.000002814316,"teacher_disagreement_score":0.86833286,"about_ca_system_score_codex":0.00005189184,"about_ca_system_score_gemma":0.00006440152,"threshold_uncertainty_score":0.5409474},"labels":[],"label_agreement":null},{"id":"W3111025719","doi":"10.1016/j.media.2020.101939","title":"Image registration: Maximum likelihood, minimum entropy and deep learning","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Mental Health; Canadian Institutes of Health Research; National Cancer Institute; National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; Ontario Trillium Foundation","keywords":"Mutual information; Pairwise comparison; Artificial intelligence; Image registration; Computer science; Discriminative model; Entropy (arrow of time); Kullback–Leibler divergence; Metric (unit); Pattern recognition (psychology); Maximum likelihood; Iterative method; Principle of maximum entropy; Upper and lower bounds; Mathematics; Algorithm; Image (mathematics); Statistics","score_opus":0.009946999533290153,"score_gpt":0.26882140144290256,"score_spread":0.2588744019096124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111025719","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000643996,0.00023323922,0.97145694,0.025814453,0.000037466925,0.00011472184,0.000001231732,0.0004674757,0.0012304985],"genre_scores_gemma":[0.1106,0.00072363066,0.8746031,0.013340737,0.00038251403,0.000043755408,0.00007363385,0.000027979615,0.00020467355],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966576,0.00028918847,0.00058021815,0.00073161366,0.0013433553,0.00039801942],"domain_scores_gemma":[0.9980039,0.00019353279,0.00020259987,0.0004165605,0.00016878315,0.0010146007],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00073890237,0.00021856994,0.00044731348,0.00020522354,0.0001655656,0.0004289121,0.0008746075,0.0001296516,0.0018923141],"category_scores_gemma":[0.0021354773,0.00019635864,0.00020779086,0.0017434912,0.0004082066,0.00081348256,0.0004146308,0.00050067715,0.00014442763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032763342,0.00025521088,0.0033866952,0.00017335043,0.0012514183,0.0021500974,0.00391349,0.0000036932106,0.044823367,0.0009446437,0.019745639,0.92331964],"study_design_scores_gemma":[0.001166369,0.0003497464,0.0016125654,0.000034961544,0.0007871262,0.000041336138,0.00048437857,0.9688911,0.018258097,0.0023474563,0.0053279996,0.0006988589],"about_ca_topic_score_codex":0.00004306147,"about_ca_topic_score_gemma":0.0000121166195,"teacher_disagreement_score":0.9688874,"about_ca_system_score_codex":0.000029571414,"about_ca_system_score_gemma":0.00008116009,"threshold_uncertainty_score":0.9990201},"labels":[],"label_agreement":null},{"id":"W3115704871","doi":"10.1016/j.compmedimag.2020.101850","title":"MR and ultrasound cardiac image dynamic visualization and synchronization over Internet for distributed heart function diagnosis","year":2020,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"School of Information Technology, King Mongkut's University of Technology Thonburi; Robarts Research Institute; Center for Advanced Surgical Technology, University of Nebraska Medical Center; College of Arts and Sciences, Boston University; Illinois State University","keywords":"Computer science; Visualization; Rendering (computer graphics); The Internet; Synchronizing; Software; Web application; Computer vision; Multimedia; Computer graphics (images); Artificial intelligence; World Wide Web","score_opus":0.008927153646253664,"score_gpt":0.28010444509971305,"score_spread":0.27117729145345937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115704871","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0147521,0.0007946073,0.9797136,0.0037895502,0.00025122028,0.00034352162,0.000024854311,0.00032829493,0.0000022051515],"genre_scores_gemma":[0.89175224,0.0031344257,0.09271478,0.011532178,0.00020844747,0.00013117945,0.0004837056,0.00003721483,0.0000058506052],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984982,0.00012754832,0.00030284215,0.0005116423,0.00035036835,0.00020940491],"domain_scores_gemma":[0.99879575,0.00045461138,0.00009136661,0.00013889324,0.00011189135,0.00040749973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003801607,0.00017810406,0.00026817687,0.00011582546,0.00013066885,0.0003645179,0.00016584256,0.00008945544,0.00001004621],"category_scores_gemma":[0.0005544242,0.00017087306,0.00004556176,0.00039345727,0.00030686866,0.0005292947,0.00022485966,0.00016200873,5.4990534e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016938602,0.00029106165,0.06641534,0.0018258974,0.00031265197,0.000045777262,0.0040173256,0.0000027581066,0.017738633,0.030798823,0.07444282,0.8039395],"study_design_scores_gemma":[0.0012547633,0.000163265,0.009663144,0.00014966913,0.000050092705,0.00002453818,0.000034714452,0.9827521,0.0009081116,0.0015167986,0.0032121122,0.00027067852],"about_ca_topic_score_codex":0.000012989949,"about_ca_topic_score_gemma":8.012637e-7,"teacher_disagreement_score":0.98274934,"about_ca_system_score_codex":0.000017977029,"about_ca_system_score_gemma":0.000036678382,"threshold_uncertainty_score":0.6968001},"labels":[],"label_agreement":null},{"id":"W3124703593","doi":"10.59275/j.melba.2021-3581","title":"FlowReg: Fast Deformable Unsupervised Medical Image Registration using Optical Flow","year":2021,"lang":"en","type":"article","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto; St. Michael's Hospital","funders":"Canadian Institutes of Health Research; York University; National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; Alzheimer's Disease Neuroimaging Initiative; U.S. Department of Defense","keywords":"Artificial intelligence; Computer science; Image registration; Pixel; Computer vision; Neuroimaging; Affine transformation; Similarity (geometry); Optical flow; Pattern recognition (psychology); Deep learning; Image (mathematics); Mathematics","score_opus":0.014996950758150045,"score_gpt":0.30271601383979846,"score_spread":0.2877190630816484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124703593","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002192823,0.0005351202,0.9809822,0.01540259,0.0004471046,0.00012267174,0.0000020165485,0.00010373113,0.00021173136],"genre_scores_gemma":[0.0882025,0.00015006794,0.9087496,0.0020640553,0.00060792593,0.0000042570264,0.00003027774,0.00003588993,0.00015544923],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99620306,0.00048243455,0.00096375815,0.000237371,0.0016583365,0.0004550209],"domain_scores_gemma":[0.9974476,0.0007265291,0.0004604829,0.00033932214,0.0005534877,0.0004725896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048240996,0.00020297643,0.00035812243,0.00018953525,0.00037514349,0.00025641287,0.0011738138,0.0000984534,0.00020376247],"category_scores_gemma":[0.004325868,0.0001368691,0.0002062395,0.00056501187,0.00040712085,0.00092422735,0.0003446637,0.0010762914,0.000007028061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011600627,0.00035927127,0.00025831943,0.00018654502,0.00012499995,0.00074512174,0.0012472346,0.00075974636,0.11180194,0.00079750875,0.004149055,0.87945426],"study_design_scores_gemma":[0.0012898673,0.0001433996,0.000049330305,0.00019588604,0.00006426318,0.00311714,0.00018051469,0.9650901,0.026303109,0.00083711604,0.0025606926,0.00016860948],"about_ca_topic_score_codex":0.00002239384,"about_ca_topic_score_gemma":0.00000216523,"teacher_disagreement_score":0.9643303,"about_ca_system_score_codex":0.00011196425,"about_ca_system_score_gemma":0.00063565286,"threshold_uncertainty_score":0.558136},"labels":[],"label_agreement":null},{"id":"W3125452826","doi":"10.15353/jcvis.v6i1.3544","title":"A Hybrid Landmark and Contour-Matching Image Registration Model","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Landmark; Artificial intelligence; Computer vision; Image registration; Computer science; Matching (statistics); Thin plate spline; Similarity (geometry); Image (mathematics); Pattern recognition (psychology); Mathematics","score_opus":0.01069668156503452,"score_gpt":0.29288845605806013,"score_spread":0.2821917744930256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125452826","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016362336,0.0010025423,0.9801762,0.0019139628,0.00018540713,0.00006048816,0.0000020838013,0.000032312233,0.00026465073],"genre_scores_gemma":[0.69607353,0.000065366345,0.3032226,0.00047915927,0.00006925364,0.0000011174117,0.00000378936,0.000005529954,0.0000796425],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986298,0.00013407713,0.00050951197,0.0001442112,0.00048536676,0.00009704548],"domain_scores_gemma":[0.99864644,0.0002023618,0.00038514045,0.000094546376,0.00054253475,0.00012897677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074869755,0.00009397635,0.0002039833,0.000118372554,0.000102560676,0.00062908756,0.00013536122,0.000016341151,0.0000026225243],"category_scores_gemma":[0.00011577519,0.00007832238,0.000040561798,0.000078671495,0.000048216163,0.0011470566,0.000073981326,0.00013808026,9.618461e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014124457,0.0007081735,0.0021166122,0.0011952467,0.00031525374,0.0024922467,0.0091386335,0.06838377,0.16484174,0.11396248,0.111427344,0.52527726],"study_design_scores_gemma":[0.00063147536,0.000042095468,0.00077821536,0.0002848039,0.000009050878,0.0025314316,0.00018479583,0.97077364,0.00097623054,0.023491265,0.00018900419,0.000108020686],"about_ca_topic_score_codex":0.0000070568294,"about_ca_topic_score_gemma":1.998259e-7,"teacher_disagreement_score":0.9023898,"about_ca_system_score_codex":0.000028430317,"about_ca_system_score_gemma":0.00014610762,"threshold_uncertainty_score":0.60663056},"labels":[],"label_agreement":null},{"id":"W3126471493","doi":"10.21013/jas.v15.n4.p1","title":"Optimal Algorithm Selection in Multimodal Medical Image Registration","year":2021,"lang":"en","type":"article","venue":"IRA-International Journal of Applied Sciences (ISSN 2455-4499)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Image registration; Computer science; Artificial intelligence; Robustness (evolution); Viewpoints; Computer vision; Machine learning; Image (mathematics)","score_opus":0.014604276890608954,"score_gpt":0.32040180033483096,"score_spread":0.305797523444222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126471493","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013608882,0.00005363552,0.97692966,0.0037133596,0.0009182589,0.00012112087,0.000002732791,0.00007077181,0.0045815567],"genre_scores_gemma":[0.23595971,0.00010049424,0.7621612,0.0011195586,0.0005252719,0.000011441504,0.0000062811673,0.000009810477,0.00010626777],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9947088,0.00013154598,0.0010307402,0.0004954019,0.0032908702,0.0003426456],"domain_scores_gemma":[0.9979883,0.00022981448,0.00061855314,0.00016018252,0.00072887685,0.00027430046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002553787,0.00019120073,0.00028706537,0.00051369984,0.00013715589,0.000577799,0.0020736016,0.00014245349,0.0003683247],"category_scores_gemma":[0.00038889932,0.00017472914,0.00012520705,0.0009831194,0.00035142584,0.0015629112,0.00026791828,0.00055499707,0.000024844609],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011214011,0.0010836793,0.000721696,0.000021872966,0.0001256911,0.0020055387,0.0022161766,0.0026262584,0.10806262,0.025058202,0.0058574155,0.8521087],"study_design_scores_gemma":[0.0034184759,0.0004390933,0.0032376752,0.00030208105,0.000020364441,0.0024488922,0.00095910433,0.40432724,0.5738363,0.008506971,0.0018343716,0.0006693931],"about_ca_topic_score_codex":0.00003081106,"about_ca_topic_score_gemma":0.00002709675,"teacher_disagreement_score":0.8514393,"about_ca_system_score_codex":0.00026620238,"about_ca_system_score_gemma":0.0011089855,"threshold_uncertainty_score":0.7125248},"labels":[],"label_agreement":null},{"id":"W3134734884","doi":"10.1007/s11548-021-02323-2","title":"Multimodal 3D ultrasound and CT in image-guided spinal surgery: public database and new registration algorithms","year":2021,"lang":"en","type":"review","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Ultrasound; Image registration; Artificial intelligence; Computed tomography; Medical imaging; Computer vision; Medicine; Radiology; Image (mathematics)","score_opus":0.09948276466953584,"score_gpt":0.37187796829688213,"score_spread":0.27239520362734626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134734884","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010389317,0.50131786,0.49663898,0.0006161186,0.0011802651,0.000099706354,0.0000097236225,0.000023342187,0.00001009867],"genre_scores_gemma":[0.0001246052,0.8037553,0.1949532,0.0003830932,0.00060717395,0.0000062943714,0.00013966598,0.000014426965,0.000016262835],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9960752,0.00096358906,0.0016709093,0.0005337191,0.0004888293,0.00026773193],"domain_scores_gemma":[0.99435204,0.003453281,0.0012590622,0.00025162325,0.00037326422,0.0003106996],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0023652047,0.00035038582,0.0016275105,0.0011903922,0.00007030231,0.0005384077,0.0005364775,0.00019120905,0.00002116028],"category_scores_gemma":[0.00089276524,0.00030087674,0.0002730063,0.0003124261,0.00029384412,0.0011605112,0.00026206987,0.00067129155,8.198718e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007677449,0.00006144889,0.00044354144,0.00030408328,0.00027201953,0.0021321736,0.000030806063,2.2831557e-7,0.0000075482003,0.0001332322,0.00790239,0.98870486],"study_design_scores_gemma":[0.0025487484,0.00035156048,0.015075621,0.033092845,0.00053106475,0.38554037,0.000044569675,0.0054346407,0.00006657764,0.0008260171,0.55410755,0.0023804363],"about_ca_topic_score_codex":0.000036428373,"about_ca_topic_score_gemma":0.000005448275,"teacher_disagreement_score":0.9863244,"about_ca_system_score_codex":0.000114956645,"about_ca_system_score_gemma":0.001025591,"threshold_uncertainty_score":0.9999443},"labels":[],"label_agreement":null},{"id":"W3135946611","doi":"10.1109/tmi.2021.3123252","title":"Efficient Pairwise Neuroimage Analysis Using the Soft Jaccard Index and 3D Keypoint Sets","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; Université de Montréal; École de Technologie Supérieure","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Mental Health; Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Jaccard index; Pairwise comparison; Pattern recognition (psychology); Kernel (algebra); Equivalence (formal languages); Set (abstract data type); Measure (data warehouse); Distance measures","score_opus":0.017804923638111092,"score_gpt":0.2974749889264786,"score_spread":0.2796700652883675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135946611","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007928754,0.00012616995,0.98651725,0.004534789,0.00038035464,0.00014141123,0.00000455108,0.00028811346,0.00007860818],"genre_scores_gemma":[0.923225,0.00005427684,0.07069212,0.005907588,0.000038288756,0.000023419067,0.0000016544284,0.00001789394,0.000039796523],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99706113,0.00036908218,0.00040875617,0.00060854014,0.0011946451,0.00035785354],"domain_scores_gemma":[0.998293,0.000458869,0.0000651422,0.0006643626,0.00014333148,0.00037532477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007813482,0.00019877579,0.0002692494,0.00028258437,0.0003891062,0.000283014,0.00050860504,0.00006603426,0.00030974054],"category_scores_gemma":[0.00013165863,0.00015441094,0.00018483498,0.0014858283,0.00033177112,0.00020284283,0.00002643911,0.00064035103,0.000009915387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011034015,0.00040379458,0.00034721108,0.00003966854,0.00029415524,0.0007756847,0.0014407597,0.018123325,0.006870737,0.00004717094,0.00031722078,0.9713292],"study_design_scores_gemma":[0.0003132927,0.000010470439,0.0003432256,0.000045130055,0.00016514245,0.00015151409,0.00015101187,0.98095375,0.017585402,0.000062040046,0.000053637188,0.00016539328],"about_ca_topic_score_codex":0.00006238538,"about_ca_topic_score_gemma":0.00001192532,"teacher_disagreement_score":0.97116387,"about_ca_system_score_codex":0.00007025206,"about_ca_system_score_gemma":0.00020522137,"threshold_uncertainty_score":0.62966955},"labels":[],"label_agreement":null},{"id":"W3138648371","doi":"10.1016/j.ynirp.2021.100006","title":"Intracranial volume segmentation for neurodegenerative populations using multicentre FLAIR MRI","year":2021,"lang":"en","type":"article","venue":"Neuroimage Reports","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Michael's Hospital; University of Toronto; Toronto Metropolitan University","funders":"National Institute on Aging; Canadian Institutes of Health Research; Canada Foundation for Innovation; National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; Alzheimer's Disease Neuroimaging Initiative; U.S. Department of Defense","keywords":"Fluid-attenuated inversion recovery; Volume (thermodynamics); Medicine; Segmentation; Nuclear medicine; Radiology; Magnetic resonance imaging; Artificial intelligence; Computer science; Physics","score_opus":0.048895379486347514,"score_gpt":0.3344686202543206,"score_spread":0.2855732407679731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138648371","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026865063,0.000045114466,0.97007924,0.00073860027,0.0011936603,0.0006090106,0.000008105648,0.00033780898,0.00012340213],"genre_scores_gemma":[0.06959658,0.000011063013,0.9286294,0.0009251651,0.00019423473,0.00006017046,0.000076404256,0.000027991855,0.00047897274],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99781907,0.00016660197,0.0005852828,0.00072240893,0.00042270566,0.00028394253],"domain_scores_gemma":[0.99842376,0.000073307216,0.0003236108,0.00060775987,0.0004252261,0.00014636357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023329709,0.00017528582,0.00019387406,0.000090065725,0.00025309122,0.00028268882,0.00018138245,0.000058480266,0.00006776689],"category_scores_gemma":[0.00054907735,0.0001917002,0.00010832922,0.00037542562,0.000043968572,0.0009477937,0.0001565132,0.00014237914,0.000005758938],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000127619605,0.00035334102,0.005455886,0.00007994453,0.0000283143,0.0043420265,0.00093568326,0.0016316291,0.8899725,0.00046017984,0.0078091808,0.088918544],"study_design_scores_gemma":[0.00063559855,0.00009760598,0.0070962952,0.000033597516,0.00005759357,0.0019072674,0.00007487422,0.50167334,0.4855122,0.0014007554,0.0010760096,0.00043488856],"about_ca_topic_score_codex":0.000021827771,"about_ca_topic_score_gemma":0.000016698721,"teacher_disagreement_score":0.50004166,"about_ca_system_score_codex":0.000076708144,"about_ca_system_score_gemma":0.00019825257,"threshold_uncertainty_score":0.7817307},"labels":[],"label_agreement":null},{"id":"W3144378772","doi":"10.1109/iembs.2006.4397380","title":"Validation and Improved Registration of Bone Segmentation Using Contour Coherency","year":2006,"lang":"en","type":"article","venue":"Conference proceedings","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Artificial intelligence; Segmentation; Similarity (geometry); Computer vision; Computer science; Image segmentation; Pattern recognition (psychology); Image registration; Parameterized complexity; Mathematics; Image (mathematics); Algorithm","score_opus":0.026597501552891686,"score_gpt":0.28008100140150327,"score_spread":0.25348349984861157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3144378772","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2721588,0.000029393761,0.7262565,0.00013624626,0.00003511116,0.0002586469,0.0000011194973,0.00010628925,0.0010178975],"genre_scores_gemma":[0.79879355,0.000011646776,0.20099749,0.00003589666,0.000025877083,0.000018204579,0.000007053369,0.000004317029,0.00010597714],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99895865,0.000011725787,0.00039183817,0.00026224757,0.00024650656,0.00012905481],"domain_scores_gemma":[0.99901,0.000020848636,0.00039535685,0.0000884333,0.00043820756,0.000047102105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031844943,0.00010394096,0.00014590855,0.000101806356,0.000061014274,0.00019207489,0.00015798771,0.0000606591,0.000018106224],"category_scores_gemma":[0.000080639074,0.00010443887,0.000019269493,0.00019591567,0.00008701906,0.0011365222,0.00005917154,0.00006661116,9.419476e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003866912,0.000026316266,0.0014084358,0.000062250976,0.0000027710057,3.0375028e-7,0.00037335113,3.3055952e-7,0.9664152,0.014745622,0.00019342275,0.01676813],"study_design_scores_gemma":[0.00041971842,0.00010644761,0.0025314414,0.00007755063,0.000012953779,0.000012829635,0.00020877918,0.050922025,0.9331867,0.0123576075,0.000011357409,0.00015258875],"about_ca_topic_score_codex":0.00020474338,"about_ca_topic_score_gemma":0.0000032199118,"teacher_disagreement_score":0.52663475,"about_ca_system_score_codex":0.000035868736,"about_ca_system_score_gemma":0.000068593865,"threshold_uncertainty_score":0.42588934},"labels":[],"label_agreement":null},{"id":"W3147539557","doi":"","title":"5 - Estimation de mouvement par maillage actif avec prise en compte de discontinuités","year":2001,"lang":"fr","type":"article","venue":"Traitement du signal","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Classification of discontinuities; Motion estimation; Vector field; Motion field; Maximum a posteriori estimation; Computer science; Markov random field; Algorithm; Mathematics; Artificial intelligence; Computer vision; Image segmentation; Image (mathematics); Mathematical analysis; Geometry","score_opus":0.021516856638995496,"score_gpt":0.2878466846116674,"score_spread":0.2663298279726719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3147539557","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08155789,0.00028240037,0.90801823,0.007594246,0.00035523518,0.00081381155,0.000015310008,0.00031070487,0.0010521935],"genre_scores_gemma":[0.56533056,0.00025153073,0.4268356,0.0026159277,0.00041479123,0.00017691952,0.000038304264,0.00003723302,0.004299148],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962761,0.0005969698,0.0007704539,0.0005912139,0.00085824303,0.00090701017],"domain_scores_gemma":[0.99841505,0.00024130117,0.00030838832,0.0004375811,0.00009832819,0.0004993238],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0017243286,0.0003719774,0.00033071518,0.00015742588,0.00022207714,0.00057401386,0.0008439402,0.00016776414,0.004197291],"category_scores_gemma":[0.00008804243,0.00039463834,0.00012788396,0.00035181196,0.00018381978,0.0010749322,0.0002529477,0.00035009635,0.00017677476],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060814833,0.00096422306,0.0020650914,0.00018186854,0.000121198995,0.0004917215,0.014183691,0.0016011867,0.047732178,0.004367998,0.020170553,0.9080595],"study_design_scores_gemma":[0.0023262724,0.00076533476,0.014113864,0.0005673595,0.00016701236,0.000234867,0.0002969805,0.7761567,0.1801121,0.004870543,0.019548552,0.00084039604],"about_ca_topic_score_codex":0.0002594613,"about_ca_topic_score_gemma":0.00003914915,"teacher_disagreement_score":0.90721905,"about_ca_system_score_codex":0.00072772783,"about_ca_system_score_gemma":0.00028403217,"threshold_uncertainty_score":0.9998506},"labels":[],"label_agreement":null},{"id":"W3148996144","doi":"10.1109/ccece.2005.1557223","title":"Extraction des contours flous et bruites","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Université du Québec en Outaouais","funders":"","keywords":"Computer science; Smoothing; Extraction (chemistry); Computer vision; Detector; Artificial intelligence; Chromatography","score_opus":0.014533764237185103,"score_gpt":0.31312004505088553,"score_spread":0.2985862808137004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3148996144","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045166714,0.000046514884,0.9687805,0.00047677895,0.00007223794,0.00007173147,2.3170863e-7,0.0006320175,0.025403325],"genre_scores_gemma":[0.27612752,0.0000094618035,0.7206964,0.0011496025,0.00003970121,0.000011812265,0.0000026693892,0.0000037598452,0.0019591283],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994,0.00004271207,0.00012038235,0.00014262034,0.00017492221,0.000119319826],"domain_scores_gemma":[0.99963266,0.00008210478,0.000034575995,0.00015901831,0.000049574992,0.00004207363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017342948,0.00005334164,0.000051729046,0.000047864472,0.0000456349,0.00012624581,0.00021036812,0.000026226387,0.0001659161],"category_scores_gemma":[0.000041227293,0.00004502379,0.000021726617,0.00012104943,0.00004820089,0.00064295455,0.000039545746,0.000053345222,0.000062017265],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021378003,0.00017969766,0.001592658,0.000011829071,0.0000058470027,0.00003066027,0.0002174368,0.000013224501,0.10751373,0.09663712,0.06276902,0.73102665],"study_design_scores_gemma":[0.00039801947,0.00010933962,0.026190624,0.000020883286,0.0000046476084,0.00006125366,0.00008655016,0.009795396,0.88806367,0.07138056,0.0036025743,0.00028647683],"about_ca_topic_score_codex":0.000618897,"about_ca_topic_score_gemma":0.00008425048,"teacher_disagreement_score":0.78054994,"about_ca_system_score_codex":0.000025987694,"about_ca_system_score_gemma":0.000016625349,"threshold_uncertainty_score":0.18360168},"labels":[],"label_agreement":null},{"id":"W3154646159","doi":"10.3390/jpm11040310","title":"Development of a Convolutional Neural Network Based Skull Segmentation in MRI Using Standard Tesselation Language Models","year":2021,"lang":"en","type":"article","venue":"Journal of Personalized Medicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Fowler Kennedy Sport Medicine Clinic; Western University","funders":"","keywords":"Convolutional neural network; Computer science; Segmentation; Artificial intelligence; Gold standard (test); Magnetic resonance imaging; Skull; Standard deviation; Pattern recognition (psychology); Sørensen–Dice coefficient; Computer vision; Image segmentation; Radiology; Medicine; Anatomy; Mathematics","score_opus":0.043060664588795886,"score_gpt":0.33795004459075123,"score_spread":0.29488938000195536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3154646159","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0649737,0.0012031419,0.9325926,0.00087106344,0.00019680722,0.00009803181,0.0000015768442,0.000013785342,0.00004933],"genre_scores_gemma":[0.1347276,0.000028886536,0.86446774,0.00059527834,0.0001378197,0.000002434716,0.000013114836,0.0000064708315,0.000020666612],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99770623,0.00018001087,0.0008330019,0.00014329063,0.0009765727,0.00016087013],"domain_scores_gemma":[0.9985516,0.00016244396,0.00057620445,0.00010877729,0.00049382856,0.000107158165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013413078,0.0001046149,0.00033408683,0.00021000193,0.00004947766,0.00001534086,0.00019292448,0.000047580572,0.00018976151],"category_scores_gemma":[0.00016928396,0.00008729311,0.000056057397,0.00052200386,0.000091693364,0.00045155696,0.00003708006,0.00018314151,2.7296844e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006635212,0.0004901843,0.0031002865,0.0004376189,0.0002226457,0.000912793,0.050040785,0.07554983,0.7539159,0.0040244595,0.0039279712,0.10671397],"study_design_scores_gemma":[0.008318827,0.000298141,0.0014538863,0.0016133258,0.00006510932,0.00022670867,0.0033203596,0.8864113,0.096127756,0.0016670385,0.00025532438,0.00024222507],"about_ca_topic_score_codex":0.000008808512,"about_ca_topic_score_gemma":0.000007683541,"teacher_disagreement_score":0.81086147,"about_ca_system_score_codex":0.0002562175,"about_ca_system_score_gemma":0.0007940873,"threshold_uncertainty_score":0.35597098},"labels":[],"label_agreement":null},{"id":"W3162717073","doi":"10.18280/ts.380207","title":"A Framework for Multi-Threshold Image Segmentation of Low Contrast Medical Images","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Contouring; Artificial intelligence; Image segmentation; Fuzzy logic; Segmentation; Pattern recognition (psychology); Entropy (arrow of time); Computer science; Membership function; Segmentation-based object categorization; Scale-space segmentation; Region growing; Mathematics; Computer vision; Fuzzy set","score_opus":0.03228202158045575,"score_gpt":0.3353799145774809,"score_spread":0.30309789299702516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162717073","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021919364,0.000107492415,0.995447,0.0012866895,0.00016252164,0.00051463983,0.000032600292,0.00016664392,0.000090512185],"genre_scores_gemma":[0.102905236,0.00003616059,0.8949715,0.0016859386,0.000104294406,0.00018004203,0.000046852416,0.0000143641955,0.000055604083],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99774265,0.00009681548,0.00058062654,0.00041039547,0.0008770904,0.00029243497],"domain_scores_gemma":[0.9986527,0.00033656275,0.00018972752,0.00028676726,0.0003225842,0.0002116791],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00065266376,0.00016540806,0.00026401447,0.00008395391,0.00007724323,0.00010888681,0.0005571318,0.000108349814,0.0011672623],"category_scores_gemma":[0.00034837882,0.00015815567,0.00013085382,0.00025789894,0.00015117635,0.00045114596,0.00013405671,0.00016564816,0.000010489018],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065569635,0.0018461603,0.00049814605,0.00052743533,0.00018243768,0.0001873417,0.0017222152,0.000015080022,0.6985007,0.03404535,0.011611606,0.250798],"study_design_scores_gemma":[0.0017667025,0.00016115868,0.0005790475,0.00020229905,0.000018704182,0.000013807121,0.00014998873,0.034193,0.95966285,0.003009311,0.00005467989,0.00018842706],"about_ca_topic_score_codex":0.000004878455,"about_ca_topic_score_gemma":0.0000038895078,"teacher_disagreement_score":0.2611622,"about_ca_system_score_codex":0.000044877346,"about_ca_system_score_gemma":0.00020214825,"threshold_uncertainty_score":0.9997458},"labels":[],"label_agreement":null},{"id":"W3163034495","doi":"10.1016/j.bspc.2021.102684","title":"Automatic segmentation of the cardiac MR images based on nested fully convolutional dense network with dilated convolution","year":2021,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Wu Jieping Medical Foundation; National Natural Science Foundation of China","keywords":"Computer science; Convolution (computer science); Segmentation; Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Path (computing); Deep learning; Computer vision; Artificial neural network","score_opus":0.006098380088395118,"score_gpt":0.22617971252940358,"score_spread":0.22008133244100847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163034495","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007707305,0.00045539925,0.98938024,0.00190976,0.0000906358,0.00026066118,0.000010198903,0.0001468555,0.000038928778],"genre_scores_gemma":[0.9698736,0.000004457548,0.028559037,0.0013825414,0.00007517479,0.000043977077,0.000021807813,0.0000075843873,0.000031806874],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980375,0.00033134734,0.00032993755,0.0003046626,0.00076912873,0.00022739128],"domain_scores_gemma":[0.99891126,0.00027787144,0.00022222131,0.00017543035,0.0002714534,0.00014174561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045312927,0.00014168909,0.00023616733,0.000050000657,0.00021789674,0.000106253785,0.00020884557,0.00008561618,0.00004330228],"category_scores_gemma":[0.0000940998,0.00009094452,0.000054004322,0.00057592854,0.00041556114,0.00018129483,0.000034020613,0.00015630001,0.0000016427269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019472152,0.0004927552,0.0049257465,0.00041561585,0.00013959518,0.000055665627,0.00025682588,0.00040709553,0.26948392,0.0005108421,0.002824101,0.7202931],"study_design_scores_gemma":[0.0031584755,0.0004351724,0.020074464,0.0007993749,0.00011403805,0.000027124099,0.00005074845,0.91348535,0.060877856,0.0006130638,0.00009931654,0.0002650292],"about_ca_topic_score_codex":0.000008828402,"about_ca_topic_score_gemma":5.843051e-7,"teacher_disagreement_score":0.9621663,"about_ca_system_score_codex":0.00004828778,"about_ca_system_score_gemma":0.0005116186,"threshold_uncertainty_score":0.370861},"labels":[],"label_agreement":null},{"id":"W3164405722","doi":"10.1007/s13239-021-00547-6","title":"A New Semi-automated Algorithm for Volumetric Segmentation of the Left Ventricle in Temporal 3D Echocardiography Sequences.","year":2022,"lang":"en","type":"article","venue":"PubMed","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian VIGOUR Centre; Virtual Materials Group (Canada); University of Alberta","funders":"Servier Canada","keywords":"Segmentation; Ventricle; Image segmentation; Pattern recognition (psychology); Cardiac imaging","score_opus":0.014808503259872565,"score_gpt":0.24660116323501616,"score_spread":0.2317926599751436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164405722","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036032205,0.00014860998,0.9934976,0.00028864038,0.0003715867,0.0016974676,0.000020186635,0.000315773,0.000056897337],"genre_scores_gemma":[0.54250884,0.000016169182,0.45375183,0.0006522422,0.00004535327,0.0027741275,0.000023049733,0.000016048865,0.00021231447],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983803,0.00018265237,0.00033597602,0.0002633323,0.0005690657,0.00026869177],"domain_scores_gemma":[0.9992699,0.000096470336,0.00020289318,0.00030062854,0.00004833094,0.00008178775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085063,0.00008898515,0.0001503547,0.0004549741,0.00010210618,0.00004739201,0.00079334126,0.0000282212,0.000018573102],"category_scores_gemma":[0.00008431508,0.00007783672,0.00011095031,0.0027185837,0.0000375669,0.0002977903,0.00026281917,0.00012197806,4.939737e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003055216,0.000057713172,0.014611149,0.000012007063,0.000019213061,0.0000035254618,0.00025562404,0.0001786161,0.00024816146,0.000021926939,0.0070580784,0.97753096],"study_design_scores_gemma":[0.003205214,0.0001863973,0.38762042,0.000018679322,0.000042338837,0.000032635675,0.00028788767,0.5090301,0.09446411,0.0027476451,0.0018755279,0.0004890345],"about_ca_topic_score_codex":0.0002751281,"about_ca_topic_score_gemma":0.0000040463215,"teacher_disagreement_score":0.9770419,"about_ca_system_score_codex":0.00016817774,"about_ca_system_score_gemma":0.000098254786,"threshold_uncertainty_score":0.31740895},"labels":[],"label_agreement":null},{"id":"W3165113708","doi":"10.1109/isbi48211.2021.9433818","title":"Intensity-Based Wasserstein Distance As A Loss Measure For Unsupervised Deformable Deep Registration","year":2021,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Centre Hospitalier de l’Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Image registration; Computer science; Artificial intelligence; Wasserstein metric; Deep learning; Metric (unit); Benchmark (surveying); Image (mathematics); Computer vision; Pattern recognition (psychology); Mathematics","score_opus":0.01967993206754578,"score_gpt":0.27336632951293954,"score_spread":0.2536863974453938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165113708","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006467137,0.00005043488,0.9914678,0.0038970755,0.00012333428,0.00029108833,0.0000017895799,0.00037598735,0.0031458202],"genre_scores_gemma":[0.26915047,0.000005493812,0.7241305,0.005126427,0.000031237865,0.00009141309,0.000047588383,0.000010370184,0.0014064775],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99868673,0.000053196833,0.0002692587,0.00036619938,0.00039435044,0.00023025519],"domain_scores_gemma":[0.99856794,0.00008755775,0.000079328485,0.0005304302,0.00060879276,0.00012594277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038387842,0.00011740463,0.000155952,0.000045910918,0.00011748551,0.0002136847,0.00040245702,0.00006532328,0.00007034822],"category_scores_gemma":[0.00035332594,0.00010585437,0.00008167208,0.00035966793,0.00004991985,0.00060599385,0.00005868858,0.000091176385,0.000023455841],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045543342,0.0012167523,0.0018912462,0.0010652916,0.00019564465,0.000662735,0.0026437724,0.0005310918,0.25829354,0.42295942,0.043197855,0.26688722],"study_design_scores_gemma":[0.0008036343,0.00011505677,0.000066858855,0.000056704772,0.000009668748,0.000022649447,0.0001334359,0.18247554,0.8046515,0.009431168,0.002020035,0.00021374354],"about_ca_topic_score_codex":0.000027436407,"about_ca_topic_score_gemma":0.00012768891,"teacher_disagreement_score":0.546358,"about_ca_system_score_codex":0.00008582976,"about_ca_system_score_gemma":0.00026586058,"threshold_uncertainty_score":0.43166158},"labels":[],"label_agreement":null},{"id":"W3171478700","doi":"10.1371/journal.pone.0251914","title":"A hybrid level set model for image segmentation","year":2021,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Sichuan Province Science and Technology Support Program; Sichuan University; Sichuan University of Science and Engineering","keywords":"Active contour model; Maxima and minima; Artificial intelligence; Level set (data structures); Image segmentation; Computer science; Segmentation; Smoothing; Computer vision; Position (finance); Pattern recognition (psychology); Level set method; Process (computing); Image (mathematics); Enhanced Data Rates for GSM Evolution; Energy (signal processing); Iterative and incremental development; Edge detection; Image processing; Mathematics","score_opus":0.1474922199911524,"score_gpt":0.31542082453412323,"score_spread":0.16792860454297084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171478700","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056617404,0.000023837063,0.9923967,0.001119514,0.000022993503,0.00029617854,0.00004782664,0.00020831391,0.00022285775],"genre_scores_gemma":[0.013421829,0.000021168873,0.9827269,0.0016941136,0.00003670492,0.00016979878,0.000083475905,0.000010216006,0.0018358309],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99899435,0.000033428496,0.0001761398,0.0002794662,0.000356795,0.00015983028],"domain_scores_gemma":[0.9992523,0.00005729168,0.000057014862,0.0003194939,0.00023361066,0.00008029343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016855379,0.0000770638,0.000114844246,0.00004344388,0.00006060207,0.00011419831,0.00027364283,0.00002136906,0.000038271526],"category_scores_gemma":[0.00018010374,0.00008359852,0.000037527327,0.000102113954,0.000023399845,0.00038495887,0.00012443453,0.00005914861,0.000027817769],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005289063,0.00068688614,0.000016646545,0.00013274465,0.00006634775,0.00001983535,0.0006071511,0.000015311627,0.964264,0.0012923685,0.012144874,0.020748561],"study_design_scores_gemma":[0.0002092075,0.000022355734,0.000009378493,0.00002433073,0.000011457892,0.0000023659054,0.000009862024,0.36008143,0.6357366,0.0038224244,0.000004209374,0.000066400724],"about_ca_topic_score_codex":0.0000020160476,"about_ca_topic_score_gemma":0.000001399236,"teacher_disagreement_score":0.36006612,"about_ca_system_score_codex":0.000040125895,"about_ca_system_score_gemma":0.000105132254,"threshold_uncertainty_score":0.34090486},"labels":[],"label_agreement":null},{"id":"W3171948477","doi":"10.1016/j.media.2021.102123","title":"Tetrahedral spectral feature-Based bayesian manifold learning for grey matter morphometry: Findings from the Alzheimer’s disease neuroimaging initiative","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institute on Aging; National Institutes of Health; National Natural Science Foundation of China; U.S. Department of Defense","keywords":"Artificial intelligence; Grey matter; Pattern recognition (psychology); Neuroimaging; Computer science; Bayesian probability; Brain morphometry; Machine learning; Mathematics; Magnetic resonance imaging; Psychology; Radiology; Medicine","score_opus":0.01994169279444881,"score_gpt":0.29284541888698273,"score_spread":0.27290372609253394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171948477","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016805598,0.00036006604,0.9577883,0.039314788,0.00012736082,0.00022210533,0.000039948533,0.00024286727,0.00022403193],"genre_scores_gemma":[0.6749655,0.000037925995,0.2805564,0.042893972,0.000337405,0.00013080811,0.000745081,0.000049831087,0.00028304814],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99614507,0.0005283105,0.0004627577,0.00091961434,0.0013847233,0.0005595186],"domain_scores_gemma":[0.9967216,0.0014344495,0.00017032522,0.00077640207,0.0002626163,0.0006345651],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007485589,0.00030252553,0.00047726012,0.00031423298,0.0003536742,0.0007237432,0.0012757255,0.00009988679,0.0032283466],"category_scores_gemma":[0.0022463095,0.00023257986,0.00058439,0.002455566,0.00022271264,0.00065920746,0.0003201552,0.00073159224,0.00006826837],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018419565,0.0013726673,0.568334,0.00014937427,0.0077762273,0.010163777,0.0029063774,0.00023118673,0.008471269,0.0016027222,0.3070937,0.0917145],"study_design_scores_gemma":[0.0018638286,0.00009558138,0.20367782,0.00015124439,0.0044287625,0.000017786519,0.0003563457,0.7286627,0.055098362,0.0027441522,0.001885186,0.0010181838],"about_ca_topic_score_codex":0.00009711583,"about_ca_topic_score_gemma":0.0000325102,"teacher_disagreement_score":0.7284316,"about_ca_system_score_codex":0.000061120794,"about_ca_system_score_gemma":0.00029856846,"threshold_uncertainty_score":0.9976828},"labels":[],"label_agreement":null},{"id":"W3172516001","doi":"10.1007/s42979-021-00704-7","title":"Biomedical Image Segmentation: A Survey","year":2021,"lang":"en","type":"article","venue":"SN Computer Science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Segmentation; Positron emission tomography; Magnetic resonance imaging; Medical imaging; Artificial intelligence; Computer vision; Image segmentation; Computer science; Market segmentation; Computed tomography; Modalities; Medical physics; Radiology; Medicine","score_opus":0.02360859321297869,"score_gpt":0.3174147246370729,"score_spread":0.2938061314240942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172516001","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023349258,0.00003754086,0.99431723,0.001178672,0.0011582688,0.000117807795,0.000004519294,0.000399314,0.0004517321],"genre_scores_gemma":[0.026122725,0.000012280175,0.9708859,0.002719085,0.00012075009,0.000013113064,0.00001680068,0.0000063095194,0.00010307801],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9967868,0.00021767916,0.00034478607,0.0008678711,0.001320847,0.00046206164],"domain_scores_gemma":[0.99782664,0.00020051734,0.0000948434,0.00087896705,0.00060995505,0.00038906338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001856991,0.00014861058,0.00017083506,0.0002266795,0.00028748316,0.00088707195,0.0020233123,0.000043565225,0.00011348542],"category_scores_gemma":[0.00027817252,0.00013871148,0.000049573304,0.0031567828,0.00078956113,0.001717216,0.0013126737,0.00016201775,0.00017986284],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027094304,0.000344748,0.0011398246,0.000027043297,0.000014699936,0.000474277,0.0010087236,0.0000056812437,0.15422612,0.004747244,0.020844994,0.81716394],"study_design_scores_gemma":[0.00085348234,0.0002513802,0.03105867,0.000067758745,0.0000054186867,0.0003914709,0.000043642878,0.15949056,0.8026647,0.003228737,0.0012538374,0.00069036137],"about_ca_topic_score_codex":0.000026426162,"about_ca_topic_score_gemma":0.000005163209,"teacher_disagreement_score":0.81647354,"about_ca_system_score_codex":0.00009956584,"about_ca_system_score_gemma":0.0008387334,"threshold_uncertainty_score":0.8554055},"labels":[],"label_agreement":null},{"id":"W3184435360","doi":"10.1109/tuffc.2022.3162800","title":"Investigating Shift Variance of Convolutional Neural Networks in Ultrasound Image Segmentation","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Computer science; Artificial intelligence; Convolutional neural network; Variance (accounting); Consistency (knowledge bases); Pattern recognition (psychology); Code (set theory); Image segmentation; Pixel; Computer vision","score_opus":0.00996634880518187,"score_gpt":0.23844688264373043,"score_spread":0.22848053383854855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184435360","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010978138,0.00029907227,0.98737085,0.00035777866,0.00028636938,0.00048266226,0.000051630883,0.000108665736,0.00006485622],"genre_scores_gemma":[0.9567168,0.000093636234,0.042124797,0.0007942336,0.00001506494,0.00021923418,0.0000102143995,0.000014686916,0.00001132866],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997761,0.00034283838,0.0005694223,0.00043475986,0.000529104,0.0003628878],"domain_scores_gemma":[0.9984331,0.0008494693,0.00023936255,0.00026518252,0.000087174085,0.00012569755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007279161,0.00019462459,0.00026725084,0.0003174392,0.00033096754,0.00008109039,0.00043441035,0.00006960948,0.000072312316],"category_scores_gemma":[0.00006810295,0.00021941977,0.0000770841,0.0011100343,0.00013337581,0.00050375564,0.0000035228586,0.0007085542,6.482074e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015378052,0.0019527951,0.00471125,0.00013788919,0.00026679886,0.00007800893,0.0038274175,0.27350473,0.45386428,0.035034854,0.0002445358,0.22622366],"study_design_scores_gemma":[0.0020793495,0.0005929704,0.0017497035,0.000018348182,0.000030220655,0.000060243692,0.00006157438,0.9767633,0.012456693,0.0058628777,0.000003267634,0.0003214179],"about_ca_topic_score_codex":0.00018178858,"about_ca_topic_score_gemma":0.00003519052,"teacher_disagreement_score":0.9457387,"about_ca_system_score_codex":0.00022423721,"about_ca_system_score_gemma":0.00015975039,"threshold_uncertainty_score":0.8947679},"labels":[],"label_agreement":null},{"id":"W3193929012","doi":"10.1101/2021.08.17.21262189","title":"Detecting orientation of Brain MR scans using deep learning","year":2021,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Orientation (vector space); Coronal plane; Artificial intelligence; Rendering (computer graphics); Computer science; Sagittal plane; DICOM; Inference; Deep learning; Neuroimaging; Computer vision; Pattern recognition (psychology); Psychology; Medicine; Radiology; Neuroscience; Mathematics","score_opus":0.0316864565828732,"score_gpt":0.32028248957584043,"score_spread":0.2885960329929672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193929012","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32637995,0.00009953473,0.672629,0.00007911219,0.00038386954,0.00013798392,4.0197105e-7,0.00019477186,0.000095369745],"genre_scores_gemma":[0.5623783,0.000016741178,0.43735093,0.00012266517,0.000056185683,0.000015363841,0.000009814253,0.00001558523,0.000034415007],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976369,0.0004247696,0.00053399004,0.0005909337,0.0005819483,0.00023142683],"domain_scores_gemma":[0.99827397,0.00025773403,0.00053640746,0.000567191,0.00026650444,0.00009816495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010677837,0.00018747806,0.00030827997,0.0002238956,0.000102718564,0.00018840368,0.00075401476,0.0001672011,0.000062817555],"category_scores_gemma":[0.00130494,0.0002056845,0.000116730946,0.00045951895,0.00006905523,0.00030195178,0.0011124271,0.0006617723,0.0000025763964],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005652649,0.00010954043,0.031741075,0.00084219174,0.00011054094,0.00012440232,0.01571607,0.010910987,0.44138956,0.00030788864,0.000030549207,0.49871156],"study_design_scores_gemma":[0.00018160456,0.00004555129,0.0028570727,0.00051190343,0.000023811093,0.000018349927,0.0005854925,0.35214114,0.64279246,0.00050042354,0.000021163129,0.0003210197],"about_ca_topic_score_codex":0.00014869074,"about_ca_topic_score_gemma":0.00001521269,"teacher_disagreement_score":0.49839053,"about_ca_system_score_codex":0.00010748301,"about_ca_system_score_gemma":0.00017755489,"threshold_uncertainty_score":0.83875704},"labels":[],"label_agreement":null},{"id":"W3198999572","doi":"10.1007/978-3-030-90439-5_5","title":"Image Prior Transfer and Ensemble Architectures for Parkinson’s Disease Detection","year":2021,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Parkinson's disease; Computer science; Artificial intelligence; Disease; Pattern recognition (psychology); Medicine; Pathology","score_opus":0.011422626325690237,"score_gpt":0.2713475290014914,"score_spread":0.25992490267580115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198999572","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044209108,0.00018933452,0.953463,0.001307252,0.00032699804,0.00033054844,0.0000023529617,0.00016766154,0.0000037036316],"genre_scores_gemma":[0.50170684,0.000006803106,0.4970614,0.001136132,0.00005400549,0.000028885886,6.228456e-7,0.0000046278965,6.568894e-7],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980758,0.00007813646,0.00022403388,0.00082997564,0.0004093491,0.00038272314],"domain_scores_gemma":[0.9987223,0.0003928712,0.0000310704,0.0004707514,0.00015601324,0.0002270025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005061447,0.00016104044,0.00016141836,0.00021421968,0.00022409303,0.00042537562,0.0006348898,0.000047407717,0.00000496727],"category_scores_gemma":[0.00039741327,0.00014423406,0.00005213236,0.0008757007,0.00031590732,0.0002933442,0.00024509817,0.00017511571,0.0000016517293],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009146869,0.00003647942,0.00016133564,0.00003855769,0.000001997923,0.00003256575,0.000601642,0.0003959723,0.07340826,0.000047705646,0.000004163764,0.92526215],"study_design_scores_gemma":[0.0003052019,0.00007155212,0.0049297246,0.00003817899,0.0000035066194,0.000032980737,5.504445e-7,0.39519733,0.58314854,0.016046697,0.0000527065,0.00017305165],"about_ca_topic_score_codex":0.000008986927,"about_ca_topic_score_gemma":0.000052908752,"teacher_disagreement_score":0.9250891,"about_ca_system_score_codex":0.000060892486,"about_ca_system_score_gemma":0.00023553088,"threshold_uncertainty_score":0.58816946},"labels":[],"label_agreement":null},{"id":"W3200413543","doi":"10.1016/j.compbiomed.2021.104879","title":"3D shearlet-based descriptors combined with deep features for the classification of Alzheimer's disease based on MRI data","year":2021,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Institute of Biomedical Imaging and Bioengineering; GE Healthcare; Johnson and Johnson; IXICO; Genentech; Janssen Research and Development; National Institutes of Health; H. Lundbeck A/S; Northern California Institute for Research and Education; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; University of Southern California; Merck; National Institute on Aging; Fujirebio Europe","keywords":"Artificial intelligence; Shearlet; Pattern recognition (psychology); Computer science; Convolutional neural network; Classifier (UML); Feature vector; Neuroimaging; Feature (linguistics); Leverage (statistics); Voxel; Wavelet; Medicine","score_opus":0.04950359666645317,"score_gpt":0.3423160572880517,"score_spread":0.29281246062159855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200413543","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00036433115,0.0009983607,0.9819667,0.01595713,0.0002926425,0.00034409622,0.000008151016,0.000041770814,0.000026869755],"genre_scores_gemma":[0.49389955,0.000094161,0.49410596,0.011285861,0.00008740343,0.000070620226,0.0004418049,0.000008700654,0.0000059391955],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990074,0.000158524,0.00020463364,0.00037574937,0.00012272867,0.00013097221],"domain_scores_gemma":[0.99808735,0.00091425056,0.00009662344,0.00073040236,0.000083698214,0.00008768983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004798853,0.000103555714,0.00019356342,0.000093498915,0.00006743794,0.000010789252,0.0005359493,0.00005205358,0.0000068854542],"category_scores_gemma":[0.00021229793,0.0000601271,0.000014386209,0.00022137353,0.00045628037,0.0000640884,0.00009681516,0.00011436142,1.6795528e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015716373,0.0009234461,0.019279776,0.00029282682,0.00023959656,0.00009177124,0.00079941377,0.00063898746,0.007147717,0.040485397,0.048054703,0.88047475],"study_design_scores_gemma":[0.0031168177,0.0010964854,0.07129881,0.00031351746,0.00007748051,0.0000033575102,0.000054676606,0.9190775,0.0029909026,0.0010259099,0.00080229674,0.00014221785],"about_ca_topic_score_codex":0.000011009038,"about_ca_topic_score_gemma":0.000011368853,"teacher_disagreement_score":0.91843855,"about_ca_system_score_codex":0.000011718445,"about_ca_system_score_gemma":0.000112509864,"threshold_uncertainty_score":0.2451912},"labels":[],"label_agreement":null},{"id":"W3201015453","doi":"10.1109/mwscas47672.2021.9531777","title":"Edge Map Extraction of an Image Based on the Gradient of its Binary Versions","year":2021,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Edge detection; Enhanced Data Rates for GSM Evolution; Binary number; Computer vision; Binary image; Deep learning; Image gradient; Pattern recognition (psychology); Image (mathematics); Canny edge detector; Image processing; Feature extraction; Mathematics","score_opus":0.02588224049400772,"score_gpt":0.3064221914154115,"score_spread":0.28053995092140377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201015453","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014168376,0.000015107494,0.9778209,0.0028749416,0.00016289362,0.00013052828,0.0000031936956,0.000083896426,0.0047401865],"genre_scores_gemma":[0.6055037,0.000013926492,0.39240173,0.0013505138,0.000017431948,0.000013850841,0.0000101214955,0.0000054238917,0.00068334327],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992389,0.000121958125,0.00015165712,0.00014662092,0.00026810414,0.00007273779],"domain_scores_gemma":[0.99915814,0.00018451319,0.000071247734,0.00040100535,0.00013533751,0.000049747978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024162095,0.000047582165,0.00006724566,0.000057556634,0.000039070892,0.000016121277,0.00028830257,0.000022011809,0.00064620643],"category_scores_gemma":[0.00009586384,0.000032732616,0.00003966821,0.00023326883,0.0000465499,0.00026429901,0.000068901274,0.00006754942,0.000015180943],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007500546,0.00055317004,0.000013305454,0.000040458217,0.0000059633094,0.000029013161,0.00020644546,0.000019145013,0.95749474,0.009938202,0.012863913,0.01882817],"study_design_scores_gemma":[0.00009925924,0.00014122679,0.00058892637,0.000027832803,0.000002801976,0.0000016665183,0.000097610086,0.05742847,0.9411904,0.0001934459,0.00019306499,0.000035271547],"about_ca_topic_score_codex":0.000008872427,"about_ca_topic_score_gemma":0.0000014066117,"teacher_disagreement_score":0.5913353,"about_ca_system_score_codex":0.000017130958,"about_ca_system_score_gemma":0.00006405136,"threshold_uncertainty_score":0.7075507},"labels":[],"label_agreement":null},{"id":"W3204810936","doi":"10.1007/978-3-030-87234-2_61","title":"SegRecon: Learning Joint Brain Surface Reconstruction and Segmentation from Images","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Voxel; Artificial intelligence; Segmentation; Computation; Surface reconstruction; Atlas (anatomy); Computer vision; Pattern recognition (psychology); Surface (topology); Representation (politics); Artificial neural network; Algorithm; Mathematics; Geometry","score_opus":0.017554499705453794,"score_gpt":0.25683165406568953,"score_spread":0.23927715436023572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204810936","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006365874,0.000684681,0.99499494,0.00140605,0.0010310668,0.00029742258,0.0000045747715,0.00027505026,0.0006696002],"genre_scores_gemma":[0.0069361846,0.00025012004,0.9903356,0.0015640693,0.00023207889,0.0000059024305,0.000026352158,0.000026165824,0.00062354835],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99672717,0.00012347379,0.0005518712,0.0014580312,0.00076972996,0.00036971227],"domain_scores_gemma":[0.9979744,0.0006324856,0.00037355605,0.0006123231,0.00022843412,0.00017880082],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009006494,0.00039131113,0.00045297138,0.00036220002,0.00023891023,0.0008564516,0.0008074821,0.0002548679,0.00014088993],"category_scores_gemma":[0.00024805547,0.00039390137,0.00007473434,0.00035361396,0.0006374433,0.001071693,0.00084973034,0.0008861743,0.000015720538],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014289477,0.0000073216556,0.000105630985,0.000018980343,0.000009834041,0.00005115275,0.0005247138,0.0011926714,0.013401454,0.000275268,0.00004877748,0.9843628],"study_design_scores_gemma":[0.00087978895,0.00034705704,0.0006690549,0.0017834353,0.000026854781,0.00044595546,0.00000850082,0.39535502,0.49417382,0.10426451,0.0003642263,0.0016817783],"about_ca_topic_score_codex":0.00006888798,"about_ca_topic_score_gemma":0.000033068824,"teacher_disagreement_score":0.982681,"about_ca_system_score_codex":0.0002560556,"about_ca_system_score_gemma":0.0003138031,"threshold_uncertainty_score":0.9998513},"labels":[],"label_agreement":null},{"id":"W3209439384","doi":"10.1016/j.compmedimag.2021.102000","title":"3D hemisphere-based convolutional neural network for whole-brain MRI segmentation","year":2021,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"National Institute of Mental Health; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; McDonnell Center for Systems Neuroscience; Genentech; National Institutes of Health; National Institute of Neurological Disorders and Stroke; IXICO; H. Lundbeck A/S; Servier; Eisai; National Institute on Aging; Alzheimer Society Research Program; Commonwealth Scientific and Industrial Research Organisation; Fondation pour la Recherche sur Alzheimer; Alzheimer Society; Alzheimer's Society; Michael Smith Health Research BC; U.S. Department of Defense; Eli Lilly and Company; Compute Canada; Northern California Institute for Research and Education; University of Southern California; Natural Sciences and Engineering Research Council of Canada; Pfizer; BioClinica; Biogen; GlaxoSmithKline; Bristol-Myers Squibb; Fondation Brain Canada; National Center for Advancing Translational Sciences; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Alzheimer's Association","keywords":"Segmentation; Computer science; Convolutional neural network; Artificial intelligence; Pattern recognition (psychology); Hausdorff distance; Minimum bounding box; Computer vision","score_opus":0.013862995202094126,"score_gpt":0.2867066519836771,"score_spread":0.272843656781583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209439384","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000536467,0.0009204344,0.9524482,0.044723812,0.00071637885,0.0002402338,0.000010441205,0.0003806718,0.00002338608],"genre_scores_gemma":[0.005193375,0.00007033116,0.95311433,0.04073824,0.00043613004,0.00009337794,0.0002372807,0.000020600404,0.00009630393],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99765694,0.00021479337,0.00043104796,0.0005810353,0.00069476705,0.00042143543],"domain_scores_gemma":[0.99799675,0.00089550944,0.00012688889,0.00033036852,0.00021482828,0.0004356869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007900707,0.00020652098,0.00027867567,0.00007175954,0.0002654235,0.0002619493,0.0004410961,0.00010341657,0.000051336654],"category_scores_gemma":[0.0003069564,0.00020383901,0.00011263889,0.00046296464,0.00032554087,0.00030361518,0.0002363646,0.00028717943,0.0000022576282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047267942,0.00026608745,0.001523889,0.00027649992,0.000078580284,0.00025821588,0.00022556355,0.00022956649,0.006627873,0.013383292,0.18501458,0.7920686],"study_design_scores_gemma":[0.0023799024,0.000046896937,0.00041283347,0.00017397528,0.000015369635,0.000089089626,0.000012673559,0.9758996,0.0024600045,0.0055188867,0.012728126,0.00026266853],"about_ca_topic_score_codex":0.000007367073,"about_ca_topic_score_gemma":0.000002785309,"teacher_disagreement_score":0.97567,"about_ca_system_score_codex":0.000027474693,"about_ca_system_score_gemma":0.0003188334,"threshold_uncertainty_score":0.83123136},"labels":[],"label_agreement":null},{"id":"W32226410","doi":"10.3389/fendo.2020.00124","title":"Implementación de un algoritmo para la registración elástica de imágenes médicas","year":2007,"lang":"en","type":"dissertation","venue":"Frontiers in Endocrinology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research","keywords":"Humanities; Physics; Art","score_opus":0.014416772520503045,"score_gpt":0.35641896296416997,"score_spread":0.3420021904436669,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W32226410","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029420794,0.0015544427,0.98715836,0.00031310052,0.0016238776,0.00041239627,0.000014938496,0.00040532538,0.005575496],"genre_scores_gemma":[0.008984098,0.0026135396,0.9847132,0.00084679545,0.0001817868,0.0002501546,0.0003383767,0.000055233522,0.0020168049],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99636006,0.00040898426,0.00076275854,0.0008037522,0.0004130355,0.0012513867],"domain_scores_gemma":[0.998303,0.00027348023,0.00035408352,0.00073909806,0.000083501596,0.00024684513],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006420005,0.00040609337,0.0005676002,0.00076648936,0.000107354106,0.000093084156,0.0016795882,0.00050188747,0.00011016738],"category_scores_gemma":[0.0003400765,0.0004592079,0.00013600735,0.00045436216,0.0002129237,0.00022132951,0.00015869424,0.0008973306,0.000017889934],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072808514,0.00021726674,0.0065798205,0.00019359466,0.00014861567,0.0027105,0.0014070577,0.0000034934726,0.0018288764,0.009375827,0.05509655,0.9223656],"study_design_scores_gemma":[0.005153559,0.0008815353,0.075865395,0.00062080106,0.00032352394,0.0021426838,0.0062435158,0.0048155645,0.6792786,0.13136013,0.089793995,0.0035207008],"about_ca_topic_score_codex":0.00024234678,"about_ca_topic_score_gemma":0.000102000646,"teacher_disagreement_score":0.9188449,"about_ca_system_score_codex":0.00045630205,"about_ca_system_score_gemma":0.0005870202,"threshold_uncertainty_score":0.99978596},"labels":[],"label_agreement":null},{"id":"W32639513","doi":"10.1007/978-3-642-04271-3_72","title":"Towards Accurate, Automatic Segmentation of the Hippocampus and Amygdala from MRI","year":2009,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Douglas Mental Health University Institute; Montreal Neurological Institute and Hospital","funders":"","keywords":"Computer science; Amygdala; Hippocampus; Segmentation; Artificial intelligence; Neuroscience; Pattern recognition (psychology); Psychology","score_opus":0.011018618152189696,"score_gpt":0.28078535783275294,"score_spread":0.26976673968056325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W32639513","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.124629565,0.00006985157,0.8725919,0.0020516375,0.0003190782,0.00022673479,0.0000010080869,0.0000994524,0.000010791939],"genre_scores_gemma":[0.532844,0.0000064161727,0.46562067,0.0015035147,0.000020532068,0.0000027896033,4.0857338e-7,0.0000015397605,1.3236668e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829435,0.00010228382,0.00030677646,0.00045459837,0.00060787745,0.00023411027],"domain_scores_gemma":[0.9988903,0.00022225008,0.00016144419,0.00056294765,0.00008555083,0.000077538796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051290233,0.00013310232,0.0001643946,0.00015239776,0.00011382038,0.0002163575,0.0014492391,0.000051845742,0.0000076462675],"category_scores_gemma":[0.00017723309,0.000091576585,0.00003108732,0.0013029673,0.0003663534,0.00072560995,0.00037177105,0.00016444187,0.0000015213275],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.8573513e-7,0.000025300076,0.0004624318,0.000005414583,0.0000013621825,0.0000022128963,0.0013060599,0.0006083853,0.0146515,0.00006878086,0.000013384738,0.9828545],"study_design_scores_gemma":[0.00021182062,0.00009321112,0.04334537,0.000087281245,0.0000026542884,0.0000094555735,0.0000015675855,0.549749,0.2765911,0.12978749,9.548122e-7,0.000120095516],"about_ca_topic_score_codex":0.000056429944,"about_ca_topic_score_gemma":0.000010295354,"teacher_disagreement_score":0.9827344,"about_ca_system_score_codex":0.000068484165,"about_ca_system_score_gemma":0.00016228436,"threshold_uncertainty_score":0.37343848},"labels":[],"label_agreement":null},{"id":"W38794867","doi":"10.5220/0001072200130021","title":"NEW INVARIANT DESCRIPTORS BASED ON THE MELLIN TRANSFORM","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais; Université de Sherbrooke","funders":"","keywords":"Invariant (physics); Artificial intelligence; Mellin transform; Computer science; Mellin inversion theorem; Pattern recognition (psychology); Mathematics; Fourier transform; Fractional Fourier transform; Mathematical analysis; Fourier analysis","score_opus":0.03861528348549951,"score_gpt":0.2459213528488594,"score_spread":0.2073060693633599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W38794867","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008688438,0.000005729306,0.9672285,0.011608344,0.000087200424,0.00018260593,2.1388861e-7,0.00030375595,0.020496797],"genre_scores_gemma":[0.10328696,0.000021420132,0.8689516,0.024206415,0.000047934824,0.000021584945,0.000001204611,0.000007783324,0.0034550834],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999135,0.000051180683,0.00014503661,0.00017156433,0.00035688825,0.00014031952],"domain_scores_gemma":[0.99933267,0.00014339338,0.000023097591,0.00036055583,0.00002185464,0.00011842176],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000244608,0.00007772279,0.00006729386,0.00004978744,0.00010419959,0.000046321908,0.00065752957,0.00003002183,0.0009360074],"category_scores_gemma":[0.000042726904,0.00004406778,0.000042382944,0.00024101329,0.000043369135,0.00019793786,0.000020966245,0.000104682054,0.0001188813],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012083566,0.00014492158,0.000058981193,0.0000066801954,0.0000124983835,0.0000745749,0.0018770756,0.000035391815,0.0050388174,0.120109715,0.56266284,0.3099664],"study_design_scores_gemma":[0.00040897992,0.00020482171,0.00023617924,0.000019834511,0.0000029199116,0.000013220087,0.00002611478,0.09853212,0.88888705,0.005534206,0.005942697,0.0001918725],"about_ca_topic_score_codex":0.00012113459,"about_ca_topic_score_gemma":0.0000066043876,"teacher_disagreement_score":0.88384825,"about_ca_system_score_codex":0.000024399413,"about_ca_system_score_gemma":0.00013487514,"threshold_uncertainty_score":0.9999773},"labels":[],"label_agreement":null},{"id":"W390146591","doi":"10.1016/j.media.2015.05.005","title":"Hierarchical max-flow segmentation framework for multi-atlas segmentation with Kohonen self-organizing map based Gaussian mixture modeling","year":2015,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"National Center for Research Resources; National Institute of Mental Health; National Institute on Aging; Canadian Institutes of Health Research","keywords":"Segmentation; Computer science; Scale-space segmentation; Artificial intelligence; Self-organizing map; Image segmentation; Segmentation-based object categorization; Pattern recognition (psychology); Minimum spanning tree-based segmentation; Atlas (anatomy); Mixture model; Computer vision; Artificial neural network","score_opus":0.028928817870087917,"score_gpt":0.3196785556511626,"score_spread":0.29074973778107466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W390146591","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010396708,0.00007949373,0.9927869,0.0046422877,0.00014192263,0.00064222893,0.000013649657,0.00061944866,0.000034429704],"genre_scores_gemma":[0.020805918,0.000014339607,0.97521603,0.0029578658,0.00019146709,0.00022566204,0.0004958267,0.00004088632,0.000051979066],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952,0.00037985534,0.0007573306,0.000937503,0.0021437637,0.00058158644],"domain_scores_gemma":[0.99714345,0.0003516479,0.00027434982,0.0007156654,0.00050481816,0.0010100702],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015578938,0.00037355226,0.00057174946,0.0006042796,0.00023692445,0.00043152762,0.0010064107,0.0003045254,0.0003330875],"category_scores_gemma":[0.0009363905,0.000302198,0.0002510137,0.0020747331,0.00014808643,0.0009886952,0.0001772619,0.00057313277,0.000041475676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013550019,0.0117360195,0.021143975,0.0025366296,0.017697735,0.0029606582,0.059326276,0.058722906,0.03925114,0.0057841553,0.05901649,0.720469],"study_design_scores_gemma":[0.0016334791,0.00017130742,0.000045676225,0.00008426271,0.0006150671,0.000006821655,0.00036432993,0.979207,0.016493274,0.0009591815,0.00004378172,0.00037586462],"about_ca_topic_score_codex":0.000074425596,"about_ca_topic_score_gemma":0.000054646764,"teacher_disagreement_score":0.92048407,"about_ca_system_score_codex":0.00026134378,"about_ca_system_score_gemma":0.00043336826,"threshold_uncertainty_score":0.999943},"labels":[],"label_agreement":null},{"id":"W39638318","doi":"10.1007/978-3-319-02267-3_24","title":"Improving Probabilistic Image Registration via Reinforcement Learning and Uncertainty Evaluation","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Pointwise; Probabilistic logic; Artificial intelligence; Entropy (arrow of time); Image registration; Probability distribution; Measure (data warehouse); Image (mathematics); Displacement (psychology); Computer vision; Pattern recognition (psychology); Data mining; Mathematics; Statistics","score_opus":0.01725708167757557,"score_gpt":0.28061525860767383,"score_spread":0.2633581769300983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W39638318","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000027309918,0.00012243833,0.99557763,0.00032587216,0.00036065702,0.0011797496,3.3561608e-7,0.0002153015,0.002190682],"genre_scores_gemma":[0.07440785,0.00003991805,0.9240549,0.00062060665,0.0002074843,0.00009004948,0.000022081287,0.000025879715,0.00053123286],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962191,0.00008606693,0.0006017115,0.0011898413,0.0014946305,0.00040862733],"domain_scores_gemma":[0.9977623,0.00031879012,0.0005112301,0.00065651815,0.00058603496,0.0001650875],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0022649502,0.00036472065,0.0003097747,0.00044066555,0.0002570961,0.00081230514,0.0010041674,0.00020381024,0.00010316472],"category_scores_gemma":[0.00065647624,0.0003330588,0.000049404458,0.00028778674,0.0006599786,0.0011286234,0.000703316,0.000667349,0.00002423149],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016482422,0.0000068748973,0.0000032490868,0.000061818144,0.0000034704767,0.000006604677,0.0003121903,0.014840647,0.0024839076,0.0016316728,0.000015901831,0.980632],"study_design_scores_gemma":[0.00019496116,0.0001939125,0.000016274316,0.00019875249,0.000010685871,0.00002410208,2.8198224e-7,0.9364811,0.002940133,0.059545025,0.000055806373,0.0003389378],"about_ca_topic_score_codex":0.00010945023,"about_ca_topic_score_gemma":0.00002685474,"teacher_disagreement_score":0.9802931,"about_ca_system_score_codex":0.00055749,"about_ca_system_score_gemma":0.0004900563,"threshold_uncertainty_score":0.99991214},"labels":[],"label_agreement":null},{"id":"W4200030210","doi":"10.1109/embc46164.2021.9629944","title":"XAI Feature Detector for Ultrasound Feature Matching","year":2021,"lang":"en","type":"article","venue":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Interpretability; Computer science; Artificial intelligence; Feature extraction; Robustness (evolution); Pattern recognition (psychology); Feature (linguistics); Matching (statistics); Artificial neural network; Computer vision; Field (mathematics); Mathematics","score_opus":0.029120655089577622,"score_gpt":0.31758373208500834,"score_spread":0.2884630769954307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200030210","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01718894,0.00038640018,0.9662857,0.012958625,0.002604747,0.0002535434,0.00007722044,0.000093237264,0.00015155734],"genre_scores_gemma":[0.33511174,0.00096107554,0.6539724,0.003030094,0.001151636,0.00014216194,0.00026284225,0.000043421638,0.005324663],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99848306,0.00008567,0.00036393874,0.0004419916,0.00032974948,0.00029560193],"domain_scores_gemma":[0.99800694,0.00063242496,0.00018389644,0.00044185808,0.0006519117,0.00008298455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057127874,0.000230295,0.0003583373,0.00010775652,0.000064411935,0.00004546672,0.0013166419,0.00025755455,0.00012162983],"category_scores_gemma":[0.0011466641,0.00017402563,0.00020264772,0.00049466157,0.00019371267,0.00024304072,0.000216447,0.0006221474,0.0000029897933],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020905962,0.000113929054,0.0014958419,0.0001937432,0.00034925737,0.000005960862,0.006380325,0.0008354594,0.8403165,0.008786301,0.13703981,0.0044620223],"study_design_scores_gemma":[0.01090558,0.00096670474,0.018373962,0.0062649897,0.00027516548,0.000641559,0.0061293347,0.104871616,0.5885254,0.031196715,0.22837606,0.003472898],"about_ca_topic_score_codex":0.00004488646,"about_ca_topic_score_gemma":0.00005080525,"teacher_disagreement_score":0.3179228,"about_ca_system_score_codex":0.000096471624,"about_ca_system_score_gemma":0.00014111193,"threshold_uncertainty_score":0.7096559},"labels":[],"label_agreement":null},{"id":"W4200036386","doi":"10.1109/embc46164.2021.9630097","title":"3D Reconstruction of Carotid Artery from Ultrasound Images","year":2021,"lang":"en","type":"article","venue":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Ellipse; Artificial intelligence; Computer science; Computer vision; Segmentation; 3D ultrasound; Ultrasound; Carotid arteries; 3D reconstruction; Iterative reconstruction; Medical ultrasound; Image segmentation; Radiology; Medicine; Mathematics","score_opus":0.027584998558744616,"score_gpt":0.2932436840565351,"score_spread":0.2656586854977905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200036386","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10725767,0.00025656947,0.8857415,0.0026200342,0.0032983157,0.00015211286,0.00011083129,0.000065787026,0.00049723533],"genre_scores_gemma":[0.7260125,0.0011873775,0.2712278,0.00045705299,0.00042934885,0.000027854883,0.00012788529,0.000015344582,0.00051482685],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99849254,0.00011491518,0.0005663367,0.00034747203,0.00029836575,0.00018039098],"domain_scores_gemma":[0.99820143,0.0004726311,0.00024501063,0.00041588847,0.00060966605,0.00005537702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004215493,0.00016691587,0.00034438798,0.000110073044,0.000027484019,0.000018731178,0.00089295174,0.00014689371,0.00038800426],"category_scores_gemma":[0.00081859576,0.00013362578,0.00013244968,0.00041543102,0.00037273543,0.0002615022,0.00017277076,0.00036941317,0.0000036459105],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053769536,0.000066335866,0.00869255,0.00004073905,0.00019730849,0.0000020246473,0.0024363555,0.0003185255,0.9758073,0.0009979253,0.0077175573,0.0037179708],"study_design_scores_gemma":[0.0027604024,0.00024885027,0.032642,0.002409336,0.00009249958,0.00022581388,0.0025211798,0.01862754,0.9301605,0.006329453,0.0031240776,0.00085833407],"about_ca_topic_score_codex":0.00024465224,"about_ca_topic_score_gemma":0.000037833073,"teacher_disagreement_score":0.61875486,"about_ca_system_score_codex":0.000063264575,"about_ca_system_score_gemma":0.00012244137,"threshold_uncertainty_score":0.54491013},"labels":[],"label_agreement":null},{"id":"W4200453233","doi":"10.1109/iros51168.2021.9636401","title":"Robot-assisted Breast Ultrasound Scanning Using Geometrical Analysis of the Seroma and Image Segmentation","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer vision; Artificial intelligence; Imaging phantom; Computer science; Trajectory; Segmentation; Robot; Robotic arm; Orientation (vector space); Ultrasound; Visual servoing; Mathematics; Acoustics; Physics; Optics","score_opus":0.06402075817554106,"score_gpt":0.3326906825915315,"score_spread":0.26866992441599047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200453233","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1279342,0.00014373027,0.8699689,0.00038818154,0.00080541056,0.00023014624,0.000051973017,0.000032800952,0.00044463508],"genre_scores_gemma":[0.98367316,0.0002347916,0.015247128,0.00016198431,0.00006974831,0.000021469887,0.00004347105,0.000010824606,0.00053744623],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973403,0.0002752203,0.0006955123,0.000570478,0.00090757303,0.00021090309],"domain_scores_gemma":[0.9980727,0.00025168931,0.00042086394,0.00039744767,0.00070929964,0.000148021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047173564,0.00021696968,0.00039831552,0.0005001552,0.00016392922,0.00068490946,0.00052976713,0.00009474279,0.00024267782],"category_scores_gemma":[0.00017009638,0.00017061977,0.00014163033,0.0013196411,0.00016924106,0.0004120477,0.00022241703,0.0002056978,0.0000041671246],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004234397,0.00052553014,0.020927947,0.00018241993,0.002286884,0.00009370743,0.0023918995,0.009567805,0.8517681,0.028604446,0.0004473797,0.083161555],"study_design_scores_gemma":[0.00046399017,0.000093104405,0.06806721,0.00063366507,0.00035519694,0.00039870723,0.0015171556,0.78186506,0.14582416,0.0002747188,0.000036387934,0.0004706429],"about_ca_topic_score_codex":0.0003411749,"about_ca_topic_score_gemma":0.00002705253,"teacher_disagreement_score":0.85573894,"about_ca_system_score_codex":0.00015013454,"about_ca_system_score_gemma":0.00012460128,"threshold_uncertainty_score":0.6957672},"labels":[],"label_agreement":null},{"id":"W4205228245","doi":"10.32920/16814956.v1","title":"Optical Flow-Based Image Registration In Flair MRI","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; Public Health Ontario; Dalhousie University","funders":"","keywords":"Artificial intelligence; Image registration; Computer science; Computer vision; Convolutional neural network; Optical flow; Orientation (vector space); Similarity (geometry); Image warping; Pattern recognition (psychology); Fluid-attenuated inversion recovery; Mutual information; Image (mathematics); Mathematics; Magnetic resonance imaging","score_opus":0.01846508689140901,"score_gpt":0.30257336044176847,"score_spread":0.28410827355035945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205228245","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00032372877,0.000029370985,0.98497915,0.0043964153,0.00031546556,0.000354989,0.0000015463963,0.0005463889,0.009052969],"genre_scores_gemma":[0.009285151,0.00001909083,0.98863566,0.0014767745,0.000052963707,0.000107461114,0.000098354016,0.000010764077,0.00031375993],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977437,0.0001539734,0.0005167487,0.0007633391,0.0005850811,0.00023713712],"domain_scores_gemma":[0.99840856,0.00011658413,0.00011936987,0.001062493,0.00015766153,0.00013532837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060684705,0.00020541564,0.000269126,0.00017536312,0.000026164782,0.0005670013,0.0008870694,0.00025883815,0.0003177748],"category_scores_gemma":[0.00026569748,0.00020265236,0.00010626376,0.000269342,0.00008779256,0.0003681688,0.0007975124,0.0006311922,0.00003240174],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005448415,0.0029717775,0.0007164499,0.0025067683,0.00012811678,0.005247287,0.0029898528,0.005231908,0.09399803,0.044392608,0.109787695,0.731975],"study_design_scores_gemma":[0.00035850258,0.000042537024,0.0007246816,0.00027424202,0.0000061345727,0.0000066057924,0.000033234715,0.49059498,0.50479686,0.0026720264,0.00008843975,0.0004017565],"about_ca_topic_score_codex":0.00010570996,"about_ca_topic_score_gemma":0.000090903355,"teacher_disagreement_score":0.7315733,"about_ca_system_score_codex":0.0001537771,"about_ca_system_score_gemma":0.00058346614,"threshold_uncertainty_score":0.82639235},"labels":[],"label_agreement":null},{"id":"W4205501949","doi":"10.32920/16814956","title":"Optical Flow-Based Image Registration In Flair MRI","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; Public Health Ontario; Dalhousie University","funders":"","keywords":"Artificial intelligence; Image registration; Computer vision; Computer science; Convolutional neural network; Optical flow; Orientation (vector space); Similarity (geometry); Fluid-attenuated inversion recovery; Image warping; Pattern recognition (psychology); Mutual information; Image (mathematics); Mathematics; Magnetic resonance imaging; Medicine","score_opus":0.01846508689140901,"score_gpt":0.30257336044176847,"score_spread":0.28410827355035945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205501949","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00032372877,0.000029370985,0.98497915,0.0043964153,0.00031546556,0.000354989,0.0000015463963,0.0005463889,0.009052969],"genre_scores_gemma":[0.009285151,0.00001909083,0.98863566,0.0014767745,0.000052963707,0.000107461114,0.000098354016,0.000010764077,0.00031375993],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977437,0.0001539734,0.0005167487,0.0007633391,0.0005850811,0.00023713712],"domain_scores_gemma":[0.99840856,0.00011658413,0.00011936987,0.001062493,0.00015766153,0.00013532837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060684705,0.00020541564,0.000269126,0.00017536312,0.000026164782,0.0005670013,0.0008870694,0.00025883815,0.0003177748],"category_scores_gemma":[0.00026569748,0.00020265236,0.00010626376,0.000269342,0.00008779256,0.0003681688,0.0007975124,0.0006311922,0.00003240174],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005448415,0.0029717775,0.0007164499,0.0025067683,0.00012811678,0.005247287,0.0029898528,0.005231908,0.09399803,0.044392608,0.109787695,0.731975],"study_design_scores_gemma":[0.00035850258,0.000042537024,0.0007246816,0.00027424202,0.0000061345727,0.0000066057924,0.000033234715,0.49059498,0.50479686,0.0026720264,0.00008843975,0.0004017565],"about_ca_topic_score_codex":0.00010570996,"about_ca_topic_score_gemma":0.000090903355,"teacher_disagreement_score":0.7315733,"about_ca_system_score_codex":0.0001537771,"about_ca_system_score_gemma":0.00058346614,"threshold_uncertainty_score":0.82639235},"labels":[],"label_agreement":null},{"id":"W4205637814","doi":"10.23952/jnva.5.2021.3.05","title":"Low patch-rank image decomposition using alternating minimization algorithms","year":2021,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Minification; Rank (graph theory); Image (mathematics); Algorithm; Decomposition; Computer science; Mathematics; Artificial intelligence; Mathematical optimization; Combinatorics; Chemistry","score_opus":0.015325027984070252,"score_gpt":0.3223679935297706,"score_spread":0.3070429655457003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205637814","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022733912,0.00006860351,0.97641313,0.0006222378,0.00009252824,0.000023506569,0.000007385368,0.0000138818705,0.00002480053],"genre_scores_gemma":[0.034577638,0.000081455066,0.9647052,0.00033042347,0.00024737406,5.1692257e-7,0.000034954654,0.000004207139,0.000018198],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859625,0.00012996308,0.0005237488,0.00015930827,0.000496887,0.00009381902],"domain_scores_gemma":[0.99835736,0.00014174888,0.00046131862,0.00010930943,0.00083258876,0.00009767176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045013716,0.00008149039,0.00022158872,0.0003120688,0.00010047852,0.0002082526,0.00016626777,0.000040204424,0.00008898067],"category_scores_gemma":[0.0001696515,0.000073799565,0.00015837878,0.00085980236,0.000018826657,0.0007710654,0.00006640671,0.00011364696,0.0000011487517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011038429,0.003091319,0.03147188,0.00024823518,0.010331667,0.0015243237,0.0044048307,0.0777202,0.4402115,0.005379236,0.0009227005,0.42458373],"study_design_scores_gemma":[0.00029778515,0.000028921795,0.0038277993,0.000025421466,0.0002876657,0.00010132236,0.000027585478,0.97079027,0.02382735,0.00068803894,0.000014989287,0.000082848936],"about_ca_topic_score_codex":0.000020459698,"about_ca_topic_score_gemma":0.0000032222722,"teacher_disagreement_score":0.89307004,"about_ca_system_score_codex":0.000049465176,"about_ca_system_score_gemma":0.00013108231,"threshold_uncertainty_score":0.3009459},"labels":[],"label_agreement":null},{"id":"W4206070684","doi":"10.5194/gi-2021-33-rc1","title":"Comment on gi-2021-33","year":2022,"lang":"en","type":"peer-review","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Robustness (evolution); Data processing; Computer science; Data pre-processing; Preprocessor; Property (philosophy); Data mining; Algorithm; Remote sensing; Artificial intelligence; Geology","score_opus":0.04780196422939596,"score_gpt":0.359275113365178,"score_spread":0.311473149135782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206070684","genre_codex":"methods","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.4058611e-9,0.0019337016,0.49058697,0.4234106,0.001996226,0.0005257118,0.000045366123,0.00035235085,0.08114908],"genre_scores_gemma":[1.6312318e-7,0.006212466,0.13061921,0.49782246,0.00016000164,0.00040141377,0.0004738197,0.00002047677,0.36428997],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99687546,0.00027375392,0.00046297157,0.0006471619,0.0014688186,0.0002718586],"domain_scores_gemma":[0.9979431,0.00022263809,0.00021529089,0.0013578793,0.00009401229,0.00016708254],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008806029,0.0002747356,0.00044556308,0.00016006205,0.00011025873,0.00011131078,0.0023251723,0.00009064105,0.042831223],"category_scores_gemma":[0.00016132767,0.00023071695,0.0001682586,0.0003807476,0.000038078324,0.00011828318,0.0012353704,0.0007051668,0.0006339747],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.7536322e-7,0.000058325535,6.5529086e-8,0.00023619838,0.000011318779,0.000021107155,0.00001289653,8.860758e-8,0.000001055675,0.0028208562,0.8201551,0.17668271],"study_design_scores_gemma":[0.00008228562,0.00015274703,3.91324e-7,0.0006057587,0.000013400382,0.000004638718,0.0000028297613,0.000110852496,0.00047657653,0.00042728987,0.9978772,0.00024601957],"about_ca_topic_score_codex":0.00009530749,"about_ca_topic_score_gemma":0.000002986755,"teacher_disagreement_score":0.35996777,"about_ca_system_score_codex":0.00024560705,"about_ca_system_score_gemma":0.00016460611,"threshold_uncertainty_score":0.95804375},"labels":[],"label_agreement":null},{"id":"W4206996524","doi":"10.1002/hbm.25782","title":"MDReg‐Net: Multi‐resolution diffeomorphic image registration using fully convolutional networks with deep self‐supervision","year":2022,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Eli Lilly and Company; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; National Institute of Mental Health; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Artificial intelligence; Diffeomorphism; Computer vision; Convolutional neural network; Image (mathematics); Computer science; Resolution (logic); Net (polyhedron); Deep learning; Pattern recognition (psychology); Image registration; Mathematics; Geometry","score_opus":0.0338856865978678,"score_gpt":0.27174203725609947,"score_spread":0.23785635065823166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206996524","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013531903,0.00011083309,0.9842566,0.0006920349,0.0001324965,0.0004905412,0.0000029500266,0.00064072816,0.00014190638],"genre_scores_gemma":[0.37034863,0.0000040627956,0.6275952,0.0014864223,0.0001571147,0.00009922519,0.00011661737,0.000027928936,0.00016480556],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99731994,0.00043815066,0.00043675455,0.00059624424,0.0008036238,0.0004052865],"domain_scores_gemma":[0.9988093,0.00012001499,0.00029908936,0.00049209,0.00015023758,0.0001292784],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0010411936,0.00021373358,0.00019607211,0.00024089364,0.0014706069,0.0002692548,0.00068307156,0.000064197724,0.00021446715],"category_scores_gemma":[0.000060307564,0.00022440961,0.00006470093,0.00056784455,0.00013891181,0.0008289142,0.00044158706,0.0004224676,0.000005605118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008656667,0.0013561106,0.0030688338,0.0002926884,0.00022896046,0.00054286356,0.01149935,0.08841233,0.81474555,0.045712177,0.01946316,0.014591422],"study_design_scores_gemma":[0.0008488888,0.00015852867,0.009276484,0.00005991739,0.0000091300335,0.00011715786,0.00022181815,0.98775274,0.00018269061,0.00057090766,0.00048647,0.00031529908],"about_ca_topic_score_codex":0.000076085096,"about_ca_topic_score_gemma":0.000018159248,"teacher_disagreement_score":0.8993404,"about_ca_system_score_codex":0.00040398652,"about_ca_system_score_gemma":0.000098008495,"threshold_uncertainty_score":0.99982935},"labels":[],"label_agreement":null},{"id":"W4207051190","doi":"10.1101/2021.08.16.456514","title":"DARQ: Deep learning of quality control for stereotaxic registration of human brain MRI","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"NIH Blueprint for Neuroscience Research; Pfizer Canada; National Institutes of Health; Government of Canada; McDonnell Center for Systems Neuroscience; McGill University; Consortium canadien en neurodégénérescence associée au vieillissement; Pfizer","keywords":"Artificial intelligence; Computer science; Image registration; Neuroimaging; Deep learning; Task (project management); Pattern recognition (psychology); Image quality; Artificial neural network; Image processing; Computer vision; Image (mathematics); Medicine","score_opus":0.02455025669975828,"score_gpt":0.2939007662242633,"score_spread":0.269350509524505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4207051190","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08669454,0.00025415013,0.9111998,0.0003408342,0.00029604146,0.00087628805,0.000039616734,0.0002912702,0.0000074548034],"genre_scores_gemma":[0.8316847,0.000025458558,0.16777587,0.00021079002,0.00008349193,0.0001777286,0.0000010984121,0.000034606437,0.000006262588],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9963067,0.00058687583,0.0012636019,0.00086112897,0.000660942,0.0003207529],"domain_scores_gemma":[0.99503094,0.0004240325,0.0017708872,0.001417028,0.001196471,0.00016062346],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0026597646,0.00032852904,0.00077187974,0.0002229867,0.00011019053,0.00019575513,0.0011001821,0.00034783565,0.00002293471],"category_scores_gemma":[0.0015660342,0.00037602798,0.0002293442,0.00038159825,0.00017951833,0.0003432716,0.0004097644,0.00051142083,0.0000010977644],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012382716,0.00013547938,0.0009796988,0.0010826222,0.000101193844,0.000006078943,0.00006056626,0.00004843517,0.9912614,0.006132183,0.00013000918,0.00004989854],"study_design_scores_gemma":[0.00096526503,0.00020487503,0.020840606,0.0005622013,0.000052707765,9.728876e-9,0.0000134665415,0.0056954958,0.9710717,0.000030082734,0.00010014257,0.00046345315],"about_ca_topic_score_codex":0.00009136675,"about_ca_topic_score_gemma":0.000005486554,"teacher_disagreement_score":0.74499017,"about_ca_system_score_codex":0.00013160259,"about_ca_system_score_gemma":0.00046283935,"threshold_uncertainty_score":0.99986917},"labels":[],"label_agreement":null},{"id":"W4210924501","doi":"10.1007/s10489-021-03062-2","title":"A training-free recursive multiresolution framework for diffeomorphic deformable image registration","year":2022,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian VIGOUR Centre; University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Transformation (genetics); Convolutional neural network; Image registration; Image (mathematics); Computer vision; Diffeomorphism; Geometric transformation; Pattern recognition (psychology); Algorithm; Mathematics","score_opus":0.05462305820158668,"score_gpt":0.3132232079417771,"score_spread":0.2586001497401904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210924501","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017807753,0.000040562678,0.99563104,0.0007804022,0.0002646774,0.0009332599,0.0000197713,0.00039154076,0.0017606511],"genre_scores_gemma":[0.15573624,0.000018777833,0.84142214,0.0011436614,0.00006050331,0.0014410657,0.000035516252,0.000015106438,0.00012697857],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981795,0.000057571553,0.00039444448,0.00048554174,0.00051863666,0.00036433214],"domain_scores_gemma":[0.9984195,0.00035770063,0.00025355248,0.0007725411,0.00009023977,0.00010647255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007301641,0.00015618025,0.00016695361,0.00011165329,0.00046808508,0.00011733048,0.0015249725,0.00006379052,0.00013410373],"category_scores_gemma":[0.0004461238,0.00016733832,0.00006997956,0.00048397723,0.00012582782,0.00034598107,0.00042250106,0.00034238433,0.00002264636],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043938777,0.00009525286,0.0000016461473,0.00002648079,0.000012469033,0.000005176007,0.0038931016,0.00019894469,0.008297122,0.7161006,0.005125969,0.26619935],"study_design_scores_gemma":[0.00020798354,0.00033705487,0.000017538092,0.000023519537,0.000010677643,0.00002421819,0.0014408724,0.06340558,0.17953262,0.7520437,0.0026202705,0.00033591647],"about_ca_topic_score_codex":0.000023108361,"about_ca_topic_score_gemma":0.0000029133591,"teacher_disagreement_score":0.26586342,"about_ca_system_score_codex":0.00018195617,"about_ca_system_score_gemma":0.00010602239,"threshold_uncertainty_score":0.6823859},"labels":[],"label_agreement":null},{"id":"W4212846736","doi":"10.1109/tuffc.2022.3151647","title":"MCAL: An Anatomical Knowledge Learning Model for Myocardial Segmentation in 2-D Echocardiography","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Science Foundation of Shandong Province; China Postdoctoral Science Foundation; National Natural Science Foundation of China; Medical Research Council; Shandong University","keywords":"Segmentation; Boundary (topology); Pattern recognition (psychology); Focus (optics); Feature (linguistics); Image segmentation; Process (computing); Pixel; Convolution (computer science)","score_opus":0.016054891386627922,"score_gpt":0.2718973369313713,"score_spread":0.2558424455447434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212846736","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061336984,0.00022749945,0.99196917,0.00016665483,0.00025178917,0.00085919234,0.00005082102,0.00026684636,0.00007435793],"genre_scores_gemma":[0.9494193,0.00018777617,0.048943967,0.00046129205,0.000020869486,0.0009002148,0.0000143958605,0.000025921589,0.000026246467],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99785954,0.0003534447,0.00040533274,0.0005836473,0.00037617236,0.00042186273],"domain_scores_gemma":[0.9989657,0.00038668435,0.00009955039,0.000268123,0.00010002961,0.00017993647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089880836,0.00021417065,0.0002981122,0.0006673125,0.00052378274,0.00012245958,0.00041597406,0.00009688388,0.000010755524],"category_scores_gemma":[0.000028432332,0.00024164027,0.00017629848,0.0010188526,0.000050507242,0.00048064176,0.0000029290043,0.00073636294,9.0038276e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002387397,0.0012837022,0.00045219768,0.00003869379,0.0001794961,0.000015078579,0.002927951,0.21165803,0.040852565,0.006536862,0.0001490079,0.73566765],"study_design_scores_gemma":[0.0021111495,0.0008001802,0.000060745213,0.000004878574,0.000037147893,0.000010298469,0.000060988637,0.9890388,0.0035607794,0.004030279,0.000022558606,0.0002621957],"about_ca_topic_score_codex":0.000037563525,"about_ca_topic_score_gemma":0.000024151024,"teacher_disagreement_score":0.94328564,"about_ca_system_score_codex":0.00026788443,"about_ca_system_score_gemma":0.00019860505,"threshold_uncertainty_score":0.9853804},"labels":[],"label_agreement":null},{"id":"W4220850818","doi":"10.3390/app12062994","title":"Autonomous Prostate Segmentation in 2D B-Mode Ultrasound Images","year":2022,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Calgary","funders":"","keywords":"Prostate; Contouring; Ultrasound; Prostate brachytherapy; Prostate cancer; Artificial intelligence; Computer science; Segmentation; Computer vision; Brachytherapy; Medicine; Image segmentation; Pixel; Radiology; Radiation therapy; Cancer; Computer graphics (images)","score_opus":0.013964037921394681,"score_gpt":0.29179414798023495,"score_spread":0.2778301100588403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220850818","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056705847,0.0000774495,0.9231753,0.0012234553,0.0003281345,0.0009606,0.000011209803,0.00058302097,0.01693498],"genre_scores_gemma":[0.78998756,0.000009720898,0.20820281,0.001190949,0.000013034252,0.0004047724,0.000006908469,0.000004722475,0.00017952763],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981306,0.00009075121,0.00026738836,0.0004952421,0.00071215106,0.00030385912],"domain_scores_gemma":[0.9994182,0.00015781629,0.00011580739,0.00022685861,0.00001597289,0.00006535719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001121937,0.00010447177,0.00011158729,0.00023390459,0.00044843717,0.00023290777,0.0011114728,0.000015685202,0.00014637469],"category_scores_gemma":[0.000029623316,0.00009971282,0.000021554499,0.0011508161,0.00025071486,0.0005579276,0.00033448654,0.00016422833,0.000022900294],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000117611735,0.0003080181,0.002571759,0.0000203814,0.0000075664843,0.0000366822,0.010722039,0.0022932307,0.730863,0.052867938,0.0070509096,0.19324672],"study_design_scores_gemma":[0.0009339487,0.00036658667,0.004221454,0.000010236201,0.0000051564034,0.000054822856,0.004465958,0.007546847,0.9274751,0.053571146,0.0007067436,0.00064198655],"about_ca_topic_score_codex":0.000087433385,"about_ca_topic_score_gemma":0.000009544755,"teacher_disagreement_score":0.73328173,"about_ca_system_score_codex":0.00013337766,"about_ca_system_score_gemma":0.00016187243,"threshold_uncertainty_score":0.4066171},"labels":[],"label_agreement":null},{"id":"W4220896388","doi":"10.18280/ts.390122","title":"Artificial Intelligence Registration of Image Series Based on Multiple Features","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"People's Government of Jilin Province","keywords":"Artificial intelligence; Fuse (electrical); Computer science; Series (stratigraphy); Image registration; Transformation (genetics); Image fusion; Feature (linguistics); Image (mathematics); Pattern recognition (psychology); Computer vision; Convolutional neural network; Feature detection (computer vision); Image processing; Engineering","score_opus":0.025898972351000575,"score_gpt":0.2743735075492609,"score_spread":0.2484745351982603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220896388","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023700802,0.000007260563,0.9949731,0.0014394125,0.00010238588,0.00027751122,0.000016802134,0.00014897225,0.00066443527],"genre_scores_gemma":[0.85992795,0.0000011701704,0.13917874,0.0006627005,0.000035943693,0.00009654317,0.000025960464,0.0000058123433,0.00006516763],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99838114,0.0001496766,0.00035076452,0.00025330015,0.0007236953,0.00014144828],"domain_scores_gemma":[0.9993078,0.0001658146,0.00016592682,0.0002494941,0.00005873714,0.00005222559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005459258,0.000099587895,0.00010814052,0.00011934724,0.00015413048,0.00006213884,0.0005070419,0.000018703673,0.0006910657],"category_scores_gemma":[0.00008632826,0.00009871534,0.000054388227,0.00026923546,0.00008669871,0.0002821827,0.00009206772,0.0001409433,0.0000043693703],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053593365,0.0016841114,0.00017982368,0.00011147477,0.000034320317,0.000090430345,0.0026827883,0.009485966,0.45074767,0.07694657,0.021704884,0.43579602],"study_design_scores_gemma":[0.000107422704,0.0008987471,0.0006259459,0.000014344995,0.0000049624978,0.0000049143805,0.00020690361,0.100434996,0.89348537,0.0037913655,0.00027968947,0.00014533984],"about_ca_topic_score_codex":0.000024200135,"about_ca_topic_score_gemma":0.000008546175,"teacher_disagreement_score":0.8575579,"about_ca_system_score_codex":0.000056237473,"about_ca_system_score_gemma":0.0000686383,"threshold_uncertainty_score":0.75666845},"labels":[],"label_agreement":null},{"id":"W4220955958","doi":"10.1109/tbme.2022.3158278","title":"Localized Statistical Shape Models for Large-Scale Problems With Few Training Data","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"Calgary Foundation; University of Calgary","keywords":"Computer science; Artificial intelligence; Kernel (algebra); USable; Machine learning; Active shape model; Kernel density estimation; Pattern recognition (psychology); Data modeling; Segmentation; Data mining; Mathematics; Estimator","score_opus":0.04427984906000604,"score_gpt":0.27707427985199096,"score_spread":0.23279443079198492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220955958","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000038938182,0.000020712541,0.99727017,0.00056035095,0.00035443032,0.00050551543,0.0005281963,0.0007032323,0.000018473518],"genre_scores_gemma":[0.13122125,0.000013596397,0.8672905,0.0004948563,0.00004309511,0.0006987603,0.00013915237,0.0000415186,0.000057268822],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99773294,0.00004681049,0.00034991006,0.0006007285,0.0008006886,0.00046893815],"domain_scores_gemma":[0.99867195,0.00030077776,0.00004850333,0.0006245591,0.000035000867,0.00031919288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006622298,0.00018966915,0.0002450576,0.00023250945,0.0002688909,0.00007259252,0.0011041628,0.000059899772,0.00021772069],"category_scores_gemma":[0.000016319489,0.0001742623,0.00004515113,0.0005933725,0.00006419658,0.0004401816,0.000027817927,0.0004530434,0.0000033960255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012132812,0.0013715584,3.2385535e-7,0.00030627678,0.00022190301,0.00008673803,0.0034956282,0.37444162,0.0055757384,0.0042399433,0.0036420566,0.6064969],"study_design_scores_gemma":[0.0010433341,0.00040293997,5.7863195e-7,0.000037302227,0.000020382617,0.000038726153,0.00010584213,0.99075925,0.0010368295,0.00021129016,0.006123976,0.00021951548],"about_ca_topic_score_codex":0.000008713815,"about_ca_topic_score_gemma":0.0000028476843,"teacher_disagreement_score":0.61631763,"about_ca_system_score_codex":0.00009949508,"about_ca_system_score_gemma":0.00014254887,"threshold_uncertainty_score":0.710621},"labels":[],"label_agreement":null},{"id":"W4223629306","doi":"10.1007/s10489-021-03072-0","title":"Image blurring and sharpening inspired three-way clustering approach","year":2022,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sharpening; Cluster analysis; Computer science; Artificial intelligence; Representation (politics); Cluster (spacecraft); Object (grammar); Pattern recognition (psychology); Image (mathematics); Boundary (topology); Data mining; Mathematics","score_opus":0.027505697004926923,"score_gpt":0.26589104450088674,"score_spread":0.23838534749595983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223629306","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018647796,0.000079112,0.9895257,0.00006446135,0.00009087644,0.00033669922,0.0000014340358,0.00044226716,0.0075946674],"genre_scores_gemma":[0.39969853,0.000011382327,0.599441,0.00053935987,0.000022628077,0.00023937956,0.0000037140105,0.000012714257,0.000031302123],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834913,0.000038867227,0.0003089729,0.0005594673,0.0004426789,0.00030090447],"domain_scores_gemma":[0.9992131,0.000084734886,0.000108004424,0.0004499288,0.00002570616,0.000118524826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005681166,0.00015562426,0.00016133618,0.00013050319,0.00037169742,0.00019632519,0.001089074,0.000029509323,0.00014188742],"category_scores_gemma":[0.000029622382,0.00016655192,0.000030004063,0.00044726874,0.00010585417,0.000345211,0.0019139919,0.0003377227,0.000020644497],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011432427,0.00008044712,0.00009822067,0.000066069835,0.000017339426,0.000022157932,0.004243139,0.0004458728,0.05274137,0.018115377,0.00028998323,0.9238686],"study_design_scores_gemma":[0.00017700972,0.00011452662,0.00030814114,0.000020372632,0.0000087746175,0.000093065755,0.0010423006,0.8334018,0.15614471,0.0077055115,0.00041464705,0.0005691616],"about_ca_topic_score_codex":0.000034436885,"about_ca_topic_score_gemma":0.0000020022867,"teacher_disagreement_score":0.92329943,"about_ca_system_score_codex":0.00006988091,"about_ca_system_score_gemma":0.000025247307,"threshold_uncertainty_score":0.679179},"labels":[],"label_agreement":null},{"id":"W4223914566","doi":"10.1002/mp.15670","title":"Uncertainty‐guided symmetric multilevel supervision network for 3D left atrium segmentation in late gadolinium‐enhanced MRI","year":2022,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Science Foundation of Shandong Province; China Postdoctoral Science Foundation; National Natural Science Foundation of China; Heilongjiang Postdoctoral Science Foundation","keywords":"Segmentation; Jaccard index; Computer science; Artificial intelligence; Hausdorff distance; Magnetic resonance imaging; Convolutional neural network; Pattern recognition (psychology); Image segmentation; Medicine; Radiology","score_opus":0.028179166246194147,"score_gpt":0.3165442977288677,"score_spread":0.28836513148267356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223914566","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047068545,0.00007163628,0.99203855,0.0010852928,0.0008440566,0.0008345539,0.000014342449,0.0002634009,0.00014131301],"genre_scores_gemma":[0.5416946,0.00015432591,0.44699177,0.00887737,0.00083829294,0.0006583823,0.00034980572,0.00005566251,0.00037980438],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964028,0.00028397993,0.0006560405,0.0005953929,0.0015278734,0.0005339248],"domain_scores_gemma":[0.9982716,0.0007213452,0.00018845648,0.00043701174,0.00012573856,0.00025587718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015996225,0.00020808661,0.00033121379,0.000152572,0.00021493204,0.000059392565,0.0010592222,0.00009437065,0.00026234437],"category_scores_gemma":[0.0004605069,0.00020627912,0.00011637643,0.0011353786,0.00008209635,0.00036116765,0.0005948396,0.0004927251,0.000018040713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004721192,0.00047103543,0.00026827407,0.00009539284,0.000026712647,0.000028495806,0.0020254704,0.018961607,0.0018231744,0.0011011369,0.02085942,0.95429206],"study_design_scores_gemma":[0.0028934407,0.00036513575,0.0004587765,0.0000821981,0.000015535343,0.0000047047956,0.0000550524,0.9434994,0.031391323,0.01945623,0.0013407368,0.00043747094],"about_ca_topic_score_codex":0.00008417457,"about_ca_topic_score_gemma":0.000008889264,"teacher_disagreement_score":0.9538546,"about_ca_system_score_codex":0.0002777644,"about_ca_system_score_gemma":0.00025258627,"threshold_uncertainty_score":0.8411818},"labels":[],"label_agreement":null},{"id":"W4230059533","doi":"10.32920/ryerson.14651931","title":"A Fast, Fully Automated Prostate Boundary Segmentation Using Probabilistic Approaches In Ultrasound Images","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Segmentation; Computer science; Smoothing; Speckle noise; Computer vision; Expectation–maximization algorithm; Image segmentation; Markov random field; Noise (video); Probabilistic logic; Pattern recognition (psychology); Scale-space segmentation; Speckle pattern; Similarity (geometry); Algorithm; Mathematics; Image (mathematics); Statistics","score_opus":0.04966282316441121,"score_gpt":0.3099492574220965,"score_spread":0.2602864342576853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230059533","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033794366,0.00021375538,0.9607199,0.0002301681,0.0003363543,0.0018163305,0.000016750708,0.0022159265,0.00065648585],"genre_scores_gemma":[0.06149146,0.000047548918,0.937179,0.00029673107,0.000043004824,0.000398249,0.0002942872,0.000036826397,0.00021288505],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961159,0.00048113568,0.0008978701,0.0012689454,0.0007578606,0.0004782741],"domain_scores_gemma":[0.99811786,0.00018935093,0.0003968433,0.00092498696,0.00021271994,0.00015824586],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008742124,0.00044577327,0.0005042129,0.00038979502,0.00011260741,0.0018298861,0.0010040213,0.0002624894,0.000085965745],"category_scores_gemma":[0.000309384,0.0004369311,0.000115665425,0.0006764935,0.00022652793,0.0010009492,0.0013528579,0.00069126993,0.000007880763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009614158,0.0042882175,0.007750366,0.010873947,0.0006730729,0.0018398712,0.053650968,0.055649504,0.5812312,0.001817433,0.008184643,0.27394468],"study_design_scores_gemma":[0.0010274308,0.00011848406,0.0041362443,0.0012049851,0.00006621246,0.00019506826,0.0019317761,0.7545922,0.22816299,0.0070237806,0.000007338819,0.0015335146],"about_ca_topic_score_codex":0.00035799047,"about_ca_topic_score_gemma":0.00005519899,"teacher_disagreement_score":0.69894266,"about_ca_system_score_codex":0.0005616126,"about_ca_system_score_gemma":0.001092489,"threshold_uncertainty_score":0.99980825},"labels":[],"label_agreement":null},{"id":"W4231631659","doi":"10.1109/icpr.2004.1334208","title":"Texture segmentation comparison using grey level co-occurrence probabilities and Markov random fields","year":2004,"lang":"en","type":"article","venue":"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Texture (cosmology); Consistency (knowledge bases); Segmentation; Feature (linguistics); Image segmentation; Confusion; Markov chain; Computer science; Boundary (topology); Random field; Image texture; Window (computing); Markov random field; Markov process; Mathematics; Gaussian; Grey level; Statistics; Image (mathematics); Psychology; Physics","score_opus":0.09093570772019158,"score_gpt":0.3348014223830179,"score_spread":0.24386571466282633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231631659","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2133573,0.00008250552,0.77736336,0.0037049183,0.00074791705,0.0011684346,0.00036434617,0.0002034839,0.0030077093],"genre_scores_gemma":[0.9629478,0.000062913816,0.035819594,0.000812745,0.00008778525,0.00007657137,0.000055973513,0.000012604751,0.000124039],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99776274,0.000034227134,0.00062181335,0.0004967778,0.00083192304,0.000252492],"domain_scores_gemma":[0.9980738,0.00007843074,0.00055649364,0.0001643175,0.0010190412,0.00010787133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043169703,0.00026454413,0.00027616596,0.00020830244,0.00016984624,0.00032558347,0.0009375039,0.00012821733,0.00019638242],"category_scores_gemma":[0.00026691947,0.00021649158,0.00008971914,0.00022224746,0.0002443255,0.0009888969,0.000157611,0.00033585992,0.000014539407],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005018851,0.0017289879,0.09918339,0.0010290341,0.00045906822,0.000009164444,0.012156317,0.00020049194,0.040732324,0.0072654784,0.01691328,0.8198206],"study_design_scores_gemma":[0.007465287,0.00066592486,0.015250657,0.003539636,0.00010780524,0.0001652881,0.0021708612,0.013981301,0.8656539,0.089631334,0.00008999057,0.0012780544],"about_ca_topic_score_codex":0.00009220368,"about_ca_topic_score_gemma":0.000009953208,"teacher_disagreement_score":0.82492155,"about_ca_system_score_codex":0.00018078742,"about_ca_system_score_gemma":0.00013939608,"threshold_uncertainty_score":0.88282704},"labels":[],"label_agreement":null},{"id":"W4231706495","doi":"10.1007/bfb0034937","title":"Medical image segmentation using topologically adaptable snakes","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Segmentation; Computer science; Merge (version control); Subdivision; Parametric statistics; Image segmentation; Artificial intelligence; Computer vision; Representation (politics); Topology (electrical circuits); Mathematics; Engineering","score_opus":0.02640009259363726,"score_gpt":0.30391874191386664,"score_spread":0.2775186493202294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231706495","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004238051,0.00027514322,0.9934095,0.0013375332,0.0008680649,0.0004455001,0.000003152828,0.00039742427,0.003221309],"genre_scores_gemma":[0.0028174764,0.00010915331,0.99145347,0.0046612173,0.0005603558,0.00001138665,0.000007929316,0.000028300496,0.00035070282],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944965,0.000077820536,0.00078950904,0.0014714279,0.0024547842,0.0007099306],"domain_scores_gemma":[0.9974914,0.00044653693,0.0003618343,0.0010185156,0.00028863605,0.00039306656],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015647691,0.0005051551,0.00053264777,0.0006855917,0.00026268052,0.0005982482,0.0035213472,0.00048923295,0.000717038],"category_scores_gemma":[0.0003503793,0.00043956572,0.00012568798,0.0005465086,0.0012805758,0.0012814504,0.0014222778,0.0009687874,0.00006434605],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032264172,0.000042070293,0.000013461384,0.00002444817,0.000008245612,0.00019142855,0.00028210852,0.0011119936,0.0026799801,0.003991178,0.00008187305,0.99157],"study_design_scores_gemma":[0.0005454405,0.00029105254,0.00004101716,0.00063767313,0.000016668726,0.0003157711,7.83802e-7,0.8840569,0.038736377,0.073400095,0.00081745855,0.0011407521],"about_ca_topic_score_codex":0.000040951898,"about_ca_topic_score_gemma":0.000045437508,"teacher_disagreement_score":0.9904292,"about_ca_system_score_codex":0.0005359673,"about_ca_system_score_gemma":0.0009779201,"threshold_uncertainty_score":0.9998056},"labels":[],"label_agreement":null},{"id":"W4232638204","doi":"10.1007/978-3-319-69251-7_16","title":"3D Imaging","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Cognitive science; Computer science; History; Psychology","score_opus":0.014361559221944554,"score_gpt":0.2582924068545299,"score_spread":0.24393084763258538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232638204","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.890035e-9,0.0000575419,0.5016933,0.00022304358,0.0001647012,0.000087757624,5.817415e-7,0.00034605988,0.497427],"genre_scores_gemma":[0.000004248705,0.000032722113,0.32367206,0.0028103956,0.00004584981,0.000001884195,0.000004641585,0.000013591229,0.6734146],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99891514,0.0000065363274,0.00020459267,0.0003670327,0.00036993495,0.00013675472],"domain_scores_gemma":[0.9990344,0.00004954742,0.00010065231,0.0006744062,0.00006437742,0.00007659839],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00012956158,0.0001625724,0.00016840751,0.00012254898,0.000022028871,0.00011653915,0.0008279006,0.00008172717,0.002750868],"category_scores_gemma":[0.000010768865,0.00014210562,0.00006605346,0.000016675152,0.000043316464,0.0002779627,0.00035921123,0.00021030707,0.0026718986],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.7944622e-7,0.0000024553744,0.0000016442567,0.000013454362,0.0000068498057,0.000022329761,0.000019519519,5.4121717e-8,0.00006227344,0.47473365,0.07728188,0.4478557],"study_design_scores_gemma":[0.00025161603,0.000049138565,0.000007646317,0.0002526166,0.000017007787,0.000049998616,0.000002575293,0.0070063267,0.005376893,0.08877502,0.8973274,0.00088374194],"about_ca_topic_score_codex":0.0000049516425,"about_ca_topic_score_gemma":5.214768e-7,"teacher_disagreement_score":0.82004553,"about_ca_system_score_codex":0.000042947577,"about_ca_system_score_gemma":0.00009258993,"threshold_uncertainty_score":0.9981608},"labels":[],"label_agreement":null},{"id":"W4232872903","doi":"10.1007/978-3-642-15352-5_4","title":"Multiregion Segmentation","year":2010,"lang":"en","type":"book-chapter","venue":"Springer topics in signal processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Partition (number theory); Mathematics; Segmentation; Image (mathematics); Domain (mathematical analysis); Plane (geometry); Geometry; Computer science; Artificial intelligence; Combinatorics; Mathematical analysis","score_opus":0.029171059465347714,"score_gpt":0.29185769908649056,"score_spread":0.26268663962114286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232872903","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000025199177,0.00033245052,0.906255,0.00027780363,0.0002386384,0.00028391732,7.291825e-7,0.00028246795,0.092303805],"genre_scores_gemma":[0.0037800937,0.00010947751,0.88057464,0.0009781598,0.0006435994,0.000045126802,0.00001740732,0.00006627383,0.11378519],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99819916,0.000018132676,0.00046819318,0.0005464455,0.00053047214,0.00023758099],"domain_scores_gemma":[0.99911004,0.00003852395,0.00030218431,0.00034185534,0.00011473134,0.00009267414],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031748816,0.0002652168,0.00025315373,0.00028931483,0.0000968497,0.00023611923,0.0006967028,0.00037811982,0.00015810099],"category_scores_gemma":[0.000020293532,0.00027484162,0.00006612646,0.00007439393,0.000108246655,0.00055694865,0.00024169641,0.0009203242,0.00003992945],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021274343,0.000019338799,0.000028411503,0.00013247784,0.000004874468,0.000049075185,0.00040915684,0.000001964085,0.0052730995,0.010903168,0.00007236541,0.98310393],"study_design_scores_gemma":[0.0022926019,0.0003281856,0.00040728028,0.00504799,0.00008554917,0.000093985036,0.00006379583,0.027657602,0.43832564,0.3852763,0.13653575,0.0038853337],"about_ca_topic_score_codex":0.00000910229,"about_ca_topic_score_gemma":0.00001292336,"teacher_disagreement_score":0.9792186,"about_ca_system_score_codex":0.00013519822,"about_ca_system_score_gemma":0.00014890498,"threshold_uncertainty_score":0.9999704},"labels":[],"label_agreement":null},{"id":"W4233419055","doi":"10.1023/a:1011225715272","title":"Complexity, Confusion, and Perceptual Grouping. Part II: Mapping Complexity","year":2001,"lang":"en","type":"article","venue":"Journal of Mathematical Imaging and Vision","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Espace pour la vie","funders":"","keywords":"Element (criminal law); Tangent; Mathematics; Texture (cosmology); Context (archaeology); Computation; Enhanced Data Rates for GSM Evolution; Orientation (vector space); Image (mathematics); Computer science; Artificial intelligence; Geometry; Algorithm","score_opus":0.06498171562809177,"score_gpt":0.34713660508713406,"score_spread":0.2821548894590423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233419055","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.085242145,0.00021974255,0.9077858,0.0058916085,0.0000892412,0.00008294019,5.2689296e-7,0.00006260396,0.00062540534],"genre_scores_gemma":[0.38825244,0.00033138655,0.6093983,0.0017703527,0.00014186517,0.0000014384613,0.000001042526,0.0000111888885,0.00009197039],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998442,0.000111242975,0.0005898691,0.00018812274,0.00047056237,0.00019817888],"domain_scores_gemma":[0.998943,0.00020881237,0.00026150234,0.00017592085,0.00015685867,0.00025391334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011555726,0.00013247179,0.00031481162,0.00014605436,0.00025381288,0.0002407415,0.00029584137,0.000034615252,0.0001395115],"category_scores_gemma":[0.0002495735,0.000098686825,0.000055951954,0.00013913545,0.00042033862,0.00068358844,0.00043040665,0.00025306703,0.0000056277063],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000602137,0.0011742878,0.0029908589,0.00048538728,0.00007346557,0.00043987448,0.014877362,0.0000014541915,0.016743341,0.12212815,0.043031037,0.79799455],"study_design_scores_gemma":[0.0031039768,0.0009949941,0.016082358,0.0029773975,0.00006471671,0.011803009,0.0027334415,0.30299953,0.0021160385,0.6491731,0.007166859,0.000784584],"about_ca_topic_score_codex":0.0000036837243,"about_ca_topic_score_gemma":2.595084e-7,"teacher_disagreement_score":0.79721,"about_ca_system_score_codex":0.000021878115,"about_ca_system_score_gemma":0.000019648438,"threshold_uncertainty_score":0.4024332},"labels":[],"label_agreement":null},{"id":"W4233516421","doi":"10.32920/ryerson.14644602.v1","title":"Improved diagnosis and navigation for CT colonography","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Contouring; Artificial intelligence; Segmentation; Computer science; Computer vision; Feature (linguistics); Texture (cosmology); Set (abstract data type); Pattern recognition (psychology); Image (mathematics); Computer graphics (images)","score_opus":0.024266224713282238,"score_gpt":0.3143882006970169,"score_spread":0.2901219759837347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233516421","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006396824,0.00020686953,0.9906426,0.0010662255,0.0003166943,0.00085056503,0.000007774236,0.0003445747,0.00016787123],"genre_scores_gemma":[0.03330648,0.00021177351,0.9634831,0.0011537602,0.00004103948,0.0016164631,0.00009841229,0.000008476233,0.000080502155],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998939,0.00005472278,0.00022828937,0.0004997199,0.0001457928,0.00013248462],"domain_scores_gemma":[0.99904454,0.00020177869,0.000121354555,0.0004077702,0.00013225974,0.00009232012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030455217,0.00013201329,0.00018003405,0.00008429099,0.00006289001,0.00042980936,0.00040872113,0.00007893531,0.000019960165],"category_scores_gemma":[0.00010775181,0.00012603051,0.0000915018,0.00009017492,0.000048029877,0.00022331013,0.0007886315,0.00020655298,4.5325885e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003325615,0.00015647883,0.0008929842,0.00068628177,0.0000930597,0.000015238779,0.0006462547,0.0000015293941,0.0030320154,0.0043388396,0.016393764,0.9737402],"study_design_scores_gemma":[0.0009164205,0.00038865328,0.0014376399,0.00067466334,0.00007693757,0.000021853504,0.00018148465,0.11813071,0.8501626,0.024144387,0.0028479844,0.0010166309],"about_ca_topic_score_codex":0.00008143371,"about_ca_topic_score_gemma":0.0000058594474,"teacher_disagreement_score":0.9727236,"about_ca_system_score_codex":0.000022776956,"about_ca_system_score_gemma":0.00006859467,"threshold_uncertainty_score":0.51393753},"labels":[],"label_agreement":null},{"id":"W4233711253","doi":"10.1109/iembs.2006.4397912","title":"Segmentation of Prostate from 3-D Ultrasound Volumes Using Shape and Intensity Priors in Level Set Framework","year":2006,"lang":"en","type":"article","venue":"Conference proceedings","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Level set (data structures); Prior probability; Computer science; Segmentation; Image segmentation; Artificial intelligence; Computer vision; Intensity (physics); Set (abstract data type); Level set method; Ultrasound; Pattern recognition (psychology); Radiology; Medicine; Bayesian probability; Physics; Optics","score_opus":0.04543537095601125,"score_gpt":0.2939300701321404,"score_spread":0.24849469917612915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233711253","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6180072,0.00002774422,0.38156694,0.000081832906,0.00003406153,0.00018413554,0.0000065311187,0.000053177213,0.00003840207],"genre_scores_gemma":[0.69569814,0.000019326157,0.30412823,0.00009994794,0.000017117602,0.0000094349625,0.000006602662,0.000005017901,0.000016161943],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988271,0.000012086238,0.00034120554,0.00036308484,0.0002724292,0.00018410808],"domain_scores_gemma":[0.9992616,0.000072199495,0.00020951632,0.000099237135,0.00030059036,0.00005686594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023962889,0.00013143582,0.00020053585,0.00012784079,0.00004663121,0.0001915015,0.000292059,0.00008610509,0.000022423927],"category_scores_gemma":[0.00022309141,0.00013257608,0.000018309498,0.00030331264,0.00016325648,0.000676465,0.0001504986,0.00016624016,0.0000014422693],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034010696,0.00008134977,0.3641256,0.00011039048,0.000012044677,0.0000041446424,0.013292068,0.0000020735636,0.5822837,0.0031798733,0.0004172552,0.036457498],"study_design_scores_gemma":[0.0005223557,0.000111665555,0.29216295,0.0005785943,0.000014967553,0.000015306076,0.0020728682,0.06284926,0.5833138,0.057972178,0.000011410434,0.0003746137],"about_ca_topic_score_codex":0.0005900141,"about_ca_topic_score_gemma":0.00001542209,"teacher_disagreement_score":0.07769097,"about_ca_system_score_codex":0.000047364556,"about_ca_system_score_gemma":0.000070191694,"threshold_uncertainty_score":0.54062957},"labels":[],"label_agreement":null},{"id":"W4234369753","doi":"10.1117/12.440246","title":"&lt;title&gt;Three-dimensional ultrasound imaging&lt;/title&gt;","year":2001,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Canadian Institutes of Health Research","keywords":"Ultrasound; 3D ultrasound; Computer science; Visualization; Ultrasound imaging; Medical diagnosis; Computer vision; Medical imaging; Artificial intelligence; Orientation (vector space); Transducer; Medical physics; Medicine; Radiology; Acoustics","score_opus":0.010198526547482961,"score_gpt":0.23838753556913023,"score_spread":0.22818900902164727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234369753","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8735815,0.00046293525,0.024635263,0.005694428,0.0011689855,0.00079015415,0.000035583213,0.00065990665,0.09297122],"genre_scores_gemma":[0.06961671,0.00017459552,0.9251456,0.000829458,0.0008072462,0.00011876298,0.00001504466,0.0000801077,0.0032124496],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983925,1.672123e-8,0.00037778585,0.0002972303,0.00066894485,0.00026351766],"domain_scores_gemma":[0.99878335,0.000106556006,0.00017676566,0.000070971204,0.0007532161,0.00010915152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003773015,0.00018507491,0.00020093091,0.00008536038,0.000054596563,0.00011305623,0.00096912734,0.000093753544,0.00020814358],"category_scores_gemma":[0.00034455527,0.00015697799,0.0002765551,0.00027750802,0.00016471744,0.00044084358,0.0001675557,0.0001950348,0.000043354055],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010457565,0.0000798826,0.00013271427,0.00008514497,0.000109767054,4.8793777e-7,0.000047287416,0.0000068592826,0.31666544,0.570375,0.10722035,0.005266618],"study_design_scores_gemma":[0.0027205888,0.00056354835,0.0024576662,0.00096229446,0.00026171328,0.00038161722,0.00021569108,0.22969961,0.40816623,0.037063036,0.3155701,0.0019379419],"about_ca_topic_score_codex":0.000002557348,"about_ca_topic_score_gemma":7.2276094e-8,"teacher_disagreement_score":0.9005104,"about_ca_system_score_codex":0.0000955602,"about_ca_system_score_gemma":0.000036024696,"threshold_uncertainty_score":0.6401376},"labels":[],"label_agreement":null},{"id":"W4234546225","doi":"10.1109/icpr.2004.1334353","title":"Unsupervised image segmentation using a simple MRF model with a new implementation scheme","year":2004,"lang":"en","type":"article","venue":"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Markov random field; Image segmentation; Weighting; Computer science; Pattern recognition (psychology); Segmentation; Scale-space segmentation; Image (mathematics); Computer vision; Segmentation-based object categorization","score_opus":0.06631868643153345,"score_gpt":0.3297550153693988,"score_spread":0.26343632893786534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234546225","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1736544,0.000008288342,0.82154226,0.0023598308,0.00013932039,0.00072828,0.00007342928,0.00017238464,0.0013217869],"genre_scores_gemma":[0.6777497,0.000024367999,0.3196899,0.002020884,0.00012346442,0.0000995475,0.00010121297,0.000039563987,0.00015137253],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972867,0.000017405628,0.000633548,0.00057592744,0.0011503504,0.00033609735],"domain_scores_gemma":[0.9976518,0.00002348231,0.0006345531,0.0002221966,0.0013018617,0.00016609962],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002969672,0.00031274668,0.00023545863,0.0002972025,0.00016507853,0.0003500906,0.001170546,0.000087507055,0.00041261315],"category_scores_gemma":[0.000070504284,0.00025364087,0.00010579403,0.0004475353,0.00012509813,0.001766361,0.00017594582,0.00025170177,0.000034268043],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035851318,0.0013032878,0.014592425,0.00034552574,0.0005782234,0.000011771657,0.0067278678,0.0012705945,0.7074996,0.013434891,0.010959362,0.24291794],"study_design_scores_gemma":[0.004394888,0.0003820177,0.0013440548,0.00070818554,0.00006574974,0.00006169429,0.0010992298,0.051277153,0.87217176,0.06783129,0.000013142909,0.0006508231],"about_ca_topic_score_codex":0.00033618545,"about_ca_topic_score_gemma":0.00003495019,"teacher_disagreement_score":0.50409526,"about_ca_system_score_codex":0.00038181627,"about_ca_system_score_gemma":0.00044509562,"threshold_uncertainty_score":0.9999916},"labels":[],"label_agreement":null},{"id":"W4236501714","doi":"10.1007/978-3-642-15352-5_5","title":"Image Models","year":2010,"lang":"en","type":"book-chapter","venue":"Springer topics in signal processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Image segmentation; Artificial intelligence; Segmentation; Image (mathematics); Scale-space segmentation; Segmentation-based object categorization; Partition (number theory); Computer science; Minimum spanning tree-based segmentation; Pattern recognition (psychology); Region growing; Statistical model; Computer vision; Mathematics; Combinatorics","score_opus":0.032315364770732075,"score_gpt":0.2861937551495238,"score_spread":0.2538783903787917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236501714","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000003152721,0.0002963295,0.7021825,0.00021677827,0.000124703,0.00014912429,8.9594806e-7,0.00023097762,0.29679558],"genre_scores_gemma":[0.0010439572,0.00007215565,0.91360396,0.0007310524,0.0004359771,0.000022067967,0.0000047616545,0.000055017856,0.084031075],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980061,0.000014631869,0.000492613,0.00061334786,0.0005710985,0.00030219316],"domain_scores_gemma":[0.9989681,0.00003689396,0.00024723547,0.00047786772,0.00015021658,0.00011967884],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037895233,0.00029936625,0.00032107055,0.0002818095,0.000090295805,0.00033827577,0.0011317242,0.00041108503,0.00021960992],"category_scores_gemma":[0.000018692795,0.00030570192,0.00008204818,0.000072483206,0.00015991114,0.0008939445,0.00044789174,0.0012906564,0.00004222547],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025567708,0.000029085462,0.000003911176,0.00025206505,0.00000837968,0.0001600643,0.0004899654,0.000005278322,0.0044304896,0.08864437,0.00017426348,0.90579957],"study_design_scores_gemma":[0.00033096195,0.00005182343,0.000008720701,0.0011095359,0.000016530217,0.000024622526,0.00000603277,0.045420203,0.030773059,0.9044874,0.016784558,0.0009865735],"about_ca_topic_score_codex":0.000007271693,"about_ca_topic_score_gemma":0.000006704765,"teacher_disagreement_score":0.904813,"about_ca_system_score_codex":0.00009519403,"about_ca_system_score_gemma":0.00022156427,"threshold_uncertainty_score":0.9999395},"labels":[],"label_agreement":null},{"id":"W4236999596","doi":"10.1109/iembs.2006.4397422","title":"Towards Real-time Registration of 4D Ultrasound Images","year":2006,"lang":"en","type":"article","venue":"Conference proceedings","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Feature (linguistics); Image registration; Tracking (education); Process (computing); Image (mathematics)","score_opus":0.014769861154974284,"score_gpt":0.26491139386667484,"score_spread":0.25014153271170053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236999596","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025227532,0.00001878436,0.8893278,0.00061158923,0.000048348513,0.00021670439,0.0000034696586,0.0004701139,0.08407562],"genre_scores_gemma":[0.7219019,0.00003804751,0.27663916,0.00006261645,0.000051353414,0.00002350439,0.000007713887,0.0000068443674,0.0012688568],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986859,0.000009797221,0.00036582458,0.00031399535,0.0004308865,0.00019361866],"domain_scores_gemma":[0.998925,0.000042593485,0.00026325724,0.00016900453,0.00053438137,0.000065818596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035711093,0.00012560851,0.00017305207,0.000096457625,0.00006421057,0.0003095808,0.0006434845,0.00006896343,0.00012167827],"category_scores_gemma":[0.00020108356,0.000118286654,0.00004010255,0.0002943123,0.00021376285,0.0009193496,0.00008283792,0.00009023523,0.000019602536],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029808523,0.000046970672,0.00045418245,0.000048321584,0.0000036962476,0.00000151361,0.00033349192,1.4325879e-7,0.8897406,0.083500266,0.013740353,0.01212751],"study_design_scores_gemma":[0.00015900376,0.00009504669,0.0055291755,0.000062605686,0.0000065093486,0.000015236955,0.000058932495,0.0012194718,0.9609803,0.031601414,0.000106073545,0.00016624099],"about_ca_topic_score_codex":0.00026732942,"about_ca_topic_score_gemma":0.0000014083741,"teacher_disagreement_score":0.69667435,"about_ca_system_score_codex":0.00003293036,"about_ca_system_score_gemma":0.00014913124,"threshold_uncertainty_score":0.482359},"labels":[],"label_agreement":null},{"id":"W4237188954","doi":"10.1007/978-3-642-15352-5_1","title":"Introduction","year":2010,"lang":"en","type":"book-chapter","venue":"Springer topics in signal processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Disjoint sets; Image (mathematics); Artificial intelligence; Image segmentation; Focus (optics); Domain (mathematical analysis); Computer science; Market segmentation; Segmentation; Scale-space segmentation; Segmentation-based object categorization; Computer vision; Set (abstract data type); Pattern recognition (psychology); Mathematics; Combinatorics","score_opus":0.01935927856788975,"score_gpt":0.27212975993579636,"score_spread":0.2527704813679066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237188954","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000064951028,0.00030441105,0.85787165,0.0014086514,0.00044637077,0.00016305772,4.26383e-7,0.00027756847,0.13952138],"genre_scores_gemma":[0.0011928783,0.000089385,0.6894098,0.0008505646,0.0044732783,0.000026872123,0.000010122738,0.00006325284,0.30388388],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983688,0.00001294573,0.0004021703,0.00056018983,0.00044008894,0.00021580468],"domain_scores_gemma":[0.99916935,0.000021290034,0.00022022177,0.0003961028,0.00011588269,0.000077165365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003659236,0.00022252214,0.00023754008,0.00026646216,0.00007860824,0.00021951202,0.00069416786,0.0003455266,0.00046841885],"category_scores_gemma":[0.000031676816,0.00022921775,0.000054339693,0.00007432991,0.000110039255,0.00046147063,0.00025345042,0.0011358053,0.00005857836],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001618286,0.000014824932,0.000008048629,0.000108255466,0.000004386289,0.000031946718,0.00017969326,0.000001388086,0.0025086747,0.044565834,0.00048618202,0.95208913],"study_design_scores_gemma":[0.0003816006,0.000099444165,0.00007021063,0.00078951,0.000023172192,0.000050681825,0.000007824342,0.0028830005,0.0914594,0.30882305,0.5942087,0.0012033803],"about_ca_topic_score_codex":0.0000037340685,"about_ca_topic_score_gemma":0.0000058545534,"teacher_disagreement_score":0.9508858,"about_ca_system_score_codex":0.00010040695,"about_ca_system_score_gemma":0.00014542587,"threshold_uncertainty_score":0.9347229},"labels":[],"label_agreement":null},{"id":"W4239077729","doi":"10.1007/978-1-4020-8658-8_1","title":"Introduction","year":2008,"lang":"en","type":"book-chapter","venue":"Computational imaging and vision","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Representation (politics); Section (typography); Context (archaeology); Object (grammar); Medial axis; Boundary (topology); Computer science; Extension (predicate logic); Cognitive science; Artificial intelligence; Mathematics; Geography; Psychology; Programming language","score_opus":0.01256837565294404,"score_gpt":0.29412810345767654,"score_spread":0.2815597278047325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239077729","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000002522992,0.0009762364,0.96362424,0.0036716182,0.0004606692,0.00011800468,0.0000028973743,0.0003591425,0.030784681],"genre_scores_gemma":[0.0012606876,0.001587276,0.7953811,0.00298438,0.002401383,0.000012458454,0.00035235393,0.000072760624,0.19594759],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986682,0.000018989524,0.00026758094,0.00050009514,0.00043030782,0.00011484603],"domain_scores_gemma":[0.9992488,0.00009117211,0.00015027988,0.00023230423,0.00017898301,0.00009843229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013889963,0.00018829835,0.00017334116,0.0002183996,0.00016307826,0.00014213564,0.00023386587,0.00006790648,0.00007265268],"category_scores_gemma":[0.000025442476,0.00018710172,0.000051588075,0.00003827458,0.00014880631,0.00043150433,0.00020455413,0.00023259752,0.000111127956],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000225147,0.000012925184,0.0000036630115,0.00002186652,0.000010799812,0.000030313868,0.00010962431,0.00007127931,0.00003906805,0.032986727,0.39400077,0.5727107],"study_design_scores_gemma":[0.0005868896,0.00013970511,0.00041787996,0.00032138862,0.00002429317,0.00081292004,0.000005114241,0.16402397,0.00030818902,0.17733002,0.6551727,0.00085694133],"about_ca_topic_score_codex":0.0000018671699,"about_ca_topic_score_gemma":6.181081e-8,"teacher_disagreement_score":0.57185376,"about_ca_system_score_codex":0.00004732207,"about_ca_system_score_gemma":0.00005766014,"threshold_uncertainty_score":0.7629787},"labels":[],"label_agreement":null},{"id":"W4241787440","doi":"10.1007/978-3-030-12029-0","title":"Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges","year":2019,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; University of Toronto","funders":"","keywords":"Computer science; Segmentation; Computational model; Artificial intelligence; Statistical model; Ventricle; Data science; Cardiology; Medicine","score_opus":0.036169264610538196,"score_gpt":0.29952407118032837,"score_spread":0.26335480656979016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241787440","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00019993688,0.0008701809,0.99657166,0.001108611,0.00048311043,0.00059911545,0.000016068905,0.00005272111,0.00009857874],"genre_scores_gemma":[0.07557431,0.00024313643,0.9233695,0.0005590005,0.00013344231,0.000010180767,0.000019572497,0.000014794023,0.00007601316],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99735725,0.00015834304,0.00046296048,0.0008363183,0.0009547692,0.000230332],"domain_scores_gemma":[0.99754226,0.0013600683,0.0002935862,0.00051873235,0.00019834508,0.00008699317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083951786,0.00023183992,0.00033338767,0.0002747063,0.00012883346,0.00021268084,0.00085468113,0.00014847929,0.0000037701752],"category_scores_gemma":[0.00015678779,0.00017862351,0.000048540194,0.00030839886,0.0009943072,0.00047843938,0.0006720949,0.00030856553,0.0000019893716],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012335493,0.00003797982,0.00010634292,0.0002851123,0.000019951305,0.0000031335485,0.0017299157,0.042830776,0.000585971,0.03276362,0.0002973149,0.92132753],"study_design_scores_gemma":[0.00031745862,0.00013530802,0.00092594454,0.00023476571,0.000011436589,0.000027289683,0.0000010677876,0.7751007,0.0023145517,0.22068684,0.000023315912,0.00022131516],"about_ca_topic_score_codex":0.00001611575,"about_ca_topic_score_gemma":0.000011166211,"teacher_disagreement_score":0.9211062,"about_ca_system_score_codex":0.00011679562,"about_ca_system_score_gemma":0.0005735145,"threshold_uncertainty_score":0.72840554},"labels":[],"label_agreement":null},{"id":"W4243897511","doi":"10.1007/978-1-0716-0843-2_15","title":"Algorithms for General Nonconvex Problems","year":2021,"lang":"en","type":"book-chapter","venue":"Texts in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Initialization; Mathematical optimization; Regular polygon; Convex optimization; Proper convex function; Optimization problem; Conic optimization; Mathematics; Space (punctuation); Computer science; Convex analysis; Algorithm","score_opus":0.041783381655550705,"score_gpt":0.31143646042116574,"score_spread":0.26965307876561506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243897511","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000004593053,0.00028359873,0.981935,0.00042218168,0.0016382751,0.0008300215,0.000008187835,0.0003296242,0.01454848],"genre_scores_gemma":[0.00003614991,0.00007503498,0.9426168,0.0018184423,0.0004607934,0.00008386377,0.000013417138,0.000030419616,0.05486505],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995876,0.000027726208,0.0006853769,0.001600352,0.0011731534,0.0006373795],"domain_scores_gemma":[0.99751765,0.00017937308,0.0002937543,0.0012932539,0.00044613093,0.0002698373],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013712018,0.00040105908,0.00049812155,0.00060200784,0.00018442613,0.00064723653,0.00364719,0.00021966455,0.00008471609],"category_scores_gemma":[0.00004511269,0.0003949235,0.00014768603,0.00041299977,0.0006761809,0.00089904154,0.0015935494,0.0004303419,0.00004683174],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.222639e-7,0.00003786112,0.000002291501,0.000067848036,0.000007986454,0.00006811124,0.00029286224,0.00007390342,0.00024523208,0.11061062,0.0032417015,0.88535076],"study_design_scores_gemma":[0.0009794207,0.00047084375,0.00006177158,0.0010312432,0.000013245005,0.0001361095,0.0000015538907,0.79789126,0.010349687,0.14091316,0.04660299,0.0015487245],"about_ca_topic_score_codex":0.000010810245,"about_ca_topic_score_gemma":0.000007806153,"teacher_disagreement_score":0.88380206,"about_ca_system_score_codex":0.0002940554,"about_ca_system_score_gemma":0.0008988062,"threshold_uncertainty_score":0.9998503},"labels":[],"label_agreement":null},{"id":"W4246141447","doi":"10.1080/10929080601017212","title":"Bone enhancement filtering: Application to sinus bone segmentation and simulation of pituitary surgery","year":2006,"lang":"en","type":"article","venue":"Computer Aided Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Segmentation; Medicine; Radiology; Computer vision","score_opus":0.01771447900193653,"score_gpt":0.2667193449385886,"score_spread":0.24900486593665208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246141447","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09614714,0.00012188183,0.9024484,0.0003534051,0.00027552494,0.00040335054,0.0000029279338,0.00022035981,0.000027006634],"genre_scores_gemma":[0.7262968,0.000022969092,0.2726393,0.0006839605,0.00015357441,0.00008390618,0.00007395323,0.0000146033535,0.000030946954],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99782133,0.00011843372,0.000839856,0.00049636455,0.00046080878,0.00026321778],"domain_scores_gemma":[0.99798167,0.0009721701,0.00030861507,0.00045492768,0.00015804441,0.00012457138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007674424,0.00018959292,0.00039372215,0.00044895604,0.00007692279,0.00009203189,0.00016871141,0.00006308818,0.000015882753],"category_scores_gemma":[0.000048938076,0.00020484586,0.000094247254,0.0004970447,0.00005258319,0.0005991939,0.00021919378,0.000070957954,0.000014761311],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026851922,0.00027355656,0.0021760787,0.00019475415,0.000022552982,0.000038371112,0.00028245788,0.00856968,0.10689265,0.0002125711,0.01633819,0.8649723],"study_design_scores_gemma":[0.00038394728,0.00013113605,0.02922288,0.00029900175,0.000023005628,0.000042436026,0.000015447245,0.45233363,0.51317817,0.0026052333,0.0009976475,0.0007674752],"about_ca_topic_score_codex":0.00007508355,"about_ca_topic_score_gemma":0.0000027869894,"teacher_disagreement_score":0.8642048,"about_ca_system_score_codex":0.00007299201,"about_ca_system_score_gemma":0.00005486942,"threshold_uncertainty_score":0.83533716},"labels":[],"label_agreement":null},{"id":"W4247257453","doi":"10.32920/ryerson.14644602","title":"Improved diagnosis and navigation for CT colonography","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Contouring; Artificial intelligence; Computer science; Segmentation; Feature (linguistics); Computer vision; Texture (cosmology); Computer graphics; Set (abstract data type); Pattern recognition (psychology); Image (mathematics); Computer graphics (images)","score_opus":0.024266224713282238,"score_gpt":0.3143882006970169,"score_spread":0.2901219759837347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247257453","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006396824,0.00020686953,0.9906426,0.0010662255,0.0003166943,0.00085056503,0.000007774236,0.0003445747,0.00016787123],"genre_scores_gemma":[0.03330648,0.00021177351,0.9634831,0.0011537602,0.00004103948,0.0016164631,0.00009841229,0.000008476233,0.000080502155],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998939,0.00005472278,0.00022828937,0.0004997199,0.0001457928,0.00013248462],"domain_scores_gemma":[0.99904454,0.00020177869,0.000121354555,0.0004077702,0.00013225974,0.00009232012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030455217,0.00013201329,0.00018003405,0.00008429099,0.00006289001,0.00042980936,0.00040872113,0.00007893531,0.000019960165],"category_scores_gemma":[0.00010775181,0.00012603051,0.0000915018,0.00009017492,0.000048029877,0.00022331013,0.0007886315,0.00020655298,4.5325885e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003325615,0.00015647883,0.0008929842,0.00068628177,0.0000930597,0.000015238779,0.0006462547,0.0000015293941,0.0030320154,0.0043388396,0.016393764,0.9737402],"study_design_scores_gemma":[0.0009164205,0.00038865328,0.0014376399,0.00067466334,0.00007693757,0.000021853504,0.00018148465,0.11813071,0.8501626,0.024144387,0.0028479844,0.0010166309],"about_ca_topic_score_codex":0.00008143371,"about_ca_topic_score_gemma":0.0000058594474,"teacher_disagreement_score":0.9727236,"about_ca_system_score_codex":0.000022776956,"about_ca_system_score_gemma":0.00006859467,"threshold_uncertainty_score":0.51393753},"labels":[],"label_agreement":null},{"id":"W4249742919","doi":"10.1007/978-3-642-15352-5_3","title":"Basic Methods","year":2010,"lang":"en","type":"book-chapter","venue":"Springer topics in signal processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Geodesic; Computer science; Edge detection; Set (abstract data type); Image (mathematics); Artificial intelligence; Segmentation; Level set (data structures); Implementation; Mathematics; Algorithm; Image processing; Geometry","score_opus":0.04064760612503763,"score_gpt":0.34894606478637874,"score_spread":0.3082984586613411,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4249742919","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000021587389,0.0005316412,0.78005683,0.00026713064,0.00024205848,0.00015402005,4.7136538e-7,0.00023820643,0.21850745],"genre_scores_gemma":[0.00016897806,0.000041463307,0.91411704,0.00087157334,0.00038044297,0.000017196762,0.0000023852501,0.00003831614,0.084362596],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980409,0.000043374235,0.00051969266,0.00063382083,0.0004672684,0.00029491808],"domain_scores_gemma":[0.99884915,0.00009574215,0.00027636805,0.0005006242,0.00012207079,0.00015604166],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00090118725,0.0002985789,0.00035905695,0.00031860193,0.00009238023,0.00026689924,0.0011454588,0.0004558417,0.0004864471],"category_scores_gemma":[0.00007872257,0.00029898382,0.000088738445,0.000095675925,0.00014580278,0.00042194777,0.00044911957,0.0014288066,0.000034112385],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.906975e-7,0.000011215477,0.000007213283,0.000108348206,0.000004892533,0.000041694253,0.0001869804,4.7072623e-7,0.0024690952,0.026532374,0.000055963832,0.97058076],"study_design_scores_gemma":[0.00045084007,0.00009623816,0.00007051019,0.001614281,0.000033592933,0.000040720246,0.000008747972,0.0054619038,0.16176282,0.583517,0.24546307,0.0014802436],"about_ca_topic_score_codex":0.0000055022388,"about_ca_topic_score_gemma":0.000005238593,"teacher_disagreement_score":0.96910053,"about_ca_system_score_codex":0.000098652505,"about_ca_system_score_gemma":0.00029014584,"threshold_uncertainty_score":0.99994624},"labels":[],"label_agreement":null},{"id":"W4250541294","doi":"10.32920/ryerson.14651931.v1","title":"A Fast, Fully Automated Prostate Boundary Segmentation Using Probabilistic Approaches In Ultrasound Images","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Segmentation; Artificial intelligence; Computer science; Smoothing; Speckle noise; Expectation–maximization algorithm; Computer vision; Image segmentation; Markov random field; Noise (video); Scale-space segmentation; Probabilistic logic; Pattern recognition (psychology); Speckle pattern; Algorithm; Mathematics; Image (mathematics); Statistics","score_opus":0.04966282316441121,"score_gpt":0.3099492574220965,"score_spread":0.2602864342576853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250541294","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033794366,0.00021375538,0.9607199,0.0002301681,0.0003363543,0.0018163305,0.000016750708,0.0022159265,0.00065648585],"genre_scores_gemma":[0.06149146,0.000047548918,0.937179,0.00029673107,0.000043004824,0.000398249,0.0002942872,0.000036826397,0.00021288505],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961159,0.00048113568,0.0008978701,0.0012689454,0.0007578606,0.0004782741],"domain_scores_gemma":[0.99811786,0.00018935093,0.0003968433,0.00092498696,0.00021271994,0.00015824586],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008742124,0.00044577327,0.0005042129,0.00038979502,0.00011260741,0.0018298861,0.0010040213,0.0002624894,0.000085965745],"category_scores_gemma":[0.000309384,0.0004369311,0.000115665425,0.0006764935,0.00022652793,0.0010009492,0.0013528579,0.00069126993,0.000007880763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009614158,0.0042882175,0.007750366,0.010873947,0.0006730729,0.0018398712,0.053650968,0.055649504,0.5812312,0.001817433,0.008184643,0.27394468],"study_design_scores_gemma":[0.0010274308,0.00011848406,0.0041362443,0.0012049851,0.00006621246,0.00019506826,0.0019317761,0.7545922,0.22816299,0.0070237806,0.000007338819,0.0015335146],"about_ca_topic_score_codex":0.00035799047,"about_ca_topic_score_gemma":0.00005519899,"teacher_disagreement_score":0.69894266,"about_ca_system_score_codex":0.0005616126,"about_ca_system_score_gemma":0.001092489,"threshold_uncertainty_score":0.99980825},"labels":[],"label_agreement":null},{"id":"W4251736467","doi":"10.1137/1.9780898718843.ch6","title":"6. Parametric Image Registration","year":2009,"lang":"en","type":"book-chapter","venue":"Society for Industrial and Applied Mathematics eBooks","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Mathematics; Parametric statistics; Measure (data warehouse); Discretization; Norm (philosophy); Remainder; A priori and a posteriori; Mathematical analysis; Computer science","score_opus":0.06270333824248432,"score_gpt":0.27485480333389173,"score_spread":0.21215146509140742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251736467","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000002388573,0.000025239011,0.64359933,0.00012747926,0.000073730436,0.0012031519,0.000018298537,0.0002531118,0.35469726],"genre_scores_gemma":[0.000033478314,0.000040473355,0.82081205,0.00045323058,0.00039458918,0.000115817034,0.000041389594,0.000038873095,0.17807013],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99826485,0.0000034461368,0.0005819908,0.0004663466,0.00044082158,0.00024252023],"domain_scores_gemma":[0.99854004,0.00025540518,0.0005320493,0.00045045893,0.00008271801,0.0001393275],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00055489544,0.00033838532,0.0004585856,0.00007382203,0.00017467605,0.00028736328,0.00045394243,0.0006799708,0.0000137169045],"category_scores_gemma":[0.000049583316,0.00031140205,0.00033102455,0.000037022204,0.00018577736,0.00007838924,0.0001259967,0.00047068225,0.000006951529],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031259776,0.000017237579,4.101788e-9,0.000101536396,0.00005286797,6.9725695e-7,0.00028597476,9.253197e-8,0.00046566708,0.68439513,0.028829107,0.28584856],"study_design_scores_gemma":[0.0010924601,0.00018084532,4.0516987e-8,0.00018618471,0.00013711,0.000009219109,0.00006008714,0.0007539565,0.011296089,0.9489459,0.036757577,0.0005805431],"about_ca_topic_score_codex":7.882191e-7,"about_ca_topic_score_gemma":2.1388152e-7,"teacher_disagreement_score":0.285268,"about_ca_system_score_codex":0.000064151754,"about_ca_system_score_gemma":0.00012555397,"threshold_uncertainty_score":0.9999338},"labels":[],"label_agreement":null},{"id":"W4251997570","doi":"10.1080/10929080500079248","title":"Automatic non-linear MRI-ultrasound registration for the correction of intra-operative brain deformations","year":2004,"lang":"en","type":"article","venue":"Computer Aided Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; Rogue Research (Canada); McGill University","funders":"","keywords":"Computer science; Ultrasound; Brain tissue; Image registration; Artificial intelligence; Surgical planning; Computer vision; Radiology; Medicine; Biomedical engineering; Image (mathematics)","score_opus":0.02178213616217592,"score_gpt":0.2941336217155363,"score_spread":0.2723514855533604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251997570","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004874917,0.000019676581,0.9910388,0.0020037896,0.0011527579,0.00060639443,0.000003900782,0.0002467081,0.000053032054],"genre_scores_gemma":[0.28364244,0.000030202422,0.71341,0.002302995,0.0002705456,0.00021023983,0.000056036573,0.000016353413,0.00006115888],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985913,0.000090925736,0.0005942247,0.00023611031,0.0002941988,0.00019323209],"domain_scores_gemma":[0.99618655,0.0027773527,0.0002865889,0.00044263687,0.00024157276,0.0000653197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009301331,0.0001378103,0.00023224522,0.00014839543,0.00021952596,0.00013072162,0.00039230517,0.00006212766,0.000013005144],"category_scores_gemma":[0.0004129538,0.000104610495,0.0001384985,0.00042206448,0.000118204494,0.00084029615,0.00006097746,0.00012442464,0.000010922363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020207668,0.000351369,0.00024105975,0.00022246187,0.00016722894,0.000007506977,0.0069118966,0.024522,0.007031744,0.004819943,0.17364612,0.7820585],"study_design_scores_gemma":[0.00038984237,0.000123094,0.0016942864,0.00017146567,0.000015094112,0.000043345095,0.0000933107,0.91558623,0.07853613,0.002804475,0.00033527258,0.00020745049],"about_ca_topic_score_codex":0.000041464147,"about_ca_topic_score_gemma":0.000009870434,"teacher_disagreement_score":0.8910642,"about_ca_system_score_codex":0.00009758115,"about_ca_system_score_gemma":0.00023891983,"threshold_uncertainty_score":0.42658922},"labels":[],"label_agreement":null},{"id":"W4253787990","doi":"10.1007/978-3-642-54268-8","title":"Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges","year":2014,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Data science","score_opus":0.026950835788959866,"score_gpt":0.27486681650116984,"score_spread":0.24791598071220997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253787990","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000043201013,0.0013960815,0.9965912,0.0012337207,0.00020251278,0.00024237116,0.000005526905,0.000063961874,0.00022140366],"genre_scores_gemma":[0.07765168,0.000113635266,0.9210692,0.0010471122,0.000077151344,0.0000061462965,0.0000023899627,0.000012243601,0.00002041405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99755436,0.0001137,0.00038842997,0.00081723306,0.0008352222,0.00029105254],"domain_scores_gemma":[0.99770635,0.0013246619,0.0001861408,0.0004918909,0.0001701136,0.00012086503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081202155,0.00024660616,0.0003544725,0.00025728438,0.00015434648,0.00020603334,0.0010484629,0.00009301897,0.0000017806894],"category_scores_gemma":[0.00008578966,0.00018607195,0.00003523202,0.00023169655,0.001458044,0.0003637231,0.0010858662,0.00038582957,5.612685e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024059952,0.000022483266,0.000047562033,0.00019168189,0.000007591662,0.000006788343,0.0011399307,0.3129178,0.000038177746,0.033121686,0.00018090717,0.652323],"study_design_scores_gemma":[0.00008122697,0.000026291407,0.00006649122,0.00021446486,0.000003276512,0.000032320535,2.222241e-7,0.6629666,0.00026343213,0.33620223,0.000017475184,0.00012595045],"about_ca_topic_score_codex":0.000012295856,"about_ca_topic_score_gemma":0.0000052243117,"teacher_disagreement_score":0.65219706,"about_ca_system_score_codex":0.00007330432,"about_ca_system_score_gemma":0.0003745492,"threshold_uncertainty_score":0.75877935},"labels":[],"label_agreement":null},{"id":"W4254522979","doi":"10.1109/icpr.2004.1334008","title":"Perceptual grouping for contour extraction","year":2004,"lang":"en","type":"article","venue":"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Salient; Artificial intelligence; Clutter; Pattern recognition (psychology); Computer vision; Computer science; Set (abstract data type); Feature extraction; Line (geometry); Mathematics; Radar; Geometry","score_opus":0.0661239255971865,"score_gpt":0.32424179121202307,"score_spread":0.2581178656148366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254522979","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03374611,0.00002330701,0.9477068,0.0070365593,0.0012126938,0.00092969235,0.000089881076,0.00027502773,0.00897991],"genre_scores_gemma":[0.9550433,0.000040058043,0.041567557,0.0022596733,0.00030205195,0.00022160937,0.000036946567,0.000025532778,0.00050327415],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978764,0.000014904281,0.00055830326,0.00049356144,0.00076976005,0.0002870436],"domain_scores_gemma":[0.997657,0.00007044345,0.00052368885,0.0001715641,0.001469638,0.00010765598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043258764,0.00023604506,0.0002134186,0.00022387345,0.0001646608,0.00024996328,0.0013137774,0.00011887637,0.00031069157],"category_scores_gemma":[0.00034531197,0.00019864959,0.00016622893,0.00021311712,0.00013329965,0.0010900391,0.00012635157,0.00026405056,0.00006259763],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025166,0.0015291471,0.0030955551,0.00034720733,0.00034977702,0.000006091569,0.0039076754,0.000051382915,0.13313186,0.06017171,0.030308323,0.76684964],"study_design_scores_gemma":[0.004670392,0.000825273,0.0073560746,0.0019754341,0.00007419372,0.00013504429,0.0013252367,0.005210339,0.7977471,0.1788818,0.0007658951,0.0010332275],"about_ca_topic_score_codex":0.000072391835,"about_ca_topic_score_gemma":0.000009861089,"teacher_disagreement_score":0.9212972,"about_ca_system_score_codex":0.00025447458,"about_ca_system_score_gemma":0.00011829757,"threshold_uncertainty_score":0.8100695},"labels":[],"label_agreement":null},{"id":"W4254782311","doi":"10.1109/icpr.2004.1334595","title":"Steerable kernels for arbitrarily-sampled spaces","year":2004,"lang":"en","type":"article","venue":"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Pixel; Image (mathematics); Computer science; Space (punctuation); Computer vision; Kernel (algebra); Artificial intelligence; Mathematics; Pure mathematics","score_opus":0.06031484864399905,"score_gpt":0.30525751302124987,"score_spread":0.24494266437725082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254782311","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031161958,0.000051361196,0.9324323,0.013174722,0.0013515532,0.0014805471,0.00023270308,0.00039439517,0.019720467],"genre_scores_gemma":[0.90635,0.000058351365,0.08704308,0.0041449033,0.00031152574,0.0003889958,0.000058061072,0.000041552725,0.0016035415],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975632,0.000015481275,0.00059358386,0.00056503556,0.00090768567,0.0003550178],"domain_scores_gemma":[0.9973732,0.00009416242,0.0005590277,0.00023080235,0.0016106155,0.00013216528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047992653,0.00027921627,0.00027970804,0.00024107323,0.00016036187,0.00038595658,0.0018359798,0.000121790836,0.00034766307],"category_scores_gemma":[0.00037374053,0.00022652208,0.00018036342,0.00029401304,0.00015339654,0.00093719136,0.00017403679,0.00027073213,0.00006733668],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005956461,0.0036369804,0.009338176,0.0010670697,0.0011370734,0.00001391645,0.0058007296,0.00019602697,0.112509355,0.24696243,0.10401421,0.51472837],"study_design_scores_gemma":[0.0027943754,0.00043685758,0.0013718819,0.0010565418,0.000042435437,0.000035816316,0.0002752239,0.0022404054,0.7143859,0.2759021,0.000860908,0.0005975253],"about_ca_topic_score_codex":0.000098124685,"about_ca_topic_score_gemma":0.000012003811,"teacher_disagreement_score":0.87518805,"about_ca_system_score_codex":0.0001940268,"about_ca_system_score_gemma":0.00018132584,"threshold_uncertainty_score":0.92373025},"labels":[],"label_agreement":null},{"id":"W4254927362","doi":"10.1109/ccv.1988.590036","title":"Organization Of Smooth Image Curves At Multiple Scales","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Image (mathematics); Computer science; Computer vision; Artificial intelligence","score_opus":0.009354881054286363,"score_gpt":0.2583366177279509,"score_spread":0.24898173667366455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254927362","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050302576,0.000055962053,0.9911903,0.0015957874,0.000026174956,0.0001056701,0.0000010977031,0.00031424168,0.0016804789],"genre_scores_gemma":[0.20705143,0.00010770953,0.7904939,0.0016827224,0.000021278802,0.000003571681,0.00000880741,0.000006183545,0.0006243908],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99932057,0.000029963647,0.00018683709,0.0001473126,0.00022578813,0.00008951632],"domain_scores_gemma":[0.9994264,0.000062524785,0.00006796495,0.0002494426,0.00014062444,0.000053016334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012990138,0.00005551817,0.0000792656,0.000046651578,0.000032958953,0.000020212583,0.0003522766,0.000022592867,0.00037527276],"category_scores_gemma":[0.00022338174,0.00004667931,0.000017052562,0.00028370778,0.00005809999,0.0005073445,0.00018498032,0.000029135686,0.000095665695],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017748781,0.00024586407,0.021893788,0.00014952837,0.000011833565,0.0000030969065,0.00039476666,0.000005871535,0.6139201,0.0029409542,0.17386304,0.18656936],"study_design_scores_gemma":[0.00012810584,0.000013729474,0.007000708,0.000024908502,0.0000014327586,0.0000021018725,0.000004719499,0.0043501067,0.9878766,0.000051667055,0.0004819359,0.0000639727],"about_ca_topic_score_codex":0.000013619841,"about_ca_topic_score_gemma":0.000027486325,"teacher_disagreement_score":0.3739565,"about_ca_system_score_codex":0.000031573207,"about_ca_system_score_gemma":0.000015167291,"threshold_uncertainty_score":0.41089734},"labels":[],"label_agreement":null},{"id":"W4255677482","doi":"10.4018/978-1-7998-3441-0.ch015","title":"Dealing With Noise and Partial Volume Effects in Alzheimer Disease Brain Tissue Classification by a Fuzzy-Possibilistic Modeling Based on Fuzzy-Genetic Initialization","year":2020,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Artificial intelligence; Partial volume; Initialization; Cluster analysis; Fuzzy logic; Computer science; Noise (video); Voxel; Segmentation; Pattern recognition (psychology); Neuroimaging; Fuzzy clustering; Image (mathematics); Neuroscience; Biology","score_opus":0.024516872348074162,"score_gpt":0.2815621546922711,"score_spread":0.25704528234419693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255677482","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010332429,0.00033469478,0.96524924,0.0004722671,0.00009927905,0.0012238728,0.000057190155,0.00031434037,0.03214578],"genre_scores_gemma":[0.959573,0.000005942716,0.03580485,0.003900397,0.00013005377,0.00016763626,0.00008612526,0.000069565234,0.00026245508],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974214,0.000120401186,0.00052480894,0.00097196555,0.0006816067,0.00027982748],"domain_scores_gemma":[0.99853605,0.00011346639,0.00025474577,0.0005426541,0.00010816322,0.0004448912],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000168184,0.000430772,0.00037640543,0.000105571824,0.00009302718,0.00024850943,0.0004026714,0.00021993571,0.0000058427427],"category_scores_gemma":[0.00013432775,0.0004284215,0.000050309143,0.00006925876,0.00012432027,0.00013432685,0.00011201965,0.00026742398,0.00002073468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039526192,0.000096366595,0.00014042221,0.00038954435,0.0000800547,0.0006111406,0.00027360604,0.004490156,0.00069392135,0.8358995,0.0010186087,0.15591145],"study_design_scores_gemma":[0.00097493,0.00042762744,0.00008972276,0.00082504924,0.00015211622,0.0000056787057,0.0000030986544,0.9378972,0.00048164764,0.058349032,0.00015182918,0.0006420919],"about_ca_topic_score_codex":0.00005294455,"about_ca_topic_score_gemma":0.000015276728,"teacher_disagreement_score":0.9594697,"about_ca_system_score_codex":0.00015757415,"about_ca_system_score_gemma":0.00027199642,"threshold_uncertainty_score":0.9998168},"labels":[],"label_agreement":null},{"id":"W4255958309","doi":"10.1007/978-3-642-36961-2","title":"Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges","year":2013,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sunnybrook Health Science Centre","funders":"","keywords":"Computer science; Computational model; Artificial intelligence","score_opus":0.028715848373016148,"score_gpt":0.2731042600856948,"score_spread":0.24438841171267867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255958309","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007266261,0.0019497492,0.99587274,0.001323562,0.00019464004,0.0003276294,0.000006093424,0.00006366628,0.00018925453],"genre_scores_gemma":[0.056263253,0.00019139801,0.9425427,0.0008864441,0.00006321914,0.000010424928,0.000002363989,0.000012379624,0.000027845095],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975741,0.00009272178,0.00039253192,0.0008065767,0.0008314211,0.00030265495],"domain_scores_gemma":[0.9978994,0.0011125832,0.00018051875,0.00047790585,0.00020191383,0.00012765244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057057623,0.000251424,0.0003376352,0.00026096584,0.00015057944,0.0002669307,0.0010564183,0.00009435204,0.000003836308],"category_scores_gemma":[0.000073602954,0.00018657286,0.00003387978,0.00024567873,0.0014714974,0.0005454835,0.0011808327,0.0003955625,0.0000010096568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017924638,0.000028712278,0.000047245943,0.00018636417,0.000009308663,0.000008184536,0.0014863403,0.23555714,0.00005625243,0.028698415,0.0003008178,0.7336194],"study_design_scores_gemma":[0.00007288142,0.00002240203,0.00008457093,0.00019636106,0.000002901791,0.000031280622,3.6818574e-7,0.6566265,0.00027491452,0.34255314,0.000009193406,0.00012544861],"about_ca_topic_score_codex":0.000025964244,"about_ca_topic_score_gemma":0.0000035705036,"teacher_disagreement_score":0.733494,"about_ca_system_score_codex":0.00008042533,"about_ca_system_score_gemma":0.0004181516,"threshold_uncertainty_score":0.760822},"labels":[],"label_agreement":null},{"id":"W4283375866","doi":"","title":"Intérêt des bornes désintégrées pour la généralisation avec des mesures de complexité","year":2022,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science","score_opus":0.03667140944554453,"score_gpt":0.29445644276849,"score_spread":0.25778503332294544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283375866","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06007934,0.0018888778,0.8888299,0.012627903,0.00037268462,0.0007720423,0.00009902922,0.0009591227,0.034371126],"genre_scores_gemma":[0.2387623,0.0023236214,0.73944724,0.0005718457,0.000075164346,0.00036449896,0.00062015123,0.00010286388,0.017732292],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9691108,0.025620127,0.0013345706,0.0016151838,0.0013118377,0.0010075029],"domain_scores_gemma":[0.9870027,0.004932108,0.0011605056,0.003107567,0.0031548727,0.0006422667],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.017205955,0.00076207216,0.00075898337,0.00047467003,0.0015634159,0.0019577378,0.0043446035,0.000469763,0.0023574354],"category_scores_gemma":[0.005772007,0.0008965157,0.0004575624,0.0011633044,0.0025515938,0.001001947,0.0046259714,0.001526639,0.00010739857],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026706151,0.0015068414,0.008574326,0.0006673823,0.00021053992,0.000070020644,0.08705053,0.00023836161,0.011152517,0.48064384,0.006429407,0.40342954],"study_design_scores_gemma":[0.0018918114,0.000007999409,0.11039768,0.006637332,0.00026968349,0.00041652136,0.0019301757,0.12663566,0.3348461,0.35739738,0.056995004,0.0025746436],"about_ca_topic_score_codex":0.006483116,"about_ca_topic_score_gemma":0.0022482402,"teacher_disagreement_score":0.4008549,"about_ca_system_score_codex":0.00095559435,"about_ca_system_score_gemma":0.001105678,"threshold_uncertainty_score":0.9997364},"labels":[],"label_agreement":null},{"id":"W4285035345","doi":"10.3390/math10142421","title":"Application of Smooth Fuzzy Model in Image Denoising and Edge Detection","year":2022,"lang":"en","type":"article","venue":"Mathematics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Agencia Estatal de Investigación; Universidad Carlos III de Madrid; Ministerio de Ciencia e Innovación; Comunidad de Madrid","keywords":"Fuzzy logic; Bounded function; Mathematics; Standard deviation; Gaussian; Impulse noise; Gaussian noise; Digital image; Impulse (physics); Algorithm; Artificial intelligence; Pattern recognition (psychology); Image (mathematics); Computer science; Image processing; Mathematical analysis; Statistics; Chemistry; Physics; Pixel","score_opus":0.014360988679170177,"score_gpt":0.27027067777956737,"score_spread":0.2559096891003972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285035345","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023967711,0.000017368417,0.97535306,0.00005294314,0.0000123517075,0.00016726427,9.632744e-7,0.000063872576,0.00036447743],"genre_scores_gemma":[0.48031223,0.0000028426389,0.51956874,0.000044865807,0.0000026154157,0.000048866656,6.132689e-7,0.000003926413,0.000015316225],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993954,0.000026427499,0.00019979723,0.000115396004,0.00019409023,0.00006890701],"domain_scores_gemma":[0.99960977,0.0000452108,0.000101735735,0.00020056753,0.000021150838,0.000021541004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003656262,0.000044610337,0.00008315288,0.00009774519,0.000046798068,0.000020274609,0.00019735243,0.000014083682,0.0000032555884],"category_scores_gemma":[0.000035761288,0.00004691933,0.000011703615,0.00021869117,0.00002625832,0.00015081919,0.00019432558,0.00007739198,0.0000010315762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033442752,0.00034413463,0.000089513516,0.00032189864,0.0000053881895,0.0000035040866,0.009134493,0.0008381795,0.59062666,0.024393754,0.00013528804,0.37410384],"study_design_scores_gemma":[0.00009799311,0.0000223755,0.000058305628,0.0000072177922,0.0000020527625,0.0000052694204,0.00012676365,0.84342694,0.091045626,0.06515232,0.000006271737,0.000048867652],"about_ca_topic_score_codex":0.000010378717,"about_ca_topic_score_gemma":0.0000025078525,"teacher_disagreement_score":0.8425888,"about_ca_system_score_codex":0.00004335413,"about_ca_system_score_gemma":0.000013961496,"threshold_uncertainty_score":0.19133148},"labels":[],"label_agreement":null},{"id":"W4285126916","doi":"10.1007/978-3-031-06767-9_11","title":"An MR Image Segmentation Method Based on Dictionary Learning Preprocessing and Probability Statistics","year":2022,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Preprocessor; Artificial intelligence; Pattern recognition (psychology); Computer science; Segmentation; Computer vision","score_opus":0.03704505967374353,"score_gpt":0.3574921288469968,"score_spread":0.3204470691732533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285126916","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00001610047,0.00003688417,0.9804914,0.00035667143,0.000080708414,0.00047401027,0.000022008307,0.00018991904,0.018332273],"genre_scores_gemma":[0.0020660963,0.00032537378,0.996179,0.0010183344,0.000011215233,0.000079607606,0.00019746019,0.000008082528,0.00011481921],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978421,0.00020698263,0.0006317737,0.0004282235,0.00072027545,0.0001706619],"domain_scores_gemma":[0.9972959,0.0005014568,0.00042222557,0.0013156465,0.00033401608,0.00013072764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025634626,0.0001973824,0.00019381038,0.00077864376,0.00091912964,0.00076687743,0.0015361648,0.000072260715,0.000045738463],"category_scores_gemma":[0.00013407838,0.00020931181,0.000019745998,0.0003617256,0.00069668674,0.008109361,0.0011481785,0.00063195964,0.0000050103076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005631449,0.000052909058,0.00008153327,0.00008291737,0.0000030181839,6.9198785e-7,0.0022187177,0.0027439774,0.000058954614,0.12196365,0.00008715372,0.87270087],"study_design_scores_gemma":[0.00021411531,0.00019984112,0.00057888124,0.000081861464,0.000005033672,0.0000101848855,0.00003578215,0.9849203,0.00011061387,0.009631137,0.003989715,0.00022248391],"about_ca_topic_score_codex":0.0000129945665,"about_ca_topic_score_gemma":0.0000017985413,"teacher_disagreement_score":0.98217636,"about_ca_system_score_codex":0.00024629457,"about_ca_system_score_gemma":0.00032914005,"threshold_uncertainty_score":0.8535488},"labels":[],"label_agreement":null},{"id":"W4288361000","doi":"10.48550/arxiv.1905.00469","title":"Fully Automatic Brain Tumor Segmentation using a Normalized Gaussian\\n Bayesian Classifier and 3D Fluid Vector Flow","year":2019,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Segmentation; Pattern recognition (psychology); Computer science; Bayesian probability; Gaussian; Mixture model; Image segmentation; Physics","score_opus":0.05307624358107846,"score_gpt":0.2260458015816849,"score_spread":0.17296955800060645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288361000","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15063089,0.000051029045,0.845214,0.00034796356,0.0008504142,0.0018515787,0.000041756313,0.00047315223,0.0005392241],"genre_scores_gemma":[0.7384875,0.00015132171,0.25775927,0.0014030122,0.00012742843,0.000008603032,0.000057942703,0.000084057116,0.0019209126],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99366426,0.0012354781,0.0010302397,0.0025546881,0.00052785897,0.000987454],"domain_scores_gemma":[0.9952356,0.00062706764,0.0011684042,0.0018092708,0.00037480282,0.00078482524],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012483366,0.0009891838,0.0010730971,0.00087652635,0.0005131377,0.0007741803,0.0018132776,0.00055092486,0.0016729466],"category_scores_gemma":[0.00029461648,0.001170664,0.0003455403,0.0013925734,0.00060059817,0.0020304092,0.002226507,0.0010377659,0.0002069915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022003825,0.0048864153,0.02248829,0.019139603,0.005501014,0.014543046,0.034174934,0.23035681,0.3560128,0.077178866,0.016690994,0.21682686],"study_design_scores_gemma":[0.0018001952,0.0002612937,0.0006706522,0.0008940269,0.0002553628,0.00006479608,0.00048841856,0.9864913,0.006008211,0.0018655839,0.000047469905,0.0011526844],"about_ca_topic_score_codex":0.00024101736,"about_ca_topic_score_gemma":0.00002324374,"teacher_disagreement_score":0.7561345,"about_ca_system_score_codex":0.0010114566,"about_ca_system_score_gemma":0.0010342107,"threshold_uncertainty_score":0.9992397},"labels":[],"label_agreement":null},{"id":"W4295935374","doi":"10.1007/978-3-031-16443-9_24","title":"Learning Tumor-Induced Deformations to Improve Tumor-Bearing Brain MR Segmentation","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Atlas (anatomy); Brain tumor; Ground truth; Deep learning; Brain atlas; Computer vision; Image segmentation; Pattern recognition (psychology); Geology; Medicine","score_opus":0.015978571254670216,"score_gpt":0.2737064840033303,"score_spread":0.2577279127486601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295935374","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005083155,0.000025317757,0.99331796,0.0013695372,0.0012385629,0.00092049915,0.0000046094274,0.00055328244,0.0020618886],"genre_scores_gemma":[0.05015275,0.0000060591456,0.9377165,0.011082231,0.0003037551,0.00014818109,0.000024975314,0.000060471193,0.0005051173],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995292,0.00011135978,0.00076522713,0.0014350468,0.0016962914,0.0007000474],"domain_scores_gemma":[0.99745154,0.0005443823,0.00045542105,0.0010028528,0.00022139882,0.0003243767],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016679737,0.00048510556,0.00043996744,0.0013440778,0.00066993746,0.0007895027,0.002897869,0.000111726185,0.00017987071],"category_scores_gemma":[0.00047026257,0.00050165696,0.00011952647,0.001083749,0.00020559363,0.0015263932,0.0024702891,0.001432923,0.00008214593],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004286546,0.000026975375,0.000027162047,0.00004621431,0.000009979764,0.0000977502,0.0029533461,0.015869802,0.02136957,0.0033723211,0.000079664635,0.9561429],"study_design_scores_gemma":[0.0008726184,0.0017658327,0.0001623022,0.0005956501,0.00002157544,0.0002882997,0.000013207035,0.7447763,0.20818937,0.03848532,0.0025111532,0.0023183713],"about_ca_topic_score_codex":0.00007525443,"about_ca_topic_score_gemma":0.000026595575,"teacher_disagreement_score":0.9538246,"about_ca_system_score_codex":0.0009645946,"about_ca_system_score_gemma":0.0005988975,"threshold_uncertainty_score":0.9997435},"labels":[],"label_agreement":null},{"id":"W4297329647","doi":"10.48550/arxiv.0903.1869","title":"Confidence Regions for Means of Random Sets using Oriented Distance\\n Functions","year":2009,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"York University; University of South Carolina","keywords":"Set (abstract data type); Image (mathematics); Mathematics; Data set; Computer science; Estimation; Algorithm; Artificial intelligence","score_opus":0.10032499422839694,"score_gpt":0.24368693347485165,"score_spread":0.1433619392464547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297329647","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017353588,0.000040313098,0.9962758,0.000097428216,0.00036627185,0.00071783335,0.000056284138,0.00027750872,0.00043323435],"genre_scores_gemma":[0.8325852,0.00012573937,0.16630158,0.0001574292,0.00003934947,0.000004587714,0.00004994264,0.000014856763,0.00072128],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840415,0.0001381361,0.0003136238,0.0007533647,0.0001380304,0.00025268583],"domain_scores_gemma":[0.99777603,0.00024051365,0.00043197212,0.00096562254,0.0004298006,0.00015603438],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031746482,0.00022051609,0.00036913037,0.00024760183,0.00016312476,0.00004783165,0.0010389605,0.00018205548,0.00002163574],"category_scores_gemma":[0.00015715686,0.00025112307,0.0002368628,0.00056641625,0.00021671064,0.0003684875,0.00042953464,0.00028469818,0.0000036174706],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017207327,0.00025031748,0.00013547971,0.00019886937,0.0001468759,0.00008088188,0.00046213486,0.033780657,0.0009571718,0.95882344,0.0030069123,0.0019851972],"study_design_scores_gemma":[0.00198264,0.00012910173,0.000057170262,0.00050755165,0.00019943881,0.000006225039,0.00018459494,0.742755,0.0058212876,0.2474643,0.0003975713,0.0004951006],"about_ca_topic_score_codex":0.000091285,"about_ca_topic_score_gemma":0.00002673598,"teacher_disagreement_score":0.8308499,"about_ca_system_score_codex":0.00018465173,"about_ca_system_score_gemma":0.00027345953,"threshold_uncertainty_score":0.9999941},"labels":[],"label_agreement":null},{"id":"W4297965442","doi":"10.1007/s11548-022-02749-2","title":"DiffeoRaptor: diffeomorphic inter-modal image registration using RaPTOR","year":2022,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Image registration; Artificial intelligence; Computer science; Computer vision; Metric (unit); Diffeomorphism; Contrast (vision); Modal; Geodesic; Image (mathematics); Mathematics","score_opus":0.027086813959899755,"score_gpt":0.2873933119284263,"score_spread":0.26030649796852656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297965442","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.122050844,0.00016347275,0.87237555,0.0020952686,0.0031895677,0.0000539592,0.0000048352663,0.000043919008,0.000022593138],"genre_scores_gemma":[0.83488464,0.000066233944,0.16250317,0.0018788779,0.00060819543,0.0000064514115,0.000014214632,0.000010654283,0.000027535423],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977545,0.0005660256,0.0007458055,0.00022919438,0.0005376814,0.00016679015],"domain_scores_gemma":[0.99804723,0.0006328993,0.00073932117,0.00017333147,0.0002934186,0.000113821414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011953276,0.00013854113,0.00033876422,0.0005384822,0.00015178547,0.00014266068,0.00079204026,0.00005679079,0.000082183324],"category_scores_gemma":[0.00009207834,0.00012798281,0.00018049803,0.00015652392,0.00016235946,0.00060001673,0.0003277318,0.00039994932,9.0270845e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007729869,0.0012995513,0.032226335,0.00005679793,0.0018544124,0.006030318,0.0021296602,0.0004792935,0.10061365,0.0055311285,0.10173645,0.7472694],"study_design_scores_gemma":[0.008503626,0.0029062545,0.20464745,0.00063494826,0.0002558946,0.16518322,0.0005042644,0.5539307,0.020533543,0.016516684,0.023617953,0.0027654457],"about_ca_topic_score_codex":0.000006931172,"about_ca_topic_score_gemma":3.4918094e-7,"teacher_disagreement_score":0.744504,"about_ca_system_score_codex":0.00015525488,"about_ca_system_score_gemma":0.00017389981,"threshold_uncertainty_score":0.52189875},"labels":[],"label_agreement":null},{"id":"W4299861534","doi":"10.48550/arxiv.1708.09300","title":"Texture and Structure Incorporated ScatterNet Hybrid Deep Learning\\n Network (TS-SHDL) For Brain Matter Segmentation","year":2017,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Scatternet; Artificial intelligence; Segmentation; Computer science; Conditional random field; Deep learning; Pattern recognition (psychology); Computer vision","score_opus":0.037958195696609645,"score_gpt":0.21535850114189342,"score_spread":0.1774003054452838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4299861534","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05506912,0.00014367094,0.9403276,0.0008221479,0.0009278339,0.0019906443,0.0000859396,0.0003038426,0.0003292409],"genre_scores_gemma":[0.9419106,0.00036952263,0.048709985,0.0020097126,0.00045939063,0.000012966688,0.0005800397,0.000085829524,0.0058619333],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947319,0.0007040767,0.0006726624,0.0026628086,0.00030806256,0.0009204986],"domain_scores_gemma":[0.9948359,0.00044036238,0.0019313307,0.00164559,0.0005695298,0.000577263],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000754019,0.00091806875,0.0008540918,0.0003539883,0.0011605739,0.0012121808,0.0025930281,0.00066452083,0.0006410645],"category_scores_gemma":[0.00019095658,0.0010661498,0.0002757359,0.0004395371,0.00079720357,0.0015445937,0.002517445,0.0015358303,0.00008425257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013123779,0.0006006433,0.1871282,0.0035481928,0.002131413,0.0017811039,0.0047560264,0.3081762,0.006158769,0.011784204,0.10646644,0.36615646],"study_design_scores_gemma":[0.003278283,0.0005284621,0.011375845,0.0007481196,0.00045363806,0.000089139656,0.00021392637,0.8897136,0.005216329,0.08428594,0.0019359194,0.0021607948],"about_ca_topic_score_codex":0.0001449208,"about_ca_topic_score_gemma":0.00006634484,"teacher_disagreement_score":0.8916176,"about_ca_system_score_codex":0.00036683062,"about_ca_system_score_gemma":0.0002582188,"threshold_uncertainty_score":0.99982464},"labels":[],"label_agreement":null},{"id":"W4300955494","doi":"","title":"Advances in low-level Image representations for processing and analysis - Special Issue Editorial","year":2016,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; Image (mathematics); Image processing; Artificial intelligence","score_opus":0.014721554191350739,"score_gpt":0.29861063203331467,"score_spread":0.28388907784196393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300955494","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021234063,0.0003611608,0.98119617,0.0053698523,0.0019639032,0.00067095424,0.000095916235,0.00022053231,0.00990918],"genre_scores_gemma":[0.008303157,0.0011381671,0.98359966,0.00011131131,0.003639814,0.00048535495,0.00035916176,0.000037257705,0.0023260964],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9956241,0.0017299062,0.0006781971,0.0010488153,0.00058490684,0.00033407626],"domain_scores_gemma":[0.9944319,0.0015438284,0.0005809349,0.0013949572,0.0018707243,0.00017763233],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0039137756,0.00027664995,0.00044320905,0.00054093316,0.0002508245,0.0007758318,0.0014791804,0.00024057663,0.00007731267],"category_scores_gemma":[0.0025807072,0.00026741254,0.00017343558,0.0007903502,0.00029494584,0.0009679346,0.0013374222,0.00037471636,0.000007484255],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021847794,0.00044647724,0.0009686718,0.00047681917,0.000098584766,0.0000056884214,0.009453049,0.00001797914,0.002113763,0.013369518,0.01706057,0.955967],"study_design_scores_gemma":[0.006603088,0.000005817787,0.010110849,0.010902385,0.0007221734,0.0000139593285,0.0006163386,0.21448004,0.37990963,0.2730224,0.0997418,0.0038715021],"about_ca_topic_score_codex":0.00014994007,"about_ca_topic_score_gemma":0.0005628999,"teacher_disagreement_score":0.9520955,"about_ca_system_score_codex":0.00012134777,"about_ca_system_score_gemma":0.00030460913,"threshold_uncertainty_score":0.9999778},"labels":[],"label_agreement":null},{"id":"W4301035368","doi":"10.1007/978-3-031-02245-6_5","title":"Segmentation with Graph Algorithms","year":2009,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on image, video, and multimedia processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Algorithm","score_opus":0.012746593313047744,"score_gpt":0.2613823546436144,"score_spread":0.24863576133056667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4301035368","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000008043896,0.0013960807,0.9499231,0.00076593575,0.00012658566,0.00074693654,0.000015540514,0.0007936522,0.04622413],"genre_scores_gemma":[0.0008530704,0.0009176743,0.9804997,0.0026426367,0.0003287131,0.00014807319,0.000042276773,0.00012134911,0.014446539],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9968707,0.000068561305,0.0005153364,0.0011248307,0.0009747369,0.00044582295],"domain_scores_gemma":[0.9979429,0.00046503494,0.0005137009,0.00053248694,0.00024881528,0.00029702237],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034971573,0.0006946843,0.00060833717,0.0005767851,0.00034241905,0.000631385,0.0006356095,0.00034228864,0.00009887099],"category_scores_gemma":[0.00021056402,0.00054113375,0.000113475704,0.00013737776,0.00038842627,0.00080071593,0.00009352017,0.0005950953,0.00002946798],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030372903,0.000038230617,0.0000015391546,0.00014440167,0.00004639046,0.0000679996,0.0004537442,0.0000020930938,0.0021162701,0.000116846786,0.0014712394,0.9955109],"study_design_scores_gemma":[0.0030258154,0.0018602228,0.00041482336,0.009575236,0.0008430834,0.00044000353,0.00015580613,0.015146491,0.90761894,0.038833734,0.01658407,0.005501791],"about_ca_topic_score_codex":0.000013317561,"about_ca_topic_score_gemma":0.000008386022,"teacher_disagreement_score":0.99000907,"about_ca_system_score_codex":0.000098500896,"about_ca_system_score_gemma":0.00019819831,"threshold_uncertainty_score":0.999704},"labels":[],"label_agreement":null},{"id":"W4311158789","doi":"","title":"Robust Automatic Graph-based Skeletonization of Hepatic Vascular Trees","year":2017,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Centre Hospitalier de l’Université de Montréal","funders":"","keywords":"Skeletonization; Computer science; Graph; Artificial intelligence; Theoretical computer science","score_opus":0.015944935455157398,"score_gpt":0.23739815328427027,"score_spread":0.22145321782911287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311158789","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02663196,0.000107233136,0.96517354,0.003293502,0.00007699034,0.0002796304,0.0000036518902,0.00035541286,0.004078088],"genre_scores_gemma":[0.5309056,0.00005387675,0.46852913,0.00009129354,0.000004576152,0.000029117371,0.00002704592,0.000013299839,0.0003460552],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963309,0.0019440424,0.00047544058,0.00042943066,0.00058561057,0.00023457652],"domain_scores_gemma":[0.99433434,0.00067054894,0.00066114176,0.0029336277,0.0012528282,0.00014752835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036143332,0.00017281344,0.00025479225,0.00020154567,0.00049105036,0.00046119408,0.0023763408,0.00009012874,0.00010175003],"category_scores_gemma":[0.0026509003,0.00017325307,0.00014522,0.00034921136,0.00035587832,0.00063759676,0.00039671347,0.00014212924,0.000017898725],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004583813,0.0015047225,0.015365173,0.00043222687,0.00012862735,0.000013043117,0.0061802096,0.00028035708,0.061209477,0.14239791,0.0014564326,0.7710272],"study_design_scores_gemma":[0.00082271505,0.0000015747655,0.04906812,0.0009238954,0.000030155996,0.0000028039747,0.000025128862,0.4783051,0.4662747,0.0040111574,0.00022252272,0.00031212717],"about_ca_topic_score_codex":0.00048523076,"about_ca_topic_score_gemma":0.00020057488,"teacher_disagreement_score":0.7707151,"about_ca_system_score_codex":0.000046581787,"about_ca_system_score_gemma":0.00016070023,"threshold_uncertainty_score":0.70650554},"labels":[],"label_agreement":null},{"id":"W4312038454","doi":"10.1002/alz.067500","title":"Problems using structural MRIs from the oldest‐old, and some solutions: Lessons learned from The 90+ Study and ADNI","year":2022,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Neuroimaging; Cohort; Segmentation; Medicine; Alzheimer's Disease Neuroimaging Initiative; Artificial intelligence; Psychology; Dementia; Computer science; Neuroscience; Pathology; Disease","score_opus":0.11437083422870513,"score_gpt":0.32771999108665073,"score_spread":0.2133491568579456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312038454","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65560496,0.14497314,0.17447327,0.020512672,0.000960038,0.0028083383,0.00016456655,0.00047953974,0.000023471584],"genre_scores_gemma":[0.9674886,0.0001306001,0.030214725,0.0019128791,0.000084917236,0.0001281648,0.000021749673,0.000014539897,0.0000038559747],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99789995,0.0005277508,0.00028732038,0.00050915603,0.000501612,0.00027422834],"domain_scores_gemma":[0.9987988,0.0003267707,0.0001560529,0.0006085656,0.000033718105,0.00007612847],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00060908985,0.00016554413,0.00015144085,0.000033455355,0.0013153829,0.00038616842,0.0008814281,0.000026611475,0.00016875286],"category_scores_gemma":[0.00003117456,0.000110809546,0.000035257814,0.00023772297,0.00018156733,0.00053434144,0.0017063994,0.00029730212,0.000003322909],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022671906,0.00030833198,0.032351274,0.000003210169,0.008433775,0.000031945878,0.03348283,0.00013490437,0.011037327,0.00469793,0.00912974,0.90036607],"study_design_scores_gemma":[0.008242507,0.0014883488,0.4337021,0.00013585684,0.024019724,0.00009116109,0.029222546,0.24401465,0.020959709,0.22569829,0.009501499,0.0029235852],"about_ca_topic_score_codex":0.0047226925,"about_ca_topic_score_gemma":0.00015578952,"teacher_disagreement_score":0.89744246,"about_ca_system_score_codex":0.000010703382,"about_ca_system_score_gemma":0.00004964887,"threshold_uncertainty_score":0.99998474},"labels":[],"label_agreement":null},{"id":"W4312086123","doi":"10.1002/alz.067505","title":"Problems using structural MRIs from the oldest‐old, and some solutions: Lessons learned from The 90+ Study and ADNI","year":2022,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Neuroimaging; Cohort; Segmentation; Medicine; Psychology; Artificial intelligence; Computer science; Neuroscience; Pathology","score_opus":0.11437083422870513,"score_gpt":0.32771999108665073,"score_spread":0.2133491568579456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312086123","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65560496,0.14497314,0.17447327,0.020512672,0.000960038,0.0028083383,0.00016456655,0.00047953974,0.000023471584],"genre_scores_gemma":[0.9674886,0.0001306001,0.030214725,0.0019128791,0.000084917236,0.0001281648,0.000021749673,0.000014539897,0.0000038559747],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99789995,0.0005277508,0.00028732038,0.00050915603,0.000501612,0.00027422834],"domain_scores_gemma":[0.9987988,0.0003267707,0.0001560529,0.0006085656,0.000033718105,0.00007612847],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00060908985,0.00016554413,0.00015144085,0.000033455355,0.0013153829,0.00038616842,0.0008814281,0.000026611475,0.00016875286],"category_scores_gemma":[0.00003117456,0.000110809546,0.000035257814,0.00023772297,0.00018156733,0.00053434144,0.0017063994,0.00029730212,0.000003322909],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022671906,0.00030833198,0.032351274,0.000003210169,0.008433775,0.000031945878,0.03348283,0.00013490437,0.011037327,0.00469793,0.00912974,0.90036607],"study_design_scores_gemma":[0.008242507,0.0014883488,0.4337021,0.00013585684,0.024019724,0.00009116109,0.029222546,0.24401465,0.020959709,0.22569829,0.009501499,0.0029235852],"about_ca_topic_score_codex":0.0047226925,"about_ca_topic_score_gemma":0.00015578952,"teacher_disagreement_score":0.89744246,"about_ca_system_score_codex":0.000010703382,"about_ca_system_score_gemma":0.00004964887,"threshold_uncertainty_score":0.99998474},"labels":[],"label_agreement":null},{"id":"W4313146159","doi":"10.1109/icpr56361.2022.9956119","title":"Towards Positive Jacobian: Learn to Postprocess for Diffeomorphic Image Registration with Matrix Exponential","year":2022,"lang":"en","type":"article","venue":"2022 26th International Conference on Pattern Recognition (ICPR)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Jacobian matrix and determinant; Image registration; Diffeomorphism; Artificial intelligence; Computer science; Computer vision; Regularization (linguistics); Deep learning; Algorithm; Mathematics; Image (mathematics); Applied mathematics; Pure mathematics","score_opus":0.0466441125091908,"score_gpt":0.32359393978265555,"score_spread":0.27694982727346473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313146159","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011909385,0.0000044770477,0.9716996,0.010004167,0.0006829761,0.0010467591,0.0008307,0.0002561811,0.0035657801],"genre_scores_gemma":[0.9250428,0.000017006205,0.06158822,0.0071086953,0.0002654997,0.0022348247,0.0021981846,0.000047979094,0.0014967888],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9969356,0.00016371658,0.0004688318,0.00078655244,0.0013024107,0.00034287266],"domain_scores_gemma":[0.9982447,0.00009550716,0.00034759965,0.00031833924,0.0007946672,0.00019916455],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00050560076,0.0002636511,0.0002230327,0.00039434337,0.00032996293,0.00045547463,0.0010971837,0.000055164983,0.0040827966],"category_scores_gemma":[0.000115591094,0.0002683724,0.000097091615,0.00032877206,0.00006621565,0.00066906644,0.00032900125,0.00037300156,0.00012672244],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015316728,0.0013058266,0.00025349262,0.00012971606,0.00028127557,0.00026874815,0.002845933,0.000074307376,0.07981456,0.0143026,0.02260196,0.8765899],"study_design_scores_gemma":[0.015392059,0.021320844,0.008047799,0.0013068998,0.00024189483,0.00088654406,0.005935131,0.15743132,0.7214882,0.056428462,0.0060362406,0.0054846266],"about_ca_topic_score_codex":0.00010481977,"about_ca_topic_score_gemma":0.000030201547,"teacher_disagreement_score":0.91313344,"about_ca_system_score_codex":0.00025546024,"about_ca_system_score_gemma":0.00022577075,"threshold_uncertainty_score":0.9999769},"labels":[],"label_agreement":null},{"id":"W4313270487","doi":"10.1109/bigmm55396.2022.00018","title":"A Dynamically Weighted Loss Function for Unsupervised Image Segmentation","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Image segmentation; Artificial intelligence; Function (biology); Image (mathematics); Scale-space segmentation; Segmentation; Computer vision; Pattern recognition (psychology); Segmentation-based object categorization","score_opus":0.016123830844137255,"score_gpt":0.29786461098085726,"score_spread":0.28174078013672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313270487","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000757784,0.000031514242,0.9915275,0.0014646281,0.0011098635,0.0020077797,0.000056157634,0.0014789564,0.0015657916],"genre_scores_gemma":[0.0029971774,0.000041518517,0.9896638,0.002219793,0.00010283028,0.002208336,0.0011075549,0.000036914225,0.0016221006],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972024,0.0001958751,0.0005895833,0.0009628598,0.0007300676,0.0003192046],"domain_scores_gemma":[0.9981993,0.00018572317,0.00027845424,0.0009119307,0.00027122375,0.00015336176],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006191251,0.00030826282,0.00031016895,0.00027981427,0.00019039158,0.0003925908,0.0013724294,0.00018846976,0.0018051689],"category_scores_gemma":[0.0000712729,0.00030334067,0.00021764298,0.0003064855,0.00006753524,0.0005175452,0.001670126,0.0004961824,0.00002940346],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028339023,0.0010699335,0.00016897722,0.0017343882,0.00047866063,0.00006788269,0.001778285,0.00014194203,0.07910693,0.037151642,0.07069138,0.8073266],"study_design_scores_gemma":[0.002336276,0.00091700046,0.00067272544,0.00013106696,0.00017906424,0.000015974225,0.00030362472,0.75686646,0.087174706,0.14656122,0.0031452691,0.0016966403],"about_ca_topic_score_codex":0.00005292184,"about_ca_topic_score_gemma":0.00000577019,"teacher_disagreement_score":0.80562997,"about_ca_system_score_codex":0.00035716232,"about_ca_system_score_gemma":0.00024437485,"threshold_uncertainty_score":0.9999419},"labels":[],"label_agreement":null},{"id":"W4313525682","doi":"10.1109/bibm55620.2022.9994849","title":"Deep Learning Based Parametrization of Diffeomorphic Image Registration for the Application of Cardiac Image Segmentation","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Alberta Hospital Edmonton","funders":"","keywords":"Artificial intelligence; Segmentation; Hausdorff distance; Diffeomorphism; Computer vision; Computer science; Deep learning; Parametrization (atmospheric modeling); Image segmentation; Transformation (genetics); Image registration; Pattern recognition (psychology); Rigid transformation; Image (mathematics); Mathematics","score_opus":0.03050761723164368,"score_gpt":0.3091556862894007,"score_spread":0.278648069057757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313525682","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016963482,0.00003109896,0.99497265,0.0018642372,0.000308784,0.00070226874,0.00006718984,0.000040575433,0.00031683742],"genre_scores_gemma":[0.8197433,0.00033540753,0.17789799,0.0005395996,0.00007348589,0.00046846023,0.00083713245,0.0000122429055,0.00009242595],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982249,0.00007395196,0.00060671323,0.00017411273,0.0008098356,0.00011045914],"domain_scores_gemma":[0.99826235,0.00034611346,0.00074291555,0.00022880794,0.00037467174,0.00004514198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009120333,0.00011815499,0.00018269477,0.00036418793,0.00017329553,0.00006595239,0.0004898681,0.00003384131,0.00006692837],"category_scores_gemma":[0.00017292699,0.00009248901,0.00006141833,0.00048557483,0.00017941651,0.000315524,0.00008770313,0.00015790542,0.0000011296478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016450127,0.0002766347,0.00033918794,0.00035348212,0.00014222295,8.5398074e-7,0.0024239193,0.00077051786,0.46723223,0.026688367,0.0020187239,0.49958935],"study_design_scores_gemma":[0.0005204054,0.0005163318,0.0003695201,0.000025485948,0.000022465354,0.0000020852615,0.0010912316,0.94022024,0.0561292,0.00065927935,0.00035108006,0.00009266063],"about_ca_topic_score_codex":0.000039500486,"about_ca_topic_score_gemma":0.0000014503802,"teacher_disagreement_score":0.9394497,"about_ca_system_score_codex":0.00006499491,"about_ca_system_score_gemma":0.000066912224,"threshold_uncertainty_score":0.37715924},"labels":[],"label_agreement":null},{"id":"W4317496750","doi":"10.1109/access.2023.3238058","title":"GMCNet: A Generative Multi-Resolution Framework for Cardiac Registration","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Image registration; Robustness (evolution); Mutual information; Pairwise comparison; Convolutional neural network; Pattern recognition (psychology); Generative model; Computer vision; Deep learning; Metric (unit); Artificial neural network; Generative grammar; Image (mathematics)","score_opus":0.10982663069349587,"score_gpt":0.4168391987583695,"score_spread":0.30701256806487365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317496750","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000928507,0.000033025462,0.9956688,0.0010656681,0.0009715251,0.000529474,0.000011360865,0.000711135,0.00008048153],"genre_scores_gemma":[0.03326188,0.000057795252,0.96433663,0.0009309331,0.00036150042,0.0005451121,0.00003676641,0.000014139337,0.00045526237],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989048,0.00007522839,0.00020588966,0.00032903053,0.00026852847,0.00021649801],"domain_scores_gemma":[0.99911565,0.00019966345,0.00011113591,0.00036929993,0.00013011554,0.00007415696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004005381,0.000096092735,0.00011968638,0.00010996751,0.00014115786,0.00031846424,0.0007039782,0.00009051504,0.000007100242],"category_scores_gemma":[0.00028114728,0.00009158938,0.000062332045,0.0005707956,0.00004259543,0.0009278692,0.00009170792,0.00010028922,0.000042858726],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037512586,0.00022120097,0.0008880163,0.00019497696,0.00013635626,0.00003233396,0.0040965825,0.002084554,0.064778075,0.09319765,0.44034585,0.39398688],"study_design_scores_gemma":[0.00047726822,0.00013082872,0.0031952246,0.000082019425,0.000015970467,0.0000014761888,0.00005087984,0.42588004,0.5122227,0.055303324,0.0022356587,0.00040462884],"about_ca_topic_score_codex":0.000037208847,"about_ca_topic_score_gemma":0.0000072284115,"teacher_disagreement_score":0.44744462,"about_ca_system_score_codex":0.000048714202,"about_ca_system_score_gemma":0.000057795864,"threshold_uncertainty_score":0.37349066},"labels":[],"label_agreement":null},{"id":"W4317795309","doi":"10.1109/tuffc.2023.3239320","title":"Toward Estimating MRI-Ultrasound Registration Error in Image-Guided Neurosurgery","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada First Research Excellence Fund","keywords":"Neuronavigation; Image registration; Ultrasound; Artificial intelligence; Convolutional neural network; Magnetic resonance imaging; Computer science; Computer vision; 3D ultrasound; Radiology; Medicine; Image (mathematics)","score_opus":0.03337137577208331,"score_gpt":0.2882193087517732,"score_spread":0.2548479329796899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317795309","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00501183,0.00006968079,0.99081075,0.0018976552,0.0006103476,0.0005762063,0.000022272681,0.0007651369,0.00023614409],"genre_scores_gemma":[0.90198874,0.00038698482,0.09637147,0.00084998435,0.00004120527,0.0002005261,0.000009769399,0.00003239169,0.00011891897],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997215,0.00021468119,0.00072554516,0.000684251,0.0005569648,0.00060355867],"domain_scores_gemma":[0.99773943,0.0012378913,0.0001997704,0.00046908236,0.00014931978,0.00020452162],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010362534,0.00029557114,0.00035164942,0.0006842182,0.00024783067,0.0003348725,0.00046280184,0.00016494251,0.000023731553],"category_scores_gemma":[0.00035632812,0.0003063794,0.00012026786,0.0020236585,0.00008702069,0.0008168949,0.0000021140377,0.0006157737,0.000028236578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012368002,0.0011347822,0.0008947467,0.00038360944,0.00021069821,0.0008663215,0.0030871574,0.021283967,0.5359963,0.007668733,0.0068808976,0.4214691],"study_design_scores_gemma":[0.0019196344,0.0003908152,0.00084244437,0.00009429593,0.000040565526,0.00018135553,0.000042822645,0.95635694,0.031521264,0.00794648,0.000030819832,0.0006325555],"about_ca_topic_score_codex":0.00016770008,"about_ca_topic_score_gemma":0.000048991456,"teacher_disagreement_score":0.93507296,"about_ca_system_score_codex":0.0001852504,"about_ca_system_score_gemma":0.00020712534,"threshold_uncertainty_score":0.99993885},"labels":[],"label_agreement":null},{"id":"W4318611754","doi":"","title":"Automatized Evaluation of the Left Ventricular Ejection Fraction from Echocardiographic Images Using Graph Cut","year":2014,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Ejection fraction; Fraction (chemistry); Cardiology; Internal medicine; Graph; Computer science; Artificial intelligence; Medicine; Heart failure; Theoretical computer science; Chemistry","score_opus":0.015526355531226084,"score_gpt":0.25740063682182207,"score_spread":0.241874281290596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318611754","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11764667,0.00016585586,0.8782708,0.0008416086,0.00020207993,0.00036834594,0.000004693623,0.0002524089,0.0022475556],"genre_scores_gemma":[0.80245936,0.000047447727,0.1972843,0.0000781131,0.000015699625,0.000020714571,0.000026456912,0.000012670614,0.000055261753],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9895145,0.008305284,0.00045033274,0.0004367467,0.0011035914,0.00018953737],"domain_scores_gemma":[0.9950194,0.0008239028,0.00056115206,0.0015181898,0.001992254,0.00008511657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008666269,0.00016024616,0.00021646863,0.0002496193,0.0003285805,0.0002075879,0.0010255944,0.000107636966,0.00006517919],"category_scores_gemma":[0.002104789,0.00013900333,0.00021004622,0.0009581093,0.00017924544,0.00056869857,0.0002938873,0.00021459564,0.0000066716643],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014405567,0.0010571169,0.040402427,0.000109163055,0.0003182461,0.0000023332318,0.0047890735,0.00075155793,0.36069128,0.027737245,0.0019326964,0.56219447],"study_design_scores_gemma":[0.000529388,5.430556e-7,0.019348891,0.00031524812,0.000077989025,0.000005827691,0.00003416101,0.32026604,0.6454885,0.013596486,0.00018229733,0.00015465826],"about_ca_topic_score_codex":0.0008330316,"about_ca_topic_score_gemma":0.00008024651,"teacher_disagreement_score":0.68481266,"about_ca_system_score_codex":0.000111419504,"about_ca_system_score_gemma":0.00011247718,"threshold_uncertainty_score":0.5668391},"labels":[],"label_agreement":null},{"id":"W4319069008","doi":"10.1109/tim.2023.3241981","title":"An Active Contour Model Based on Local Pre-Piecewise Fitting Bias Corrections for Fast and Accurate Segmentation","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Suzhou Municipal Science and Technology Bureau; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Active contour model; Robustness (evolution); Image segmentation; Piecewise; Artificial intelligence; Segmentation; Smoothing; Computer science; Computer vision; Mathematics; Algorithm; Pattern recognition (psychology)","score_opus":0.07549587995217782,"score_gpt":0.32153591751332244,"score_spread":0.24604003756114462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319069008","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018266905,0.0000025388524,0.9791999,0.00048934174,0.00042258392,0.0010826638,0.000052570937,0.0004281082,0.00005541484],"genre_scores_gemma":[0.95018274,0.000035407644,0.047989912,0.0009179513,0.000016339145,0.00074897025,0.000025958037,0.000018037983,0.00006466675],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817914,0.0001319623,0.0003414122,0.0004871166,0.0006227028,0.00023768468],"domain_scores_gemma":[0.99906784,0.00015134305,0.00014323527,0.00022620315,0.0002088115,0.00020257602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060455006,0.00020187648,0.00015453865,0.00035563397,0.00052244816,0.00019910916,0.00013578474,0.00007071895,0.000013090008],"category_scores_gemma":[0.00002562166,0.0002011968,0.000052755953,0.00034137495,0.00008192049,0.0007960897,0.0000023913997,0.00014834717,0.0000053027593],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011863805,0.00021182522,0.000012824093,0.000036756606,0.00003065416,7.425356e-7,0.0016388275,0.07000603,0.023423003,0.00008560567,0.00013315692,0.90430194],"study_design_scores_gemma":[0.0013746498,0.00047798583,0.00025240085,0.000056523313,0.000025421978,0.000001069154,0.00095862953,0.703521,0.2930297,0.00014719859,0.0000083647255,0.0001470665],"about_ca_topic_score_codex":0.000032246106,"about_ca_topic_score_gemma":0.000060855786,"teacher_disagreement_score":0.9319159,"about_ca_system_score_codex":0.00027307076,"about_ca_system_score_gemma":0.00011834822,"threshold_uncertainty_score":0.82045674},"labels":[],"label_agreement":null},{"id":"W4321021200","doi":"10.1109/lra.2023.3244415","title":"A Hybrid Approach to 3D Shape Estimation of Catheters Using Ultrasound Images","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer vision; Computer science; 3D ultrasound; Artificial intelligence; Robustness (evolution); Imaging phantom; Kalman filter; Visualization; Ultrasound; Catheter; Radiology; Medicine","score_opus":0.024018009657285948,"score_gpt":0.2792205530442587,"score_spread":0.25520254338697274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321021200","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09837081,0.0000035807043,0.900124,0.00083567254,0.000120216195,0.0001949795,0.0000036674935,0.00032179718,0.000025283998],"genre_scores_gemma":[0.32023028,0.000004467149,0.6786362,0.0010788164,0.000016464355,0.00000943707,0.000010426905,0.0000076487395,0.0000063101893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990081,0.00004729361,0.00026287668,0.00023041054,0.00029129122,0.00015998681],"domain_scores_gemma":[0.999456,0.000099726676,0.00011878397,0.00020677333,0.000040952553,0.00007776828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002832951,0.00010000811,0.00013087959,0.00025303624,0.00007428827,0.00012717536,0.00021938537,0.00002249159,0.000001956317],"category_scores_gemma":[0.000067296634,0.0000990714,0.000030008496,0.00038015892,0.000056213645,0.0004115754,0.000049448267,0.000053987864,0.000009899529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013793698,0.000044150336,0.000052765772,0.0001141882,0.000019447369,0.0000049446044,0.0010646236,0.5299864,0.42038575,0.00039411025,0.0048818393,0.04305043],"study_design_scores_gemma":[0.0000955785,0.000017402544,0.0008097589,0.000033350356,0.000007620241,0.000014245538,0.000013686932,0.9352214,0.0635538,0.00012389789,0.0000031776124,0.00010612834],"about_ca_topic_score_codex":0.000011133615,"about_ca_topic_score_gemma":4.866143e-8,"teacher_disagreement_score":0.405235,"about_ca_system_score_codex":0.000032377142,"about_ca_system_score_gemma":0.000017423568,"threshold_uncertainty_score":0.40400144},"labels":[],"label_agreement":null},{"id":"W4321794875","doi":"10.20517/ir.2023.02","title":"An overview of intelligent image segmentation using active contour models","year":2023,"lang":"en","type":"article","venue":"Intelligence & Robotics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Image segmentation; Active contour model; Pattern recognition (psychology); Computer vision; Machine learning; Data mining","score_opus":0.18734206977712617,"score_gpt":0.42077245827709875,"score_spread":0.23343038849997258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321794875","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025171083,0.00012879202,0.9960765,0.000114977534,0.00026488214,0.00036649266,0.000008331002,0.00040664853,0.00011622793],"genre_scores_gemma":[0.16027978,0.0011029921,0.8381429,0.00031194766,0.000048246577,0.000020325739,0.000023870482,0.000025681931,0.000044260792],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997884,0.00015654763,0.0006084286,0.0004172333,0.000606948,0.0003268519],"domain_scores_gemma":[0.99839944,0.00015974104,0.00027445902,0.00063068676,0.0003651482,0.0001705501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050681777,0.00019154487,0.00026601696,0.00027309128,0.000079631085,0.000105227366,0.0010162623,0.00008377586,0.000054062963],"category_scores_gemma":[0.00009452096,0.00019075468,0.00008912737,0.001030035,0.0001502086,0.0015953399,0.00023411095,0.00016566187,0.00006670248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015333842,0.00027293427,0.00005603472,0.00015098993,0.000055485467,0.00004819124,0.0071788067,0.28499046,0.1353751,0.040465683,0.00019687199,0.5311941],"study_design_scores_gemma":[0.00002865312,0.00008811689,0.000020720903,0.000066223,0.000009753423,0.000004362619,0.0005306932,0.5496847,0.43169183,0.017747514,0.0000032838363,0.00012413476],"about_ca_topic_score_codex":0.0001131331,"about_ca_topic_score_gemma":0.0000072115045,"teacher_disagreement_score":0.53107,"about_ca_system_score_codex":0.0001308541,"about_ca_system_score_gemma":0.00011945197,"threshold_uncertainty_score":0.777875},"labels":[],"label_agreement":null},{"id":"W4324121743","doi":"10.1007/978-3-031-27324-7_9","title":"Segmentation of Intraoperative 3D Ultrasound Images Using a Pyramidal Blur-Pooled 2D U-Net","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"3D ultrasound; Computer science; Artificial intelligence; Coronal plane; Segmentation; Computer vision; Sagittal plane; Ultrasound; Slicing; Resection; Medicine; Radiology; Computer graphics (images); Surgery","score_opus":0.02261612445414831,"score_gpt":0.2933212259432114,"score_spread":0.2707051014890631,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324121743","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022451697,0.000100238,0.9970676,0.00015562697,0.0009654732,0.0006440323,0.00002015177,0.0003343188,0.00048806405],"genre_scores_gemma":[0.018417504,0.00006719012,0.9801781,0.0007540365,0.00023112312,0.000016316635,0.000020004842,0.000046545574,0.00026915444],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9957574,0.0000922451,0.00084052345,0.0013055066,0.0014580112,0.0005463211],"domain_scores_gemma":[0.9970337,0.00087956525,0.00052853714,0.0009387583,0.00044589926,0.00017355333],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011181826,0.00050469855,0.000609935,0.001024249,0.00019429557,0.00048014562,0.0022586084,0.00027826818,0.0000584429],"category_scores_gemma":[0.00031141017,0.00047000125,0.000116316456,0.0010012737,0.0013776908,0.0010276993,0.0008831346,0.00067924167,0.000027611923],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012477039,0.00007968269,0.0000742514,0.00017587769,0.000068046815,0.00015578914,0.0037481051,0.015865313,0.16641659,0.0037313045,0.000085496256,0.80958706],"study_design_scores_gemma":[0.0005475649,0.00034913083,0.00014619982,0.00093294465,0.000041082574,0.00010211465,0.0000030783442,0.29092333,0.64708936,0.05885774,0.000016198272,0.0009912791],"about_ca_topic_score_codex":0.00007190911,"about_ca_topic_score_gemma":0.00003054503,"teacher_disagreement_score":0.8085958,"about_ca_system_score_codex":0.0003635557,"about_ca_system_score_gemma":0.0006248794,"threshold_uncertainty_score":0.9997752},"labels":[],"label_agreement":null},{"id":"W4328106170","doi":"10.1016/j.compbiomed.2023.106792","title":"ACU<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si3.svg\" display=\"inline\" id=\"d1e1379\"><mml:msup><mml:mrow/><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math>E-Net: A novel predict–refine attention network for segmentation of soft-tissue structures in ultrasound images","year":2023,"lang":"lv","type":"article","venue":"Computers in Biology and Medicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Exfo Electro-Optical Engineering (Canada); University of Alberta","funders":"Mitacs","keywords":"Segmentation; Computer science; Artificial intelligence; Residual; Computer vision; Pattern recognition (psychology); Algorithm","score_opus":0.01984302615290382,"score_gpt":0.29114764641790236,"score_spread":0.2713046202649985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4328106170","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63093024,0.0013926264,0.35950047,0.001996356,0.0032253119,0.00017704781,0.00026740052,0.00021752964,0.0022930303],"genre_scores_gemma":[0.93360746,0.0017039707,0.058368664,0.001730048,0.001423888,0.00043783683,0.0025337278,0.000110569534,0.000083832994],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99593294,0.00024279121,0.0012232278,0.0010023853,0.0006618041,0.00093683775],"domain_scores_gemma":[0.9962752,0.0016974072,0.00089885097,0.0007156859,0.0000984864,0.00031435367],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014490114,0.0004208578,0.0003537552,0.00038784908,0.00035242946,0.00015170194,0.0009073092,0.0008367077,0.001879061],"category_scores_gemma":[0.0007847262,0.000546556,0.00022965515,0.00079648197,0.0014244828,0.00052870176,0.0007207223,0.00064536376,0.000043368404],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063652056,0.00022034418,0.00024094331,0.001301456,0.00048263083,0.0003147883,0.0030793264,0.0008955099,0.027993381,0.8727694,0.060533002,0.03153266],"study_design_scores_gemma":[0.0035832955,0.0029305746,0.0034347007,0.0016950044,0.0003683493,0.0005760443,0.0009463165,0.8076511,0.17674692,0.00055046653,0.00087898225,0.00063822983],"about_ca_topic_score_codex":0.0004191879,"about_ca_topic_score_gemma":0.00019452871,"teacher_disagreement_score":0.87221897,"about_ca_system_score_codex":0.0000176563,"about_ca_system_score_gemma":0.00029629047,"threshold_uncertainty_score":0.9996986},"labels":[],"label_agreement":null},{"id":"W4353047532","doi":"10.1016/j.brs.2023.03.011","title":"Automatic analysis of skull thickness, scalp-to-cortex distance and association with age and sex in cognitively normal elderly","year":2023,"lang":"en","type":"letter","venue":"Brain stimulation","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Helmut Horten Stiftung; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Universität Zürich; Eisai; Eidgenössische Technische Hochschule Zürich; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Pfizer; Biogen; BioClinica; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Meso Scale Diagnostics; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Scalp; Skull; Association (psychology); Audiology; Cortex (anatomy); Medicine; Psychology; Neuroscience; Anatomy","score_opus":0.012851647861415693,"score_gpt":0.27589721157831887,"score_spread":0.2630455637169032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353047532","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020340055,0.000016184158,0.93417704,0.044459064,0.000040771127,0.0006034215,0.000046082812,0.00022650587,0.00009087663],"genre_scores_gemma":[0.5234291,0.0000435856,0.23715492,0.2318974,0.0005104416,0.0003400753,0.0021403115,0.00014538984,0.0043387604],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9977814,0.00024903985,0.00047172786,0.0005017668,0.0007669573,0.00022911541],"domain_scores_gemma":[0.9977861,0.0013352901,0.00044645448,0.00023634103,0.0001390572,0.000056739256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077357824,0.00020078449,0.00048114217,0.0008227205,0.00005253333,0.00015584806,0.000209265,0.00029745518,0.000013909745],"category_scores_gemma":[0.00038521088,0.00019247003,0.000041152907,0.0016980228,0.0000701986,0.00039874486,0.00010217743,0.00039445882,0.0000029533724],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050001112,0.00017127916,0.08734511,0.0016157175,0.0016733742,0.0010568398,0.013725693,0.0016873422,0.0014251617,0.00019584963,0.29579133,0.5952623],"study_design_scores_gemma":[0.00070761633,0.00022460589,0.5100499,0.00042156785,0.00031083182,0.0000029152716,0.000038675338,0.4863614,0.00015201469,0.00073817564,0.00056773296,0.00042455064],"about_ca_topic_score_codex":0.00008926054,"about_ca_topic_score_gemma":0.00012244201,"teacher_disagreement_score":0.69702214,"about_ca_system_score_codex":0.00012302797,"about_ca_system_score_gemma":0.000051395484,"threshold_uncertainty_score":0.78487},"labels":[],"label_agreement":null},{"id":"W4362604439","doi":"10.1117/12.2654364","title":"Skeletal modeling of the tricuspid valve via cylindrical parameterization","year":2023,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Tricuspid valve; Computer science; Cardiology; Medicine","score_opus":0.027702713060848722,"score_gpt":0.2862108476837822,"score_spread":0.2585081346229335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362604439","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007997147,0.0000037879342,0.9901043,0.00046815985,0.0001436816,0.00013691424,3.9164397e-7,0.00034774732,0.0007978264],"genre_scores_gemma":[0.834567,0.000008082731,0.16483629,0.00037825652,0.000020140995,0.000014993157,0.0000027638657,0.000004760622,0.00016769463],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990803,0.000066304696,0.00022643752,0.00015944992,0.0003478721,0.00011961376],"domain_scores_gemma":[0.99946904,0.000070234135,0.000057541838,0.00030608123,0.000056986486,0.000040143666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024946127,0.00005532322,0.000081261904,0.00007430925,0.000043281874,0.000028406974,0.00054029515,0.000036679445,0.000026293284],"category_scores_gemma":[0.00013253563,0.000034946588,0.000087101806,0.0009049314,0.000032870077,0.00019658337,0.00023747215,0.0000689839,0.00003304735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043034975,0.0002093459,0.0005469489,0.00007518732,0.000058037193,0.0000080634945,0.0012815717,0.012896346,0.08626053,0.021501604,0.0043304907,0.8728276],"study_design_scores_gemma":[0.00007052206,0.00002172486,0.00024983124,0.0000061991896,0.0000026555654,0.0000016268707,0.000008932513,0.9543472,0.04171733,0.003517776,0.000011286501,0.000044908924],"about_ca_topic_score_codex":0.000014066543,"about_ca_topic_score_gemma":4.151993e-7,"teacher_disagreement_score":0.94145083,"about_ca_system_score_codex":0.0000131315655,"about_ca_system_score_gemma":0.000024328487,"threshold_uncertainty_score":0.14250804},"labels":[],"label_agreement":null},{"id":"W4366588666","doi":"10.3174/ajnr.a7845","title":"3D Capsule Networks for Brain Image Segmentation","year":2023,"lang":"en","type":"article","venue":"American Journal of Neuroradiology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Advancing Health Outcomes","funders":"National Center for Advancing Translational Sciences; Engineering and Physical Sciences Research Council; RSNA Research and Education Foundation; Radiological Society of North America; Yale University; National Institutes of Health; Alzheimer's Disease Neuroimaging Initiative","keywords":"Medicine; Capsule; Artificial intelligence; Segmentation; Computer vision; Anatomy; Pattern recognition (psychology); Computer science; Paleontology; Geology","score_opus":0.015110276302550395,"score_gpt":0.3130004469947647,"score_spread":0.2978901706922143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366588666","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021156069,0.000014014139,0.97176206,0.006300484,0.00048386413,0.00013699463,0.0000018950694,0.00011611402,0.000028510698],"genre_scores_gemma":[0.14586535,0.00016006258,0.8390523,0.014255171,0.00048105998,0.000036981146,0.000012232495,0.000031503765,0.00010532041],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986585,0.0002936323,0.00042665907,0.00018438895,0.00016873931,0.00026808723],"domain_scores_gemma":[0.99822587,0.0007668504,0.0005254946,0.00019606567,0.00015478453,0.00013095474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007017405,0.00009857037,0.00028255302,0.0002514955,0.00005857775,0.00004249504,0.00059728726,0.000026939446,0.000010296377],"category_scores_gemma":[0.0005123108,0.000086808104,0.000091778515,0.0006367742,0.00023336562,0.00034072585,0.0000690347,0.00017520934,0.00001024852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000380783,0.000049203103,0.0002504533,0.000011161456,0.00005681425,0.00021187805,0.00054039207,0.0007486519,0.12749903,0.0006431992,0.16179545,0.7081557],"study_design_scores_gemma":[0.009162499,0.038910843,0.037555248,0.0001723611,0.0001907636,0.008211439,0.0018880446,0.71538633,0.14147647,0.012154341,0.032695625,0.0021960204],"about_ca_topic_score_codex":0.0000055227324,"about_ca_topic_score_gemma":3.5685835e-7,"teacher_disagreement_score":0.7146377,"about_ca_system_score_codex":0.000031885487,"about_ca_system_score_gemma":0.000063043626,"threshold_uncertainty_score":0.35399318},"labels":[],"label_agreement":null},{"id":"W4366606355","doi":"10.1212/wnl.0000000000207102","title":"Section 1: Images by Subspecialty","year":2023,"lang":"en","type":"article","venue":"Neurology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Section (typography); Subspecialty; Medicine; Medical physics; Artificial intelligence; Computer science; Pathology","score_opus":0.013651567307175797,"score_gpt":0.2762422479830521,"score_spread":0.26259068067587626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366606355","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026725404,0.000014454196,0.95845556,0.008943596,0.0011610162,0.0001070508,0.000002512323,0.0018059275,0.0027845027],"genre_scores_gemma":[0.8327423,0.0007483927,0.06707925,0.088654995,0.0023215578,0.0003422687,0.00011876173,0.000096323965,0.007896153],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99925405,0.00009693834,0.00010483876,0.00023539286,0.00013694078,0.00017182808],"domain_scores_gemma":[0.99959075,0.00008456292,0.000033359047,0.00021691987,0.000023303013,0.000051086714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015601539,0.000052893134,0.0000660952,0.00008948366,0.000050398452,0.00003146912,0.0003225695,0.00005608671,0.000078024714],"category_scores_gemma":[0.00006022757,0.00005187872,0.000019567384,0.00037639154,0.000048714377,0.00017810102,0.00012476908,0.00013550997,0.00030868177],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000070609267,0.000022718772,0.0005167722,0.000004006148,0.0000031606694,0.00007004078,0.00009100706,0.0000021366627,0.08050947,0.0008609098,0.8254321,0.09248059],"study_design_scores_gemma":[0.0010246867,0.0019251662,0.055007644,0.0000040448836,0.0000075979556,0.000179263,0.000007104329,0.012844851,0.67197615,0.015899586,0.24060465,0.000519265],"about_ca_topic_score_codex":0.000022554184,"about_ca_topic_score_gemma":0.0000031400482,"teacher_disagreement_score":0.89137626,"about_ca_system_score_codex":0.0000052710207,"about_ca_system_score_gemma":0.0000119240085,"threshold_uncertainty_score":0.39675826},"labels":[],"label_agreement":null},{"id":"W4366826390","doi":"10.3390/bdcc7020082","title":"Analysis of 2D and 3D Convolution Models for Volumetric Segmentation of the Human Hippocampus","year":2023,"lang":"en","type":"article","venue":"Big Data and Cognitive Computing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; BioClinica; F. Hoffmann-La Roche; University of Southern California; Biogen; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Hippocampus; Segmentation; Computer science; Convolution (computer science); Task (project management); Similarity (geometry); Artificial intelligence; Hippocampal formation; Pattern recognition (psychology); Atrophy; Dice; Market segmentation; Neuroscience; Psychology; Medicine; Pathology; Mathematics; Statistics; Artificial neural network","score_opus":0.1165656099801303,"score_gpt":0.3512443843753714,"score_spread":0.23467877439524112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366826390","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21267232,0.000097907476,0.7867922,0.000021200904,0.000046594636,0.00020984988,0.000105787374,0.000035499987,0.000018646006],"genre_scores_gemma":[0.98199445,0.00002783806,0.017637683,0.0000722391,0.00001656572,0.000005173294,0.00023615647,0.0000031715185,0.0000066974476],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990846,0.000085458596,0.0002554564,0.0002785342,0.00019258751,0.000103358405],"domain_scores_gemma":[0.99890417,0.00042895557,0.00022653813,0.0002359613,0.00017377354,0.000030579533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061554305,0.000065311,0.00016451592,0.0003271412,0.00013263829,0.000035414865,0.00032232542,0.00002722205,7.7342526e-7],"category_scores_gemma":[0.00021494633,0.00005423645,0.00002936664,0.0014159504,0.00011915619,0.00024146779,0.00066835684,0.000040964478,1.610472e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000022727768,0.00001772298,0.0056177815,0.0000534673,0.0001335612,2.5160583e-7,0.00047381833,0.0000336027,0.0028358686,0.00024822215,0.00007895363,0.9905045],"study_design_scores_gemma":[0.0003214273,0.000041956613,0.037172027,0.000065955806,0.00025928867,6.078221e-7,0.00027279503,0.95504636,0.0054337936,0.0013212042,0.0000016970527,0.00006288334],"about_ca_topic_score_codex":0.000038614788,"about_ca_topic_score_gemma":0.0000066795105,"teacher_disagreement_score":0.9904416,"about_ca_system_score_codex":0.000006631054,"about_ca_system_score_gemma":0.000021095248,"threshold_uncertainty_score":0.22116981},"labels":[],"label_agreement":null},{"id":"W4367833740","doi":"10.1007/978-3-031-35302-4_25","title":"Extraction of Volumetric Indices from Echocardiography: Which Deep Learning Solution for Clinical Use?","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; Université de Lyon; Grand Équipement National De Calcul Intensif; Agence Nationale de la Recherche","keywords":"Extraction (chemistry); Computer science; Artificial intelligence; Chromatography; Chemistry","score_opus":0.05970480299960167,"score_gpt":0.3505284822115734,"score_spread":0.2908236792119717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367833740","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014776744,0.00025280315,0.9961174,0.00011828971,0.002229198,0.0005933119,0.000008531018,0.00042116252,0.000111510475],"genre_scores_gemma":[0.03171909,0.0005635297,0.9667725,0.00027189686,0.00045524194,0.000028330503,0.00003923091,0.000038751125,0.00011143563],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957406,0.00013716574,0.0010184895,0.0014116535,0.0012336551,0.00045845783],"domain_scores_gemma":[0.99355656,0.00401175,0.0008424774,0.0008205935,0.0006031753,0.00016545886],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0026511904,0.00035155745,0.00064508984,0.0019614205,0.0002146155,0.00039596934,0.0019253843,0.0005165309,0.000010682808],"category_scores_gemma":[0.0017000661,0.0003477893,0.00029396766,0.0020348493,0.0005428416,0.00114714,0.0006926127,0.001061283,0.000015574413],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008382861,0.000024959912,0.001832912,0.00003129149,0.000032104457,0.000007998603,0.00017547351,0.0014727626,0.00022410837,0.00033386494,0.000046081124,0.99581003],"study_design_scores_gemma":[0.00044329133,0.00053193077,0.012156758,0.00046759043,0.000044066717,0.0000064901524,4.844055e-7,0.9324555,0.0030206414,0.049828514,0.00044806904,0.00059669913],"about_ca_topic_score_codex":0.00013235417,"about_ca_topic_score_gemma":0.000075231226,"teacher_disagreement_score":0.9952134,"about_ca_system_score_codex":0.0001404579,"about_ca_system_score_gemma":0.00027045634,"threshold_uncertainty_score":0.9998974},"labels":[],"label_agreement":null},{"id":"W4367841510","doi":"10.32920/22734380","title":"A novel Accelerated Greedy Snake Algorithm for active contours","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Convergence (economics); Algorithm; Greedy algorithm; Object (grammar); Pixel; Similarity (geometry); Relaxation (psychology); Computer science; Mathematics; Artificial intelligence; Mathematical optimization; Image (mathematics)","score_opus":0.12499159895777094,"score_gpt":0.3696387095472299,"score_spread":0.24464711058945893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367841510","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000016383694,0.0000104910305,0.99219275,0.0016251291,0.0012043762,0.0015918855,0.0001548055,0.0023761422,0.0008280687],"genre_scores_gemma":[0.00017447336,0.00002240555,0.9916406,0.0013759598,0.00018702092,0.0010239432,0.00019303437,0.000038088816,0.005344475],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99772316,0.0000435423,0.00043096082,0.0009256573,0.000491922,0.0003847307],"domain_scores_gemma":[0.9979595,0.00031073103,0.00026427946,0.0007962075,0.00047493182,0.00019435844],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004401682,0.0003068011,0.0003959073,0.00023879933,0.0000853641,0.00040004117,0.0018078705,0.00031805117,0.00008903761],"category_scores_gemma":[0.00019131677,0.00027821597,0.00017136481,0.00030168772,0.00007633991,0.00032637257,0.0017314535,0.0004719682,0.0000601855],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043674486,0.00010720848,0.000001383803,0.000053051936,0.00011441295,0.000011667859,0.00042084992,0.000008229186,0.0020716107,0.001243112,0.038126133,0.957838],"study_design_scores_gemma":[0.0016113686,0.00022313539,0.0005200654,0.00022581931,0.000046491943,0.000011859611,0.00018518273,0.7464263,0.22849588,0.019941265,0.0012754581,0.0010372072],"about_ca_topic_score_codex":0.0003752935,"about_ca_topic_score_gemma":0.0000423358,"teacher_disagreement_score":0.95680076,"about_ca_system_score_codex":0.00013867847,"about_ca_system_score_gemma":0.00033106384,"threshold_uncertainty_score":0.999967},"labels":[],"label_agreement":null},{"id":"W4367849106","doi":"10.32920/22734380.v1","title":"A novel Accelerated Greedy Snake Algorithm for active contours","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Convergence (economics); Algorithm; Greedy algorithm; Similarity (geometry); Object (grammar); Relaxation (psychology); Pixel; Computer science; Mathematics; Artificial intelligence; Mathematical optimization; Image (mathematics)","score_opus":0.12499159895777094,"score_gpt":0.3696387095472299,"score_spread":0.24464711058945893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367849106","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000016383694,0.0000104910305,0.99219275,0.0016251291,0.0012043762,0.0015918855,0.0001548055,0.0023761422,0.0008280687],"genre_scores_gemma":[0.00017447336,0.00002240555,0.9916406,0.0013759598,0.00018702092,0.0010239432,0.00019303437,0.000038088816,0.005344475],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99772316,0.0000435423,0.00043096082,0.0009256573,0.000491922,0.0003847307],"domain_scores_gemma":[0.9979595,0.00031073103,0.00026427946,0.0007962075,0.00047493182,0.00019435844],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004401682,0.0003068011,0.0003959073,0.00023879933,0.0000853641,0.00040004117,0.0018078705,0.00031805117,0.00008903761],"category_scores_gemma":[0.00019131677,0.00027821597,0.00017136481,0.00030168772,0.00007633991,0.00032637257,0.0017314535,0.0004719682,0.0000601855],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043674486,0.00010720848,0.000001383803,0.000053051936,0.00011441295,0.000011667859,0.00042084992,0.000008229186,0.0020716107,0.001243112,0.038126133,0.957838],"study_design_scores_gemma":[0.0016113686,0.00022313539,0.0005200654,0.00022581931,0.000046491943,0.000011859611,0.00018518273,0.7464263,0.22849588,0.019941265,0.0012754581,0.0010372072],"about_ca_topic_score_codex":0.0003752935,"about_ca_topic_score_gemma":0.0000423358,"teacher_disagreement_score":0.95680076,"about_ca_system_score_codex":0.00013867847,"about_ca_system_score_gemma":0.00033106384,"threshold_uncertainty_score":0.999967},"labels":[],"label_agreement":null},{"id":"W4376121429","doi":"10.1007/s11548-023-02915-0","title":"3D US-CT/MRI registration for percutaneous focal liver tumor ablations","year":2023,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lawson Health Research Institute; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Ontario Institute for Cancer Research","keywords":"Image registration; Medicine; Artificial intelligence; Percutaneous; Segmentation; Imaging phantom; Radiology; Computer vision; Patient registration; Computer science; 3D ultrasound; Real-time MRI; Workflow; Pipeline (software); Magnetic resonance imaging; Ultrasound; Image (mathematics)","score_opus":0.027721356914126964,"score_gpt":0.29468898366489105,"score_spread":0.2669676267507641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376121429","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029901998,0.000108511864,0.9646351,0.0031159031,0.0019882133,0.000094223695,0.000006112966,0.00010041139,0.00004952515],"genre_scores_gemma":[0.52277267,0.0005038728,0.47124365,0.003847006,0.0013787387,0.000017808363,0.0000618146,0.000017872722,0.00015659339],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985139,0.00016214041,0.0006219022,0.00020171041,0.00033008,0.00017031528],"domain_scores_gemma":[0.9971535,0.0017397539,0.00040587818,0.00012762923,0.00046764733,0.000105577026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093386864,0.000112947,0.00026367817,0.0004892975,0.00009459975,0.00013036863,0.00045568545,0.000051336214,0.000017406346],"category_scores_gemma":[0.00022817809,0.00010004652,0.00015826053,0.00017945346,0.0001167732,0.00046389332,0.000078386736,0.00017117625,0.0000063130883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026229085,0.00024913903,0.0070565,0.000056895078,0.0007964155,0.008195255,0.0006847312,0.0011363158,0.0030314813,0.0069690854,0.2716653,0.6998966],"study_design_scores_gemma":[0.0015963023,0.00075217866,0.109831065,0.00037139934,0.000092701914,0.118045405,0.000059513142,0.72249264,0.0031501388,0.008230395,0.034591008,0.00078723015],"about_ca_topic_score_codex":0.0000045414863,"about_ca_topic_score_gemma":0.0000019055627,"teacher_disagreement_score":0.72135633,"about_ca_system_score_codex":0.000055562043,"about_ca_system_score_gemma":0.00015276301,"threshold_uncertainty_score":0.40797788},"labels":[],"label_agreement":null},{"id":"W4377717228","doi":"10.1109/tnb.2023.3276867","title":"Deep Learning Based Parameterization of Diffeomorphic Image Registration for Cardiac Image Segmentation","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on NanoBioscience","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Alberta Innovates","keywords":"Segmentation; Artificial intelligence; Computer science; Computer vision; Diffeomorphism; Image segmentation; Hausdorff distance; Transformation (genetics); Pattern recognition (psychology); Geometric transformation; Image registration; Image (mathematics); Mathematics","score_opus":0.019795262818563763,"score_gpt":0.2857287332215809,"score_spread":0.2659334704030171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377717228","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006149939,0.0000035858557,0.99174553,0.00031319697,0.0005140057,0.0006836877,0.000022024875,0.0005334172,0.000034582703],"genre_scores_gemma":[0.62327945,0.000043307966,0.37597805,0.0001386,0.000014945665,0.000296056,0.000030298243,0.000015819667,0.00020345682],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99815184,0.00015469805,0.00039906485,0.00047706626,0.0005426502,0.0002746679],"domain_scores_gemma":[0.9987339,0.00035645234,0.00023989998,0.00036062454,0.00021620514,0.000092923045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007089822,0.00015290383,0.00018314336,0.00039780454,0.00031683545,0.00014927663,0.00045043643,0.000070648406,0.000018458612],"category_scores_gemma":[0.000100614096,0.0001548933,0.0001267553,0.0015670285,0.00022749462,0.0010108703,0.0000035864423,0.00012114256,0.00002376253],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011692047,0.000063979074,0.0000059602985,0.00005145898,0.000004803377,0.0000011939889,0.0002082984,0.0029805386,0.95901555,0.0000581873,0.000080958234,0.037517384],"study_design_scores_gemma":[0.0002094479,0.00022998256,0.00012187722,0.00002755571,0.000009788846,6.9391706e-7,0.000051474,0.30804366,0.6910741,0.000107279644,0.0000134080765,0.00011076687],"about_ca_topic_score_codex":0.000016888858,"about_ca_topic_score_gemma":0.00000244328,"teacher_disagreement_score":0.6171295,"about_ca_system_score_codex":0.00007736272,"about_ca_system_score_gemma":0.000093248804,"threshold_uncertainty_score":0.63163656},"labels":[],"label_agreement":null},{"id":"W4378550181","doi":"10.1016/j.ijleo.2023.170997","title":"An active contour model for image segmentation using morphology and nonlinear Poisson’s equation","year":2023,"lang":"en","type":"article","venue":"Optik","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Mathematical morphology; Dilation (metric space); Active contour model; Computer science; Partial differential equation; Image segmentation; Nonlinear system; Poisson's equation; Artificial intelligence; Segmentation; Image processing; Poisson distribution; Segmentation-based object categorization; Computer vision; Image (mathematics); Scale-space segmentation; Pattern recognition (psychology); Algorithm; Mathematics; Mathematical analysis; Geometry","score_opus":0.06797799348408919,"score_gpt":0.3720553004785104,"score_spread":0.3040773069944212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378550181","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1030263,0.0000037127543,0.8959699,0.0003120835,0.000059843158,0.00033722617,0.000016476935,0.00024353692,0.000030871302],"genre_scores_gemma":[0.056812625,0.000011991308,0.9425375,0.00035986328,0.000040924664,0.00006242409,0.00007258659,0.000010468219,0.00009160176],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921954,0.000045873752,0.00015057328,0.00027311462,0.00014657396,0.00016434497],"domain_scores_gemma":[0.9994859,0.00007306498,0.000083643245,0.00017799358,0.00010260093,0.000076764125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029603843,0.00007962844,0.00009631614,0.00011150057,0.00009675274,0.00009530964,0.00017815278,0.000051502873,0.000007239144],"category_scores_gemma":[0.00006421334,0.00008141955,0.000019757317,0.0001603129,0.000042122527,0.0009647969,0.00006204301,0.000051350806,0.000007731569],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017393739,0.00004330876,0.000025910596,0.000018629917,0.000010254964,0.0000076253477,0.0022477666,0.00146631,0.9040328,0.0008835823,0.0005053511,0.09074106],"study_design_scores_gemma":[0.00031717148,0.000070034126,0.000094661904,0.000005244132,0.0000054604247,0.0000042086363,0.00014127613,0.8631876,0.13396809,0.00212778,0.0000025732916,0.00007587808],"about_ca_topic_score_codex":0.000023891596,"about_ca_topic_score_gemma":0.0000031103345,"teacher_disagreement_score":0.86172134,"about_ca_system_score_codex":0.000051564813,"about_ca_system_score_gemma":0.0000448327,"threshold_uncertainty_score":0.3320193},"labels":[],"label_agreement":null},{"id":"W4378979186","doi":"10.1016/j.neucom.2023.126411","title":"Mutually aided uncertainty incorporated dual consistency regularization with pseudo label for semi-supervised medical image segmentation","year":2023,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Central South University; Natural Science Foundation of Hunan Province; Royal Society","keywords":"Artificial intelligence; Regularization (linguistics); Computer science; Consistency (knowledge bases); Dual (grammatical number); Segmentation; Image segmentation; Computer vision; Image (mathematics); Pattern recognition (psychology); Mathematics","score_opus":0.025100268079534117,"score_gpt":0.29133261480518885,"score_spread":0.2662323467256547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378979186","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03420331,0.000004181812,0.96077144,0.0018674813,0.00022454884,0.0009661505,0.000006507316,0.0017613695,0.00019498612],"genre_scores_gemma":[0.106842,0.000013077842,0.88761294,0.0043408563,0.00024807345,0.00021647479,0.00041066704,0.000072753035,0.00024317237],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969926,0.00024525955,0.00060558936,0.0007004787,0.0010065706,0.00044951285],"domain_scores_gemma":[0.9980262,0.00059562415,0.00029988887,0.0003965026,0.0004378732,0.00024392297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001002963,0.00024193928,0.00026928386,0.0002536921,0.00034107867,0.00025329585,0.0005883597,0.00012491341,0.0000259919],"category_scores_gemma":[0.0006772494,0.00021890525,0.000052750074,0.0015037984,0.0001434475,0.0005959234,0.00030333363,0.00023833184,0.000029036739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002094099,0.000583494,0.0011923217,0.0007228924,0.00017221288,0.0009864728,0.0046528373,0.006807641,0.3360322,0.012345776,0.026488684,0.60980606],"study_design_scores_gemma":[0.0020706316,0.00033470607,0.00032746902,0.00012389396,0.00001537328,0.00006297847,0.00012611784,0.9659466,0.029361514,0.001317383,0.000040664207,0.0002726385],"about_ca_topic_score_codex":0.000017077527,"about_ca_topic_score_gemma":0.000006211963,"teacher_disagreement_score":0.959139,"about_ca_system_score_codex":0.00006722264,"about_ca_system_score_gemma":0.00035861324,"threshold_uncertainty_score":0.8926697},"labels":[],"label_agreement":null},{"id":"W4379931088","doi":"10.1109/tcbb.2023.3284215","title":"AR-UNet: A Deformable Image Registration Network with Cyclic Training","year":2023,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Image registration; Computer science; Robustness (evolution); Discriminative model; Computer vision; Generative model; Residual; Pattern recognition (psychology); Generative grammar; Image (mathematics); Algorithm","score_opus":0.0287029518165341,"score_gpt":0.29277755278078166,"score_spread":0.26407460096424756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379931088","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025565906,0.000009259869,0.99470174,0.0012067502,0.00019950593,0.00024307997,0.00002057123,0.0005528053,0.0005096722],"genre_scores_gemma":[0.103606515,0.00006777776,0.89468634,0.0012906308,0.000050756567,0.000054729142,0.00011952148,0.000009081929,0.00011465219],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998764,0.000057685895,0.0004176293,0.00021779594,0.00022819095,0.0003147264],"domain_scores_gemma":[0.9989778,0.00038922246,0.00016224816,0.00025894947,0.000098417855,0.00011336111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040934404,0.00017101048,0.0001786813,0.00021872074,0.00040847374,0.00012240394,0.0003273132,0.000113078386,0.000016471513],"category_scores_gemma":[0.000023866472,0.00013673065,0.000043962602,0.0007174225,0.00021226004,0.00071016524,0.000012506109,0.00022954434,0.000075895594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000097788674,0.0001306628,0.00015571968,0.00014981985,0.00020186928,0.00002399415,0.0047959746,0.15110554,0.0004984198,0.009173331,0.0038928594,0.829774],"study_design_scores_gemma":[0.0008655598,0.0006047841,0.0007068968,0.000082703096,0.000021190577,0.00014147408,0.00033980957,0.9640188,0.0015617346,0.030717706,0.0005950919,0.00034424663],"about_ca_topic_score_codex":0.000007035433,"about_ca_topic_score_gemma":0.000008722268,"teacher_disagreement_score":0.8294298,"about_ca_system_score_codex":0.000029797098,"about_ca_system_score_gemma":0.000115311894,"threshold_uncertainty_score":0.5575714},"labels":[],"label_agreement":null},{"id":"W4380029951","doi":"10.1109/icit58465.2023.10143160","title":"Human Age Prediction Based on Brain MRI Using Density-Based Regression","year":2023,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Multivariate statistics; Artificial intelligence; Maximization; Regression; Covariance; Correlation; Machine learning; Regression analysis; Expectation–maximization algorithm; Pattern recognition (psychology); Data mining; Statistics; Mathematics; Maximum likelihood","score_opus":0.047451829860351395,"score_gpt":0.34334308383500295,"score_spread":0.29589125397465155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380029951","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008923211,6.491519e-7,0.98534334,0.0023756858,0.0001654595,0.00019839962,0.0000014016399,0.0019923858,0.0009994789],"genre_scores_gemma":[0.35693055,7.682605e-7,0.62516457,0.016086252,0.00011664597,0.000030238889,0.00007560008,0.000026236017,0.0015691306],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985516,0.0001523462,0.00020448593,0.00035159563,0.00053922186,0.0002007448],"domain_scores_gemma":[0.9991194,0.00017119401,0.000070691465,0.0004790007,0.000048761456,0.00011097447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006009925,0.00011087926,0.00010566211,0.00031191434,0.00020146205,0.00011368466,0.0003460169,0.00007031318,0.00009110034],"category_scores_gemma":[0.00013503517,0.00009119704,0.00004933846,0.0006430513,0.000048062637,0.00022548002,0.00008728007,0.00013000588,0.000060398106],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018338884,0.00027997707,0.0014619147,0.000099831705,0.000011424894,0.00050892186,0.00030839138,0.00487299,0.5738547,0.0033625425,0.36454374,0.05067728],"study_design_scores_gemma":[0.00031242458,0.00010949003,0.0017591518,0.000102516926,0.0000022137956,8.854521e-7,0.000007227969,0.75142473,0.24550535,0.00049715984,0.00018326574,0.000095599215],"about_ca_topic_score_codex":0.00003379458,"about_ca_topic_score_gemma":0.0000044539615,"teacher_disagreement_score":0.74655175,"about_ca_system_score_codex":0.000077318015,"about_ca_system_score_gemma":0.00005397217,"threshold_uncertainty_score":0.37189075},"labels":[],"label_agreement":null},{"id":"W4381185916","doi":"10.1007/s11548-023-02982-3","title":"Unsupervised synthesis of realistic coronary artery X-ray angiogram","year":2023,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; École de Technologie Supérieure","funders":"National Heart, Lung, and Blood Institute; Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; National Institute of Biomedical Imaging and Bioengineering; Nvidia","keywords":"Computer science; Coronary arteries; Fluoroscopy; Artificial intelligence; Computer vision; Artery; Radiology; Medicine; Cardiology","score_opus":0.0253731270447306,"score_gpt":0.28155198873135473,"score_spread":0.2561788616866241,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381185916","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10357686,0.00019455534,0.89186186,0.0021894695,0.0019647193,0.000054680764,0.0000075817134,0.000097967895,0.000052332103],"genre_scores_gemma":[0.93225133,0.0005415056,0.06608853,0.00077490264,0.00029880297,0.000005594241,0.000013209245,0.000008504372,0.000017615366],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99821275,0.00031775105,0.00074728014,0.00017057896,0.00039418673,0.00015743735],"domain_scores_gemma":[0.99613875,0.0027809772,0.0004256125,0.0001646484,0.000380674,0.000109309934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011052731,0.00011389041,0.00039574996,0.00079197227,0.000035747573,0.000047927413,0.00064037123,0.00009103811,0.000020543122],"category_scores_gemma":[0.00025793238,0.000096154494,0.00020986651,0.0003008494,0.00017507566,0.00031925985,0.00013417887,0.00015147385,0.000004147729],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012612472,0.0001984896,0.028981632,0.00003998551,0.0010017939,0.002836292,0.0003691861,0.00018623182,0.002722811,0.0009243882,0.04942816,0.9131849],"study_design_scores_gemma":[0.00085075834,0.0003063246,0.94280857,0.00053406693,0.00007711019,0.008563312,0.00005831669,0.03902399,0.0033006433,0.0029831836,0.00110832,0.0003854015],"about_ca_topic_score_codex":0.0000036750298,"about_ca_topic_score_gemma":3.2562127e-7,"teacher_disagreement_score":0.91382694,"about_ca_system_score_codex":0.000027680015,"about_ca_system_score_gemma":0.00010511996,"threshold_uncertainty_score":0.39210665},"labels":[],"label_agreement":null},{"id":"W4382137505","doi":"10.3390/rs15133251","title":"Level Sets Guided by SoDEF-Fitting Energy for River Channel Detection in SAR Images","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Synthetic aperture radar; Computer science; Quadratic function; Gradient descent; Grayscale; Channel (broadcasting); Exponential function; Energy (signal processing); Function (biology); Algorithm; Artificial intelligence; Remote sensing; Quadratic equation; Image (mathematics); Mathematics; Geology; Statistics; Geometry","score_opus":0.05146565938222718,"score_gpt":0.3140126808839177,"score_spread":0.2625470215016905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382137505","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007919201,0.000027388693,0.9903322,0.0005861528,0.00024614265,0.00016039006,0.0000035848607,0.0005961536,0.00012878077],"genre_scores_gemma":[0.24678628,0.00005386157,0.75144345,0.0010412126,0.00009936017,2.2980132e-7,0.000020347605,0.000031951156,0.0005232945],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986217,0.00009168536,0.00029839724,0.00038425185,0.0002534891,0.00035045127],"domain_scores_gemma":[0.99927783,0.000189211,0.00010941641,0.00025568873,0.0000961631,0.00007167975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005927524,0.0001281995,0.00015693657,0.00024981858,0.00012601959,0.00008836283,0.00020159956,0.00008145469,8.8406375e-7],"category_scores_gemma":[0.00032751408,0.00013724992,0.000054583317,0.0005970706,0.000043064378,0.00032717653,0.00014217998,0.000098169454,0.000010890347],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017364123,0.0000033993558,4.533624e-7,0.000013870921,0.000003799792,0.000015419815,0.00033922234,0.000040519266,0.16736637,0.0000086654645,0.004195,0.8280115],"study_design_scores_gemma":[0.0001835937,0.000014641898,0.000027932878,0.000048247264,0.000001740589,0.000012587772,0.000028488836,0.53944564,0.4571808,0.0027260052,0.00023084765,0.00009946955],"about_ca_topic_score_codex":0.00050458807,"about_ca_topic_score_gemma":0.000029113728,"teacher_disagreement_score":0.8279121,"about_ca_system_score_codex":0.00010034488,"about_ca_system_score_gemma":0.000028322318,"threshold_uncertainty_score":0.559689},"labels":[],"label_agreement":null},{"id":"W4382679206","doi":"10.7554/elife.88404.1","title":"Evaluation of surface-based hippocampal registration using ground-truth subfield definitions","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; McGill University; Montreal Neurological Institute and Hospital; Western University","funders":"Canadian Institutes of Health Research; Montreal Neurological Institute and Hospital; Horizon 2020 Framework Programme; Natural Sciences and Engineering Research Council of Canada; European Commission; Forschungszentrum Jülich; Canada Research Chairs; McGill University; Robarts Research Institute; Hospital for Sick Children; University of Pennsylvania","keywords":"Ground truth; Hippocampal formation; Surface (topology); Computer science; Artificial intelligence; Mathematics; Neuroscience; Geometry; Psychology","score_opus":0.3735625535598835,"score_gpt":0.3982384989904934,"score_spread":0.024675945430609914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382679206","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022940028,0.00004769274,0.9729709,0.00044046674,0.00075197173,0.0006413379,0.00001576434,0.00063105725,0.0015607784],"genre_scores_gemma":[0.4901257,0.000022641632,0.5094529,0.00012451195,0.000043094395,0.00004932169,0.00013362226,0.000015451767,0.00003277529],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960003,0.00055591186,0.00068085705,0.00057277415,0.0019935488,0.00019659346],"domain_scores_gemma":[0.99689883,0.00040433422,0.00054781936,0.0009723271,0.0010887121,0.000087979235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039003175,0.00020489209,0.0002664579,0.00023664898,0.0000823045,0.00020104425,0.000727935,0.00027162855,0.0001652472],"category_scores_gemma":[0.00094647025,0.0002117665,0.00013595463,0.0004176275,0.000094774514,0.00029090318,0.0003667302,0.0003434503,0.000019595724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000073361916,0.002293535,0.0019880787,0.0032687394,0.0009776539,0.00007501328,0.0040889103,0.31505588,0.10973611,0.23809771,0.021245526,0.30309948],"study_design_scores_gemma":[0.00032713814,0.000049801347,0.00069687515,0.00024026651,0.00013670075,0.000001974179,0.000058786154,0.7973531,0.060383763,0.14048672,0.0000018725967,0.00026299607],"about_ca_topic_score_codex":0.0006196278,"about_ca_topic_score_gemma":0.00006814873,"teacher_disagreement_score":0.48229724,"about_ca_system_score_codex":0.00026614274,"about_ca_system_score_gemma":0.0014385708,"threshold_uncertainty_score":0.8635587},"labels":[],"label_agreement":null},{"id":"W4383563349","doi":"10.1016/b978-0-12-805320-1.00010-5","title":"Regularized mutual information","year":2023,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Mutual information; Discriminative model; Computer science; Segmentation; Artificial intelligence; Generative grammar; Cluster analysis; Pointwise mutual information; Entropy (arrow of time); Pattern recognition (psychology); Machine learning","score_opus":0.0170710413826792,"score_gpt":0.25009174601844664,"score_spread":0.23302070463576743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383563349","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.5063299e-7,0.000019900335,0.14009485,0.00015655771,0.0005011453,0.0004562543,0.000009801153,0.0011922164,0.85756916],"genre_scores_gemma":[0.0000023172415,0.00003862454,0.07053656,0.0013997344,0.0001175576,0.00005527777,0.000060790808,0.00003191565,0.9277572],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99805635,0.000029776822,0.00059187284,0.0003172945,0.00076015055,0.0002445417],"domain_scores_gemma":[0.99840635,0.000083165825,0.0003387213,0.00086006295,0.00015610173,0.00015557701],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00044370006,0.0002889908,0.00033312154,0.00033609336,0.00008675813,0.00023494613,0.0010077284,0.00030790363,0.00013125993],"category_scores_gemma":[0.000073237294,0.00027729815,0.00015724021,0.000034174704,0.000111841786,0.0005144553,0.0004912214,0.0004225277,0.0029721234],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016563343,0.0000012294959,5.143765e-8,0.000034993038,0.000021875454,0.000015365062,0.00025113684,4.5614794e-8,0.000025020901,0.051011004,0.0031732458,0.9454644],"study_design_scores_gemma":[0.0002597682,0.000054662585,0.000002729855,0.0002427571,0.000020549876,0.000013687254,0.0000034218003,0.00031597316,0.00089256617,0.06823733,0.929568,0.00038857953],"about_ca_topic_score_codex":2.9908063e-7,"about_ca_topic_score_gemma":0.0000013955972,"teacher_disagreement_score":0.9450758,"about_ca_system_score_codex":0.00008558784,"about_ca_system_score_gemma":0.00015596344,"threshold_uncertainty_score":0.99996793},"labels":[],"label_agreement":null},{"id":"W4383563371","doi":"10.1016/b978-0-12-805320-1.00009-9","title":"Regularized model fitting","year":2023,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Segmentation; Cluster analysis; Artificial intelligence; Mathematics; Pattern recognition (psychology); Generative model; Parametric statistics; Kernel (algebra); Scale-space segmentation; Image segmentation; Regularization (linguistics); Probabilistic logic; Computer science; Generative grammar; Statistics; Combinatorics","score_opus":0.03237540609788989,"score_gpt":0.2740478668305849,"score_spread":0.24167246073269502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383563371","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.4931636e-7,0.00004354913,0.30135468,0.00014433132,0.00022166884,0.00033296502,0.000005469878,0.0011544331,0.6967428],"genre_scores_gemma":[0.000002834993,0.000035944642,0.24574076,0.00095883146,0.000107471744,0.000046308647,0.000011273498,0.00006691805,0.75302964],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99761444,0.00003193734,0.0005687436,0.000703226,0.0007469275,0.00033472682],"domain_scores_gemma":[0.99810547,0.00011567593,0.00031412073,0.001146265,0.00012737805,0.00019108706],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005870595,0.00036027454,0.0004503536,0.000245494,0.00011719428,0.00016928258,0.0013318146,0.00033066678,0.0000595457],"category_scores_gemma":[0.00007445171,0.00035217192,0.00022385964,0.000029389344,0.00012034701,0.00014117123,0.00074960024,0.0005603714,0.00053163926],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.797421e-7,0.0000020565485,4.109562e-8,0.000038403537,0.000026167165,0.000045831745,0.00015045522,0.000001955558,0.00018421409,0.04620935,0.0013008207,0.9520397],"study_design_scores_gemma":[0.0005518491,0.00007628681,9.116489e-7,0.001220773,0.000075503776,0.000027056998,0.0000043650125,0.06338646,0.0036032556,0.6905076,0.23926525,0.0012806784],"about_ca_topic_score_codex":2.1409647e-7,"about_ca_topic_score_gemma":0.0000014240568,"teacher_disagreement_score":0.95075905,"about_ca_system_score_codex":0.00008369549,"about_ca_system_score_gemma":0.00019037329,"threshold_uncertainty_score":0.999893},"labels":[],"label_agreement":null},{"id":"W4383563436","doi":"10.1016/b978-0-12-805320-1.00011-7","title":"Examples of high-order functionals","year":2023,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Prior probability; Matching (statistics); Cluster analysis; Segmentation; Order (exchange); Graph; Mathematics; Computer science; Artificial intelligence; Pattern recognition (psychology); Distribution (mathematics); Algorithm; Combinatorics; Statistics; Mathematical analysis; Bayesian probability","score_opus":0.03375204840517524,"score_gpt":0.26962166324233056,"score_spread":0.23586961483715532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383563436","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000001022498,0.00015925443,0.13409144,0.0001290199,0.0006170257,0.00035623927,0.000025431986,0.0005299579,0.8640906],"genre_scores_gemma":[0.000018668195,0.00009901909,0.088896744,0.00054617686,0.00014857885,0.000045652345,0.000029123607,0.000049389517,0.9101666],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99788785,0.0000354637,0.00058298686,0.00050803757,0.0007740217,0.00021164885],"domain_scores_gemma":[0.9981479,0.00023700387,0.00036739086,0.0008379167,0.00029164448,0.00011811818],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004022678,0.00027968216,0.00045443772,0.00028399535,0.00005478097,0.000052864012,0.0008609395,0.0002332135,0.0004963453],"category_scores_gemma":[0.00007327904,0.0002601941,0.0001474333,0.000046220743,0.00018075063,0.00010342678,0.00043580783,0.000294662,0.0004174298],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010623066,0.0000044714675,3.3170787e-7,0.00005544875,0.000050115137,0.000012970321,0.00006188719,3.1662523e-7,0.00012464034,0.099600665,0.0018985883,0.8981895],"study_design_scores_gemma":[0.00025397638,0.000121340134,0.000045426976,0.0005487952,0.00004971779,0.000010658301,0.0000041699313,0.00003257484,0.00547737,0.27235246,0.7205849,0.0005185852],"about_ca_topic_score_codex":0.0000022738107,"about_ca_topic_score_gemma":0.0000060487305,"teacher_disagreement_score":0.8976709,"about_ca_system_score_codex":0.00004097054,"about_ca_system_score_gemma":0.0001738257,"threshold_uncertainty_score":0.99998504},"labels":[],"label_agreement":null},{"id":"W4384575240","doi":"10.23952/jnva.7.2023.4.03","title":"Weighted-type image segmentation model via coupling heat kernel convolution with high-order total variation","year":2023,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Robustness (evolution); Segmentation; Kernel (algebra); Image segmentation; Computer science; Scale-space segmentation; Active contour model; Artificial intelligence; Segmentation-based object categorization; Algorithm; Convolution (computer science); Pattern recognition (psychology); Mathematical optimization; Mathematics","score_opus":0.010919541068349428,"score_gpt":0.26884858868153494,"score_spread":0.2579290476131855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384575240","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055274393,0.000026439819,0.9433902,0.0010592695,0.00008741115,0.00007887133,0.000008187316,0.0000640865,0.000011169903],"genre_scores_gemma":[0.2107705,0.000091627364,0.78854334,0.00019945108,0.00014781358,0.0000037627121,0.00013358681,0.000009158514,0.000100762],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829817,0.000055222416,0.0005284642,0.00021996454,0.0007488253,0.00014934318],"domain_scores_gemma":[0.9982355,0.00013607644,0.00035128728,0.00013853551,0.0010204795,0.00011810385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007505668,0.00012836342,0.0002672483,0.0006530956,0.00014274479,0.00014948951,0.00017614366,0.000063976804,0.00004844668],"category_scores_gemma":[0.00006797575,0.0001013125,0.00008895204,0.0022009073,0.000034919107,0.0010496356,0.00005609511,0.00015653958,0.000009948351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022187737,0.00050390605,0.0041535557,0.000059982838,0.0035124293,0.000068338035,0.0021649739,0.8368193,0.14003928,0.0049259914,0.0007885892,0.0067418157],"study_design_scores_gemma":[0.00052561203,0.00012457804,0.015526913,0.000011523433,0.0003815762,0.000021411948,0.000028464368,0.9796558,0.0018680133,0.0017375576,0.0000030881938,0.00011546762],"about_ca_topic_score_codex":0.00005781588,"about_ca_topic_score_gemma":0.0000042697793,"teacher_disagreement_score":0.1554961,"about_ca_system_score_codex":0.000082967104,"about_ca_system_score_gemma":0.00016989378,"threshold_uncertainty_score":0.4131404},"labels":[],"label_agreement":null},{"id":"W4385329910","doi":"10.1007/s11517-023-02882-3","title":"3D biplanar reconstruction of lower limbs using nonlinear statistical models","year":2023,"lang":"en","type":"article","venue":"Medical & Biological Engineering & Computing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Centre Hospitalier de l’Université de Montréal; Centrale des Syndicats du Québec","funders":"","keywords":"Artificial intelligence; Iterative reconstruction; Computer science; Computer vision; Radiography; 3D reconstruction; Tomography; Reduction (mathematics); Mathematics; Geometry; Radiology","score_opus":0.041064459040018035,"score_gpt":0.2935228139990676,"score_spread":0.25245835495904956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385329910","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07290408,0.000037113696,0.9252844,0.00014264711,0.0005724109,0.000119960954,0.000005889898,0.0008475677,0.00008594383],"genre_scores_gemma":[0.3973932,0.000030095962,0.60223913,0.0001490443,0.00015439291,0.0000030521194,0.000015311925,0.000011391388,0.0000043955383],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99768746,0.00009753493,0.00064849283,0.00046389073,0.0006336434,0.00046896836],"domain_scores_gemma":[0.9984672,0.0007330169,0.000118278265,0.0002771809,0.00008268158,0.00032165684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010687103,0.00019593968,0.00036897277,0.00019938758,0.000071915245,0.00003847987,0.00066486426,0.00024139631,0.00013952187],"category_scores_gemma":[0.0012193783,0.00015848578,0.00007680572,0.00078433286,0.00017629219,0.000158216,0.00047946296,0.00042482556,0.000038550243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030357414,0.00051461585,0.00085565326,0.00025299683,0.00010229052,0.00057956367,0.00029914416,0.07743932,0.019669123,0.015994703,0.0006985714,0.88356364],"study_design_scores_gemma":[0.00017384,0.0002093012,0.00050899124,0.00016743447,0.0000039848287,0.000082254475,0.000008821296,0.99702424,0.0011500379,0.00043099985,0.000063320855,0.00017676539],"about_ca_topic_score_codex":0.000022196251,"about_ca_topic_score_gemma":1.9262939e-7,"teacher_disagreement_score":0.91958493,"about_ca_system_score_codex":0.000049604558,"about_ca_system_score_gemma":0.00007493088,"threshold_uncertainty_score":0.64628625},"labels":[],"label_agreement":null},{"id":"W4386449347","doi":"10.20944/preprints202309.0223.v1","title":"Deep Learning in Medical Image Registration: Introduction and Survey","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Service Public de Wallonie","keywords":"Image registration; Artificial intelligence; Computer science; Computer vision; Affine transformation; Medical imaging; Pyramid (geometry); Image processing; Segmentation; Rotation (mathematics); Image (mathematics); Mathematics","score_opus":0.12195358946202878,"score_gpt":0.3856695918370166,"score_spread":0.2637160023749878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386449347","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07058899,0.000070195325,0.91762036,0.008182258,0.0010087052,0.0005427065,0.0000017008934,0.0012980176,0.00068706006],"genre_scores_gemma":[0.9195273,0.001886148,0.07267675,0.0005639392,0.0010635542,0.0004744876,0.00032705578,0.000087049775,0.003393702],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99578834,0.00089890347,0.0007163801,0.0013567228,0.0009339664,0.00030570826],"domain_scores_gemma":[0.9978856,0.00031397233,0.00031908954,0.0010745439,0.00018164619,0.00022516027],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0059619644,0.00025059667,0.00035534555,0.00030002816,0.00008139454,0.00012803635,0.0011669578,0.0003824609,0.00041351342],"category_scores_gemma":[0.006452876,0.0002752021,0.00005518817,0.0004260454,0.00020564142,0.00038821137,0.0035394118,0.0017243921,0.00038977445],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003739857,0.00023570051,0.89868104,0.00052347954,0.00008332298,0.0003573338,0.0030418057,0.00037525492,0.00414485,0.0014231084,0.0022261203,0.088870555],"study_design_scores_gemma":[0.0003039431,0.00002367202,0.95378524,0.00017120712,0.0000067330507,0.00003129228,0.00004808869,0.028765326,0.011969762,0.0041630906,0.00033479757,0.00039685806],"about_ca_topic_score_codex":0.0008288156,"about_ca_topic_score_gemma":0.00036606763,"teacher_disagreement_score":0.84893835,"about_ca_system_score_codex":0.00013551916,"about_ca_system_score_gemma":0.00023514434,"threshold_uncertainty_score":0.99997},"labels":[],"label_agreement":null},{"id":"W4386594373","doi":"10.1080/13682199.2023.2256504","title":"Multi-object 3D segmentation of brain structures using a geometric deformable model with a priori knowledge","year":2023,"lang":"en","type":"article","venue":"The Imaging Science Journal","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Segmentation; Artificial intelligence; Computer science; A priori and a posteriori; Computer vision; Object (grammar); Probabilistic logic; Pattern recognition (psychology); Image segmentation","score_opus":0.03769657898039932,"score_gpt":0.3503620714422515,"score_spread":0.3126654924618522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386594373","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09743731,0.00006654397,0.90160686,0.0003891005,0.00014645563,0.000158128,8.251164e-7,0.000119609904,0.00007514197],"genre_scores_gemma":[0.36802799,0.000013290346,0.63161826,0.00022763138,0.000028229293,0.0000032013525,2.7247643e-7,0.0000074802756,0.00007360882],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979211,0.00007537067,0.00033145683,0.00024634198,0.0009923026,0.00043341593],"domain_scores_gemma":[0.9987293,0.00010955508,0.00032884104,0.0003314107,0.0003637775,0.000137119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033231997,0.00012222854,0.00013860363,0.001068297,0.0006912736,0.00049383426,0.0015257188,0.00001438592,0.0000064460637],"category_scores_gemma":[0.00026550062,0.00007319148,0.000039830742,0.0051616128,0.0005413854,0.002289418,0.00031148404,0.00025839717,0.000004587381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014071355,0.00007150439,0.0015971299,0.000040872772,0.000020516964,0.000031050975,0.012887444,0.064256564,0.62994885,0.00023868922,0.00091715634,0.28997615],"study_design_scores_gemma":[0.00031397308,0.000031570286,0.0014880958,0.00004889402,0.0000074004456,0.00031514422,0.00036470123,0.90296125,0.09354428,0.0008275073,0.0000031360669,0.00009406653],"about_ca_topic_score_codex":0.000025624431,"about_ca_topic_score_gemma":0.0000023117195,"teacher_disagreement_score":0.83870465,"about_ca_system_score_codex":0.00018727801,"about_ca_system_score_gemma":0.00083113444,"threshold_uncertainty_score":0.53167874},"labels":[],"label_agreement":null},{"id":"W4386965001","doi":"10.1016/j.media.2023.102974","title":"Learning joint surface reconstruction and segmentation, from brain images to cortical surface parcellation","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Bell (Canada)","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Segmentation; Voxel; Computer science; Hausdorff distance; Surface reconstruction; Computer vision; Pattern recognition (psychology); Surface (topology); Computation; Mathematics; Algorithm; Geometry","score_opus":0.014258781679771555,"score_gpt":0.29365168947209586,"score_spread":0.2793929077923243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386965001","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28085107,0.000021761638,0.7107212,0.0075654127,0.00007408969,0.0001359972,0.000004141948,0.0005343642,0.00009197303],"genre_scores_gemma":[0.5026198,0.00022164601,0.49401727,0.0019806738,0.00010127004,0.00001779181,0.00016397158,0.0000240596,0.00085354026],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966074,0.0005740635,0.0006009105,0.0007069284,0.001161359,0.00034930627],"domain_scores_gemma":[0.997914,0.0008091058,0.00014789117,0.00037359004,0.00017775326,0.0005776646],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0017778769,0.00018680925,0.0003883212,0.00032789598,0.00023409627,0.0003154734,0.00035532087,0.00011910532,0.0010485916],"category_scores_gemma":[0.0024264115,0.00017707815,0.00012247951,0.0028434715,0.00019591552,0.00061153836,0.0003383255,0.00037029493,0.00028312526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019179894,0.000103900136,0.04787663,0.00003733017,0.00084515224,0.00025048165,0.0034454886,0.0021987013,0.48718852,0.000078959216,0.027947463,0.4300082],"study_design_scores_gemma":[0.0006594749,0.00014846363,0.06746883,0.000068003836,0.00034123083,0.000013177055,0.0010168187,0.75045496,0.17831416,0.0008023921,0.00017726824,0.00053523254],"about_ca_topic_score_codex":0.000378055,"about_ca_topic_score_gemma":0.000032726894,"teacher_disagreement_score":0.74825627,"about_ca_system_score_codex":0.00006586213,"about_ca_system_score_gemma":0.00006513544,"threshold_uncertainty_score":0.9998646},"labels":[],"label_agreement":null},{"id":"W4387211866","doi":"10.1007/978-3-031-43898-1_21","title":"Asymmetric Contour Uncertainty Estimation for Medical Image Segmentation","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Artificial intelligence; Image segmentation; Segmentation; Multivariate normal distribution; Pattern recognition (psychology); Contouring; Multivariate statistics; Machine learning","score_opus":0.020768812109621206,"score_gpt":0.31235797915945945,"score_spread":0.2915891670498382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387211866","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000028560823,0.00007407223,0.9940804,0.001700312,0.0015965269,0.0011006364,0.000013605908,0.0006668316,0.0007647506],"genre_scores_gemma":[0.0011020944,0.00007081783,0.9953717,0.0023973791,0.00034320264,0.00010366529,0.000063687316,0.00004466052,0.00050277356],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9948967,0.0000553115,0.0008025465,0.0013717614,0.002268949,0.0006047299],"domain_scores_gemma":[0.99611276,0.0018312294,0.00040879942,0.0008643139,0.00046453942,0.0003183508],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0025647106,0.00044061147,0.0004969161,0.00143501,0.00023742439,0.00058935303,0.0027518559,0.00040873754,0.000048389244],"category_scores_gemma":[0.0017836574,0.00040812188,0.00014761422,0.0011651377,0.00063327077,0.00090418616,0.00078588864,0.0006107741,0.00008533432],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004184522,0.000019097952,0.0000021677054,0.00006606374,0.000009068061,0.000040500217,0.00018253339,0.0021239188,0.00015782737,0.0056024403,0.00045743046,0.9913348],"study_design_scores_gemma":[0.0004634225,0.00018266229,0.00001955913,0.00031048874,0.000009186846,0.000022431099,2.6353396e-7,0.8528315,0.0067685237,0.13886921,0.00011052376,0.00041225587],"about_ca_topic_score_codex":0.00003479364,"about_ca_topic_score_gemma":0.00004700374,"teacher_disagreement_score":0.9909225,"about_ca_system_score_codex":0.00050009927,"about_ca_system_score_gemma":0.0008813292,"threshold_uncertainty_score":0.99983704},"labels":[],"label_agreement":null},{"id":"W4387211919","doi":"10.1007/978-3-031-43996-4_64","title":"Towards Multi-modal Anatomical Landmark Detection for Ultrasound-Guided Brain Tumor Resection with Contrastive Learning","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Landmark; Computer science; Artificial intelligence; Convolutional neural network; Computer vision; Image registration; Pattern recognition (psychology); Image (mathematics)","score_opus":0.02369054555861806,"score_gpt":0.29713503758273374,"score_spread":0.2734444920241157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387211919","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021591166,0.000024955569,0.9964206,0.00043085442,0.00091869413,0.001059544,0.000008035774,0.0007429151,0.00017846371],"genre_scores_gemma":[0.10296832,0.000016313586,0.89425504,0.001386716,0.00048019688,0.00013988849,0.000024310071,0.000082438884,0.00064674596],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99616486,0.000098779376,0.0005321731,0.0015069044,0.0010495486,0.00064772414],"domain_scores_gemma":[0.9966572,0.0017293247,0.0003580445,0.000553042,0.0004851595,0.00021724479],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001668458,0.0004680116,0.00050289684,0.0008539348,0.00038737903,0.00053658517,0.0013828829,0.0002764635,0.000009433172],"category_scores_gemma":[0.0014047597,0.00039992508,0.00012058381,0.00076930266,0.0006768019,0.0006166312,0.00037582946,0.0010516348,0.000014317412],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015071766,0.000070731425,0.00013021867,0.00015293946,0.00006190623,0.00016181715,0.0014053306,0.0108352285,0.013564405,0.0022423223,0.00018966522,0.9710347],"study_design_scores_gemma":[0.0016664914,0.0014103521,0.00060849835,0.0006130976,0.000020136311,0.00036494082,0.0000021755902,0.86496437,0.104322016,0.024830373,0.00022017183,0.0009774026],"about_ca_topic_score_codex":0.00008162275,"about_ca_topic_score_gemma":0.00032838347,"teacher_disagreement_score":0.9700573,"about_ca_system_score_codex":0.00051895366,"about_ca_system_score_gemma":0.0005228245,"threshold_uncertainty_score":0.99984527},"labels":[],"label_agreement":null},{"id":"W4387429075","doi":"10.31219/osf.io/k23r9","title":"Hessian-based Similarity Metric for Multimodal Medical Image Registration","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Image registration; Metric (unit); Hessian matrix; Artificial intelligence; Affine transformation; Robustness (evolution); Similarity (geometry); Computer science; Medical imaging; Pattern recognition (psychology); Computation; Context (archaeology); Computer vision; Mathematics; Image (mathematics); Algorithm; Geometry; Applied mathematics","score_opus":0.06153101939060815,"score_gpt":0.3776232136431117,"score_spread":0.31609219425250357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387429075","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000020933923,0.00002041553,0.98145294,0.012776301,0.00086036156,0.0011714875,0.00003433882,0.002417662,0.0012455758],"genre_scores_gemma":[0.008214915,0.000020880014,0.98719954,0.0024859633,0.00020990927,0.00065979816,0.00028289895,0.0000332595,0.0008928088],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99615437,0.00017986601,0.0007192141,0.0010265377,0.0015440476,0.00037596928],"domain_scores_gemma":[0.9968478,0.00096138584,0.00033314445,0.0011660907,0.00033070036,0.0003608725],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002251467,0.0003022176,0.00039839707,0.00047429895,0.00011225721,0.00042336358,0.0021717171,0.00060297234,0.00023740427],"category_scores_gemma":[0.00279432,0.00027377316,0.0002499165,0.000559578,0.00015183729,0.00028402737,0.00087978464,0.00071949593,0.000057601526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052517258,0.0010449823,0.00023814409,0.0026415796,0.00019382895,0.00033584062,0.00031254464,0.0003261936,0.0015772871,0.02497017,0.4635004,0.5048065],"study_design_scores_gemma":[0.00069159654,0.00008767781,0.00043735842,0.00016253543,0.000021831678,0.0000022951608,0.0000104644705,0.9244802,0.058926515,0.014365254,0.0003660342,0.0004482338],"about_ca_topic_score_codex":0.0002848171,"about_ca_topic_score_gemma":0.00006232792,"teacher_disagreement_score":0.924154,"about_ca_system_score_codex":0.0001549909,"about_ca_system_score_gemma":0.0010368186,"threshold_uncertainty_score":0.99997145},"labels":[],"label_agreement":null},{"id":"W4388099570","doi":"10.1007/978-3-031-46914-5","title":"Shape in Medical Imaging","year":2023,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"York University","keywords":"Computer science; Medical imaging; Artificial intelligence; Geometric modeling; Computer vision; Shape analysis (program analysis); Geometric analysis; Computer graphics (images); Geometry; Mathematics; Static analysis","score_opus":0.01524131705399677,"score_gpt":0.3024412454106108,"score_spread":0.287199928356614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388099570","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000011913156,0.00021071672,0.9927704,0.0027596822,0.0018566085,0.00034961416,0.0000016223457,0.0006718372,0.0013676217],"genre_scores_gemma":[0.004393108,0.00017310512,0.9762028,0.016359389,0.0010015238,0.00007785381,0.000020182064,0.00008664681,0.0016854258],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99399364,0.00011672893,0.0007450005,0.0016245617,0.0026504488,0.0008696027],"domain_scores_gemma":[0.9970866,0.0010772084,0.00020163057,0.0011553171,0.00014174014,0.0003375385],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.002796317,0.00041254127,0.0005120443,0.0017003176,0.00010907618,0.000493781,0.0059789354,0.00031334674,0.00010476751],"category_scores_gemma":[0.0009597799,0.0003857585,0.000090224676,0.0026466197,0.0009201056,0.0007791005,0.0026685675,0.0014365434,0.00015666285],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.416523e-7,0.00002449291,0.00011301912,0.000035110716,0.0000018145793,0.0008262239,0.00040316244,0.0002508768,0.000037993537,0.00034919457,0.0010678009,0.9968895],"study_design_scores_gemma":[0.00027146764,0.00003536709,0.0003270875,0.0009239358,0.0000016930092,0.00008019479,2.2220624e-7,0.92567974,0.0011475453,0.07072305,0.00032302484,0.000486667],"about_ca_topic_score_codex":0.00003477894,"about_ca_topic_score_gemma":0.000099211,"teacher_disagreement_score":0.9964028,"about_ca_system_score_codex":0.0006776715,"about_ca_system_score_gemma":0.0025100135,"threshold_uncertainty_score":0.99985945},"labels":[],"label_agreement":null},{"id":"W4388447480","doi":"10.1109/ius51837.2023.10306435","title":"Dense Error Map Estimation for MRI-Ultrasound Registration in Brain Tumor Surgery Using Swin UNETR","year":2023,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Rendering (computer graphics); Image registration; Artificial intelligence; Surgical planning; Brain tumor; Medicine; Computer vision; Radiology; Image (mathematics)","score_opus":0.06272749201262243,"score_gpt":0.3492907463334946,"score_spread":0.28656325432087215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388447480","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024812978,0.000006160697,0.97118706,0.002715189,0.00021148977,0.00042335832,0.000002801443,0.000570794,0.00007016769],"genre_scores_gemma":[0.1131702,0.0000029387368,0.8844339,0.001350347,0.000052749263,0.000098841905,0.0000773238,0.00001582888,0.00079783896],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986395,0.000090548376,0.00040950818,0.00031790702,0.00029491802,0.00024756926],"domain_scores_gemma":[0.9981651,0.001288193,0.00013122224,0.00027982693,0.00006611639,0.00006955161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013802031,0.0001033996,0.00014378845,0.0003260224,0.00007242658,0.00016221617,0.00021043596,0.000047475234,0.00002975883],"category_scores_gemma":[0.0012063979,0.000103302955,0.00005387357,0.00076286943,0.000038799524,0.00072984945,0.000046449695,0.00006871694,0.000034287896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005485421,0.00027207888,0.004659662,0.0005921024,0.000033694676,0.00021810949,0.0019951668,0.005247214,0.18845315,0.020656155,0.6664541,0.11136375],"study_design_scores_gemma":[0.00024198831,0.000033684235,0.0030361516,0.000080904436,0.0000033026733,0.00002034342,0.00008180431,0.91879064,0.06764218,0.0096179005,0.00023942211,0.00021165767],"about_ca_topic_score_codex":0.0001244181,"about_ca_topic_score_gemma":0.00007039395,"teacher_disagreement_score":0.91354346,"about_ca_system_score_codex":0.00009616552,"about_ca_system_score_gemma":0.00013245344,"threshold_uncertainty_score":0.42125723},"labels":[],"label_agreement":null},{"id":"W4388626321","doi":"10.7554/elife.88404.4.sa3","title":"Author Response: Evaluation of surface-based hippocampal registration using ground-truth subfield definitions","year":2023,"lang":"en","type":"peer-review","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; McGill University","funders":"","keywords":"Ground truth; Hippocampal formation; Computer science; Artificial intelligence; Neuroscience; Psychology","score_opus":0.34546716007263745,"score_gpt":0.4450249946421555,"score_spread":0.09955783456951806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388626321","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00026537327,0.0017299337,0.96066505,0.03204536,0.001979044,0.001326331,0.00010678078,0.00068433443,0.0011978014],"genre_scores_gemma":[0.0054174843,0.00077785546,0.940139,0.0050248858,0.00029451938,0.00037529116,0.0024419564,0.00012665201,0.045402367],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99302256,0.0018318612,0.0011576861,0.0007179455,0.0029612575,0.00030867566],"domain_scores_gemma":[0.99428344,0.0016265605,0.00092956336,0.0012630966,0.0017494886,0.0001478473],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.010262765,0.00032910728,0.0005679342,0.00037922713,0.00013902008,0.00016150375,0.00094901625,0.0003674436,0.0007525821],"category_scores_gemma":[0.0055375663,0.00032150067,0.00023419698,0.0012916687,0.00013287776,0.00040206325,0.00015385087,0.0004279638,0.00006112645],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028593195,0.00010881351,0.0000031953405,0.0014535233,0.000051729185,0.000009963866,0.00007698832,0.0001982441,0.0016260261,0.0021088542,0.97289807,0.021436002],"study_design_scores_gemma":[0.004488345,0.0016729134,0.0011246341,0.035414666,0.0035714323,0.00009775954,0.00027016262,0.75512064,0.074960895,0.062323585,0.056366723,0.0045882487],"about_ca_topic_score_codex":0.00032672318,"about_ca_topic_score_gemma":0.0000694273,"teacher_disagreement_score":0.9165313,"about_ca_system_score_codex":0.00037552443,"about_ca_system_score_gemma":0.002711768,"threshold_uncertainty_score":0.9999237},"labels":[],"label_agreement":null},{"id":"W4390430504","doi":"10.18280/ts.400616","title":"Improving Segmentation of Pilocytic Astrocytoma in MRI Using Genomic Cluster-Shape Feature Analysis","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pilocytic astrocytoma; Segmentation; Feature (linguistics); Cluster (spacecraft); Pattern recognition (psychology); Artificial intelligence; Astrocytoma; Computer science; Medicine; Glioma; Cancer research; Linguistics","score_opus":0.020533234842657885,"score_gpt":0.28209995615629546,"score_spread":0.26156672131363756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390430504","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32974246,0.000017391949,0.6696623,0.00012304792,0.00003836875,0.0002623624,0.000006294196,0.0001330968,0.000014699989],"genre_scores_gemma":[0.7698303,0.0000074790305,0.22983186,0.00019167265,0.00003413011,0.00002895545,0.00004484142,0.000009828639,0.00002097638],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823314,0.0001323643,0.00047036,0.0003720362,0.00050105475,0.00029102687],"domain_scores_gemma":[0.9992669,0.000106793195,0.00024240879,0.00023984993,0.00006135261,0.00008269988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006614894,0.00014940617,0.00026008775,0.0008397163,0.000064545326,0.000082780614,0.00046637366,0.000055239285,0.00015259527],"category_scores_gemma":[0.000019423851,0.00015047357,0.00010944761,0.002024145,0.00003799199,0.00044312913,0.00018590491,0.00012486616,0.000014148407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005851644,0.00015233093,0.006697836,0.00014514154,0.00025084044,0.00005441972,0.0023695552,0.017824609,0.8501056,0.00012686533,0.0007100113,0.121504284],"study_design_scores_gemma":[0.0006664606,0.00013681504,0.009136718,0.000029186662,0.000079840036,0.0000027744345,0.00017453496,0.9124928,0.07707106,0.00006586926,0.0000044884227,0.00013942823],"about_ca_topic_score_codex":0.000083505285,"about_ca_topic_score_gemma":0.00003415679,"teacher_disagreement_score":0.8946682,"about_ca_system_score_codex":0.00022129148,"about_ca_system_score_gemma":0.000080153535,"threshold_uncertainty_score":0.6136134},"labels":[],"label_agreement":null},{"id":"W4391036464","doi":"10.1007/978-3-031-50069-5_21","title":"MRI-GAN: Generative Adversarial Network for Brain Segmentation","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Image segmentation; Deep learning; Generative adversarial network; Pattern recognition (psychology); Sørensen–Dice coefficient; Generative grammar; White matter; Metric (unit); Computer vision; Generative model; Magnetic resonance imaging; Radiology; Medicine","score_opus":0.017448868451000063,"score_gpt":0.2890999962991844,"score_spread":0.27165112784818435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391036464","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000010938933,0.0003764656,0.98871076,0.0034679605,0.004315161,0.0012044746,0.00001744182,0.00040546712,0.001501203],"genre_scores_gemma":[0.0003572797,0.000034416335,0.9895949,0.00615186,0.002038005,0.00008183617,0.000040565264,0.000041613992,0.0016595531],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961366,0.000044373144,0.00060411735,0.00159368,0.0009956749,0.00062556576],"domain_scores_gemma":[0.9976312,0.00087050063,0.00026084093,0.00080073625,0.000249668,0.00018707394],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013156821,0.00048520113,0.00044931998,0.0005271504,0.00025497933,0.00079498475,0.002071225,0.00031204484,0.00004415791],"category_scores_gemma":[0.0001266878,0.0004448923,0.00016733992,0.0005927999,0.0005300879,0.0007161098,0.0006811751,0.0006145323,0.000049349346],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011776694,0.00001931526,0.0000025488434,0.00008573886,0.000033590313,0.000061751045,0.0012171691,0.026085097,0.0009916793,0.05364909,0.0059330524,0.91190916],"study_design_scores_gemma":[0.00032850902,0.0002802546,0.0000023095197,0.00033963873,0.000015449134,0.000021609938,2.8831366e-7,0.56783026,0.016978336,0.4105921,0.0030712013,0.0005399933],"about_ca_topic_score_codex":0.000010620477,"about_ca_topic_score_gemma":0.00003931218,"teacher_disagreement_score":0.9113692,"about_ca_system_score_codex":0.00045187477,"about_ca_system_score_gemma":0.00054649793,"threshold_uncertainty_score":0.99980026},"labels":[],"label_agreement":null},{"id":"W4391056022","doi":"10.1016/j.media.2024.103090","title":"Fighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site data","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Mental Health; Horizon 2020; HORIZON EUROPE Framework Programme; Horizon 2020 Framework Programme; McGill University","keywords":"Computer science; Robustness (evolution); Scanner; Segmentation; Artificial intelligence; Convolutional neural network; Software portability; Population; Data acquisition; Pattern recognition (psychology); Machine learning","score_opus":0.04749079695162298,"score_gpt":0.3614109178070805,"score_spread":0.3139201208554575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391056022","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030094336,0.0003093456,0.98866785,0.0072447853,0.00006261337,0.00042783062,0.000022207587,0.00018164658,0.00007430538],"genre_scores_gemma":[0.33347294,0.00006475888,0.6587576,0.005741359,0.0003485674,0.0003364635,0.00064876693,0.000051865747,0.00057766284],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962678,0.0007245323,0.00054734375,0.0007408031,0.0013632899,0.00035623222],"domain_scores_gemma":[0.9970673,0.0015210889,0.00016626478,0.0009956541,0.00007618452,0.0001735402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035728002,0.00021390623,0.0004113823,0.00037693323,0.00009461859,0.00027019082,0.0014868131,0.00008639,0.00030116088],"category_scores_gemma":[0.0010129085,0.00011895214,0.0001259893,0.0038302536,0.00029033588,0.00066555507,0.00039230904,0.00045206875,0.000023428638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007612119,0.00034354161,0.004941222,0.00028099012,0.0018709479,0.0019972397,0.0041187597,0.0006999655,0.0023636434,0.00021386238,0.032082178,0.95101154],"study_design_scores_gemma":[0.0006695566,0.00021860271,0.0023757021,0.0005049322,0.00037335322,0.000009422305,0.000076843615,0.98814696,0.007249135,0.00006325207,0.00010993231,0.00020232306],"about_ca_topic_score_codex":0.00015547409,"about_ca_topic_score_gemma":0.00034749295,"teacher_disagreement_score":0.98744696,"about_ca_system_score_codex":0.00005759889,"about_ca_system_score_gemma":0.00014946911,"threshold_uncertainty_score":0.48507276},"labels":[],"label_agreement":null},{"id":"W4391139655","doi":"10.23977/jaip.2024.070102","title":"Conditional Diffusion Model for X-Ray Segmentation Data Generation","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Segmentation; Diffusion; Computer science; Artificial intelligence; Physics","score_opus":0.1984553765418792,"score_gpt":0.440134561485128,"score_spread":0.24167918494324878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391139655","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020571108,0.00024406049,0.99221927,0.0059785466,0.0009902297,0.00022902947,0.000021327074,0.000062193234,0.000049652383],"genre_scores_gemma":[0.06395738,0.00023017808,0.9338561,0.0010851974,0.00069934997,0.000012580104,0.00006418719,0.000010826658,0.00008418792],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980592,0.0001230048,0.00075742695,0.00028550174,0.0006347874,0.00014007374],"domain_scores_gemma":[0.9977015,0.0008662459,0.00039444416,0.00032788288,0.0006103981,0.00009952244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023296005,0.00010257018,0.00012602481,0.00020793415,0.00012401927,0.0006172793,0.00079893693,0.000058501406,0.00004494639],"category_scores_gemma":[0.0019325374,0.00008842594,0.000063898864,0.0002690649,0.000048989627,0.0062276116,0.00013690189,0.00023974513,0.000029693736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057730547,0.00025903393,6.6989264e-7,0.000038721468,0.00006794366,0.000049458155,0.00165345,0.010328281,0.1642597,0.059634868,0.01765651,0.7459936],"study_design_scores_gemma":[0.000024825425,0.0001368705,5.895899e-7,0.00003348316,0.000043636766,0.00009011301,0.00021236128,0.8925573,0.08230149,0.023102045,0.0014127219,0.00008451247],"about_ca_topic_score_codex":0.000004010966,"about_ca_topic_score_gemma":0.0000030441283,"teacher_disagreement_score":0.8822291,"about_ca_system_score_codex":0.000090041794,"about_ca_system_score_gemma":0.00030961892,"threshold_uncertainty_score":0.5952439},"labels":[],"label_agreement":null},{"id":"W4391465159","doi":"10.1007/978-3-031-47425-5_23","title":"Hessian-Based Similarity Metric for Multimodal Medical Image Registration","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Image registration; Computer science; Hessian matrix; Similarity (geometry); Metric (unit); Artificial intelligence; Computer vision; Image (mathematics); Mathematics","score_opus":0.0290223093919264,"score_gpt":0.3173664020879044,"score_spread":0.288344092695978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391465159","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000002047045,0.000052662148,0.99236697,0.0036239077,0.0013178475,0.0008833283,0.0000160664,0.0007400608,0.0009970985],"genre_scores_gemma":[0.004393535,0.000020575844,0.9908659,0.003788075,0.0004186587,0.000072493654,0.00003969188,0.000046667883,0.00035439126],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9942909,0.00006488869,0.0007860339,0.0016513322,0.0025402901,0.00066659006],"domain_scores_gemma":[0.99549717,0.0020107205,0.0004035533,0.001266358,0.00044086343,0.00038134662],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0029155742,0.00047860638,0.0005425199,0.0013496241,0.00026708323,0.0005902163,0.0037339944,0.00057295494,0.000057968162],"category_scores_gemma":[0.0017086545,0.00044109236,0.00019740555,0.0012165734,0.001013896,0.0006621695,0.00065092253,0.000908417,0.0000395761],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000865008,0.000057471087,0.000010814565,0.0001387655,0.00001009515,0.00015773615,0.00012374652,0.001122941,0.00024613968,0.008178052,0.0005228668,0.98942274],"study_design_scores_gemma":[0.00052435714,0.00020759003,0.00005701222,0.00036650637,0.000009245754,0.00001468994,1.0552104e-7,0.91307575,0.0165731,0.06837235,0.0002584604,0.000540849],"about_ca_topic_score_codex":0.00003446195,"about_ca_topic_score_gemma":0.000079406636,"teacher_disagreement_score":0.9888819,"about_ca_system_score_codex":0.00034239734,"about_ca_system_score_gemma":0.0015768018,"threshold_uncertainty_score":0.9998041},"labels":[],"label_agreement":null},{"id":"W4391876934","doi":"10.1117/12.3008783","title":"3D U-Net with region of interest segmentation of kidneys and masses in computed tomography scans","year":2024,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; University of Alberta","funders":"","keywords":"Computed tomography; Segmentation; Image segmentation; Computer science; Artificial intelligence; Radiology; Medicine","score_opus":0.028269562369142964,"score_gpt":0.2806294429983758,"score_spread":0.2523598806292328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391876934","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03047165,0.00009224237,0.96833533,0.00030205623,0.00004219781,0.00015709635,0.0000013443271,0.0001191371,0.0004789294],"genre_scores_gemma":[0.66938543,0.00002848145,0.3304448,0.00010285486,0.000003913585,0.000006569042,0.0000037878162,0.0000033148747,0.000020854375],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99932724,0.000048053273,0.00022608349,0.00018819363,0.0001352495,0.0000751563],"domain_scores_gemma":[0.99963063,0.00008099287,0.00005530008,0.00014153006,0.00004527666,0.00004629102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013842821,0.00006751482,0.000114555194,0.00032670677,0.000007589485,0.000042862863,0.00016400087,0.000025668714,0.000016059003],"category_scores_gemma":[0.000010353249,0.000051177503,0.000015736325,0.0006519202,0.00010555442,0.0003637021,0.00006808011,0.000057502708,4.383643e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060633116,0.00043482048,0.04492152,0.001972479,0.00015248974,0.00018999928,0.005857975,0.000037865248,0.07239767,0.05764295,0.013481668,0.80284995],"study_design_scores_gemma":[0.0009937832,0.0010731752,0.016676594,0.0018378071,0.000024084058,0.00007322397,0.0005226551,0.10554249,0.8694342,0.0033948054,0.000108364686,0.00031881323],"about_ca_topic_score_codex":0.00011572541,"about_ca_topic_score_gemma":0.000049881044,"teacher_disagreement_score":0.8025311,"about_ca_system_score_codex":0.000011972812,"about_ca_system_score_gemma":0.000036107154,"threshold_uncertainty_score":0.2086958},"labels":[],"label_agreement":null},{"id":"W4391930116","doi":"10.1109/bibe60311.2023.00049","title":"Image Registration for Multi-View Three-Dimensional Echocardiography Sequences","year":2023,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta Hospital; Alberta Hospital Edmonton; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Image registration; Computer science; Computer vision; Artificial intelligence; Image (mathematics)","score_opus":0.06400180773616161,"score_gpt":0.3469591455193515,"score_spread":0.2829573377831899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391930116","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023492884,0.000037167993,0.99598473,0.0013845362,0.00017663893,0.00043658653,0.000004756787,0.0012896847,0.00045096184],"genre_scores_gemma":[0.0030201122,0.000028353823,0.99548316,0.00085758965,0.000039151237,0.00016955398,0.00002864218,0.0000069474895,0.0003664837],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885726,0.000036438105,0.00023225807,0.00033279424,0.000337979,0.00020328035],"domain_scores_gemma":[0.9992736,0.00012172549,0.00006920159,0.00031921343,0.00013249635,0.000083763116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006342792,0.00009737706,0.00011902453,0.00016994492,0.0001120888,0.000133909,0.00043943888,0.000045146506,0.0000321581],"category_scores_gemma":[0.000104804356,0.00007994451,0.00011606471,0.00073657243,0.00008528499,0.00060725445,0.00008907482,0.000058007372,0.00009475987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012084181,0.00015581255,0.0011007517,0.00021967653,0.00011080025,0.000070092145,0.0002583023,0.0000445282,0.07026252,0.04115922,0.34615633,0.54044986],"study_design_scores_gemma":[0.0009153001,0.00024724682,0.005735379,0.000087917244,0.000018020652,0.000014012137,0.00003922776,0.8463949,0.11058414,0.032860074,0.0025831095,0.00052066566],"about_ca_topic_score_codex":0.00004079818,"about_ca_topic_score_gemma":0.000017722678,"teacher_disagreement_score":0.8463504,"about_ca_system_score_codex":0.000014835586,"about_ca_system_score_gemma":0.000053064894,"threshold_uncertainty_score":0.32600427},"labels":[],"label_agreement":null},{"id":"W4392033722","doi":"10.32920/25266604.v1","title":"Image Enhancement for the Improved Extraction of Local Image Features Using Image Offsets","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Image (mathematics); Artificial intelligence; Computer vision; Extraction (chemistry); Computer science; Pattern recognition (psychology); Chemistry; Chromatography","score_opus":0.023988930377243967,"score_gpt":0.35894268888160147,"score_spread":0.3349537585043575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392033722","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001353721,0.00049659,0.9929806,0.0016372163,0.0014351594,0.0018558825,0.000041120653,0.0004051845,0.0010128862],"genre_scores_gemma":[0.012162864,0.00012168333,0.985756,0.00041649299,0.00019724219,0.0003666444,0.000034824952,0.000038789305,0.00090549263],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9973394,0.00010008807,0.0007313207,0.00086173706,0.000605904,0.0003615128],"domain_scores_gemma":[0.99746364,0.00037131016,0.0004449609,0.001161646,0.00045265432,0.00010577714],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009920374,0.00037433647,0.00039478828,0.00020058597,0.00012474097,0.00058045954,0.0013919957,0.00023933596,0.00017965917],"category_scores_gemma":[0.00012571811,0.00025927895,0.0003266519,0.00022324159,0.00034239423,0.0004462159,0.0022226535,0.0008016875,0.00001826612],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018520182,0.00009470666,1.1345642e-7,0.0007166655,0.0000925672,0.000008982254,0.00025940491,0.000005011052,0.84001154,0.0007869186,0.01792417,0.1400814],"study_design_scores_gemma":[0.00016706507,0.00006450672,0.000015015194,0.00017761107,0.000077211145,0.000013081743,0.00012527283,0.208511,0.7836541,0.0068229944,0.00014407687,0.00022809766],"about_ca_topic_score_codex":0.00049273745,"about_ca_topic_score_gemma":0.000017034183,"teacher_disagreement_score":0.20850599,"about_ca_system_score_codex":0.00023206501,"about_ca_system_score_gemma":0.00033861466,"threshold_uncertainty_score":0.99998593},"labels":[],"label_agreement":null},{"id":"W4392172899","doi":"10.1109/tmrb.2024.3369769","title":"A Very Fast and Robust Method for Refinement of Putative Matches of Features in MIS Images for Robotic-Assisted Surgery","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Robotics and Bionics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Robotic surgery; Computer vision","score_opus":0.03354829404901978,"score_gpt":0.3270563960431876,"score_spread":0.2935081019941678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392172899","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011385136,0.0004980905,0.9948225,0.0038790696,0.00025836765,0.0003355601,0.000042414737,0.000044094933,0.0000060391676],"genre_scores_gemma":[0.049879123,0.00092881534,0.9487941,0.00020982459,0.000015982789,0.00007520786,0.000008931592,0.000012863559,0.000075130636],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986803,0.00007067113,0.00044883002,0.00029453493,0.00033939155,0.00016626489],"domain_scores_gemma":[0.9973521,0.0022093656,0.000079284,0.0001321402,0.000102754006,0.00012436265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090484146,0.00012708387,0.00032811996,0.00025243429,0.000047717793,0.00004857046,0.00014370464,0.0001300485,0.0000059002336],"category_scores_gemma":[0.00013800108,0.000101841935,0.00010295447,0.00030707376,0.00016609281,0.00011250595,0.000005238785,0.00016833638,1.0911866e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000086062464,0.0004805186,0.000007789643,0.0017749537,0.000159301,0.000015049635,0.0009585103,0.013877248,0.006239566,0.0021537165,0.0014100114,0.97283727],"study_design_scores_gemma":[0.0008989875,0.0006157657,0.00024137045,0.0011216063,0.00013383616,0.000036624766,0.00041255628,0.7443845,0.24953169,0.0022866311,0.000059118694,0.0002772973],"about_ca_topic_score_codex":0.00003434387,"about_ca_topic_score_gemma":0.000022822458,"teacher_disagreement_score":0.97256,"about_ca_system_score_codex":0.000028166314,"about_ca_system_score_gemma":0.00016675584,"threshold_uncertainty_score":0.41529936},"labels":[],"label_agreement":null},{"id":"W4392906883","doi":"10.32920/25412872.v1","title":"Methods for Improved Efficacy in Segmentation and Tracking of Echocardiographic Images","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Segmentation; Computer vision; Artificial intelligence; Robustness (evolution); Endocardium; Computer science; Image segmentation; Speckle noise; Cardiac imaging; Speckle pattern; Medicine; Radiology; Cardiology","score_opus":0.03338118030855837,"score_gpt":0.40984262442530106,"score_spread":0.3764614441167427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392906883","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00069618446,0.0009932094,0.9958903,0.00041739488,0.00030735493,0.0012184951,0.000008068502,0.00027208656,0.00019685988],"genre_scores_gemma":[0.016172143,0.0002934246,0.98308605,0.00012510587,0.00002510035,0.00023292571,0.00001424277,0.000015379364,0.00003561584],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998416,0.00018880056,0.00051260705,0.0005903015,0.00013031322,0.00016195374],"domain_scores_gemma":[0.99877685,0.0005082479,0.00016713368,0.00038988458,0.000099647244,0.000058245107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016298763,0.00018235188,0.00036542834,0.00058793783,0.000018085275,0.00019971796,0.0004517848,0.00015778524,0.000003836932],"category_scores_gemma":[0.00019622475,0.00016432743,0.00015121924,0.0003484127,0.000076733086,0.00016477676,0.0009257133,0.00033285742,3.5437031e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037686145,0.00003780902,0.0000309071,0.0005325785,0.000042561824,0.0000010441291,0.00036584013,0.000008219462,0.103523545,0.00064228824,0.00018264663,0.89462876],"study_design_scores_gemma":[0.0004924234,0.00008771205,0.0010914395,0.0003353606,0.000048394795,0.0000028056775,0.00005704179,0.040222064,0.890701,0.06668747,0.000017616036,0.00025666287],"about_ca_topic_score_codex":0.00005383724,"about_ca_topic_score_gemma":0.0000043241284,"teacher_disagreement_score":0.8943721,"about_ca_system_score_codex":0.000035594603,"about_ca_system_score_gemma":0.000071815746,"threshold_uncertainty_score":0.6701078},"labels":[],"label_agreement":null},{"id":"W4393540329","doi":"10.5281/zenodo.6653674","title":"Tiny-ImageNet-R","year":2022,"lang":"en","type":"dataset","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.01254330195433238,"score_gpt":0.25836902129897366,"score_spread":0.24582571934464129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393540329","genre_codex":"methods","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000024826893,0.0007515799,0.5762743,0.0012899485,0.0004179648,0.00081794,0.41873544,0.00158617,0.00012420946],"genre_scores_gemma":[0.000009249383,0.0012895933,0.33343744,0.012756794,0.00025169403,0.0023864352,0.6484803,0.00009311616,0.0012953852],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99383986,0.0006127866,0.0010952291,0.0014569277,0.0017184035,0.0012768066],"domain_scores_gemma":[0.99392605,0.0002800261,0.0007957496,0.004118525,0.00016780739,0.000711827],"candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001589267,0.0008541899,0.00083849585,0.0012400859,0.0005411336,0.0007530506,0.0061894692,0.00066867564,0.011164084],"category_scores_gemma":[0.0005156177,0.00092375587,0.0004004723,0.0014272631,0.0001863108,0.0007762752,0.0034083156,0.0020722693,0.00017195685],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010218449,0.0002463197,0.000020565101,0.000081142956,0.000053858348,0.00040820078,0.000038211827,0.000036993548,0.0003418661,0.00062350923,0.9719998,0.026139311],"study_design_scores_gemma":[0.00033417437,0.00026168567,0.00012820937,0.00005168374,0.000054803153,0.0002544343,0.000018054065,0.004316753,0.0045974664,0.0011095242,0.98785496,0.0010182572],"about_ca_topic_score_codex":0.009109959,"about_ca_topic_score_gemma":0.0007431412,"teacher_disagreement_score":0.2428368,"about_ca_system_score_codex":0.0011624,"about_ca_system_score_gemma":0.0009021207,"threshold_uncertainty_score":0.9993213},"labels":[],"label_agreement":null},{"id":"W4393702513","doi":"10.5281/zenodo.3580962","title":"Neuromod Natural Image Bank","year":2019,"lang":"en","type":"dataset","venue":"Figshare","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Natural (archaeology); Image (mathematics); Computer science; Business; Computer vision; Geography; Archaeology","score_opus":0.03414170576146574,"score_gpt":0.30789083506838594,"score_spread":0.2737491293069202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393702513","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.31694975e-8,0.00011972328,0.0008095477,0.00010260172,0.0004475946,0.00035229893,0.9977131,0.00028470965,0.00017044494],"genre_scores_gemma":[3.974779e-7,0.000007825154,0.0073895818,0.0024394183,0.00016486326,0.00012977213,0.9894445,0.000014613638,0.00040901656],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99816924,0.00008339489,0.00025312233,0.00059863465,0.0005920592,0.00030353534],"domain_scores_gemma":[0.99793845,0.00017166643,0.00021529848,0.0014140682,0.00013680532,0.00012372038],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00005099028,0.00027289407,0.00025815208,0.00015217173,0.000047801233,0.00039352087,0.0028669324,0.00020215118,0.17398278],"category_scores_gemma":[0.0011081139,0.0002502303,0.00011294302,0.00024699976,0.0000089626565,0.0006103194,0.0011609005,0.0007106528,0.034059234],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.379795e-7,0.000014738938,7.997317e-9,0.00021944004,0.000005646242,0.00010660089,0.0000039286315,4.6320814e-8,0.00002029686,4.5012257e-7,0.9957446,0.0038838226],"study_design_scores_gemma":[0.00009294703,0.000035564328,0.0000123221325,0.00068331405,0.000004071069,0.00002322864,4.3011374e-7,0.00024451458,0.0011453785,0.000013021071,0.9974684,0.0002767999],"about_ca_topic_score_codex":0.000006790845,"about_ca_topic_score_gemma":0.000001551772,"teacher_disagreement_score":0.13992354,"about_ca_system_score_codex":0.000047115365,"about_ca_system_score_gemma":0.00014412278,"threshold_uncertainty_score":0.999995},"labels":[],"label_agreement":null},{"id":"W4393970837","doi":"10.1002/9781119763222.ch15","title":"Extraction of Quasi‐Static Images","year":2024,"lang":"en","type":"other","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Extraction (chemistry); Computer science; Artificial intelligence; Computer vision; Pattern recognition (psychology); Computer graphics (images); Chromatography; Chemistry","score_opus":0.014519549107051975,"score_gpt":0.33560393263984295,"score_spread":0.32108438353279095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393970837","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.5051885e-8,0.00021680018,0.55175126,0.00012472377,0.0002044459,0.000078761004,0.0000024691672,0.0005809126,0.4470406],"genre_scores_gemma":[0.000011984867,0.00008831297,0.3984452,0.00011857947,0.000035670862,0.000010165873,0.0000022414258,0.000060792383,0.60122705],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99919003,0.000033052092,0.00018282695,0.00023738442,0.00027252015,0.0000841656],"domain_scores_gemma":[0.9994372,0.00003603885,0.00011842994,0.00034730244,0.000019829386,0.000041192638],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00012429473,0.00010327241,0.00015124115,0.0002567289,0.000003868147,0.000048080852,0.00035884758,0.000085557505,0.0035341473],"category_scores_gemma":[0.000031780615,0.000083187515,0.000046276466,0.0001705773,0.000045982473,0.000092354494,0.000083340805,0.000111752895,0.0007710337],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.2063254e-7,0.000023341197,1.8867163e-7,0.00018187874,0.000013133839,0.000009923897,0.00003024892,6.8629884e-9,0.0012620067,0.0034466607,0.954471,0.040561497],"study_design_scores_gemma":[0.00032970408,0.0003603908,0.000010710137,0.0020869349,0.00008983494,0.000056869492,0.00011429759,0.0017250205,0.3282127,0.052394502,0.6136718,0.0009472665],"about_ca_topic_score_codex":0.00013732906,"about_ca_topic_score_gemma":0.0000041814105,"teacher_disagreement_score":0.3407992,"about_ca_system_score_codex":0.000014789486,"about_ca_system_score_gemma":0.000032816748,"threshold_uncertainty_score":0.99737674},"labels":[],"label_agreement":null},{"id":"W4393971060","doi":"10.1002/9781119763222.ch16","title":"Discrete Complex Image Method","year":2024,"lang":"en","type":"other","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Image (mathematics); Computer science; Artificial intelligence; Computer vision; Computer graphics (images); Geology","score_opus":0.028500250520438582,"score_gpt":0.3855002025162273,"score_spread":0.3569999519957887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393971060","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.12697726e-10,0.00008590394,0.5052421,0.0004321305,0.00016111869,0.00010960787,0.0000061352857,0.0019002612,0.49206272],"genre_scores_gemma":[7.930794e-8,0.00001598753,0.5101654,0.0007760701,0.00007482571,0.0000147973415,0.000007991434,0.00010973018,0.48883513],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99871415,0.00008354423,0.0001789733,0.000497745,0.0003447565,0.00018082424],"domain_scores_gemma":[0.9991056,0.0000340479,0.000070703565,0.00066402496,0.000016059981,0.00010962065],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00022544716,0.00019147286,0.00022199018,0.00023636887,0.0000110455185,0.00024743288,0.00096823525,0.0001192356,0.015838873],"category_scores_gemma":[0.000023231876,0.00014447262,0.00008351387,0.00023071608,0.00005904868,0.00010226019,0.00046712472,0.00019165549,0.005091419],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.9546204e-8,0.00000462625,2.8789122e-8,0.000060001737,0.000018244069,0.000036554517,0.000024335326,2.7409712e-9,0.00071074907,0.011981026,0.9511394,0.036024958],"study_design_scores_gemma":[0.00006168516,0.000020950605,5.8928003e-7,0.000121675956,0.000013196447,0.000019374562,0.000006605277,0.002340253,0.005681996,0.0074960734,0.983963,0.00027458227],"about_ca_topic_score_codex":0.00013145454,"about_ca_topic_score_gemma":0.000007506724,"teacher_disagreement_score":0.035750378,"about_ca_system_score_codex":0.000021927135,"about_ca_system_score_gemma":0.0000290454,"threshold_uncertainty_score":0.99568325},"labels":[],"label_agreement":null},{"id":"W4393987804","doi":"10.1111/ejn.16332","title":"ArcheD, a residual neural network for prediction of cerebrospinal fluid amyloid‐beta from amyloid PET images","year":2024,"lang":"en","type":"article","venue":"European Journal of Neuroscience","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; U.S. Department of Defense; Eli Lilly and Company; China Scholarship Council; Eisai; Alzheimer's Disease Neuroimaging Initiative; Academy of Finland; Northern California Institute for Research and Education; Helsingin Yliopisto; F. Hoffmann-La Roche; University of Southern California; Pfizer; BioClinica; Biogen; Bristol-Myers Squibb; Meso Scale Diagnostics; Novartis Pharmaceuticals Corporation; Alzheimer's Association","keywords":"Cerebrospinal fluid; Amyloid (mycology); Residual; Amyloid beta; Medicine; Neuroscience; Pathology; Psychology; Computer science; Disease","score_opus":0.03572346916080987,"score_gpt":0.28610221727913154,"score_spread":0.25037874811832167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393987804","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057093404,0.00040534366,0.9383963,0.0012382417,0.0022519992,0.00018916173,0.000083810155,0.00015490937,0.00018684356],"genre_scores_gemma":[0.73730946,0.00016903615,0.2604747,0.000782857,0.0010613237,0.0000038283956,0.0000047635576,0.000036620724,0.00015738179],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9968783,0.0005539464,0.0008756237,0.0004714931,0.00086384866,0.00035676738],"domain_scores_gemma":[0.99842674,0.00031756258,0.00041608064,0.00039035748,0.00021053386,0.00023873581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017033033,0.00019091307,0.00026511503,0.0002232786,0.00014188656,0.0004228014,0.0017667157,0.00001417639,0.000012836299],"category_scores_gemma":[0.0004421255,0.00015617862,0.00018413791,0.0006574386,0.0003466784,0.0013282654,0.00030549036,0.00036717413,0.0000062201457],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046668407,0.0000581777,0.00015300582,0.000044053802,0.000009768794,0.0006842359,0.00028077315,0.00049852347,0.953693,0.0003186191,0.015805902,0.0284073],"study_design_scores_gemma":[0.0027381028,0.009444457,0.3154649,0.0018273921,0.00018364284,0.0045587923,0.00011204274,0.20386712,0.44673008,0.0028619785,0.011199474,0.0010120134],"about_ca_topic_score_codex":0.0000035438766,"about_ca_topic_score_gemma":2.4794574e-7,"teacher_disagreement_score":0.6802161,"about_ca_system_score_codex":0.000033661534,"about_ca_system_score_gemma":0.00013565889,"threshold_uncertainty_score":0.6368779},"labels":[],"label_agreement":null},{"id":"W4394048112","doi":"10.5281/zenodo.3580961","title":"Neuromod Natural Image Bank","year":2019,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Natural (archaeology); Image (mathematics); Business; Computer science; Geography; Computer vision; Archaeology","score_opus":0.030458894285305637,"score_gpt":0.27837541926391224,"score_spread":0.2479165249786066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394048112","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000067478213,0.0000995005,0.18349423,0.0009179069,0.00083848933,0.00096773333,0.80403024,0.0021638845,0.007481269],"genre_scores_gemma":[0.000072482006,0.00017889484,0.0070287944,0.000992271,0.0001951905,7.432878e-8,0.9899183,0.00081431726,0.0007996797],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9966793,0.00058852363,0.00040364466,0.00088678324,0.00093762006,0.0005041127],"domain_scores_gemma":[0.9970888,0.000055214157,0.00025939697,0.0017296681,0.0006217123,0.00024521316],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00072325155,0.00029648247,0.00028613073,0.0004546781,0.0010381589,0.00243596,0.005696926,0.00015876253,0.012766656],"category_scores_gemma":[0.0010145288,0.00030316773,0.00010110616,0.0007169637,0.0001987115,0.00083073205,0.004827953,0.0009554268,0.03736486],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000064893043,0.00007294928,9.2115515e-9,0.000106504005,0.0000204177,0.00005441947,0.000058823945,6.3538295e-7,0.0008702848,0.00011818316,0.9495854,0.049105916],"study_design_scores_gemma":[0.00027536124,0.00018412093,0.000012709299,0.000051222763,0.000012913214,0.00015748128,0.000010628211,0.00036586108,0.00072876597,0.00007046914,0.99782443,0.00030603947],"about_ca_topic_score_codex":0.000017206965,"about_ca_topic_score_gemma":1.1966426e-7,"teacher_disagreement_score":0.18588805,"about_ca_system_score_codex":0.00017418046,"about_ca_system_score_gemma":0.000011468923,"threshold_uncertainty_score":0.99994206},"labels":[],"label_agreement":null},{"id":"W4394597994","doi":"10.1109/wacv57701.2024.00729","title":"PAIR : Perception Aided Image Restoration for Natural Driving Conditions","year":2024,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Faurecia (Canada)","funders":"","keywords":"Natural (archaeology); Perception; Image restoration; Computer science; Computer vision; Artificial intelligence; Image (mathematics); Image processing; Geology; Psychology; Neuroscience","score_opus":0.016293368878592535,"score_gpt":0.33320666903949026,"score_spread":0.3169133001608977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394597994","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021711218,0.000021756345,0.99140954,0.003354525,0.0006701181,0.00029104608,0.000002629017,0.001459767,0.0006194697],"genre_scores_gemma":[0.3943588,0.0000041426038,0.60176736,0.0006212878,0.00010423255,0.00011749195,0.000052641954,0.000007232414,0.0029667825],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926245,0.000036569803,0.00016224467,0.00024120344,0.00017499822,0.00012252855],"domain_scores_gemma":[0.99951875,0.00014955064,0.000021423233,0.00017645182,0.00008392056,0.000049926133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024934462,0.00006894543,0.00005876222,0.00011778946,0.000102995844,0.00037122623,0.00020296428,0.000033755732,0.00016919454],"category_scores_gemma":[0.00011435866,0.000059371472,0.00005363343,0.00020350856,0.000038168957,0.0014940163,0.00004809297,0.00008823719,0.00009035781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013514268,0.000029361285,0.000022264214,0.00006525445,0.000011614138,0.000007994864,0.0008647732,0.000001600045,0.48195606,0.049575694,0.3385737,0.12889034],"study_design_scores_gemma":[0.00029432683,0.00016915517,0.0034405617,0.00014625983,0.000016967088,0.000026492087,0.00027004044,0.81266004,0.1462405,0.028919201,0.0074537112,0.0003627463],"about_ca_topic_score_codex":0.000007897979,"about_ca_topic_score_gemma":0.0000071361183,"teacher_disagreement_score":0.8126584,"about_ca_system_score_codex":0.00007727684,"about_ca_system_score_gemma":0.000045393223,"threshold_uncertainty_score":0.3579743},"labels":[],"label_agreement":null},{"id":"W4394629102","doi":"10.1109/csce60160.2023.00094","title":"Deep Learning-Based MR Image Re-parameterization","year":2023,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University; Sunnybrook Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Computer vision; Image (mathematics); Deep learning","score_opus":0.01918460237407105,"score_gpt":0.2960074112913717,"score_spread":0.27682280891730066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394629102","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00062717113,0.0000029275789,0.9923476,0.0011160308,0.000094439885,0.00011554531,1.4608733e-7,0.0026856058,0.0030105077],"genre_scores_gemma":[0.104802646,0.000013035846,0.890228,0.0022451787,0.000031644897,0.000051913445,0.00003937179,0.00001449718,0.002573769],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990377,0.000091227186,0.00016077881,0.00024204931,0.00029192306,0.00017636632],"domain_scores_gemma":[0.99940914,0.00012269088,0.000051343406,0.00027386603,0.00006332245,0.00007966672],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00031118313,0.000072565126,0.0000750934,0.00015487793,0.000073737196,0.00015699066,0.00039803187,0.000037495593,0.00035678246],"category_scores_gemma":[0.00029090475,0.00006562756,0.000032065273,0.0007875034,0.00004084013,0.00039170697,0.00010860779,0.00009443607,0.0007860879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009643066,0.0001399596,0.00074036594,0.00009871426,0.000021265438,0.0001450611,0.0011861242,0.0030679496,0.25606105,0.0063720397,0.050871905,0.6812859],"study_design_scores_gemma":[0.0001240263,0.000059698952,0.000380975,0.000006555227,0.0000012440219,6.227455e-7,0.000019921397,0.7861833,0.21187499,0.0006025774,0.0006557199,0.00009035011],"about_ca_topic_score_codex":0.000008473341,"about_ca_topic_score_gemma":0.0000017404902,"teacher_disagreement_score":0.7831154,"about_ca_system_score_codex":0.000020648205,"about_ca_system_score_gemma":0.00002391525,"threshold_uncertainty_score":0.9999919},"labels":[],"label_agreement":null},{"id":"W4394714706","doi":"10.5220/0008912100002513","title":"Exploiting Bilateral Symmetry in Brain Lesion Segmentation with Reflective Registration","year":2020,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Bilateral symmetry; Segmentation; Computer vision; Computer science; Artificial intelligence; Image segmentation; Image registration; Image (mathematics); Engineering","score_opus":0.04794187359080684,"score_gpt":0.31994658118883795,"score_spread":0.27200470759803114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394714706","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015631339,0.0000036731158,0.9729312,0.007951383,0.000019482579,0.00023875991,2.501502e-7,0.00031827064,0.0029056068],"genre_scores_gemma":[0.44793165,0.0000035309156,0.54616296,0.0057596415,0.000027529972,0.000026935099,0.0000074240475,0.000005961108,0.000074380696],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989369,0.00009826835,0.00022450961,0.0003141949,0.00029258418,0.00013357686],"domain_scores_gemma":[0.99958074,0.00007287319,0.000091227,0.00012819395,0.000044638047,0.000082314036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022385333,0.000086534594,0.00009017647,0.00008866466,0.000040438237,0.000109570596,0.00020650483,0.00003398717,0.000022448572],"category_scores_gemma":[0.000102371065,0.00007030219,0.000014076701,0.0005806188,0.000023892015,0.0010431878,0.00005612472,0.00011448254,0.000012549702],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008593517,0.000119330376,0.0042186175,0.00008210746,0.00001562888,0.00013015069,0.010921733,0.000057219906,0.5695736,0.016474573,0.005633816,0.39268732],"study_design_scores_gemma":[0.0008693034,0.0005823769,0.0025297776,0.0000765804,0.0000024593153,0.000011693643,0.000924365,0.034552056,0.95843655,0.0017346481,0.000038777747,0.00024138295],"about_ca_topic_score_codex":0.000053247033,"about_ca_topic_score_gemma":0.000021436668,"teacher_disagreement_score":0.43230033,"about_ca_system_score_codex":0.0000699708,"about_ca_system_score_gemma":0.000042329877,"threshold_uncertainty_score":0.286684},"labels":[],"label_agreement":null},{"id":"W4394744135","doi":"10.1109/access.2024.3388293","title":"Two-Step Rigid and Non-Rigid Image Registration for the Alignment of Three-Dimensional Echocardiography Sequences From Multiple Views","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Artificial intelligence; Computer vision; Image registration; Hausdorff distance; Computer science; Mutual information; Image fusion; Image quality; Real-time MRI; Speckle noise; Speckle pattern; Pattern recognition (psychology); Magnetic resonance imaging; Image (mathematics); Radiology; Medicine","score_opus":0.044057896388160454,"score_gpt":0.34402039458646005,"score_spread":0.2999624981982996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394744135","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013548324,0.0007236367,0.9834933,0.0006757858,0.00057125394,0.00067649985,0.000047431815,0.00012831189,0.0001354385],"genre_scores_gemma":[0.7310806,0.00011349985,0.26790187,0.00047400847,0.00015197665,0.00023187362,0.000016083926,0.000010726064,0.0000193461],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986392,0.000049297905,0.00033869402,0.0004014998,0.00042011734,0.00015113642],"domain_scores_gemma":[0.9986408,0.00067182357,0.00012499046,0.0004116225,0.00008852904,0.00006222907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005303828,0.00013288966,0.0001745362,0.000095754185,0.00010522039,0.00045093123,0.0007643516,0.00004253806,0.000013304635],"category_scores_gemma":[0.00003578543,0.00008797547,0.00011178061,0.00030535102,0.00019097287,0.0010266067,0.000121934005,0.00009097144,0.000003483002],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000088488465,0.00022443008,0.008027336,0.00054806715,0.00071647717,0.000081247774,0.0013874907,0.0004067377,0.39559865,0.003988889,0.11659338,0.47233883],"study_design_scores_gemma":[0.00066326506,0.00014179538,0.0047544446,0.00028588853,0.00008279797,0.000007064906,0.000033980978,0.32467306,0.64949864,0.018043457,0.0015126271,0.0003030078],"about_ca_topic_score_codex":0.0008539191,"about_ca_topic_score_gemma":0.0001286163,"teacher_disagreement_score":0.7175323,"about_ca_system_score_codex":0.000020492302,"about_ca_system_score_gemma":0.00006089017,"threshold_uncertainty_score":0.434834},"labels":[],"label_agreement":null},{"id":"W4395016395","doi":"10.2139/ssrn.4798852","title":"Semi-Supervised Segmentation of Medical Images Focused on the Pixels with Unreliable Predictions","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Pixel; Segmentation; Artificial intelligence; Computer science; Computer vision; Image segmentation; Pattern recognition (psychology)","score_opus":0.013649818018445262,"score_gpt":0.2796145069971601,"score_spread":0.26596468897871484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395016395","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003155722,0.0013598546,0.9767274,0.016401676,0.0006104384,0.0005869243,0.000013873284,0.00029137963,0.0008527247],"genre_scores_gemma":[0.8795582,0.026137616,0.08466511,0.0031507905,0.0015604424,0.00070852967,0.00007903071,0.00022525777,0.003914989],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99473804,0.00036865796,0.0006898198,0.0005359252,0.0024341238,0.0012334473],"domain_scores_gemma":[0.99805224,0.00035577881,0.00041999927,0.00072815095,0.00025288953,0.00019096318],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0042941086,0.00033341406,0.00035379487,0.00032452872,0.00021018051,0.00037208825,0.0022947344,0.00027754542,0.00016375168],"category_scores_gemma":[0.00024482966,0.00020276025,0.00019086417,0.00049532746,0.00020756498,0.00022833269,0.0008213276,0.006491857,0.000029169656],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001864437,0.0010535778,0.00018954542,0.000654305,0.0030909283,0.00014918727,0.0038587195,0.0016636295,0.005796133,0.5332989,0.044971824,0.40508682],"study_design_scores_gemma":[0.0016295806,0.0022011918,0.00006308323,0.0030709233,0.00032792712,0.00076532416,0.0016145952,0.044430897,0.068616666,0.87627447,0.00024763282,0.0007577388],"about_ca_topic_score_codex":0.00010806632,"about_ca_topic_score_gemma":0.000061905805,"teacher_disagreement_score":0.8920623,"about_ca_system_score_codex":0.0008594634,"about_ca_system_score_gemma":0.007018288,"threshold_uncertainty_score":0.998611},"labels":[],"label_agreement":null},{"id":"W4395038909","doi":"10.1007/s11548-024-03118-x","title":"Laryngeal surface reconstructions from monocular endoscopic videos: a structure from motion pipeline for periodic deformations","year":2024,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Michael's Hospital; University of Toronto","funders":"Temerty Faculty of Medicine, University of Toronto; University of Toronto Mississauga","keywords":"Monocular; Computer vision; Artificial intelligence; Computer science; Surface (topology); Motion (physics); Pipeline (software); Computer graphics (images); Structure from motion; Mathematics; Geometry","score_opus":0.016381050576511815,"score_gpt":0.2744096543343647,"score_spread":0.2580286037578529,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395038909","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13260444,0.0012276242,0.8589289,0.002459913,0.004492663,0.000081519225,0.00011875152,0.000079801306,0.00000635852],"genre_scores_gemma":[0.5374587,0.00026658428,0.46044362,0.0006753855,0.00096502714,0.0000047753683,0.00016666691,0.000008950405,0.000010272292],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849445,0.00018593851,0.00069551653,0.00023899545,0.00024984995,0.00013524182],"domain_scores_gemma":[0.9975921,0.0015758707,0.0002635047,0.00013815735,0.00032606017,0.00010433965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029938004,0.0001380835,0.00031207377,0.0003249365,0.00008898542,0.00029001982,0.00040558758,0.00012982926,0.0000670973],"category_scores_gemma":[0.000113114525,0.00011700461,0.00018684464,0.00012587727,0.000105003535,0.000889804,0.0000705515,0.00027570353,0.0000023952691],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054754008,0.00006708561,0.0056174165,0.000026207417,0.000920537,0.00023848996,0.0010368642,0.00041493247,0.0063528325,0.00089687674,0.019521505,0.9648525],"study_design_scores_gemma":[0.0015454165,0.00016537715,0.036987714,0.0007530584,0.00018324927,0.0037446301,0.00012342718,0.9008593,0.009928663,0.037435006,0.007771384,0.0005027776],"about_ca_topic_score_codex":0.000028764409,"about_ca_topic_score_gemma":0.0000055177297,"teacher_disagreement_score":0.96434975,"about_ca_system_score_codex":0.00008045633,"about_ca_system_score_gemma":0.00016516479,"threshold_uncertainty_score":0.47713095},"labels":[],"label_agreement":null},{"id":"W4395047885","doi":"10.21203/rs.3.rs-4281942/v1","title":"VoxelFSD: voxel-based fully sparse detector with sparse convolution for 3D object detection","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Education and Child Care","funders":"Government of Jiangsu Province","keywords":"Convolution (computer science); Artificial intelligence; Detector; Computer science; Voxel; Object (grammar); Pattern recognition (psychology); Computer vision; Artificial neural network","score_opus":0.06763481634759909,"score_gpt":0.3866355715863989,"score_spread":0.3190007552387998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395047885","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004339917,0.0005627144,0.9874025,0.00088287296,0.0007229166,0.0042310553,0.000104298655,0.0014579614,0.00029574241],"genre_scores_gemma":[0.5962309,0.0000755623,0.3970927,0.00018834302,0.0006278037,0.004775043,0.00015329491,0.00013226921,0.00072404277],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9935964,0.00076900475,0.000583598,0.0016752696,0.0023178787,0.001057855],"domain_scores_gemma":[0.9952494,0.00083451404,0.0002103734,0.0016655279,0.0015919569,0.00044825298],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0034308515,0.00047704493,0.00048831664,0.0013077421,0.0003714473,0.0010022622,0.0015618143,0.00052727916,0.00008641257],"category_scores_gemma":[0.0009927272,0.00041194947,0.00025179167,0.0012622331,0.00039523543,0.00028824474,0.0016088325,0.002338108,0.00021156513],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010813083,0.0007762281,0.00020972226,0.015454196,0.00041290038,0.00056862255,0.0016352771,0.0013210346,0.04032063,0.0019979174,0.016984686,0.9192375],"study_design_scores_gemma":[0.0015903974,0.00311946,0.00049896276,0.0034110525,0.00008076927,0.000033086148,0.00015935862,0.5720778,0.3992387,0.014438215,0.004223251,0.0011289266],"about_ca_topic_score_codex":0.00034549812,"about_ca_topic_score_gemma":0.00030701305,"teacher_disagreement_score":0.9181085,"about_ca_system_score_codex":0.0010259192,"about_ca_system_score_gemma":0.001964082,"threshold_uncertainty_score":0.9999635},"labels":[],"label_agreement":null},{"id":"W4395959965","doi":"10.1088/978-0-7503-6244-3ch9","title":"U-Nets for image segmentation and diffusion models for image generation","year":2024,"lang":"en","type":"book-chapter","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Image (mathematics); Computer science; Artificial intelligence; Image segmentation; Computer vision; Diffusion; Segmentation; Pattern recognition (psychology); Physics","score_opus":0.0381268383980871,"score_gpt":0.3019199624720288,"score_spread":0.2637931240739417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395959965","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000029139342,0.00022452936,0.9532492,0.00089356565,0.00036009058,0.0020591405,0.000082889106,0.0004582807,0.042669363],"genre_scores_gemma":[0.00002293065,0.0002449727,0.758481,0.00079715444,0.00021171002,0.00035871114,0.00037349956,0.000049637616,0.23946038],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982677,0.0000100995185,0.00044003996,0.0007429263,0.0003368471,0.00020235984],"domain_scores_gemma":[0.9990149,0.00012423037,0.00016153224,0.0003454043,0.00023524454,0.00011867783],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030084956,0.00030385028,0.0002629602,0.00021856042,0.00012607232,0.0005247571,0.00029652214,0.00021331683,0.0000793495],"category_scores_gemma":[0.000022088367,0.00027054,0.0001282371,0.000029937308,0.00007416973,0.0009712704,0.0002106428,0.00012850066,0.000021730779],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009638978,0.000021861699,3.045198e-8,0.00039079852,0.000044889624,0.000008167774,0.00031068205,0.000003061494,0.11075903,0.589732,0.0934962,0.20522366],"study_design_scores_gemma":[0.00045399353,0.00019296937,1.2543875e-7,0.00010312426,0.000058167385,0.000011292012,0.000008325539,0.5664086,0.06600203,0.3603202,0.006066498,0.00037469465],"about_ca_topic_score_codex":0.000006679853,"about_ca_topic_score_gemma":0.0000073374904,"teacher_disagreement_score":0.56640553,"about_ca_system_score_codex":0.00009232025,"about_ca_system_score_gemma":0.000054745695,"threshold_uncertainty_score":0.99997467},"labels":[],"label_agreement":null},{"id":"W4396219078","doi":"10.18280/ts.410244","title":"An Enhanced CT Liver Segmentation Framework Using Differential Evolution-Optimized Rényi Entropy","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Segmentation; Entropy (arrow of time); Computer science; Artificial intelligence; Mathematics; Physics; Thermodynamics","score_opus":0.017825815962401143,"score_gpt":0.2969158829438746,"score_spread":0.2790900669814735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396219078","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03846473,0.00013884416,0.95933354,0.00011598028,0.00057871995,0.0004783754,0.000008427137,0.00082961156,0.000051783147],"genre_scores_gemma":[0.59471667,0.000018622803,0.40471688,0.00020407475,0.00022754174,0.000050537667,0.000024605137,0.000014705277,0.000026348976],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976332,0.00022325829,0.00045010482,0.0005922981,0.0007462112,0.0003549346],"domain_scores_gemma":[0.99916905,0.0001395238,0.0000982848,0.0003193174,0.00007674134,0.00019709284],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002963165,0.00023486701,0.00020140567,0.00022309681,0.00016931877,0.0005828692,0.00056189555,0.0000614113,0.00230618],"category_scores_gemma":[0.000019495636,0.00021780407,0.00011508381,0.00039844227,0.00008085194,0.0012956331,0.000093035924,0.00023748926,0.00006872209],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004399573,0.00036799387,0.00003496953,0.0000991152,0.00011920512,0.000101353944,0.0024657457,0.0009744617,0.86584663,0.022408238,0.0009918835,0.10654642],"study_design_scores_gemma":[0.00055477966,0.00020251867,0.00024694545,0.00016637317,0.00005151226,0.000014606973,0.00007388245,0.6796422,0.31520212,0.0035086372,0.000038286278,0.00029816752],"about_ca_topic_score_codex":0.0000415917,"about_ca_topic_score_gemma":0.0000014561631,"teacher_disagreement_score":0.6786677,"about_ca_system_score_codex":0.00029435736,"about_ca_system_score_gemma":0.00011408785,"threshold_uncertainty_score":0.99860585},"labels":[],"label_agreement":null},{"id":"W4396519717","doi":"10.18280/ts.410203","title":"Applications of Multiscale Geometric Analysis in Image Texture Recognition and Classification","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Texture (cosmology); Pattern recognition (psychology); Computer science; Image texture; Image (mathematics); Computer vision; Image processing","score_opus":0.025850322637445248,"score_gpt":0.29138892076376116,"score_spread":0.2655385981263159,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396519717","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051346933,0.00024106212,0.9935426,0.00030027266,0.000013486115,0.00031058665,0.00002022395,0.00011634143,0.00032072846],"genre_scores_gemma":[0.8578576,0.00009164256,0.14166367,0.00006960136,0.000021627076,0.00018212077,0.000073572584,0.000004396152,0.000035788056],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990276,0.00005306799,0.00030223659,0.00027848047,0.00024224109,0.00009635854],"domain_scores_gemma":[0.9995834,0.000101643636,0.00006158274,0.00014308315,0.000059943355,0.000050316325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038551973,0.00007217512,0.00011838989,0.000991341,0.000023517574,0.00009193522,0.0001793258,0.000039594546,0.0001714808],"category_scores_gemma":[0.000016384036,0.0000668823,0.000047064972,0.0028796184,0.000054042026,0.0003801238,0.000039875908,0.00008737347,0.000019320625],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015595443,0.00008794833,0.00033067347,0.000060541184,0.000042546886,0.000002549095,0.00028786896,0.0000031578832,0.026015086,0.00041411567,0.00024379433,0.97251016],"study_design_scores_gemma":[0.0009339056,0.00022777228,0.17291592,0.00016450854,0.0003876435,0.000010181976,0.00028109492,0.69393873,0.120506525,0.008644525,0.0014622699,0.0005269213],"about_ca_topic_score_codex":0.000028807268,"about_ca_topic_score_gemma":0.000012771928,"teacher_disagreement_score":0.97198325,"about_ca_system_score_codex":0.000037818296,"about_ca_system_score_gemma":0.000022006514,"threshold_uncertainty_score":0.2727381},"labels":[],"label_agreement":null},{"id":"W4398231329","doi":"10.17504/protocols.io.n2bvj326blk5/v1","title":"Graphing area from an image series using ImageJ: a simple method v1","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Simple (philosophy); Series (stratigraphy); Image (mathematics); Computer science; Computer vision; Computer graphics (images); Artificial intelligence; Algorithm; Geology","score_opus":0.09547196118386325,"score_gpt":0.40825754645126344,"score_spread":0.3127855852674002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398231329","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014187717,0.000028786659,0.9926677,0.0008037395,0.0005965193,0.0004572429,0.000074585354,0.0034841665,0.00046849862],"genre_scores_gemma":[0.00072150066,0.000042299373,0.9972519,0.0011866232,0.00015784564,0.00007376689,0.00021107189,0.00005673782,0.00029824924],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963367,0.00045921694,0.00065182353,0.0013703605,0.00072863424,0.00045324964],"domain_scores_gemma":[0.9969955,0.0003340504,0.00033763633,0.0018579876,0.00022674423,0.0002481021],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011195764,0.00042887536,0.0005385485,0.00042820926,0.00018287619,0.0013355858,0.0023150754,0.00027045663,0.00031663818],"category_scores_gemma":[0.00031806453,0.0004076578,0.0002023064,0.0005111051,0.00013514006,0.001888059,0.004059055,0.000740838,0.000041336498],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005433355,0.00054194307,0.00079251616,0.0007610843,0.00074197055,0.001814171,0.014467959,0.0009495652,0.55280596,0.008335264,0.069857985,0.34887725],"study_design_scores_gemma":[0.00021760896,0.00005957855,0.00046759014,0.00019712876,0.00005755114,0.000019998368,0.0004220774,0.39305797,0.30060717,0.30376866,0.00017757813,0.00094708503],"about_ca_topic_score_codex":0.007135441,"about_ca_topic_score_gemma":0.00016537588,"teacher_disagreement_score":0.39210838,"about_ca_system_score_codex":0.000101552236,"about_ca_system_score_gemma":0.00020857294,"threshold_uncertainty_score":0.9998375},"labels":[],"label_agreement":null},{"id":"W4399938967","doi":"10.1109/access.2024.3418936","title":"3D–3D Rigid Registration of Echocardiographic Images With Significant Overlap Using Particle Filter","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"Health and Care Research Wales","keywords":"Computer vision; Particle filter; Image registration; Artificial intelligence; Computer science; Filter (signal processing); Image (mathematics)","score_opus":0.04106049413381614,"score_gpt":0.3340327887946799,"score_spread":0.2929722946608638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399938967","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07267196,0.00010847717,0.9259156,0.00016499916,0.00020983082,0.00018741455,0.000005082426,0.00029513938,0.0004415089],"genre_scores_gemma":[0.90045327,0.000032207336,0.09921031,0.00015716263,0.00006125494,0.000020897316,0.0000018633052,0.000010951106,0.000052077103],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99865866,0.000071587034,0.0002800803,0.00034256192,0.00046256944,0.00018457117],"domain_scores_gemma":[0.999204,0.000088408124,0.00009885786,0.00043609328,0.000098663004,0.000073973875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034120234,0.000113545095,0.00015079025,0.00013661532,0.000048962404,0.0004979359,0.0005869793,0.00004005932,0.000028971119],"category_scores_gemma":[0.000021510508,0.000088559216,0.000054505606,0.0007994905,0.00013699205,0.0017391758,0.00006853901,0.00011097363,0.0000057287793],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005707106,0.00027502258,0.013127668,0.0007297743,0.00029927294,0.000561624,0.0012105405,0.0019326346,0.7800357,0.0028931114,0.015947973,0.1829296],"study_design_scores_gemma":[0.00015289092,0.00010321098,0.0014017192,0.00019150882,0.000031513446,0.000017814667,0.000014271588,0.07999611,0.91691,0.00089787913,0.00010817137,0.0001748963],"about_ca_topic_score_codex":0.00014927064,"about_ca_topic_score_gemma":0.0000037341267,"teacher_disagreement_score":0.8277813,"about_ca_system_score_codex":0.000027165239,"about_ca_system_score_gemma":0.00008613044,"threshold_uncertainty_score":0.48016074},"labels":[],"label_agreement":null},{"id":"W4400315327","doi":"10.1109/isivc61350.2024.10577938","title":"Neonatal Brain MRI Image Segmentation Using U-Net With Enhanced Edge Detection Layers","year":2024,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; Toronto Metropolitan University","funders":"","keywords":"Image segmentation; Computer vision; Artificial intelligence; Edge detection; Segmentation; Computer science; Image (mathematics); Enhanced Data Rates for GSM Evolution; Image processing","score_opus":0.01061820644721126,"score_gpt":0.2883057470217483,"score_spread":0.27768754057453704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400315327","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009392998,0.000038559603,0.98758423,0.0005280422,0.00026592772,0.0002659728,0.000001521687,0.0010726333,0.0008500975],"genre_scores_gemma":[0.2290229,0.000007281521,0.7695866,0.00075611245,0.000067078974,0.000030695533,0.000007496354,0.000015711366,0.0005061239],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987987,0.0000638292,0.0001888487,0.00039563508,0.00035708016,0.00019588403],"domain_scores_gemma":[0.9994934,0.00008979885,0.000042246604,0.00022561586,0.00005944804,0.000089500296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025180492,0.00012730292,0.0000901362,0.00017106775,0.00008219618,0.00040831254,0.00024573738,0.00004423316,0.00014777099],"category_scores_gemma":[0.000022732305,0.00010281572,0.000033300872,0.0005460503,0.000063550375,0.0015802814,0.00007960029,0.00013208212,0.000064947904],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000530745,0.000009759301,0.0000024223823,0.00003212437,0.000014665699,0.000029576337,0.0007525612,0.000017544402,0.695455,0.0003791873,0.0010633753,0.30223843],"study_design_scores_gemma":[0.00015976797,0.000100034995,0.000019030773,0.000043281776,0.0000060936063,0.00003772608,0.00015162671,0.11632267,0.8825425,0.0003207007,0.00015985468,0.00013671585],"about_ca_topic_score_codex":0.00006821907,"about_ca_topic_score_gemma":0.000027524382,"teacher_disagreement_score":0.30210173,"about_ca_system_score_codex":0.00012884325,"about_ca_system_score_gemma":0.000076742755,"threshold_uncertainty_score":0.41927034},"labels":[],"label_agreement":null},{"id":"W4400578518","doi":"10.32388/gvuqvj","title":"Review of: \"On n-Dimensional Maxwell and Dirac Equations in Curved Space-Time and Its Applications in SO(P,Q) Group Theoretic Image Processing\"","year":2024,"lang":"en","type":"peer-review","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Group (periodic table); Dirac (video compression format); Image (mathematics); Space (punctuation); Mathematical physics; Curved space; Space time; Spacetime; Maxwell's equations; Physics; Computer science; Classical mechanics; Quantum mechanics; Computer vision; Engineering","score_opus":0.023136324265507477,"score_gpt":0.3370121687668004,"score_spread":0.3138758445012929,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400578518","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00000210873,0.6841314,0.2553768,0.05224886,0.00012971785,0.003715325,0.000111486304,0.00023599263,0.004048364],"genre_scores_gemma":[0.00025805653,0.6359515,0.26266098,0.04197196,0.00020006744,0.0040571797,0.0031939766,0.00015511732,0.051551156],"study_design_codex":"not_applicable","study_design_gemma":"systematic_review","domain_scores_codex":[0.9973137,0.00025620108,0.00085860107,0.00079893006,0.00054503785,0.00022752551],"domain_scores_gemma":[0.9981094,0.00078461587,0.00029721076,0.00048124706,0.00020357857,0.00012394435],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016365148,0.00032150227,0.00070263643,0.00038903748,0.000050923925,0.00011091969,0.00051826186,0.00014108754,0.00024496918],"category_scores_gemma":[0.0007801211,0.00025520136,0.00006817521,0.001037143,0.00021539239,0.00030211464,0.00035601415,0.00050158234,0.000049325165],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043338996,0.0005110925,0.0000030307567,0.1959457,0.000036464593,0.00003117498,0.0002474127,0.0000011342693,0.00060649746,0.049217556,0.5638824,0.1895132],"study_design_scores_gemma":[0.00089891034,0.00037789138,0.000062386614,0.548076,0.00043880896,0.000047881116,0.000016877244,0.057696916,0.0020997562,0.035697363,0.35257062,0.002016637],"about_ca_topic_score_codex":0.00003066456,"about_ca_topic_score_gemma":0.000020291947,"teacher_disagreement_score":0.35213026,"about_ca_system_score_codex":0.00006360023,"about_ca_system_score_gemma":0.00023125204,"threshold_uncertainty_score":0.99999005},"labels":[],"label_agreement":null},{"id":"W4400882015","doi":"10.1007/s11760-024-03400-0","title":"An instance segmentation model based on improved SOLOv2 and Chan–Vese","year":2024,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Natural Science Foundation of Anhui Province","keywords":"Segmentation; Artificial intelligence; Pattern recognition (psychology); Computer vision; Computer science; Mathematics","score_opus":0.015005967583954186,"score_gpt":0.30918260882517534,"score_spread":0.29417664124122117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400882015","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003908756,0.0005304229,0.99419135,0.0005313338,0.000032188833,0.00018027711,0.0000031405314,0.000402227,0.00022029766],"genre_scores_gemma":[0.65251935,0.00002762802,0.3456063,0.0017066242,0.000037770085,0.0000338529,0.000004787926,0.000012908497,0.000050783376],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882174,0.000055989796,0.0001937698,0.000494828,0.00023595315,0.00019771868],"domain_scores_gemma":[0.9995214,0.00006122029,0.000052849937,0.00015538839,0.00006869682,0.00014046983],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00042613884,0.00014978493,0.000116772695,0.00015324033,0.00016249156,0.0010516903,0.00018196355,0.000050374794,0.000009562536],"category_scores_gemma":[0.000025870248,0.00012918157,0.000018358522,0.0002341477,0.00010520591,0.0024750251,0.00005183451,0.00016061416,0.0000025440165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009630917,0.000027186365,0.000005171118,0.0001748969,0.000002281027,0.000015821399,0.0007605659,0.000039025555,0.39211187,0.00011926163,0.00008591114,0.6066484],"study_design_scores_gemma":[0.00016938972,0.00012553064,0.000010306887,0.00017758735,0.000006122127,0.0000048711804,0.000050365164,0.7721307,0.22515392,0.0020402565,0.0000074473464,0.00012349179],"about_ca_topic_score_codex":0.0000062232266,"about_ca_topic_score_gemma":0.0000012320768,"teacher_disagreement_score":0.7720917,"about_ca_system_score_codex":0.000028869974,"about_ca_system_score_gemma":0.00011841876,"threshold_uncertainty_score":0.99998534},"labels":[],"label_agreement":null},{"id":"W4400904851","doi":"10.1007/978-3-031-66958-3_17","title":"Confounder-Aware Image Synthesis for Pathology Segmentation in New Magnetic Resonance Imaging Sequences","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Magnetic resonance imaging; Segmentation; Image segmentation; Artificial intelligence; Computer vision; Pattern recognition (psychology); Radiology; Medicine","score_opus":0.019033459825611426,"score_gpt":0.29038706250437063,"score_spread":0.2713536026787592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400904851","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000008009693,0.0029606244,0.99133676,0.0021944984,0.0011817494,0.00094081525,0.000015695367,0.0003097742,0.0010520504],"genre_scores_gemma":[0.004512239,0.00019230135,0.99116147,0.0026668024,0.0003024817,0.00011121225,0.000010505288,0.000049797232,0.0009931857],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99591887,0.00005679202,0.0007520667,0.0017739838,0.00081927626,0.0006789973],"domain_scores_gemma":[0.99738544,0.0011718774,0.00023512891,0.00083620055,0.0001927901,0.00017858324],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011449412,0.0005012888,0.0005292197,0.0011214708,0.00013172267,0.00079600886,0.0023313824,0.00022815776,0.00006721081],"category_scores_gemma":[0.00026547528,0.00047986,0.00011970592,0.0006718059,0.0008919415,0.0009538829,0.0007092349,0.0006258769,0.000041425017],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000047803596,0.000012712029,0.000033601817,0.00009325142,0.0000020039067,0.000302302,0.00063258427,0.000112491405,0.001674476,0.0066999123,0.0001558487,0.99027604],"study_design_scores_gemma":[0.0005268695,0.00027135332,0.00017389093,0.0019895935,0.000024059033,0.0002487044,0.000003805321,0.37721333,0.04248634,0.5748478,0.0010490573,0.0011652082],"about_ca_topic_score_codex":0.00006790116,"about_ca_topic_score_gemma":0.000112498696,"teacher_disagreement_score":0.9891108,"about_ca_system_score_codex":0.00052901875,"about_ca_system_score_gemma":0.0010328847,"threshold_uncertainty_score":0.99976534},"labels":[],"label_agreement":null},{"id":"W4400960057","doi":"10.23952/jano.6.2024.3.01","title":"EDITORIAL: SPECIAL ISSUE ON IMAGE RESTORATION MODELS, ALGORITHMS, AND THEIR APPLICATIONS","year":2024,"lang":"en","type":"editorial","venue":"Journal of Applied and Numerical Optimization","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Guangzhou University","keywords":"Image restoration; Computer science; Image (mathematics); Algorithm; Artificial intelligence; Image processing","score_opus":0.007375064360661626,"score_gpt":0.2628097290762185,"score_spread":0.25543466471555687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400960057","genre_codex":"methods","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.23026e-8,0.000113561386,0.62500346,0.00023356013,0.3733619,0.00021642256,0.000015773412,0.00006111918,0.0009941902],"genre_scores_gemma":[0.0000031042468,0.0011624522,0.27165282,0.00007656484,0.72693664,0.000032504922,0.000057693847,0.000027641272,0.00005058584],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976952,0.000061288505,0.0007107243,0.00043927258,0.0009186989,0.00017481246],"domain_scores_gemma":[0.9980512,0.00039532557,0.0006185056,0.0002476347,0.00047043385,0.0002168719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005742829,0.00029908758,0.00047369464,0.00030963004,0.000123986,0.0005649016,0.000416368,0.00048744184,0.00001811504],"category_scores_gemma":[0.00011602366,0.00023264618,0.000073126575,0.00033048532,0.00009713791,0.0006263971,0.00015409199,0.0009500047,0.000008826119],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004310588,0.000057765872,5.8402465e-9,0.000049779235,0.000027200309,0.0000024712697,0.00023854742,0.0018796091,0.000051392803,0.0007920892,0.929296,0.067562014],"study_design_scores_gemma":[0.00040173938,0.00030873483,6.330917e-8,0.000094069466,0.0000466797,0.000005432136,0.000037005644,0.1107948,0.0003322876,0.010935038,0.87678033,0.00026383466],"about_ca_topic_score_codex":0.0000030205133,"about_ca_topic_score_gemma":9.671073e-8,"teacher_disagreement_score":0.35357475,"about_ca_system_score_codex":0.00012314184,"about_ca_system_score_gemma":0.00021740186,"threshold_uncertainty_score":0.9487036},"labels":[],"label_agreement":null},{"id":"W4401307149","doi":"10.48550/arxiv.2408.00273","title":"UKAN-EP: Enhancing U-KAN with Efficient Attention and Pyramid Aggregation for 3D Multi-Modal MRI Brain Tumor Segmentation","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Goddard Space Flight Center; York University","keywords":"Modal; Segmentation; Computer science; Artificial intelligence; Medicine; Materials science","score_opus":0.03433399960217007,"score_gpt":0.2236604565977175,"score_spread":0.18932645699554743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401307149","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16931397,0.00004825497,0.828458,0.00023420295,0.00029960315,0.0011272852,0.000019420224,0.00045012895,0.000049126993],"genre_scores_gemma":[0.75125223,0.0000294751,0.24738182,0.0002278644,0.00006055912,0.00002349171,0.00008687779,0.00003048325,0.00090719317],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787825,0.00012090411,0.00029670593,0.001205851,0.00019503683,0.0003032668],"domain_scores_gemma":[0.99864954,0.00015926272,0.00032596773,0.00049417,0.00020624793,0.00016481636],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00050916895,0.00031690625,0.00026912778,0.00039497227,0.00018245613,0.00028756398,0.0004963283,0.00015332081,0.000010041606],"category_scores_gemma":[0.000059526545,0.00032642417,0.00010508414,0.0004380762,0.00015523507,0.00031157123,0.0007943227,0.00038800997,0.000017974638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001124472,0.0027966942,0.0053335405,0.013566267,0.001977642,0.0026080853,0.019936023,0.3709159,0.17859401,0.14585657,0.004621974,0.25266883],"study_design_scores_gemma":[0.0009407515,0.00018094886,0.0003929896,0.0005502633,0.000110522225,0.000010997417,0.0002741169,0.97046304,0.023291072,0.0033497459,0.00001974428,0.0004158127],"about_ca_topic_score_codex":0.00009327909,"about_ca_topic_score_gemma":0.00007158884,"teacher_disagreement_score":0.59954715,"about_ca_system_score_codex":0.00034947513,"about_ca_system_score_gemma":0.00016737723,"threshold_uncertainty_score":0.99991876},"labels":[],"label_agreement":null},{"id":"W4401683276","doi":"10.1016/j.media.2024.103305","title":"Neural implicit surface reconstruction of freehand 3D ultrasound volume with geometric constraints","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Innovative Research Group Project of the National Natural Science Foundation of China; Government of Alberta","keywords":"Artificial intelligence; Computer science; Computer vision; Point cloud; Robustness (evolution); Segmentation; Surface reconstruction; Visualization; Imaging phantom; Iterative closest point; 3D reconstruction; Surface (topology); Mathematics","score_opus":0.00790621936922627,"score_gpt":0.2694161975329444,"score_spread":0.2615099781637181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401683276","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041062478,0.00030733654,0.956726,0.0005889162,0.00013165978,0.00011990795,0.000016004984,0.00029673002,0.0007509725],"genre_scores_gemma":[0.75327057,0.00009362936,0.24591297,0.00025739323,0.00005710436,0.00000961914,0.000026000873,0.000012476475,0.0003602094],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970906,0.00015026494,0.00056930794,0.0005801666,0.0012912942,0.00031840734],"domain_scores_gemma":[0.99825114,0.00051425037,0.00013740953,0.0005255235,0.000219397,0.000352291],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009226406,0.00018890334,0.00045750992,0.0008513376,0.00006588726,0.00025900835,0.00070560933,0.00011979511,0.0043012057],"category_scores_gemma":[0.0006953438,0.00014360879,0.00020765427,0.00628579,0.0008566971,0.00073992927,0.00011868257,0.0003463396,0.00005990962],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008743032,0.00013036461,0.012605649,0.00014800987,0.001642993,0.00049319165,0.00029240112,0.00006542138,0.015475695,0.00018332468,0.0042792116,0.964675],"study_design_scores_gemma":[0.0011360918,0.0006159433,0.01596743,0.00037086665,0.0026443845,0.0013026899,0.00029155263,0.86616474,0.10941393,0.00069362187,0.00034095833,0.0010578193],"about_ca_topic_score_codex":0.00017051189,"about_ca_topic_score_gemma":0.000023508404,"teacher_disagreement_score":0.96361715,"about_ca_system_score_codex":0.00005897662,"about_ca_system_score_gemma":0.000154581,"threshold_uncertainty_score":0.996609},"labels":[],"label_agreement":null},{"id":"W4401938832","doi":"10.1101/2024.08.25.609595","title":"Vasculature segmentation in 3D hierarchical phase-contrast tomography images of human kidneys","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; NIH Office of the Director; National Institutes of Health; Common Fund; Royal Academy of Engineering; European Synchrotron Radiation Facility; National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; Medical Research Council; Canadian Institute for Advanced Research; Silicon Valley Community Foundation","keywords":"Phase contrast microscopy; Contrast (vision); Segmentation; Computed tomography; Tomography; Artificial intelligence; Computer vision; Computer science; Radiology; Medicine; Physics; Optics","score_opus":0.010944462705779636,"score_gpt":0.269280079755584,"score_spread":0.25833561704980434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401938832","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30726865,0.0022842363,0.68431556,0.00059980195,0.0013863349,0.0020484156,0.0003080014,0.0017409659,0.000048062037],"genre_scores_gemma":[0.80818164,0.0001279523,0.1908843,0.00027549278,0.0001810059,0.00027761044,0.0000016195636,0.00006743959,0.0000029131925],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99608886,0.00031306833,0.0009757351,0.0012672308,0.0008500772,0.0005050071],"domain_scores_gemma":[0.99744356,0.000095361924,0.0004120317,0.0013902881,0.00033704375,0.00032172963],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010553629,0.0005282206,0.0006798496,0.0011077118,0.00007917831,0.00042192734,0.0013967901,0.0005180675,0.00005125802],"category_scores_gemma":[0.00016715567,0.00054682925,0.00027002473,0.0013851289,0.00027713884,0.00039362605,0.0011567952,0.001529043,0.000016610891],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008295915,0.00038535998,0.00084487256,0.00079827645,0.000131388,0.0001177011,0.000051758358,0.000006805637,0.99434125,0.0020260229,0.0011275094,0.00016073347],"study_design_scores_gemma":[0.0010307976,0.00013377224,0.011629391,0.001079352,0.00007744914,3.0533524e-8,0.000002743473,0.0015440873,0.98363614,0.00016816382,0.00006875383,0.0006293227],"about_ca_topic_score_codex":0.000060469545,"about_ca_topic_score_gemma":0.0000016193358,"teacher_disagreement_score":0.500913,"about_ca_system_score_codex":0.00019797846,"about_ca_system_score_gemma":0.00043707318,"threshold_uncertainty_score":0.99969834},"labels":[],"label_agreement":null},{"id":"W4401994484","doi":"10.3390/biomedinformatics4030106","title":"Diffusion-Based Image Synthesis or Traditional Augmentation for Enriching Musculoskeletal Ultrasound Datasets","year":2024,"lang":"en","type":"article","venue":"BioMedInformatics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Novo Nordisk","keywords":"Diffusion; Ultrasound; Computer science; Image (mathematics); Artificial intelligence; Data science; Computer vision; Medicine; Radiology; Physics","score_opus":0.030661816417430664,"score_gpt":0.3204265865983731,"score_spread":0.28976477018094243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401994484","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00069511554,0.000020894127,0.99591345,0.0007631837,0.0006057272,0.00047975,0.000819392,0.00058810884,0.000114361515],"genre_scores_gemma":[0.01034927,0.000026013171,0.98630726,0.0009471395,0.00016892656,0.00032597955,0.0018050788,0.000017084903,0.000053265467],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838954,0.00003244131,0.000502226,0.0002186187,0.0005999428,0.00025724326],"domain_scores_gemma":[0.9972715,0.0021045874,0.00010520902,0.0003050093,0.000056172765,0.00015755613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056803395,0.00017168635,0.00014864918,0.00033851646,0.00016977466,0.0005903988,0.00056686474,0.00007118907,0.00022297482],"category_scores_gemma":[0.0006168495,0.0001302806,0.0001034412,0.000482631,0.00010700458,0.0019409918,0.00006174934,0.00010559552,0.00006826084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003505161,0.00031886928,0.000005950504,0.002963647,0.000116125906,0.000041668656,0.0024711473,0.000007908734,0.056100648,0.007299838,0.21994938,0.7106898],"study_design_scores_gemma":[0.0012192361,0.00051195093,0.00017706171,0.0006511836,0.0001356345,0.00010011115,0.0006115539,0.7866725,0.17359337,0.005157323,0.030250505,0.00091960205],"about_ca_topic_score_codex":0.000004184469,"about_ca_topic_score_gemma":0.0000010695555,"teacher_disagreement_score":0.78666455,"about_ca_system_score_codex":0.00011802832,"about_ca_system_score_gemma":0.00019973637,"threshold_uncertainty_score":0.56932294},"labels":[],"label_agreement":null},{"id":"W4402062094","doi":"10.1016/b978-0-443-15999-2.00009-8","title":"Deployment, feature extraction, and selection in computer vision and medical imaging","year":2024,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Software deployment; Computer vision; Computer science; Artificial intelligence; Feature selection; Feature extraction; Selection (genetic algorithm); Medical physics; Medicine; Software engineering","score_opus":0.0073459542732033,"score_gpt":0.28258487164212764,"score_spread":0.27523891736892436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402062094","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000036122412,0.0065822788,0.45166567,0.0036577655,0.00090154575,0.0010674514,0.0000049177165,0.0010452105,0.535039],"genre_scores_gemma":[0.00058090495,0.0014711411,0.116923325,0.0033515473,0.0006542147,0.00005876204,0.000011059056,0.00010543979,0.87684363],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981355,0.000039305494,0.00034326027,0.0007049192,0.00057506125,0.00020191736],"domain_scores_gemma":[0.9993649,0.000094699135,0.000112767724,0.0001658733,0.00005477069,0.00020698029],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047449564,0.0002964554,0.00029771347,0.00035150524,0.00007017843,0.00028962994,0.0002503764,0.00027856373,0.000051753956],"category_scores_gemma":[0.000016227494,0.00025991598,0.000049627375,0.000037761063,0.00014159977,0.00025475974,0.00040378064,0.00088081334,0.000019841813],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000022689385,0.000005214578,0.000011887901,0.00008375167,0.000013583938,0.00015556118,0.00012528557,5.9334607e-8,0.00012347703,0.0028963087,0.0014780207,0.99510455],"study_design_scores_gemma":[0.000826677,0.00021826458,0.00039538878,0.0047852453,0.00008388357,0.0020831244,0.0000069388,0.046147417,0.00091575715,0.06830604,0.8750902,0.0011411052],"about_ca_topic_score_codex":0.0000016643661,"about_ca_topic_score_gemma":0.00002811386,"teacher_disagreement_score":0.9939635,"about_ca_system_score_codex":0.00009606756,"about_ca_system_score_gemma":0.00007849529,"threshold_uncertainty_score":0.9999853},"labels":[],"label_agreement":null},{"id":"W4402263203","doi":"10.62973/04-051","title":"OWS1.2 Image Handling Design","year":2004,"lang":"en","type":"report","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"3v Geomatics (Canada)","funders":"","keywords":"Image (mathematics); Computer science; Computer graphics (images); Computer vision","score_opus":0.08126242197685403,"score_gpt":0.35807966676126585,"score_spread":0.27681724478441183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402263203","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.6052557e-7,0.0003131282,0.8920587,0.00021832078,0.00069213816,0.00036288804,0.0000013860257,0.0012747977,0.10507851],"genre_scores_gemma":[0.000038358798,0.00060923636,0.9824661,0.0007518646,0.00021873017,0.000059092265,0.000013479163,0.00002770508,0.015815426],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9970142,0.00008535633,0.00049853965,0.0006196368,0.0014563045,0.00032592454],"domain_scores_gemma":[0.9980988,0.00013669289,0.00025458873,0.00084968586,0.00047512856,0.00018511036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014307612,0.00026779558,0.0003513384,0.00023039065,0.00008165372,0.0003758152,0.0013086823,0.00025354954,0.00053954736],"category_scores_gemma":[0.00052499626,0.00022412035,0.00012517678,0.00028656522,0.000081147875,0.00048878376,0.00035737237,0.0004066651,0.00029781694],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016482702,0.00009872613,0.0000027350013,0.00024182536,0.00006289186,0.0004737294,0.00018340649,0.000010839176,0.003256096,0.0008798672,0.6020449,0.39274335],"study_design_scores_gemma":[0.00087515305,0.00037931997,0.000039862156,0.0013977089,0.00008458496,0.0006495873,0.00002744891,0.004285649,0.8856422,0.022457745,0.0820683,0.0020924818],"about_ca_topic_score_codex":0.0002218318,"about_ca_topic_score_gemma":0.0000027635397,"teacher_disagreement_score":0.8823861,"about_ca_system_score_codex":0.00044484585,"about_ca_system_score_gemma":0.002577992,"threshold_uncertainty_score":0.9139362},"labels":[],"label_agreement":null},{"id":"W4402307358","doi":"10.18280/ts.410430","title":"Frameless Registration Method Using a Depth Camera for Robot-Assisted Stereotactic Brain Surgery","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Türkiye Bilimsel ve Teknolojik Araştırma Kurumu","keywords":"Computer vision; Artificial intelligence; Computer science; Robot; Stereotactic surgery; Medicine; Surgery","score_opus":0.08030058685703133,"score_gpt":0.3684809155124791,"score_spread":0.28818032865544774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402307358","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021604013,0.00011342,0.99424297,0.0019924133,0.00031827547,0.000553939,0.0000077609,0.0005267565,0.00008409363],"genre_scores_gemma":[0.25912172,0.0000044496587,0.73902154,0.0013662709,0.0001744896,0.00013750888,0.000029459981,0.000022701975,0.000121837125],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793,0.00024692618,0.0005237936,0.0005049019,0.00048621732,0.00030818177],"domain_scores_gemma":[0.99804896,0.0013207436,0.00014291947,0.0002615573,0.0000972461,0.0001285831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015386817,0.0001840858,0.00024347033,0.00023959733,0.00011741584,0.0005959904,0.00033165375,0.000076706965,0.00010542723],"category_scores_gemma":[0.00012345465,0.00017303794,0.00015723184,0.00044107527,0.000041275925,0.00081494945,0.00005301174,0.00015111215,0.0000065932813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021509435,0.00012639236,0.000056875466,0.00032610542,0.000112067304,0.00007511113,0.0007483132,0.00011728545,0.10145493,0.0046735927,0.013293135,0.8789947],"study_design_scores_gemma":[0.0004954419,0.00021481201,0.0013343283,0.00043595702,0.000082419356,0.00011456303,0.00010451933,0.8832384,0.1082846,0.0025166704,0.002643696,0.00053461315],"about_ca_topic_score_codex":0.00005626797,"about_ca_topic_score_gemma":0.000017070615,"teacher_disagreement_score":0.8831211,"about_ca_system_score_codex":0.0001537498,"about_ca_system_score_gemma":0.0002442868,"threshold_uncertainty_score":0.7056283},"labels":[],"label_agreement":null},{"id":"W4402401801","doi":"10.1109/tmi.2024.3457228","title":"Point Cloud Registration in Laparoscopic Liver Surgery Using Keypoint Correspondence Registration Network","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Image registration; Point cloud; Artificial intelligence; Computer vision; Computer science; Image (mathematics)","score_opus":0.03771306787816494,"score_gpt":0.30651473123656375,"score_spread":0.26880166335839883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402401801","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024666213,0.0003676731,0.9900853,0.0026205757,0.0033599408,0.0002577464,0.0000022566255,0.0005357476,0.0003041328],"genre_scores_gemma":[0.9204694,0.0004851446,0.07472697,0.0029323096,0.00049595616,0.00008719068,0.000007786238,0.000040756786,0.0007544996],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99675906,0.0003317321,0.0007358503,0.0006407665,0.0010959835,0.0004366061],"domain_scores_gemma":[0.9982649,0.00084478664,0.00010672131,0.00045224902,0.00006588946,0.00026543497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019332601,0.00021895155,0.00026279676,0.0003905931,0.00016350891,0.00038051163,0.00040074586,0.00012688813,0.000564638],"category_scores_gemma":[0.00014837924,0.00021739461,0.00012930643,0.001112794,0.0002027522,0.00130363,0.0000072594344,0.0007541385,0.0001383416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000090111695,0.000539983,0.00019214531,0.00049012597,0.00006400397,0.0032492115,0.0017171872,0.007929765,0.00951444,0.0034423263,0.06670682,0.90606385],"study_design_scores_gemma":[0.00023530406,0.000033657136,0.00009069743,0.0016902408,0.000017183369,0.00026850865,0.000048314552,0.9693926,0.02636066,0.0010264964,0.00052517385,0.0003111362],"about_ca_topic_score_codex":0.0002607711,"about_ca_topic_score_gemma":0.00007481296,"teacher_disagreement_score":0.96146286,"about_ca_system_score_codex":0.0002901711,"about_ca_system_score_gemma":0.0005605867,"threshold_uncertainty_score":0.8865095},"labels":[],"label_agreement":null},{"id":"W4402423810","doi":"10.24908/iqurcp18069","title":"Leveraging SAM for automatic prostate segmentation on micro-ultrasound images","year":2024,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Computer vision; Ultrasound; Prostate; Medicine; Radiology; Internal medicine","score_opus":0.09316639379710263,"score_gpt":0.40141753928563734,"score_spread":0.3082511454885347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402423810","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010969806,0.00012265831,0.9566989,0.026663773,0.0004268488,0.0022904992,0.000013551332,0.0017222902,0.001091672],"genre_scores_gemma":[0.79655284,0.00048009033,0.19912171,0.00046440106,0.00022369936,0.0012460712,0.00003559786,0.00006225645,0.001813317],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9954907,0.00013536726,0.00058824045,0.001172101,0.0015999025,0.0010136967],"domain_scores_gemma":[0.99672604,0.0012523095,0.00011878389,0.00033519138,0.0012495106,0.00031819765],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0030258878,0.00034580365,0.00030479924,0.0009351109,0.00046329584,0.0037106562,0.0012418963,0.00012370302,0.000033889613],"category_scores_gemma":[0.00093425583,0.00031056316,0.000113522365,0.0013009286,0.00052538724,0.0023012534,0.00032585123,0.00074007054,0.00017250598],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042337648,0.00018995666,0.00016250095,0.001906597,0.00013226092,0.00004604139,0.015705258,0.0000028464785,0.5031239,0.09049258,0.104224145,0.2839716],"study_design_scores_gemma":[0.0006520735,0.00086949585,0.00018382733,0.0015374311,0.000019630876,0.00005059701,0.0022785068,0.02474588,0.715333,0.2521448,0.0015641602,0.00062058325],"about_ca_topic_score_codex":0.000051619172,"about_ca_topic_score_gemma":7.4594885e-7,"teacher_disagreement_score":0.785583,"about_ca_system_score_codex":0.00048658415,"about_ca_system_score_gemma":0.0005977509,"threshold_uncertainty_score":0.9999347},"labels":[],"label_agreement":null},{"id":"W4402474868","doi":"10.1109/ccece59415.2024.10667319","title":"Assessing the Performance of Foundation Models in Prostate Segmentation Across Different Ultrasound Modalities","year":2024,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Queen's University","funders":"","keywords":"Modalities; Foundation (evidence); Computer science; Segmentation; Artificial intelligence; Image segmentation; Geography","score_opus":0.03479306637790007,"score_gpt":0.3544605530889108,"score_spread":0.3196674867110107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402474868","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4648383,0.000028082564,0.53444415,0.00016259152,0.00008304453,0.00015281915,6.507227e-7,0.000101686244,0.00018869035],"genre_scores_gemma":[0.97100985,0.000085933025,0.028500156,0.00009854431,0.000012262493,0.000060299335,0.000008863963,0.0000053598505,0.00021875802],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893963,0.00007699189,0.00030135756,0.00019439879,0.0003348096,0.00015282085],"domain_scores_gemma":[0.99946445,0.0002152222,0.000055741595,0.00019553368,0.000046916422,0.000022121874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051452906,0.00008430637,0.00008685215,0.00004732242,0.000069208436,0.0006445241,0.00027682757,0.000020089738,0.00002120282],"category_scores_gemma":[0.00001793132,0.00005267539,0.000024684514,0.0002833628,0.000082997954,0.0029056936,0.000079202946,0.00010214424,0.0000040766668],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007176348,0.00013744205,0.004182317,0.0006469852,0.000033147848,0.000006377626,0.038061995,0.004542006,0.11175298,0.029925734,0.00019147302,0.81051236],"study_design_scores_gemma":[0.00009388862,0.000035324952,0.0034730264,0.00011486837,0.000002104272,0.000004923533,0.0010180952,0.7173871,0.2733876,0.0044041076,0.0000041234034,0.00007485485],"about_ca_topic_score_codex":0.0000696941,"about_ca_topic_score_gemma":0.000013003011,"teacher_disagreement_score":0.8104375,"about_ca_system_score_codex":0.00008010716,"about_ca_system_score_gemma":0.00003672595,"threshold_uncertainty_score":0.6215161},"labels":[],"label_agreement":null},{"id":"W4402502922","doi":"10.48550/arxiv.2408.08949","title":"Demonstration of hybrid foreground removal on CHIME data","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Office of Science; Alliance de recherche numérique du Canada; Industry Canada; Canada Research Chairs; National Research Council Canada; University of Toronto; University of Utah; Smithsonian Astrophysical Observatory; Western Canada Research Grid; Canadian Institute for Advanced Research; Natural Sciences and Engineering Research Council of Canada; Institut Périmètre de physique théorique; Alfred P. Sloan Foundation; McGill University; U.S. Department of Energy; Government of Canada; National Science Foundation","keywords":"Computer science; Geology","score_opus":0.12710058967291332,"score_gpt":0.2413632924819403,"score_spread":0.11426270280902698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402502922","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035241727,0.000051143666,0.95632005,0.00015110537,0.00046175494,0.00028608568,0.00007068484,0.00037096706,0.0070464727],"genre_scores_gemma":[0.9616865,0.00011258709,0.03650679,0.00011957881,0.00006791491,6.219475e-7,0.00015602438,0.000014982858,0.0013349983],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982041,0.00011132276,0.00025231743,0.0010708168,0.00018130889,0.00018012112],"domain_scores_gemma":[0.99731183,0.00010407771,0.00022713021,0.0021575214,0.00009053015,0.00010889956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041194513,0.00020802207,0.00024030692,0.00027831882,0.000039067978,0.00010856911,0.002776466,0.000110376895,0.000027615333],"category_scores_gemma":[0.000056615125,0.00022424998,0.00009718158,0.00035439146,0.00014233445,0.00038836076,0.0040657553,0.00054128503,0.00007397764],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074889496,0.00044669278,0.00021411596,0.000860743,0.00039271088,0.0039854785,0.0002645638,0.006296013,0.0015402107,0.88355976,0.027136995,0.07522785],"study_design_scores_gemma":[0.00027556656,0.00012797929,0.00006670471,0.00044482815,0.000107593936,0.00003927903,0.000030202005,0.7790045,0.022142889,0.19703045,0.0003117306,0.00041827327],"about_ca_topic_score_codex":0.00007009242,"about_ca_topic_score_gemma":0.000009518769,"teacher_disagreement_score":0.92644477,"about_ca_system_score_codex":0.00011341854,"about_ca_system_score_gemma":0.00024517547,"threshold_uncertainty_score":0.91446483},"labels":[],"label_agreement":null},{"id":"W4402510942","doi":"10.1109/space63117.2024.10668387","title":"PLANET: Multi-Class Patch Layer Adaptive Network for Satellite Image Segmentation","year":2024,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Class (philosophy); Segmentation; Planet; Satellite; Image segmentation; Layer (electronics); Artificial intelligence; Satellite image; Computer vision; Image (mathematics); Remote sensing; Geology; Astronomy; Physics; Materials science","score_opus":0.03747193908784335,"score_gpt":0.31917059855836194,"score_spread":0.2816986594705186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402510942","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000049486283,0.00032968805,0.9954886,0.0005733106,0.00048026603,0.0006117177,0.000019114657,0.0010088251,0.0014390275],"genre_scores_gemma":[0.005294059,0.00008817562,0.99075896,0.0015296891,0.00017439938,0.00017282076,0.0000922285,0.000017738812,0.0018719615],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987286,0.000058753405,0.00026041453,0.00041312122,0.00025039376,0.00028876605],"domain_scores_gemma":[0.9992862,0.00026912827,0.00004292437,0.00023686353,0.000060435577,0.00010449241],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036508567,0.00014052866,0.000121947116,0.00006843556,0.00007389348,0.00032941028,0.00034136648,0.00006275592,0.00014546678],"category_scores_gemma":[0.000017347438,0.00011649876,0.00004971648,0.00025557273,0.000042114345,0.00071664446,0.00008986561,0.000109512504,0.0002034731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019603924,0.00007850705,0.00007772176,0.000107307875,0.00007628226,0.000053931108,0.0011253331,0.00005116932,0.02174816,0.022806594,0.16874625,0.78510916],"study_design_scores_gemma":[0.000642819,0.0003007904,0.00027185996,0.00010051018,0.000024126408,0.000015444906,0.00016811024,0.79466724,0.17037337,0.004938511,0.028052561,0.00044466445],"about_ca_topic_score_codex":0.00006466037,"about_ca_topic_score_gemma":0.00003145221,"teacher_disagreement_score":0.79461604,"about_ca_system_score_codex":0.000060569797,"about_ca_system_score_gemma":0.00005109827,"threshold_uncertainty_score":0.47506815},"labels":[],"label_agreement":null},{"id":"W4402620540","doi":"10.1007/s11548-024-03249-1","title":"Robust unsupervised texture segmentation for motion analysis in ultrasound images","year":2024,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; Polytechnique Montréal; Université de Montréal; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Computer vision; Artificial intelligence; Computer science; Modality (human–computer interaction); Segmentation; Ultrasound; Texture (cosmology); Motion analysis; Radiology; Medicine; Image (mathematics)","score_opus":0.024711248084202272,"score_gpt":0.2935403640400008,"score_spread":0.2688291159557985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402620540","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0155470865,0.00075961754,0.9801952,0.0018921059,0.001461897,0.00007937829,0.0000071219415,0.00004544108,0.000012120082],"genre_scores_gemma":[0.723752,0.00047786016,0.2743115,0.0008832331,0.0004683848,0.000011959016,0.000060571736,0.000008193871,0.00002627358],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9985983,0.00019868753,0.00059992855,0.00022127872,0.00025633298,0.00012552312],"domain_scores_gemma":[0.9976054,0.0017877693,0.00018411803,0.000088934765,0.00026976533,0.00006402453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010873901,0.0001087374,0.00030050124,0.001253211,0.000031148058,0.00023088044,0.00033093314,0.00009019726,0.000020645688],"category_scores_gemma":[0.000110825684,0.00009078589,0.00022438481,0.0004438091,0.000064009655,0.00070675084,0.000036668134,0.0001819433,8.235333e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000098077435,0.00018799226,0.035248857,0.00006940109,0.0023685326,0.0006206398,0.00091644924,0.002915998,0.008237623,0.001224196,0.017714372,0.93039787],"study_design_scores_gemma":[0.0019838302,0.00033839763,0.5354418,0.0005615662,0.00048543877,0.005415477,0.0001386041,0.43506846,0.012088462,0.00662015,0.0011471058,0.0007106575],"about_ca_topic_score_codex":0.0000045317397,"about_ca_topic_score_gemma":0.0000025043894,"teacher_disagreement_score":0.9296872,"about_ca_system_score_codex":0.000084283405,"about_ca_system_score_gemma":0.00007848395,"threshold_uncertainty_score":0.3702141},"labels":[],"label_agreement":null},{"id":"W4403066950","doi":"10.1007/978-3-031-72114-4_1","title":"3D-SAutoMed: Automatic Segment Anything Model for 3D Medical Image Segmentation from Local-Global Perspective","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Perspective (graphical); Artificial intelligence; Computer vision; Image segmentation; Segmentation; Image (mathematics); Computer graphics (images)","score_opus":0.017267585698856384,"score_gpt":0.3117010159272551,"score_spread":0.2944334302283987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403066950","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000010008378,0.0005606905,0.99196905,0.0022116688,0.0016232322,0.0012612251,0.00006891042,0.0009941593,0.0013010585],"genre_scores_gemma":[0.0031382528,0.000051224535,0.99206644,0.0039007596,0.000394116,0.00011625578,0.00004162941,0.00005231532,0.00023898874],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99285376,0.00005780189,0.0009819756,0.002339828,0.0029552116,0.00081139914],"domain_scores_gemma":[0.996783,0.00074519706,0.00037073568,0.0011473533,0.00048422563,0.00046946402],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001502065,0.00073536305,0.00072630454,0.0006298903,0.0002682263,0.0010322572,0.0032271235,0.0005271474,0.00013049222],"category_scores_gemma":[0.00031956926,0.0006695357,0.0002180422,0.00067310134,0.0012065851,0.0011868423,0.0017085271,0.0009875363,0.00008215979],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005812407,0.00005874335,0.0000019710503,0.00012479363,0.000051230167,0.000158101,0.0030893062,0.007577795,0.00022485618,0.011859044,0.00024722307,0.9766011],"study_design_scores_gemma":[0.00030783098,0.00010663072,0.0000030426593,0.00073861616,0.00002915965,0.00003087222,0.0000036709625,0.73173636,0.0015406404,0.26499116,0.000026881029,0.00048513705],"about_ca_topic_score_codex":0.00012873253,"about_ca_topic_score_gemma":0.000113597955,"teacher_disagreement_score":0.976116,"about_ca_system_score_codex":0.002911399,"about_ca_system_score_gemma":0.0016346985,"threshold_uncertainty_score":0.9995756},"labels":[],"label_agreement":null},{"id":"W4403081519","doi":"10.1007/978-3-031-72658-3_18","title":"G3R: Gradient Guided Generalizable Reconstruction","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.024203898349290067,"score_gpt":0.27811868445973337,"score_spread":0.2539147861104433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403081519","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000117027075,0.00064039906,0.983608,0.0009506681,0.004344297,0.0003981599,0.0000037924897,0.00064278394,0.009400182],"genre_scores_gemma":[0.0010344986,0.0001605656,0.99266976,0.0021276458,0.0005436033,0.000022061613,0.000006872768,0.00003896013,0.0033960515],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.996002,0.000029852317,0.00067045895,0.0016300721,0.0010971521,0.00057044125],"domain_scores_gemma":[0.9979129,0.00014808195,0.000225419,0.0012529003,0.00022916861,0.00023154104],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000915044,0.0004605098,0.0004264804,0.0010839448,0.00017504935,0.00080383266,0.0024993403,0.00028996944,0.00011031613],"category_scores_gemma":[0.00007372523,0.00041275923,0.00014234649,0.0007392115,0.00073425064,0.0007422331,0.0011702065,0.0007545237,0.00017128981],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010010231,0.000012426116,0.0000060879474,0.000058538633,0.000011740135,0.0001239809,0.0003057496,0.0021242732,0.0005693562,0.034889426,0.0007398177,0.9611576],"study_design_scores_gemma":[0.00015523272,0.00013188597,0.0000055951273,0.00071987335,0.000012490817,0.0004618478,1.2601176e-7,0.427239,0.023564953,0.54367924,0.0033501636,0.00067956914],"about_ca_topic_score_codex":0.000036897993,"about_ca_topic_score_gemma":0.000023640418,"teacher_disagreement_score":0.960478,"about_ca_system_score_codex":0.00053263793,"about_ca_system_score_gemma":0.00042218363,"threshold_uncertainty_score":0.99983245},"labels":[],"label_agreement":null},{"id":"W4403090316","doi":"10.1007/978-3-031-72069-7_41","title":"Synchronous Image-Label Diffusion with Anisotropic Noise for Stroke Lesion Segmentation on Non-Contrast CT","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Anisotropic diffusion; Contrast (vision); Noise (video); Artificial intelligence; Diffusion; Segmentation; Computer vision; Lesion; Image (mathematics); Image noise; Image segmentation; Stroke (engine); Medicine; Physics; Pathology","score_opus":0.01305184304516936,"score_gpt":0.2723251604414096,"score_spread":0.2592733173962402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403090316","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00027197844,0.00010873777,0.9949218,0.00057738414,0.0010906259,0.0015562488,0.000030774547,0.00036094512,0.0010814922],"genre_scores_gemma":[0.02531099,0.00008152041,0.97134423,0.0016984654,0.00038436425,0.00010817695,0.00003981044,0.000070938,0.0009615211],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957324,0.000026597838,0.00055394985,0.0017644161,0.0013003147,0.00062227924],"domain_scores_gemma":[0.99776185,0.00046214942,0.00030128888,0.0009928633,0.0002594843,0.00022235131],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005055993,0.00061941444,0.0005200272,0.0008659419,0.00028902624,0.00085585844,0.0018269514,0.00017045211,0.000024835797],"category_scores_gemma":[0.000051925963,0.00048465087,0.000111843394,0.0004185995,0.0005701835,0.00075639604,0.0005927499,0.0006776074,0.00006049384],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003417156,0.00010653839,0.000009076235,0.00019632216,0.000020542762,0.00019702956,0.0003933532,0.0006908975,0.024291804,0.0030174784,0.000121636505,0.97092116],"study_design_scores_gemma":[0.0021930113,0.0032570024,0.00009093675,0.0025669471,0.000061563966,0.00014501133,0.0000016195416,0.74593955,0.20773977,0.036325604,0.0002980254,0.0013809652],"about_ca_topic_score_codex":0.000024063123,"about_ca_topic_score_gemma":0.0000342952,"teacher_disagreement_score":0.9695402,"about_ca_system_score_codex":0.00061213534,"about_ca_system_score_gemma":0.00042708468,"threshold_uncertainty_score":0.9997605},"labels":[],"label_agreement":null},{"id":"W4403150376","doi":"10.1007/978-3-031-72111-3_13","title":"CT-Based Brain Ventricle Segmentation via Diffusion Schrödinger Bridge without target domain ground truths","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Segmentation; Bridge (graph theory); Diffusion; Domain (mathematical analysis); Ground truth; Artificial intelligence; Diffusion MRI; Computer vision; Algorithm; Physics; Anatomy; Mathematics; Medicine; Mathematical analysis; Radiology; Magnetic resonance imaging; Quantum mechanics","score_opus":0.013519843877144808,"score_gpt":0.27549865087030145,"score_spread":0.2619788069931566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403150376","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030764553,0.00044281833,0.9935759,0.0017179418,0.0016873556,0.00082889193,0.000010782294,0.0006729032,0.00075579336],"genre_scores_gemma":[0.099952005,0.0000174952,0.8940292,0.0050119665,0.00044701077,0.000046161837,0.000041814343,0.000073201096,0.00038115305],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947126,0.000087393826,0.000749081,0.0018858045,0.0018446198,0.00072050263],"domain_scores_gemma":[0.99740225,0.0005455467,0.0003546944,0.0012143544,0.00017783673,0.00030534156],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0013972363,0.00063324725,0.0005403457,0.0012803783,0.00029361207,0.0011377754,0.0025904838,0.00021890558,0.00014032997],"category_scores_gemma":[0.00009223873,0.0005805853,0.00018190937,0.0010661377,0.00071518216,0.00090011983,0.0008551996,0.00093513285,0.00014672053],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001485879,0.00012354039,0.00015582217,0.00018951294,0.000025775924,0.0005271618,0.0011072253,0.001955192,0.024867233,0.008530608,0.00043090567,0.96207213],"study_design_scores_gemma":[0.0007143718,0.00031202915,0.00017740317,0.0008538189,0.000017757464,0.00011860625,5.864728e-7,0.62566674,0.074736506,0.29500207,0.0013383068,0.0010618322],"about_ca_topic_score_codex":0.000046789133,"about_ca_topic_score_gemma":0.000013510987,"teacher_disagreement_score":0.96101034,"about_ca_system_score_codex":0.0007150306,"about_ca_system_score_gemma":0.00045906336,"threshold_uncertainty_score":0.99989915},"labels":[],"label_agreement":null},{"id":"W4403152097","doi":"10.1007/978-3-031-72111-3_61","title":"Simulation-Based Segmentation of Blood Vessels in Cerebral 3D OCTA Images","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Western Hospital","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Computer vision; Image segmentation; Computer graphics (images); Pattern recognition (psychology)","score_opus":0.015244089347269087,"score_gpt":0.2876389065978036,"score_spread":0.27239481725053455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403152097","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000089358815,0.00031604417,0.9970673,0.00034626803,0.0006633204,0.00052733504,0.000011435522,0.0002087709,0.0007701888],"genre_scores_gemma":[0.22498529,0.000012307418,0.7738134,0.00082558376,0.00012456151,0.000017178822,0.000014603645,0.000029703304,0.00017734552],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963865,0.00004783672,0.0008018322,0.001182647,0.0011781006,0.00040302615],"domain_scores_gemma":[0.9971965,0.0012676144,0.0003253663,0.0008628815,0.0002320542,0.000115586],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008498593,0.00039299304,0.000457971,0.0014299862,0.00006436916,0.0003293966,0.0018529507,0.00023630622,0.00006315869],"category_scores_gemma":[0.00021948396,0.00037260016,0.00010016039,0.0009610753,0.0005905916,0.00069377106,0.0005552403,0.0006102484,0.000019883388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000072802904,0.00011552365,0.00018580294,0.0003431721,0.000021285763,0.00015665042,0.0010606868,0.44011068,0.0049706604,0.0031631899,0.00001732438,0.5498478],"study_design_scores_gemma":[0.00035658455,0.0001556952,0.000066655964,0.0008135088,0.000017185217,0.0000041228786,2.6251354e-7,0.8535073,0.098248094,0.046428733,0.000020287134,0.00038159746],"about_ca_topic_score_codex":0.00003553023,"about_ca_topic_score_gemma":0.000024328496,"teacher_disagreement_score":0.54946613,"about_ca_system_score_codex":0.00018731195,"about_ca_system_score_gemma":0.00052629755,"threshold_uncertainty_score":0.99987257},"labels":[],"label_agreement":null},{"id":"W4403587220","doi":"10.3174/ajnr.a8544","title":"Comprehensive Segmentation of Gray Matter Structures on T1-Weighted Brain MRI: A Comparative Study of Convolutional Neural Network, Convolutional Neural Network Hybrid-Transformer or -Mamba Architectures","year":2024,"lang":"en","type":"article","venue":"American Journal of Neuroradiology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; BioClinica; Deutsches Krebsforschungszentrum; Bristol-Myers Squibb; Eli Lilly and Company; Biogen; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association","keywords":"Medicine; Segmentation; Gray (unit); Artificial intelligence; Magnetic resonance imaging; Pattern recognition (psychology); Neuroscience; Radiology; Computer science","score_opus":0.026279003274724176,"score_gpt":0.32102610207223004,"score_spread":0.2947470987975059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403587220","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79079574,0.00023744338,0.20609377,0.0014622406,0.00083752366,0.00047542187,0.00002778333,0.000050223993,0.000019866806],"genre_scores_gemma":[0.97657025,0.000019560506,0.019755699,0.003255239,0.00033224313,0.0000159954,0.000018444813,0.000020396254,0.000012173372],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9955737,0.0015853437,0.0012468674,0.0004529949,0.0007001759,0.00044092775],"domain_scores_gemma":[0.9956765,0.0025362228,0.0009705184,0.0002710298,0.00036591783,0.00017977427],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032406746,0.00034206262,0.00098655,0.00040481082,0.000103401966,0.000046978475,0.00074246345,0.000047043046,0.00011134283],"category_scores_gemma":[0.000044818484,0.00024778582,0.00021968171,0.0008242458,0.0010634353,0.00024153032,0.000079202655,0.0007081888,0.000003803357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008037203,0.0020284962,0.042543925,0.0003769104,0.0040017134,0.0020621566,0.016508127,0.59968287,0.06561996,0.00514038,0.1800966,0.073901646],"study_design_scores_gemma":[0.009745329,0.084188,0.54994553,0.0006203342,0.00065872964,0.014496981,0.0036834292,0.303188,0.015850583,0.01420355,0.0014141317,0.0020054157],"about_ca_topic_score_codex":0.000045128116,"about_ca_topic_score_gemma":0.000009975358,"teacher_disagreement_score":0.5074016,"about_ca_system_score_codex":0.00006781666,"about_ca_system_score_gemma":0.00020146467,"threshold_uncertainty_score":0.99999744},"labels":[],"label_agreement":null},{"id":"W4404030983","doi":"10.1109/icccnt61001.2024.10725719","title":"2D Rises Wavelet Transform for Multiscale Texture Enhancement in Medical Imaging","year":2024,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Impact","funders":"","keywords":"Wavelet transform; Texture (cosmology); Wavelet; Medical imaging; Artificial intelligence; Computer science; Computer vision; Materials science; Image (mathematics)","score_opus":0.011009008355785183,"score_gpt":0.32260401563794794,"score_spread":0.3115950072821628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404030983","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007182761,0.00036979615,0.9824277,0.012477217,0.00027791515,0.00041705678,0.0000026502835,0.0004294845,0.0035263565],"genre_scores_gemma":[0.21217851,0.00024129094,0.7776152,0.006172793,0.00016969958,0.0005108859,0.00001851184,0.000023175218,0.0030699496],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985991,0.000023959912,0.0002945652,0.0003548412,0.00046909458,0.00025843907],"domain_scores_gemma":[0.9994542,0.00019085445,0.0000144696805,0.00018006095,0.000028133472,0.00013225485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005262153,0.00010750566,0.00012210084,0.00013225543,0.000033628752,0.00014193896,0.0004834333,0.00005360713,0.0006934374],"category_scores_gemma":[0.00007105264,0.00008205924,0.000059153524,0.0002528794,0.000053543,0.00046252558,0.000069330206,0.00016162211,0.00003215014],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016084268,0.000043679636,0.00000775865,0.00006425457,0.0000044199955,0.00004229286,0.0004678663,1.4422469e-7,0.0013162168,0.0029649446,0.019318644,0.97576815],"study_design_scores_gemma":[0.000627789,0.000065170396,0.000068360816,0.00030154493,0.000005607482,0.00002623456,0.0001040993,0.6239471,0.3368171,0.0069967015,0.030780487,0.00025981813],"about_ca_topic_score_codex":0.00003787841,"about_ca_topic_score_gemma":0.000037930415,"teacher_disagreement_score":0.97550833,"about_ca_system_score_codex":0.000063213025,"about_ca_system_score_gemma":0.00010039598,"threshold_uncertainty_score":0.7592653},"labels":[],"label_agreement":null},{"id":"W4404163952","doi":"10.1038/s41598-024-77582-5","title":"Deep learning for 3D vascular segmentation in hierarchical phase contrast tomography: a case study on kidney","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Common Fund; National Institute of Diabetes and Digestive and Kidney Diseases; Canadian Institute for Advanced Research; Bundesministerium für Gesundheit; NIH Office of the Director; National Cancer Institute; Royal Academy of Engineering; National Institutes of Health; Bundesministerium für Bildung und Forschung; European Synchrotron Radiation Facility; Wellcome Trust; Medical Research Council; Silicon Valley Community Foundation","keywords":"Contrast (vision); Phase contrast microscopy; Segmentation; Computer science; Computed tomography; Tomography; Artificial intelligence; Kidney; Radiology; Medicine; Internal medicine; Physics","score_opus":0.019719209794234114,"score_gpt":0.33489176648895974,"score_spread":0.3151725566947256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404163952","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13779618,0.00008387334,0.8577914,0.000118903685,0.002279225,0.001434011,9.217227e-7,0.0004200783,0.000075378026],"genre_scores_gemma":[0.9141409,0.0000019970505,0.084845364,0.00019751482,0.000057557423,0.0004922256,0.000045933426,0.000018358101,0.00020017679],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99693173,0.00021312105,0.0006202236,0.0011394297,0.00076039555,0.00033510767],"domain_scores_gemma":[0.99872965,0.00017548053,0.00011825862,0.0006100867,0.00010454416,0.0002619826],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0033704948,0.00016369719,0.00019355587,0.00073003076,0.00027454607,0.0010826093,0.00022212738,0.0000509003,0.00003858672],"category_scores_gemma":[0.00043311156,0.00014625622,0.00012260351,0.0012653631,0.00013302272,0.00057704275,0.00008918679,0.00027386702,0.000007983904],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022665337,0.0021521521,0.0021061155,0.00017456613,0.00011490462,0.07118984,0.015386733,0.00021888908,0.028467916,0.0003530676,0.0065404084,0.8732727],"study_design_scores_gemma":[0.005195218,0.0034611863,0.00040582274,0.0007044057,0.00015593134,0.0075032813,0.0057649924,0.8531402,0.09094816,0.0135117695,0.017806292,0.0014027429],"about_ca_topic_score_codex":0.000044115306,"about_ca_topic_score_gemma":0.000017668228,"teacher_disagreement_score":0.87187,"about_ca_system_score_codex":0.00008648704,"about_ca_system_score_gemma":0.00016779163,"threshold_uncertainty_score":0.99995434},"labels":[],"label_agreement":null},{"id":"W4404820184","doi":"10.1007/978-3-031-72848-8_19","title":"VQA-Diff: Exploiting VQA and Diffusion for Zero-Shot Image-to-3D Vehicle Asset Generation in Autonomous Driving","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; York University","funders":"","keywords":"Computer science; Shot (pellet); Zero (linguistics); Asset (computer security); Image (mathematics); Computer vision; Diffusion; Artificial intelligence; Computer security; Physics","score_opus":0.027226868663055057,"score_gpt":0.29198741081212204,"score_spread":0.26476054214906697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404820184","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027118567,0.00016482561,0.9934729,0.0012992423,0.00086261693,0.00088962767,0.0000070484484,0.00028794463,0.00030398514],"genre_scores_gemma":[0.06652574,0.00002995683,0.9305288,0.002185441,0.00038714934,0.00008284443,0.000019139936,0.000044420638,0.00019647664],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963654,0.000039768267,0.0006519595,0.0016289754,0.00073602016,0.0005778694],"domain_scores_gemma":[0.99824816,0.00049725553,0.00018799135,0.000680989,0.00016362111,0.00022196732],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001269503,0.00043252078,0.0004377509,0.0010405463,0.00022130029,0.0011522319,0.0013545004,0.00024008869,0.000011091836],"category_scores_gemma":[0.00026243963,0.00041361686,0.00007266193,0.00054649194,0.00029209346,0.0008669274,0.0014300611,0.0006055002,0.000015468382],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031774116,0.000032328782,0.00004581148,0.000114647504,0.000005812143,0.00007896307,0.00242079,0.0013594747,0.061221607,0.0031992232,0.0001244719,0.9313937],"study_design_scores_gemma":[0.00024247197,0.00019159028,0.0001134283,0.0006050927,0.000006837704,0.00002679565,6.8590316e-7,0.9284033,0.031001844,0.038578395,0.00029796202,0.000531581],"about_ca_topic_score_codex":0.000037661714,"about_ca_topic_score_gemma":0.00010050749,"teacher_disagreement_score":0.9308621,"about_ca_system_score_codex":0.000388152,"about_ca_system_score_gemma":0.0002094198,"threshold_uncertainty_score":0.99988467},"labels":[],"label_agreement":null},{"id":"W4404935291","doi":"10.1038/s41598-024-80206-7","title":"An effective and open source interactive 3D medical image segmentation solution","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Shenzhen Key Laboratory Fund","keywords":"Computer science; Segmentation; Image segmentation; Scale-space segmentation; Segmentation-based object categorization; Computation; Software; Feature (linguistics); Source code; Computer vision; Annotation; Artificial intelligence; Scheme (mathematics); Data mining; Algorithm; Programming language","score_opus":0.010309920020639301,"score_gpt":0.3394454397802187,"score_spread":0.3291355197595794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404935291","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013985164,0.00010114397,0.97985184,0.00045370348,0.0034650145,0.0007591377,8.240354e-7,0.00053184945,0.0008513193],"genre_scores_gemma":[0.65136206,0.000012982977,0.3460574,0.00045391035,0.00014207697,0.00032860658,0.000086075706,0.000028319808,0.0015285576],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972142,0.00023180745,0.00037902955,0.0010727831,0.000878163,0.00022401418],"domain_scores_gemma":[0.9987202,0.0001146534,0.0001328621,0.00061143434,0.00013972157,0.00028108194],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003447866,0.00013084059,0.00014743926,0.0002313659,0.00026986605,0.0037422492,0.00057757646,0.00006895369,0.00020195341],"category_scores_gemma":[0.00035410287,0.00011198889,0.000033497046,0.000557058,0.0003362865,0.004156053,0.00063040253,0.00020469895,0.000037477508],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003647028,0.00007225497,0.00005720418,0.000036365258,0.000017257733,0.00061222457,0.0027672702,0.0000012092669,0.1037367,0.00016634272,0.011791085,0.88073844],"study_design_scores_gemma":[0.0003309257,0.0003291902,0.0011544167,0.00051962456,0.000030958625,0.001926604,0.00041571513,0.2784731,0.6868932,0.021878067,0.007501408,0.0005467729],"about_ca_topic_score_codex":0.00012464626,"about_ca_topic_score_gemma":0.00001545323,"teacher_disagreement_score":0.8801917,"about_ca_system_score_codex":0.00011593128,"about_ca_system_score_gemma":0.0001955677,"threshold_uncertainty_score":0.997292},"labels":[],"label_agreement":null},{"id":"W4405489431","doi":"10.1109/embc53108.2024.10782048","title":"SimICL: A Simple Visual In-context Learning Framework for Ultrasound Segmentation","year":2024,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Simple (philosophy); Segmentation; Context (archaeology); Artificial intelligence; Computer vision; Image segmentation; Geology","score_opus":0.018397308092935858,"score_gpt":0.3544214020764311,"score_spread":0.3360240939834952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405489431","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005563036,0.000096010386,0.99232745,0.0004250482,0.0001893749,0.00035558356,9.1043904e-7,0.00072941056,0.0003131558],"genre_scores_gemma":[0.5185257,0.00001645061,0.47989312,0.0010392654,0.000054603333,0.000107345004,0.0000121285,0.0000108849745,0.00034048],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99895024,0.000055673387,0.00024153265,0.00033282788,0.00020714694,0.00021257291],"domain_scores_gemma":[0.99864376,0.0011125465,0.00003174275,0.00011250407,0.00003507804,0.00006436693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038873235,0.00009749049,0.00010205492,0.00014947595,0.000058483944,0.0003723976,0.00024504348,0.00006680095,0.0001909033],"category_scores_gemma":[0.00038136172,0.00008840858,0.000049846265,0.0003932084,0.000026836893,0.0006221629,0.000056657143,0.00020293143,0.00005393282],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000674005,0.000059521382,0.00042018242,0.00009567071,0.000018484345,0.000015150602,0.0035278099,0.00004371012,0.025098866,0.08627948,0.004296441,0.8801379],"study_design_scores_gemma":[0.00056225876,0.0005279399,0.0003760266,0.00023841044,0.000012500413,0.000020662006,0.002354787,0.29918262,0.5230201,0.16825786,0.004927596,0.0005192644],"about_ca_topic_score_codex":0.000034269764,"about_ca_topic_score_gemma":0.000018290364,"teacher_disagreement_score":0.8796187,"about_ca_system_score_codex":0.000078784535,"about_ca_system_score_gemma":0.00005215481,"threshold_uncertainty_score":0.36051974},"labels":[],"label_agreement":null},{"id":"W4405558092","doi":"10.23952/jnva.9.2025.1.02","title":"Gradient approximation algorithms for the $\\sigma$-OSS + concave function maximization","year":2024,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sigma; Function (biology); Maximization; Concave function; Algorithm; Mathematics; Mathematical optimization; Applied mathematics; Combinatorics; Computer science; Physics; Geometry","score_opus":0.022660044313509913,"score_gpt":0.29863115728155554,"score_spread":0.2759711129680456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405558092","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000114594426,0.00049572607,0.9961352,0.0027871549,0.00029649388,0.000112210524,0.000010985438,0.000029711853,0.000017971384],"genre_scores_gemma":[0.07447766,0.0003176523,0.92341363,0.00068758766,0.00080577645,0.000021405747,0.00007506971,0.000008965587,0.00019227172],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989454,0.00005345144,0.0003946126,0.00013921384,0.00038973684,0.00007758771],"domain_scores_gemma":[0.9987965,0.00044994638,0.00021137371,0.000096627686,0.00039072998,0.000054779186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001005866,0.00007228332,0.00014215574,0.00032437788,0.00012314424,0.00027576264,0.00017411735,0.00003860079,0.000032917338],"category_scores_gemma":[0.00012689811,0.00004546998,0.00017956347,0.0007943929,0.00002564626,0.0005505358,0.000028098086,0.000101757396,0.0000010298972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010180692,0.00029790183,0.00061259273,0.00014902253,0.006503716,0.000015159707,0.002302037,0.03249968,0.0012586627,0.12687406,0.005205793,0.8241796],"study_design_scores_gemma":[0.0001705794,0.00009241933,0.0019211858,0.000011237265,0.0006519691,0.00001519389,0.00003565215,0.9890771,0.00027449516,0.006494137,0.0012026662,0.00005339044],"about_ca_topic_score_codex":0.0000062271374,"about_ca_topic_score_gemma":0.0000017257333,"teacher_disagreement_score":0.9565774,"about_ca_system_score_codex":0.00004447734,"about_ca_system_score_gemma":0.00007689683,"threshold_uncertainty_score":0.26591852},"labels":[],"label_agreement":null},{"id":"W4405591760","doi":"10.1002/hbm.70082","title":"Subject‐Level Segmentation Precision Weights for Volumetric Studies Involving Label Fusion","year":2024,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; National Institute of Neurological Disorders and Stroke; National Institute of General Medical Sciences; Northern California Institute for Research and Education; National Institute of Mental Health; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association","keywords":"Segmentation; Weighting; Artificial intelligence; Computer science; Pattern recognition (psychology); Neuroimaging; Region of interest; Market segmentation; Volume (thermodynamics); Psychology; Medicine; Neuroscience; Radiology","score_opus":0.13998585122374044,"score_gpt":0.38085942406392576,"score_spread":0.24087357284018532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405591760","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014115585,0.0024634649,0.9801617,0.00091646204,0.0005966066,0.0007259648,0.0000042951706,0.0008741066,0.00014180991],"genre_scores_gemma":[0.07010586,0.0002265567,0.92374825,0.0016300374,0.00042159963,0.0003790917,0.000055778313,0.000045216686,0.0033876037],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980375,0.00012850792,0.00045376696,0.00059025607,0.00049000996,0.00029991998],"domain_scores_gemma":[0.998246,0.0010564184,0.000108521985,0.00032326634,0.00018308939,0.00008268111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001387122,0.00018457833,0.00020784924,0.00077729445,0.0004977608,0.0004451971,0.00050406903,0.000072118506,0.000030950527],"category_scores_gemma":[0.00062884635,0.00016769243,0.00007994964,0.0010349279,0.000059849965,0.0010176711,0.00032065852,0.00015459514,0.00003429008],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003672555,0.000055249067,0.00008458462,0.00058282795,0.00007160326,0.000017650082,0.007792168,0.0000017696868,0.54583615,0.019935492,0.055921752,0.3696971],"study_design_scores_gemma":[0.003943192,0.0016218541,0.015957985,0.0068929708,0.00010725686,0.000055914188,0.0046316953,0.3605355,0.20458017,0.379221,0.019869342,0.0025831233],"about_ca_topic_score_codex":0.000011329544,"about_ca_topic_score_gemma":0.0000073938754,"teacher_disagreement_score":0.36711398,"about_ca_system_score_codex":0.00022070268,"about_ca_system_score_gemma":0.000050099665,"threshold_uncertainty_score":0.68382984},"labels":[],"label_agreement":null},{"id":"W4405602834","doi":"10.2316/j.2025.206-1048","title":"IMAGE STYLE MIGRATION BASED ON CYCLEGAN WITH SAME MAPPING LOSS, 23-32.","year":2024,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Style (visual arts); Image (mathematics); Computer science; Artificial intelligence; Computer vision; Art; Visual arts","score_opus":0.009722148558883544,"score_gpt":0.2729533958971858,"score_spread":0.2632312473383023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405602834","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005273823,0.000052115167,0.9837395,0.010203706,0.00042133132,0.00006250039,0.0000023643352,0.00009664763,0.00014799643],"genre_scores_gemma":[0.5034742,0.00004218559,0.49570602,0.0005794462,0.00013874624,0.0000018295123,0.000009404375,0.000007126336,0.0000410614],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880123,0.00004124498,0.00031125662,0.00012834163,0.0006414646,0.00007647403],"domain_scores_gemma":[0.9992357,0.00011605867,0.00019004426,0.000085159794,0.0003119739,0.000061090286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034289787,0.00008577648,0.00009262792,0.00035381093,0.000034976947,0.0005973639,0.0002768966,0.00003194505,0.000014067666],"category_scores_gemma":[0.00006279289,0.00006541505,0.000039876686,0.00015565725,0.0000378303,0.0009687729,0.000028717232,0.00014054748,0.000008327121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012373192,0.0005739363,0.0015277613,0.00030200067,0.0004676381,0.0017521544,0.004511811,0.037943404,0.060864065,0.05993902,0.012670717,0.8193238],"study_design_scores_gemma":[0.00030855322,0.00018535223,0.0020037415,0.0005119224,0.000009234137,0.00014335079,0.00003122266,0.988905,0.005796866,0.0015600391,0.00045266107,0.000092022536],"about_ca_topic_score_codex":0.0000046608984,"about_ca_topic_score_gemma":0.0000018916293,"teacher_disagreement_score":0.95096165,"about_ca_system_score_codex":0.00008211983,"about_ca_system_score_gemma":0.000088570676,"threshold_uncertainty_score":0.57603943},"labels":[],"label_agreement":null},{"id":"W4405713400","doi":"10.29173/hsi482","title":"Image assemblage","year":2022,"lang":"en","type":"article","venue":"Health Science Inquiry","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Assemblage (archaeology); Geology; Computer science; Paleontology","score_opus":0.06903694147594527,"score_gpt":0.40604564201390664,"score_spread":0.3370087005379614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405713400","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045496607,0.00007830239,0.984597,0.007098167,0.0013396922,0.00029172288,0.0000013944809,0.00057663786,0.0014673797],"genre_scores_gemma":[0.23784702,0.00002611626,0.73466843,0.026924614,0.000116425246,0.00018468518,0.0000029266375,0.0000106736325,0.00021908846],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99697614,0.000106170155,0.00032156444,0.00055461435,0.0014587253,0.0005828125],"domain_scores_gemma":[0.9986396,0.00004581721,0.00016441122,0.0006974194,0.00008027707,0.00037248654],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0035843472,0.0000907382,0.00012851082,0.00032737225,0.0013352641,0.00021429954,0.002436567,0.000011712792,0.00024271589],"category_scores_gemma":[0.00010035181,0.00009072382,0.000030704337,0.0021270025,0.0005994553,0.0013688115,0.0013995045,0.00027298235,0.000070732385],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005843565,0.00039938494,0.00057998154,0.000085687476,0.0000032638911,0.000118383694,0.015056982,0.00002419645,0.05866166,0.038965266,0.20110068,0.6849987],"study_design_scores_gemma":[0.004244798,0.008308478,0.0877621,0.00021786198,0.000011672045,0.0020573973,0.019409174,0.35281092,0.32561198,0.057518743,0.13730296,0.0047439206],"about_ca_topic_score_codex":0.00004998213,"about_ca_topic_score_gemma":7.511859e-7,"teacher_disagreement_score":0.68025476,"about_ca_system_score_codex":0.0004046296,"about_ca_system_score_gemma":0.0013206943,"threshold_uncertainty_score":0.99996483},"labels":[],"label_agreement":null},{"id":"W4406213437","doi":"10.1016/j.cageo.2025.105853","title":"Addressing class imbalance in micro-CT image segmentation: A modified U-Net model with pixel-level class weighting","year":2025,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Class (philosophy); Weighting; Pixel; Segmentation; Artificial intelligence; Image (mathematics); Computer science; Net (polyhedron); Image segmentation; Pattern recognition (psychology); Computer vision; Mathematics; Medicine; Radiology","score_opus":0.046415702474236036,"score_gpt":0.31670576651272137,"score_spread":0.2702900640384853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406213437","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03522173,0.00010722407,0.95969385,0.0020387226,0.0004010152,0.00040570163,0.0000059525155,0.00034210522,0.0017837163],"genre_scores_gemma":[0.26933223,0.000020888889,0.7276942,0.002503689,0.000028216316,0.000058728358,0.000006808877,0.000008458119,0.00034677636],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99694985,0.00016365701,0.00056148454,0.0009868401,0.000712837,0.00062535366],"domain_scores_gemma":[0.99864805,0.00023707432,0.00025562182,0.00056462415,0.00014278434,0.00015183436],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007871455,0.0002977485,0.00034442457,0.0004969964,0.0003531133,0.0008238571,0.0019898557,0.00005383185,0.000005131192],"category_scores_gemma":[0.00005332586,0.0002533698,0.00005972201,0.0017856676,0.00053638034,0.0017065022,0.00054825353,0.00030532235,0.0000069961575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000104057544,0.0009958174,0.0116580995,0.0005456605,0.000104841936,0.0004969938,0.008017703,0.033958632,0.34681508,0.025775656,0.020469429,0.55105805],"study_design_scores_gemma":[0.0007333125,0.000066199944,0.0018621656,0.00040230673,0.000006572573,0.000021788741,0.00017101571,0.9577952,0.036221914,0.0023073796,0.00008275687,0.00032937585],"about_ca_topic_score_codex":0.00009539212,"about_ca_topic_score_gemma":0.00004556213,"teacher_disagreement_score":0.9238366,"about_ca_system_score_codex":0.0001859289,"about_ca_system_score_gemma":0.00044631024,"threshold_uncertainty_score":0.99999183},"labels":[],"label_agreement":null},{"id":"W4406264718","doi":"10.1109/cce62852.2024.10771056","title":"Evaluation of Segmentation Quality in Magnetic Resonance Images Using Singular Value Decomposition: A Feasibility Study","year":2024,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Singular value decomposition; Decomposition; Magnetic resonance imaging; Segmentation; Quality (philosophy); Computer science; Image segmentation; Artificial intelligence; Computer vision; Nuclear magnetic resonance; Pattern recognition (psychology); Physics; Chemistry; Radiology; Medicine","score_opus":0.09619900559892551,"score_gpt":0.45612137577924833,"score_spread":0.3599223701803228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406264718","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37762618,0.0008176715,0.6201965,0.00006466941,0.00008753406,0.00083225686,0.0000016929158,0.0001323008,0.00024116573],"genre_scores_gemma":[0.72069806,0.0000044345006,0.27919298,0.000037955353,0.0000115074845,0.00003561584,0.0000027484543,0.000004733234,0.00001199498],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960754,0.0014247746,0.0006157737,0.00047630415,0.0012690355,0.000138703],"domain_scores_gemma":[0.9989764,0.00016248302,0.00008011838,0.00041467475,0.00032201989,0.000044312583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073569394,0.000110315086,0.000173246,0.00020279209,0.000040195424,0.00014102347,0.00027900308,0.00003641603,0.000120140954],"category_scores_gemma":[0.00024311704,0.000103488186,0.000042565065,0.0007696902,0.000059841117,0.0007909337,0.00010972542,0.000100271485,0.0000045277093],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017764445,0.0015013424,0.011058799,0.00012991315,0.000018715025,0.000024893337,0.0043958127,0.0004511248,0.1679491,0.0026228568,0.00012252443,0.81170714],"study_design_scores_gemma":[0.0009908268,0.00035383113,0.12737942,0.00017195434,0.00005058696,0.000006688313,0.00044042245,0.6359484,0.2140366,0.020410184,0.0000011353198,0.00020994639],"about_ca_topic_score_codex":0.00059566845,"about_ca_topic_score_gemma":0.000031355135,"teacher_disagreement_score":0.8114972,"about_ca_system_score_codex":0.00038782752,"about_ca_system_score_gemma":0.00022168983,"threshold_uncertainty_score":0.4220126},"labels":[],"label_agreement":null},{"id":"W4406316916","doi":"10.48550/arxiv.2501.05633","title":"Regularized Top-$k$: A Bayesian Framework for Gradient Sparsification","year":2025,"lang":"en","type":"preprint","venue":"Open MIND","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bayesian probability; Computer science; Algorithm; Mathematics; Artificial intelligence; Econometrics","score_opus":0.0663815686913643,"score_gpt":0.3841924460814526,"score_spread":0.3178108773900883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406316916","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000909853,0.000046980716,0.9891994,0.003385565,0.00074899243,0.0026858924,0.000046662524,0.000027202774,0.0037683204],"genre_scores_gemma":[0.001111642,0.000022052858,0.9937752,0.00044187985,0.00007786955,0.00077346055,0.00012072178,0.000008909342,0.0036682922],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99787885,0.00012425354,0.0004759451,0.0009672408,0.00030035083,0.00025335123],"domain_scores_gemma":[0.9975318,0.00025337812,0.00033462612,0.0015798804,0.00016560723,0.00013475194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085712096,0.00022081753,0.00035371605,0.0001639993,0.00011102736,0.00093985605,0.0036368652,0.00034538138,0.0003259222],"category_scores_gemma":[0.0004346413,0.0002233746,0.00012742289,0.00024441566,0.000055931276,0.00024919613,0.0026095565,0.00040753148,0.000037424965],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020419337,0.000115505805,0.000009653899,0.00007877478,0.00004783075,0.0000042852876,0.0012238659,0.000011813993,0.00026245136,0.018425921,0.0050548413,0.9747446],"study_design_scores_gemma":[0.0011466946,0.00018922403,0.00014237636,0.002116441,0.00012492666,0.0000055300816,0.0001273245,0.08442766,0.25309068,0.5985802,0.05894042,0.0011085272],"about_ca_topic_score_codex":0.00003405717,"about_ca_topic_score_gemma":0.0000059566014,"teacher_disagreement_score":0.9736361,"about_ca_system_score_codex":0.00013008236,"about_ca_system_score_gemma":0.0004118655,"threshold_uncertainty_score":0.91089517},"labels":[],"label_agreement":null},{"id":"W4406785230","doi":"10.48550/arxiv.2501.13193","title":"Revisiting Data Augmentation for Ultrasound Images","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Ultrasound; Computer vision; Computer science; Artificial intelligence; Computer graphics (images); Medicine; Radiology","score_opus":0.10874175426895351,"score_gpt":0.3885910580925978,"score_spread":0.2798493038236443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406785230","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020685357,0.00032510996,0.99264884,0.0016605933,0.00076250505,0.0009041692,0.00022941234,0.00060321094,0.0007976266],"genre_scores_gemma":[0.020853838,0.0004535125,0.97114545,0.0026472977,0.0005781282,0.00033508704,0.0019665451,0.000024086727,0.0019960832],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976068,0.00012839513,0.00054586225,0.001093352,0.00034167615,0.00028391404],"domain_scores_gemma":[0.996402,0.0006994383,0.0003671453,0.0022346599,0.00020420425,0.00009254894],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010188604,0.00025084257,0.00030470252,0.00015078983,0.00014988326,0.00033739177,0.0031867302,0.0001634738,0.000044722492],"category_scores_gemma":[0.0012989735,0.00025886903,0.000089359375,0.00020509417,0.00006776782,0.0007325191,0.003056477,0.0003872661,0.000029245493],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028829041,0.00023730368,0.053425107,0.004012927,0.0004255645,0.00003877183,0.0013867761,0.00006146921,0.049793616,0.0029136129,0.20984499,0.67783105],"study_design_scores_gemma":[0.0026174875,0.00018877481,0.06517348,0.004982897,0.00051745045,0.000030146659,0.0004074291,0.025372515,0.8622319,0.022561,0.01287514,0.0030417969],"about_ca_topic_score_codex":0.000058589518,"about_ca_topic_score_gemma":0.0000019362346,"teacher_disagreement_score":0.81243825,"about_ca_system_score_codex":0.000102464204,"about_ca_system_score_gemma":0.00023933269,"threshold_uncertainty_score":0.99998635},"labels":[],"label_agreement":null},{"id":"W4407133000","doi":"10.1016/j.patrec.2025.01.031","title":"Geometric insights into focal loss: Reducing curvature for enhanced model calibration","year":2025,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Curvature; Calibration; Artificial intelligence; Computer science; Mathematics; Computer vision; Algorithm; Geometry; Statistics","score_opus":0.01948090364215348,"score_gpt":0.2825082187832532,"score_spread":0.2630273151410997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407133000","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026406461,0.00004569392,0.96645015,0.0056747682,0.00040542567,0.000556094,0.000006454857,0.00035419944,0.00010074902],"genre_scores_gemma":[0.6585613,0.00003097957,0.30627987,0.03430428,0.00012789955,0.00044025888,0.00017692873,0.000016263211,0.00006224773],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99853456,0.0000787362,0.0003702304,0.0005091391,0.00027190504,0.00023541522],"domain_scores_gemma":[0.9991582,0.0001883125,0.00013769607,0.00030171455,0.00013399501,0.00008006172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001987003,0.00016690773,0.00016987987,0.00058439485,0.00015116716,0.00018877514,0.00041461445,0.00010605426,0.000019590223],"category_scores_gemma":[0.00016840824,0.0001666569,0.00008699936,0.00079542026,0.000051686115,0.0009327577,0.0000988017,0.00018869736,0.000017176866],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010525847,0.000064263506,0.000036293113,0.0001435177,0.000032860873,0.0000039177266,0.0008045848,0.0002537048,0.13140236,0.00006148778,0.015255792,0.8519307],"study_design_scores_gemma":[0.00060162065,0.000043405696,0.000072152536,0.00018433762,0.000020452151,0.0000013970559,0.000016213526,0.329515,0.6611233,0.008061782,0.000106522704,0.00025379614],"about_ca_topic_score_codex":0.000023726614,"about_ca_topic_score_gemma":0.000004676513,"teacher_disagreement_score":0.8516769,"about_ca_system_score_codex":0.000115265546,"about_ca_system_score_gemma":0.000055190998,"threshold_uncertainty_score":0.67960715},"labels":[],"label_agreement":null},{"id":"W4407269637","doi":"10.1007/978-3-031-79103-1_12","title":"Generative Style Transfer for MR Image Segmentation: A Case of Glioma Segmentation in Sub-Saharan Africa","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University; University of British Columbia; Artificial Intelligence in Medicine (Canada); Lawson Health Research Institute","funders":"","keywords":"Segmentation; Style (visual arts); Generative grammar; Artificial intelligence; Computer science; Image segmentation; Computer vision; Pattern recognition (psychology); Natural language processing; Geography","score_opus":0.03798201701427577,"score_gpt":0.32664313038377163,"score_spread":0.28866111336949585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407269637","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000112586924,0.0004254009,0.9849281,0.0004953567,0.0001301105,0.001449079,0.00008236002,0.000066065935,0.012310942],"genre_scores_gemma":[0.008308042,0.0024315955,0.98763967,0.0005834667,0.000019765013,0.00040357577,0.00019508443,0.000010554264,0.00040825427],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977857,0.00007834611,0.0011593372,0.00034611323,0.00040349123,0.00022703236],"domain_scores_gemma":[0.9974192,0.00042046458,0.00029900047,0.0011529698,0.0006211912,0.000087198314],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013093977,0.00024075333,0.00033757766,0.0017418001,0.00027335453,0.00034578115,0.0016198212,0.00012644204,0.000010549334],"category_scores_gemma":[0.00006715701,0.00025345187,0.00006614233,0.0008430114,0.000736421,0.0060388953,0.0006578147,0.00027225266,0.000004808536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020679761,0.00011864943,0.000006022464,0.0003324115,0.000024611223,0.000013834979,0.012842689,0.00009645889,0.004400645,0.16139653,0.002795102,0.81795233],"study_design_scores_gemma":[0.0038670981,0.0004999797,0.00010543568,0.0012666526,0.000048996397,0.00024446743,0.0004287915,0.8973111,0.07163632,0.013133251,0.010294521,0.0011634133],"about_ca_topic_score_codex":0.00002244455,"about_ca_topic_score_gemma":0.000036280388,"teacher_disagreement_score":0.8972146,"about_ca_system_score_codex":0.00023272696,"about_ca_system_score_gemma":0.0003868504,"threshold_uncertainty_score":0.9999918},"labels":[],"label_agreement":null},{"id":"W4407573296","doi":"10.1117/12.3047042","title":"Efficient real-time 3D tracking of liver targets through image registration and LightGBM","year":2025,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Image registration; Tracking (education); Image (mathematics)","score_opus":0.011659765626251255,"score_gpt":0.2850959394823316,"score_spread":0.2734361738560803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407573296","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026296075,0.000079462625,0.9611236,0.00050708966,0.000041213098,0.00016750861,6.60524e-7,0.00019916514,0.035251737],"genre_scores_gemma":[0.089402,0.0000949157,0.90853167,0.00035101525,0.000010634382,0.0000080380305,0.0000021382039,0.0000033207602,0.0015962906],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99914694,0.000048159767,0.00024823225,0.00023872983,0.00020711437,0.00011081257],"domain_scores_gemma":[0.9994182,0.00009449774,0.00008852676,0.00026338786,0.00010345398,0.000031923468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026744336,0.00007517717,0.00011669393,0.000063959604,0.00004974426,0.00007164712,0.00023312334,0.000040168492,0.00007675034],"category_scores_gemma":[0.000080151796,0.000063865285,0.000023653025,0.00022650046,0.00009376731,0.00026299874,0.00011232467,0.00005250788,0.000009277913],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010186862,0.00023277405,0.00006659029,0.00018138542,0.000026763377,0.000027818158,0.0024891663,0.000018157236,0.7582467,0.075993985,0.02235825,0.1403482],"study_design_scores_gemma":[0.00022920112,0.000064876476,0.0015926878,0.00008298794,0.000008880425,0.0000039146194,0.0000280424,0.09378462,0.9018577,0.0020339754,0.0001998397,0.00011328641],"about_ca_topic_score_codex":0.00012668163,"about_ca_topic_score_gemma":0.0000029324904,"teacher_disagreement_score":0.14361095,"about_ca_system_score_codex":0.000020820253,"about_ca_system_score_gemma":0.000041808224,"threshold_uncertainty_score":0.26043507},"labels":[],"label_agreement":null},{"id":"W4407590819","doi":"10.1007/s11548-025-03330-3","title":"Leveraging deep learning for nonlinear shape representation in anatomically parameterized statistical shape models","year":2025,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Parameterized complexity; Representation (politics); Computer science; Artificial intelligence; Nonlinear system; Deep learning; Theoretical computer science; Computer vision; Algorithm; Physics","score_opus":0.03346051803779087,"score_gpt":0.3284559460568094,"score_spread":0.2949954280190185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407590819","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07923774,0.00014522563,0.918103,0.0015098077,0.0008592639,0.00008975277,0.0000014118003,0.000032136308,0.000021656637],"genre_scores_gemma":[0.4703306,0.00015622996,0.52796984,0.0013779274,0.000121616686,0.000009524124,0.00002070759,0.0000052408445,0.000008330352],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981726,0.00035101737,0.00081846997,0.0002485903,0.00023949977,0.00016985943],"domain_scores_gemma":[0.9959749,0.0031700023,0.00032199992,0.0000919406,0.0003687655,0.000072399045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010558844,0.00011423909,0.00038592436,0.00064708566,0.00004966084,0.00012774038,0.00043223563,0.000097012715,0.000012897934],"category_scores_gemma":[0.00046307076,0.00010599011,0.00011157471,0.00017729904,0.00009453758,0.00047831246,0.00011531988,0.00029924433,3.8247686e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027400893,0.000117082665,0.008804549,0.000020160784,0.00023486653,0.00024481773,0.0003019483,0.001780338,0.0007170301,0.0024783004,0.0011771073,0.98384976],"study_design_scores_gemma":[0.000812054,0.00005502147,0.0209402,0.00010357561,0.000010196919,0.00029219186,0.000018213606,0.97014654,0.00029433152,0.007072679,0.00016013831,0.000094874464],"about_ca_topic_score_codex":0.0000038339704,"about_ca_topic_score_gemma":6.7696647e-7,"teacher_disagreement_score":0.98375493,"about_ca_system_score_codex":0.00007481377,"about_ca_system_score_gemma":0.000132,"threshold_uncertainty_score":0.43221512},"labels":[],"label_agreement":null},{"id":"W4407599638","doi":"10.1016/j.media.2025.103501","title":"Neighbor-aware calibration of segmentation networks with penalty-based constraints","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Ministère de l’Emploi et de la Solidarité Sociale (Québec)","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Computer science; Calibration; Artificial intelligence; Computer vision; Pattern recognition (psychology); Mathematics","score_opus":0.005711010240022647,"score_gpt":0.28069177106980087,"score_spread":0.27498076082977824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407599638","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00044546003,0.000034336488,0.99666137,0.0018840742,0.000044150434,0.00018474426,0.000004208704,0.00017141226,0.0005702461],"genre_scores_gemma":[0.70117813,0.000019871071,0.29465127,0.0038027272,0.000025961855,0.000049947917,0.00015145572,0.0000074797886,0.00011312669],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976215,0.00023150562,0.0005415003,0.0003976483,0.0009883882,0.00021950164],"domain_scores_gemma":[0.99853396,0.00029965033,0.00022364066,0.00048243205,0.00026117978,0.0001991042],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006713295,0.00015314374,0.00036993963,0.0005215077,0.000077006815,0.00010612334,0.00063193945,0.000111843816,0.0011204999],"category_scores_gemma":[0.00024101321,0.000119967786,0.00014829681,0.0030207168,0.0004563906,0.0004425587,0.00009361044,0.00020555519,0.0000025920353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012950119,0.0012886871,0.04490748,0.0004203147,0.0039545638,0.00045520318,0.00060065836,0.007369347,0.007389649,0.007126217,0.016117766,0.9102406],"study_design_scores_gemma":[0.0006447902,0.00006793241,0.0011743683,0.00008779681,0.0003929422,0.0000014762468,0.000069241294,0.9575278,0.039707985,0.0001815997,0.000009395699,0.0001346815],"about_ca_topic_score_codex":0.00008426035,"about_ca_topic_score_gemma":0.000049893915,"teacher_disagreement_score":0.9501584,"about_ca_system_score_codex":0.00005745165,"about_ca_system_score_gemma":0.0004008229,"threshold_uncertainty_score":0.99979264},"labels":[],"label_agreement":null},{"id":"W4407687303","doi":"10.1007/978-3-031-81854-7_13","title":"RepViT-MedSAM: Efficient Segment Anything in the Medical Images","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Computer vision; Computer graphics (images); Artificial intelligence","score_opus":0.012691956725138674,"score_gpt":0.28407882433469306,"score_spread":0.2713868676095544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407687303","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000017668168,0.0005379569,0.9852475,0.007221081,0.0012476448,0.0006926709,0.0000028833508,0.00022879493,0.0048038336],"genre_scores_gemma":[0.018612761,0.00021020684,0.95382196,0.026224744,0.00045679236,0.0000757298,0.000009277655,0.000031167234,0.00055737176],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99328464,0.00018888655,0.00089209114,0.0016142318,0.003283316,0.0007368152],"domain_scores_gemma":[0.9959356,0.0016080965,0.00027823745,0.001785837,0.00018209298,0.00021010476],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.005051844,0.00050097855,0.0005293638,0.0011099095,0.00024547253,0.00069506274,0.006762064,0.00038345324,0.00007150572],"category_scores_gemma":[0.0008773474,0.00036016063,0.0001347872,0.001193028,0.0010423049,0.00034449014,0.0022154946,0.0017431572,0.00002378613],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025166607,0.00007259709,0.000013172028,0.000054095028,0.000005798597,0.0006130635,0.001325931,0.0013377072,0.00011182877,0.005016767,0.00062137377,0.9908252],"study_design_scores_gemma":[0.0010835488,0.00035696174,0.00035789594,0.0043984773,0.000023138202,0.0005478086,0.000004782774,0.87632126,0.017443266,0.09525915,0.0025058743,0.0016978255],"about_ca_topic_score_codex":0.000056919103,"about_ca_topic_score_gemma":0.00004369945,"teacher_disagreement_score":0.98912734,"about_ca_system_score_codex":0.00043924412,"about_ca_system_score_gemma":0.0011319118,"threshold_uncertainty_score":0.999885},"labels":[],"label_agreement":null},{"id":"W4407717702","doi":"10.1049/htl2.12117","title":"Deep regression 2D‐3D ultrasound registration for liver motion correction in focal tumour thermal ablation","year":2025,"lang":"en","type":"article","venue":"Healthcare Technology Letters","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lawson Health Research Institute; Robarts Clinical Trials; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Institute for Cancer Research","keywords":"Computer science; Artificial intelligence; Computer vision; Translation (biology); Image registration; Rotation (mathematics); Centroid; Ablation; Image (mathematics); Medicine","score_opus":0.014625207260121221,"score_gpt":0.30305341737144476,"score_spread":0.2884282101113235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407717702","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030051598,0.00014307472,0.92622936,0.041229263,0.00070094725,0.00077951513,8.1765984e-7,0.00081290444,0.000052527947],"genre_scores_gemma":[0.86451703,0.000044522934,0.13039695,0.0045858584,0.00004764832,0.0003082073,0.000033730208,0.000010778393,0.000055252804],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983993,0.00014441143,0.0004370753,0.0004961595,0.0002065281,0.0003165123],"domain_scores_gemma":[0.99904233,0.00018815561,0.00020675121,0.0003969883,0.00012307438,0.000042703934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004939901,0.00015443035,0.00018174379,0.00074569305,0.00017080944,0.00004968468,0.00038482586,0.0003168609,0.000004970828],"category_scores_gemma":[0.0003708109,0.00015381034,0.00004211113,0.00089872064,0.00012148667,0.00045365776,0.000060507293,0.00038840348,0.0000052451037],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048513848,0.000093656454,0.0092380075,0.00016174684,0.00001002527,0.000020799058,0.0004183088,0.00016142665,0.14229469,0.009409128,0.005915089,0.8322286],"study_design_scores_gemma":[0.0024663557,0.00056133984,0.0993558,0.0010773742,0.000029686857,0.00011883191,0.000490163,0.41230237,0.46804538,0.0141340615,0.00062508066,0.00079357135],"about_ca_topic_score_codex":0.00017125321,"about_ca_topic_score_gemma":0.00022002672,"teacher_disagreement_score":0.83446544,"about_ca_system_score_codex":0.0003821906,"about_ca_system_score_gemma":0.00007501617,"threshold_uncertainty_score":0.62722033},"labels":[],"label_agreement":null},{"id":"W4408061740","doi":"10.18280/ts.420145","title":"MSF-TransUNet: A Multi-Scale Feature Fusion Transformer-Based U-Net for Medical Image Segmentation with Uniform Attention","year":2025,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Zhejiang Province","keywords":"Artificial intelligence; Segmentation; Computer science; Computer vision; Image segmentation; Transformer; Pattern recognition (psychology); Feature (linguistics); Scale (ratio); Cartography; Geography; Engineering; Electrical engineering; Voltage","score_opus":0.009767675175327109,"score_gpt":0.2819626240038401,"score_spread":0.272194948828513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408061740","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041012624,0.000030134803,0.98640674,0.007044996,0.00012067684,0.0016529551,0.000031693147,0.0003661893,0.00024534698],"genre_scores_gemma":[0.072975025,0.000021588672,0.9216696,0.0038569658,0.000061498016,0.0006841886,0.00034386347,0.000023076585,0.0003641949],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976725,0.000089636975,0.00044506704,0.0005132632,0.000905618,0.00037391591],"domain_scores_gemma":[0.99913603,0.00012730053,0.00011602259,0.00024408066,0.00018412642,0.0001924542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006937422,0.00025440866,0.00023987026,0.00023998685,0.00022151033,0.00015843633,0.0005730633,0.00014834866,0.00025108355],"category_scores_gemma":[0.000016343147,0.00020580245,0.00013148387,0.0004775097,0.00013038123,0.0006058011,0.000023427401,0.00020605998,0.000006050486],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000526323,0.0014985845,0.0008127389,0.00071771146,0.00014625903,0.000033446722,0.0011275326,0.00007876921,0.3716403,0.0014050384,0.014518926,0.60749435],"study_design_scores_gemma":[0.01477127,0.0014262437,0.005209962,0.0006848141,0.00014605084,0.000012096789,0.00030203894,0.43128946,0.5430155,0.000483903,0.0020216836,0.0006369909],"about_ca_topic_score_codex":0.00001837697,"about_ca_topic_score_gemma":0.00014293354,"teacher_disagreement_score":0.60685736,"about_ca_system_score_codex":0.0001241954,"about_ca_system_score_gemma":0.00025316764,"threshold_uncertainty_score":0.83923805},"labels":[],"label_agreement":null},{"id":"W4408110112","doi":"10.1007/978-3-031-82475-3_2","title":"Medical Image Denosing via Explainable AI Feature Preserving Loss","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Feature (linguistics); Image (mathematics); Pattern recognition (psychology); Computer vision; Linguistics","score_opus":0.009270335879796882,"score_gpt":0.28136037096117084,"score_spread":0.27209003508137397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408110112","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000031037878,0.0005723929,0.9844339,0.0072703464,0.0014361949,0.00052857056,0.0000031717939,0.0005394109,0.0052128895],"genre_scores_gemma":[0.0017367891,0.00009829521,0.9822127,0.012682315,0.0004915057,0.00002744539,0.000010698405,0.00003848443,0.0027017465],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9937025,0.00009578607,0.0006596553,0.0019080242,0.0026932894,0.0009407845],"domain_scores_gemma":[0.9961345,0.000819456,0.00028590809,0.0018348196,0.00048338002,0.00044189382],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.0017770528,0.0006315645,0.00066337665,0.0010807501,0.00040684425,0.0010647977,0.006894485,0.0007014066,0.00021455239],"category_scores_gemma":[0.00076556636,0.0005807795,0.000161076,0.0010288202,0.0011365758,0.0015802067,0.0042951037,0.0020750486,0.00003711002],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000051415027,0.00004457076,0.00002688805,0.00021315936,0.000017299006,0.0009649482,0.00050246506,0.00026169242,0.0005582102,0.0046928306,0.0038049174,0.9889079],"study_design_scores_gemma":[0.00059024733,0.0001927601,0.000035391287,0.0034474349,0.000021364991,0.00035590524,4.3935174e-7,0.7373894,0.047662456,0.20419522,0.00468703,0.0014223551],"about_ca_topic_score_codex":0.000051203013,"about_ca_topic_score_gemma":0.000047287336,"teacher_disagreement_score":0.9874855,"about_ca_system_score_codex":0.0004943138,"about_ca_system_score_gemma":0.0014627597,"threshold_uncertainty_score":0.99997216},"labels":[],"label_agreement":null},{"id":"W4408120978","doi":"10.1145/3704137.3704167","title":"Attention Mechanisms vs. Frequency filters in Medical Image Segmentation: a Comparative Study","year":2024,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Image segmentation; Computer science; Computer vision; Artificial intelligence; Segmentation","score_opus":0.026343011696890305,"score_gpt":0.3540092373500399,"score_spread":0.3276662256531496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408120978","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007482893,0.000033260698,0.9857242,0.0017579385,0.00033526646,0.00070583314,0.0000014720041,0.00072149007,0.0032376293],"genre_scores_gemma":[0.5284489,0.000011058631,0.4700037,0.00081595505,0.00003501228,0.00024985813,0.000013465465,0.000009640373,0.00041246545],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99769,0.00024584972,0.00043884924,0.00050665124,0.0009004382,0.0002181919],"domain_scores_gemma":[0.9993353,0.00013477015,0.000040971358,0.00028067286,0.000055823948,0.00015248252],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008574742,0.0001502807,0.00019103823,0.00028274313,0.000048382502,0.0003223383,0.0006134321,0.0000570163,0.0014667723],"category_scores_gemma":[0.000044176435,0.00012407228,0.00005021671,0.00076088647,0.000061795894,0.0011658915,0.00018617394,0.00024169571,0.00027589974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044042245,0.0051254043,0.0025584693,0.00035904424,0.00039619365,0.007423286,0.053522717,0.000009240624,0.19489108,0.28690884,0.0765958,0.3721659],"study_design_scores_gemma":[0.006027497,0.0041993735,0.017782712,0.0011029576,0.000069158494,0.0002911513,0.020894065,0.49882415,0.29550207,0.15299985,0.000120735305,0.002186276],"about_ca_topic_score_codex":0.00017112942,"about_ca_topic_score_gemma":0.00007277454,"teacher_disagreement_score":0.52096593,"about_ca_system_score_codex":0.000120896824,"about_ca_system_score_gemma":0.00011574146,"threshold_uncertainty_score":0.99944603},"labels":[],"label_agreement":null},{"id":"W4408355271","doi":"10.1109/icassp49660.2025.10888433","title":"Principal Curvatures Estimation with Applications to Single Cell Data","year":2025,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Principal curvature; Principal (computer security); Estimation; Curvature; Mathematics; Computer security; Engineering; Geometry","score_opus":0.0332548691768697,"score_gpt":0.33751911548433766,"score_spread":0.30426424630746796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408355271","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000033116372,0.000012279109,0.9731824,0.0018331162,0.000024643306,0.00039527562,0.0000026057003,0.0003987996,0.02411779],"genre_scores_gemma":[0.017039327,0.000001053159,0.9781588,0.002650432,0.000009841337,0.00009769192,0.000030154946,0.0000027072026,0.0020100027],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992701,0.000017211814,0.000120548444,0.00031154547,0.00017999666,0.00010061233],"domain_scores_gemma":[0.9987923,0.000053088985,0.000029630555,0.0010026024,0.000058115213,0.00006429817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013567263,0.00006025458,0.00005705542,0.00008887543,0.000058763162,0.00013453652,0.0011449038,0.000022814378,0.000021704776],"category_scores_gemma":[0.00003486079,0.000046801917,0.0000058484834,0.0005204222,0.00002277596,0.00041313973,0.00048521266,0.00005582537,0.000043189593],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003914652,0.0002433635,0.00009602955,0.000045257697,0.000008636722,0.0000018256853,0.00014944244,0.00008297185,0.0114062615,0.049273267,0.06806356,0.8706255],"study_design_scores_gemma":[0.00048939907,0.00019898158,0.0016335117,0.00008209144,0.000021019154,0.0000042466413,0.000054719403,0.16898297,0.7391539,0.006628199,0.082352445,0.0003984801],"about_ca_topic_score_codex":0.000017345541,"about_ca_topic_score_gemma":0.000013880506,"teacher_disagreement_score":0.870227,"about_ca_system_score_codex":0.00002478256,"about_ca_system_score_gemma":0.00006693227,"threshold_uncertainty_score":0.21275352},"labels":[],"label_agreement":null},{"id":"W4408355344","doi":"10.1109/icassp49660.2025.10890821","title":"Beyond Point Annotation: A Weakly Supervised Network Guided by Multi-Level Labels Generated from Four-Point Annotation for Thyroid Nodule Segmentation in Ultrasound Image","year":2025,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Annotation; Nodule (geology); Artificial intelligence; Computer science; Segmentation; Point (geometry); Image segmentation; Image retrieval; Thyroid; Image (mathematics); Pattern recognition (psychology); Medicine; Mathematics; Biology","score_opus":0.03252410338237473,"score_gpt":0.3046278886458953,"score_spread":0.2721037852635206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408355344","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017763438,0.00017132775,0.97598994,0.0027949815,0.0004359457,0.001775486,0.00014864819,0.00047961605,0.0004406055],"genre_scores_gemma":[0.020439649,0.000058126974,0.9697354,0.0064763734,0.000094679875,0.0005978052,0.0013312517,0.000028512122,0.0012381962],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9967555,0.00031226827,0.0010599316,0.00088470575,0.00047138723,0.00051621546],"domain_scores_gemma":[0.9977677,0.00068394805,0.00026067602,0.000568519,0.0005715714,0.00014758555],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009578432,0.00034374636,0.0003781455,0.00026876744,0.00022382586,0.0004975716,0.0006919398,0.00016613715,0.00016847166],"category_scores_gemma":[0.00045912448,0.00034442465,0.000100245095,0.0012585013,0.000087724104,0.0017315425,0.00014165456,0.0001956086,0.000027164999],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004053916,0.00023144309,0.00052126474,0.00003721734,0.00005782812,0.000007710477,0.0011210158,0.00025061634,0.7821889,0.0012422376,0.18647386,0.02782737],"study_design_scores_gemma":[0.005215355,0.00017473262,0.005388035,0.00010515627,0.000034034965,0.0000041252147,0.0004018959,0.24503161,0.7305008,0.012430748,0.00014363378,0.0005698637],"about_ca_topic_score_codex":0.00047847623,"about_ca_topic_score_gemma":0.00026405818,"teacher_disagreement_score":0.24478099,"about_ca_system_score_codex":0.0002792226,"about_ca_system_score_gemma":0.0001857997,"threshold_uncertainty_score":0.99990076},"labels":[],"label_agreement":null},{"id":"W4408577766","doi":"10.23952/jano.7.2025.1.03","title":"DC programming and algorithm for nonconvex log total variation image reconstruction","year":2025,"lang":"en","type":"article","venue":"Journal of Applied and Numerical Optimization","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Guangxi Province; National Natural Science Foundation of China","keywords":"Variation (astronomy); Image (mathematics); Algorithm; Computer science; Mathematics; Artificial intelligence","score_opus":0.005105190725417419,"score_gpt":0.2499684202268943,"score_spread":0.24486322950147688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408577766","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015701393,0.0000358683,0.99878776,0.00041271324,0.0001663792,0.00022264573,6.399683e-7,0.00003471926,0.00018225216],"genre_scores_gemma":[0.0060551753,0.00006048793,0.99360114,0.00018723962,0.000060816394,0.000014165414,0.0000023064147,0.0000037978093,0.000014858871],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929523,0.000019384024,0.00033025595,0.00013957689,0.00012508979,0.00009046076],"domain_scores_gemma":[0.99936646,0.00008895388,0.0002553668,0.000058424284,0.00016548642,0.00006529866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000300792,0.00007282008,0.00015359418,0.00012875986,0.00008138793,0.00014638466,0.000080108075,0.000057117755,0.000004860923],"category_scores_gemma":[0.000044296878,0.000063246815,0.000027484839,0.00019353395,0.000044881483,0.0004565382,0.000037418187,0.00009175992,1.510033e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017715967,0.000030230027,0.0000060644547,0.00001759546,0.00001576369,6.835881e-7,0.00013196998,0.00041115226,0.0019257597,0.0013635615,0.000077615994,0.9960019],"study_design_scores_gemma":[0.0009061611,0.00018931471,0.00020410876,0.000037290793,0.000030199093,0.000058211048,0.00006545738,0.9867106,0.007530106,0.0040860935,0.00008759584,0.00009486864],"about_ca_topic_score_codex":0.0000013037659,"about_ca_topic_score_gemma":1.5004137e-8,"teacher_disagreement_score":0.995907,"about_ca_system_score_codex":0.000030378851,"about_ca_system_score_gemma":0.00004296532,"threshold_uncertainty_score":0.25791302},"labels":[],"label_agreement":null},{"id":"W4408634392","doi":"10.1016/j.media.2025.103547","title":"Medical SAM adapter: Adapting segment anything model for medical image segmentation","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":334,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Ministry of Education - Singapore; Ministry of Education; National University of Singapore","keywords":"Artificial intelligence; Computer vision; Segmentation; Computer science; Adapter (computing); Image segmentation; Image (mathematics)","score_opus":0.014697149688372452,"score_gpt":0.33526344771584266,"score_spread":0.3205662980274702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408634392","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005427894,0.0002195633,0.9738762,0.022494564,0.0002731392,0.00059211283,0.000014316825,0.00075128494,0.0012360492],"genre_scores_gemma":[0.03355499,0.00039913863,0.9393429,0.024094481,0.00030232844,0.000542451,0.00022920409,0.00004571771,0.0014888006],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9892675,0.00048194645,0.0018233623,0.0013710029,0.0060915677,0.0009646146],"domain_scores_gemma":[0.99504775,0.0013983644,0.00037094278,0.0011129868,0.0005340101,0.0015359642],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006977229,0.00048928306,0.0010077376,0.0010954047,0.00044271653,0.0004691545,0.0030316943,0.00059430156,0.002889996],"category_scores_gemma":[0.009115491,0.000434802,0.0007162925,0.0027551935,0.0005469976,0.0012446182,0.0011931311,0.00094516337,0.000050349096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005525917,0.00090184674,0.00043880852,0.00033597127,0.0023153198,0.00059779966,0.0015913452,0.0001742413,0.004025926,0.0047522318,0.05010478,0.93470645],"study_design_scores_gemma":[0.0013612771,0.000060922914,0.00006310279,0.00024889133,0.00055284204,0.000015152026,0.00018183325,0.9857798,0.009410754,0.0016155287,0.00031813275,0.00039171806],"about_ca_topic_score_codex":0.00018982968,"about_ca_topic_score_gemma":0.00018885545,"teacher_disagreement_score":0.9856056,"about_ca_system_score_codex":0.00030903777,"about_ca_system_score_gemma":0.0014681001,"threshold_uncertainty_score":0.9998104},"labels":[],"label_agreement":null},{"id":"W4409316844","doi":"10.1117/12.3047231","title":"Scribble-based weakly supervised method for segmentation of neonatal cerebral ventricles from 3D ultrasound images","year":2025,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"London Health Sciences Centre; Western University; University of Guelph","funders":"","keywords":"Segmentation; Artificial intelligence; Image segmentation; Computer science; Lateral ventricles; Ultrasound; Computer vision; Pattern recognition (psychology); Anatomy; Radiology; Medicine","score_opus":0.013889835800200494,"score_gpt":0.3125171159681305,"score_spread":0.29862728016793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409316844","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00074950955,0.00009483987,0.9968235,0.00080747355,0.00018653646,0.00055317575,0.00007125558,0.00026529623,0.00044842507],"genre_scores_gemma":[0.01451208,0.0000045059437,0.9834162,0.0015888843,0.0000223849,0.000090863316,0.00010811292,0.0000074990076,0.00024947082],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99858296,0.00013680363,0.00039607185,0.00037704603,0.00030880177,0.00019832894],"domain_scores_gemma":[0.9980669,0.0012064206,0.00011154783,0.00036962272,0.00017748462,0.00006799459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004028264,0.00013662512,0.00021066431,0.00019784356,0.000074727985,0.00011694532,0.0006107319,0.000062224244,0.00033419448],"category_scores_gemma":[0.00023860797,0.00012487543,0.00009896177,0.0004188552,0.00006456585,0.00049131527,0.000085410706,0.00007010745,0.0000046966616],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053610933,0.00015053926,0.0011012541,0.00010913721,0.0000668904,0.0000025895963,0.00029015134,0.00004490852,0.6075523,0.0035856047,0.01484241,0.37220058],"study_design_scores_gemma":[0.0010720916,0.00007000542,0.0007796654,0.00003948507,0.000023446586,5.484568e-7,0.0001430184,0.027820235,0.96539485,0.0044383095,0.00010266587,0.00011568275],"about_ca_topic_score_codex":0.00031677212,"about_ca_topic_score_gemma":0.0000094165325,"teacher_disagreement_score":0.37208492,"about_ca_system_score_codex":0.00004585069,"about_ca_system_score_gemma":0.00014738535,"threshold_uncertainty_score":0.5092272},"labels":[],"label_agreement":null},{"id":"W4409333090","doi":"10.1111/cgf.70023","title":"2D Neural Fields with Learned Discontinuities","year":2025,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Classification of discontinuities; Computer science; Computer graphics (images); Artificial intelligence; Artificial neural network; Computer vision; Field (mathematics); Mathematics","score_opus":0.01288096629670099,"score_gpt":0.2692795891248917,"score_spread":0.2563986228281907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409333090","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021599494,0.00008761637,0.9888225,0.0067209504,0.00046100654,0.00018339371,9.1778907e-7,0.0005406697,0.0010229907],"genre_scores_gemma":[0.6922167,0.000046426157,0.28600195,0.019965773,0.00009254171,0.0000622851,0.0000102701315,0.000016423694,0.0015876092],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987794,0.00006184134,0.00021905858,0.00036608553,0.0002576943,0.00031591632],"domain_scores_gemma":[0.9990963,0.000110037035,0.000065114284,0.00055957155,0.00008985264,0.00007912601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014995612,0.00015546697,0.00017602493,0.00024656163,0.00014561982,0.0002928741,0.0009399013,0.00007326775,0.0000090475705],"category_scores_gemma":[0.000011828465,0.00012330087,0.000073398594,0.0005953915,0.0001626162,0.00047693134,0.00045195743,0.00025653045,0.000004694766],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013351489,0.00008811268,0.006588738,0.0000514659,0.00006769545,0.00004750951,0.00053944805,0.00002595638,0.00003433487,0.5600098,0.050669566,0.38186407],"study_design_scores_gemma":[0.0029622756,0.0015943254,0.015357278,0.0005870539,0.00006114837,0.00008116533,0.00027160678,0.7394966,0.024639701,0.19316882,0.020270647,0.0015094002],"about_ca_topic_score_codex":0.000028605902,"about_ca_topic_score_gemma":0.000046562734,"teacher_disagreement_score":0.7394706,"about_ca_system_score_codex":0.000013339717,"about_ca_system_score_gemma":0.000046914894,"threshold_uncertainty_score":0.5028063},"labels":[],"label_agreement":null},{"id":"W4409581507","doi":"10.1109/lgrs.2025.3562276","title":"Optimizing Relative Radiometric Normalization: Minimizing Residual Distortions in Multispectral Bitemporal Images Using Trust-Region Reflective and Laplacian Pyramid Fusion","year":2025,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Multispectral image; Normalization (sociology); Residual; Artificial intelligence; Computer science; Computer vision; Radiometric dating; Image fusion; Fusion; Radiometry; Pyramid (geometry); Remote sensing; Image (mathematics); Geology; Mathematics; Algorithm","score_opus":0.020065294269589275,"score_gpt":0.29200846951671205,"score_spread":0.2719431752471228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409581507","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2471313,0.00013985627,0.74997675,0.002077591,0.00030266622,0.00018951103,6.279033e-7,0.0001002394,0.00008145198],"genre_scores_gemma":[0.29921672,0.00010902761,0.699016,0.0015339695,0.00004155112,3.0131662e-7,0.0000019657475,0.000007913844,0.00007260838],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813944,0.0001839887,0.0003664826,0.0006304423,0.00030728758,0.00037233692],"domain_scores_gemma":[0.99926794,0.0001639898,0.00016723754,0.00022916749,0.000069150956,0.00010253189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004980145,0.00018723632,0.00022223212,0.0013220474,0.0005254889,0.0002761453,0.00020055576,0.00008484872,3.368725e-7],"category_scores_gemma":[0.00019328727,0.00018154345,0.000033496613,0.0026889415,0.00054208335,0.0013031546,0.00013406188,0.0002607278,3.1649463e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048420345,0.00005587211,0.0029075008,0.00009989721,0.000028949727,0.0005009744,0.011821204,0.00067411736,0.51155263,0.0002962288,0.000506829,0.4715074],"study_design_scores_gemma":[0.0011165004,0.00010385496,0.016406571,0.0008638861,0.000034033153,0.00024829854,0.0005397744,0.90168136,0.07744465,0.0009233887,0.000048151964,0.00058952154],"about_ca_topic_score_codex":0.0008017768,"about_ca_topic_score_gemma":0.000049337967,"teacher_disagreement_score":0.90100724,"about_ca_system_score_codex":0.00022637224,"about_ca_system_score_gemma":0.00008286108,"threshold_uncertainty_score":0.7403127},"labels":[],"label_agreement":null},{"id":"W4409763082","doi":"10.1109/tip.2025.3562425","title":"Unrolling Plug-and-Play Gradient Graph Laplacian Regularizer for Image Restoration","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Image restoration; Computer science; Laplace operator; Graph; Artificial intelligence; Plug-in; Image processing; Image (mathematics); Computer vision; Mathematics; Theoretical computer science","score_opus":0.013760350903647297,"score_gpt":0.29592087669782347,"score_spread":0.28216052579417616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409763082","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00047848542,0.000111541594,0.99614954,0.0014742622,0.00034824826,0.00050940475,0.000007242319,0.00052456284,0.000396739],"genre_scores_gemma":[0.17025772,0.000042652006,0.8276015,0.0009868043,0.000031082902,0.00029263643,0.0000045345832,0.000020620591,0.0007624332],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99845386,0.000069658185,0.00038662637,0.00053278106,0.00026474879,0.00029233837],"domain_scores_gemma":[0.99906796,0.00013565295,0.0001230039,0.00032862843,0.00023879959,0.00010597944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045899706,0.00020121883,0.00020008335,0.00046681165,0.0006357172,0.00056751637,0.0003445346,0.00009616633,0.0000073449314],"category_scores_gemma":[0.00003548027,0.00019996324,0.000088934125,0.0007023729,0.00016899554,0.0014923511,0.0000048942807,0.0002477532,0.000004635797],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008156669,0.000246198,0.000004404806,0.0004204202,0.000045496363,0.000012104342,0.0013899207,0.00016327557,0.17387532,0.00095016067,0.0013400275,0.8214711],"study_design_scores_gemma":[0.0011154801,0.00012805946,0.000037624603,0.0003761051,0.00006208315,0.000013552225,0.00011819618,0.08260595,0.905376,0.009466523,0.0003651177,0.00033527045],"about_ca_topic_score_codex":0.000012051976,"about_ca_topic_score_gemma":0.000008020107,"teacher_disagreement_score":0.8211358,"about_ca_system_score_codex":0.00009193293,"about_ca_system_score_gemma":0.00016007185,"threshold_uncertainty_score":0.8154264},"labels":[],"label_agreement":null},{"id":"W4410020272","doi":"10.1016/j.media.2025.103596","title":"Towards Foundation Models and Few-Shot Parameter-Efficient Fine-Tuning for Volumetric Organ Segmentation","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Université du Québec","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec","keywords":"Artificial intelligence; Segmentation; Computer science; Computer vision; Shot (pellet); Foundation (evidence); Pattern recognition (psychology); Materials science; Geography","score_opus":0.02816809076505809,"score_gpt":0.3418809072649658,"score_spread":0.3137128164999077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410020272","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043067196,0.00012620517,0.9922553,0.0022057283,0.00011209384,0.00036451424,0.0000035789735,0.00022574466,0.0004001744],"genre_scores_gemma":[0.24509253,0.00006424865,0.7523202,0.0018129819,0.000041481588,0.0001705104,0.00012223974,0.000011023653,0.00036481544],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974887,0.00015467618,0.0005589555,0.00060587295,0.00089168496,0.00030010665],"domain_scores_gemma":[0.9983419,0.000515731,0.0001592198,0.00043982232,0.00029731443,0.00024598042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013309082,0.00017909212,0.00037440163,0.0011672286,0.00020408953,0.0003878144,0.00059764815,0.00011450653,0.00021589774],"category_scores_gemma":[0.0020474566,0.00016237368,0.00016890386,0.0039865943,0.00015891362,0.00057582936,0.0002810376,0.00016816905,0.000007024145],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009686257,0.00017999098,0.00032427697,0.00008092897,0.000489403,0.00001279455,0.00044771546,0.00026380035,0.0034417063,0.002466682,0.0018896367,0.9903934],"study_design_scores_gemma":[0.00050332805,0.00005125476,0.00040001262,0.00002759488,0.00040759673,0.0000015478932,0.00007001109,0.98134965,0.01332019,0.0036499414,0.00006396549,0.00015492663],"about_ca_topic_score_codex":0.00012665997,"about_ca_topic_score_gemma":0.000023526592,"teacher_disagreement_score":0.9902384,"about_ca_system_score_codex":0.00011812855,"about_ca_system_score_gemma":0.00017823922,"threshold_uncertainty_score":0.6621406},"labels":[],"label_agreement":null},{"id":"W4410048232","doi":"10.1016/j.bspc.2025.107956","title":"Pseudo Label-Guided Data Fusion and output consistency for semi-supervised medical image segmentation","year":2025,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Consistency (knowledge bases); Artificial intelligence; Computer science; Segmentation; Fusion; Image (mathematics); Pattern recognition (psychology); Computer vision; Image fusion; Image segmentation","score_opus":0.03138742140786215,"score_gpt":0.32767980202959557,"score_spread":0.2962923806217334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410048232","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010315864,0.0013324887,0.9851978,0.011413972,0.00010641072,0.0005040132,0.00003603916,0.0002457776,0.00013194542],"genre_scores_gemma":[0.5885895,0.0003408313,0.392733,0.017047383,0.00029984498,0.00026217746,0.00028373318,0.000025850783,0.00041769035],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976683,0.00012755454,0.0005278483,0.0006789079,0.0006886992,0.0003086797],"domain_scores_gemma":[0.9985726,0.00045164893,0.00012271825,0.00030613688,0.00017572184,0.00037117107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001349176,0.0001880345,0.00031151128,0.00014971815,0.00032947367,0.0003242992,0.0007265296,0.00018732324,0.00003706433],"category_scores_gemma":[0.0006446055,0.0001498977,0.000026681417,0.0003054549,0.00049890514,0.00058302126,0.0003909287,0.00018183712,0.0000019618506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033049553,0.0001147157,0.000066470675,0.00027623546,0.000026268059,0.0000123003965,0.00010640563,1.7431852e-8,0.027308611,0.00027577585,0.009536003,0.96224415],"study_design_scores_gemma":[0.008793388,0.00030296817,0.00021213257,0.0006760005,0.00011729443,0.000050537266,0.0001591001,0.9761616,0.0056388746,0.004311512,0.0032085804,0.0003680331],"about_ca_topic_score_codex":0.00001996527,"about_ca_topic_score_gemma":0.0000014460772,"teacher_disagreement_score":0.97616154,"about_ca_system_score_codex":0.000027098713,"about_ca_system_score_gemma":0.00044104224,"threshold_uncertainty_score":0.61126506},"labels":[],"label_agreement":null},{"id":"W4410167451","doi":"10.2139/ssrn.5244734","title":"Accelerated 3d-3d Rigid Registration of Echocardiographic Images Obtained from Apical Window Using Particle Filter","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Window (computing); Particle filter; Particle (ecology); Filter (signal processing); Computer vision; Artificial intelligence; Computer science; Materials science; Geology","score_opus":0.026636770371282103,"score_gpt":0.30526696944832316,"score_spread":0.27863019907704106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410167451","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040119886,0.0013022857,0.9569491,0.00054917304,0.0003479142,0.00032635714,0.00001964974,0.00018183142,0.00020378598],"genre_scores_gemma":[0.79486334,0.0026773002,0.20148256,0.00026951733,0.0003253239,0.000021968726,0.000046856923,0.00002318015,0.00028997133],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99586785,0.0005025549,0.00094819546,0.00060110167,0.00078280055,0.0012975256],"domain_scores_gemma":[0.9977913,0.0001425609,0.0007631004,0.0007635622,0.00039953733,0.00013994459],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0019200015,0.0003214447,0.00053757033,0.0003131803,0.00016145155,0.00037499922,0.0014274974,0.00030128946,0.00003912508],"category_scores_gemma":[0.00020781303,0.00030312873,0.00029196776,0.00058349466,0.0001453947,0.00055221736,0.0005212987,0.0030530242,0.0000030149445],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005703965,0.0015524798,0.016408581,0.0005009297,0.0060441927,0.00023685733,0.0021710752,0.006968196,0.26463205,0.041470934,0.0039528916,0.6554914],"study_design_scores_gemma":[0.002581433,0.0005645411,0.0035004583,0.001057454,0.00050341344,0.00020623831,0.00026872987,0.14187334,0.33147648,0.5166239,0.00009719471,0.0012467977],"about_ca_topic_score_codex":0.00042313585,"about_ca_topic_score_gemma":0.00005137022,"teacher_disagreement_score":0.7554666,"about_ca_system_score_codex":0.0006500649,"about_ca_system_score_gemma":0.0036939792,"threshold_uncertainty_score":0.99994206},"labels":[],"label_agreement":null},{"id":"W4410167591","doi":"10.2139/ssrn.5244736","title":"Accelerated 3d-3d Rigid Registration of Echocardiographic Images Obtained from Apical Window Using Particle Filter","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Window (computing); Particle filter; Particle (ecology); Computer vision; Filter (signal processing); Artificial intelligence; Computer science; Materials science; Computer graphics (images); Geology","score_opus":0.026636770371282103,"score_gpt":0.30526696944832316,"score_spread":0.27863019907704106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410167591","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040119886,0.0013022857,0.9569491,0.00054917304,0.0003479142,0.00032635714,0.00001964974,0.00018183142,0.00020378598],"genre_scores_gemma":[0.79486334,0.0026773002,0.20148256,0.00026951733,0.0003253239,0.000021968726,0.000046856923,0.00002318015,0.00028997133],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99586785,0.0005025549,0.00094819546,0.00060110167,0.00078280055,0.0012975256],"domain_scores_gemma":[0.9977913,0.0001425609,0.0007631004,0.0007635622,0.00039953733,0.00013994459],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0019200015,0.0003214447,0.00053757033,0.0003131803,0.00016145155,0.00037499922,0.0014274974,0.00030128946,0.00003912508],"category_scores_gemma":[0.00020781303,0.00030312873,0.00029196776,0.00058349466,0.0001453947,0.00055221736,0.0005212987,0.0030530242,0.0000030149445],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005703965,0.0015524798,0.016408581,0.0005009297,0.0060441927,0.00023685733,0.0021710752,0.006968196,0.26463205,0.041470934,0.0039528916,0.6554914],"study_design_scores_gemma":[0.002581433,0.0005645411,0.0035004583,0.001057454,0.00050341344,0.00020623831,0.00026872987,0.14187334,0.33147648,0.5166239,0.00009719471,0.0012467977],"about_ca_topic_score_codex":0.00042313585,"about_ca_topic_score_gemma":0.00005137022,"teacher_disagreement_score":0.7554666,"about_ca_system_score_codex":0.0006500649,"about_ca_system_score_gemma":0.0036939792,"threshold_uncertainty_score":0.99994206},"labels":[],"label_agreement":null},{"id":"W4410196803","doi":"10.1016/j.hpb.2025.03.045","title":"The use of artificial intelligence in generating 3D liver volumetrics","year":2025,"lang":"en","type":"article","venue":"HPB","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Medicine; Artificial intelligence; Computer science","score_opus":0.08002144032001782,"score_gpt":0.32588065242482994,"score_spread":0.24585921210481212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410196803","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015221533,0.00011387856,0.9980601,0.00002441033,0.00012280894,0.00008597443,2.9451095e-7,0.000038162692,0.00003220489],"genre_scores_gemma":[0.00043966516,0.00007103673,0.9988085,0.00040468524,0.000010372541,0.0000084524845,2.9934685e-7,0.0000014634079,0.00025550864],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993218,0.00006855863,0.00023711742,0.00011864722,0.00015640074,0.0000974809],"domain_scores_gemma":[0.9992366,0.0003963497,0.000051780156,0.00023609362,0.00006181606,0.000017394676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033812513,0.00003895685,0.000057481724,0.00012257716,0.000051921066,0.00010119851,0.00037638584,0.000024696596,0.00000895788],"category_scores_gemma":[0.0007359356,0.000029790523,0.000016602089,0.0008317796,0.00005979845,0.00018349547,0.00015923376,0.00008193779,0.000005994669],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.6474513e-7,0.0000144546675,0.00013504642,0.0000040644436,0.0000012572826,0.000002394055,0.00010219308,0.00006738074,0.000942635,0.0017235588,0.000832096,0.9961743],"study_design_scores_gemma":[0.000009400418,0.000010841981,0.0002732892,0.000016298412,8.31452e-7,2.768256e-7,0.000013129457,0.99041444,0.008561642,0.00009032972,0.000581281,0.000028210201],"about_ca_topic_score_codex":0.00006993842,"about_ca_topic_score_gemma":0.000033314027,"teacher_disagreement_score":0.996146,"about_ca_system_score_codex":0.00002351551,"about_ca_system_score_gemma":0.000044629258,"threshold_uncertainty_score":0.12148222},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low"}],"label_agreement":"split"},{"id":"W4410327697","doi":"10.23952/jnva.9.2025.4.02","title":"Image regularity conditions based on nonconvex separation with applications","year":2025,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Separation (statistics); Image (mathematics); Artificial intelligence; Computer science; Mathematics; Computer vision; Machine learning","score_opus":0.007371884202857864,"score_gpt":0.31959459141440755,"score_spread":0.3122227072115497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410327697","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00032965283,0.000012672929,0.99509084,0.0037814544,0.000018401784,0.00008701987,0.0000152524535,0.000020239375,0.00064447674],"genre_scores_gemma":[0.078727536,0.000010940774,0.9194395,0.0015409451,0.00006906156,0.000015854708,0.000055658267,0.0000025045729,0.0001380145],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999028,0.00007420123,0.00033146984,0.00014229973,0.0003577188,0.00006626469],"domain_scores_gemma":[0.9987364,0.00022389136,0.00026545083,0.00017667,0.0005283089,0.00006932073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041751214,0.000073678806,0.00017992296,0.00057542365,0.00012696588,0.0001143366,0.00019995052,0.00003768893,0.000056901026],"category_scores_gemma":[0.000057215224,0.0000561133,0.00009973508,0.0012366232,0.00004913768,0.00033958448,0.00002033538,0.0001273979,0.0000023886391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007806553,0.008619268,0.057962216,0.0004001786,0.014450151,0.00015865502,0.0012457353,0.06531919,0.032400027,0.70340645,0.023872325,0.09138513],"study_design_scores_gemma":[0.0006442524,0.00014227483,0.044014864,0.000026141475,0.00056587544,0.0000068253626,0.000014860954,0.94720376,0.0017142347,0.004849514,0.00071638223,0.000100990714],"about_ca_topic_score_codex":0.0000063006764,"about_ca_topic_score_gemma":0.000004942088,"teacher_disagreement_score":0.8818846,"about_ca_system_score_codex":0.00004113063,"about_ca_system_score_gemma":0.00021424108,"threshold_uncertainty_score":0.2288234},"labels":[],"label_agreement":null},{"id":"W4410601944","doi":"10.1007/s11042-025-20732-2","title":"Diagnosing early stages of Alzheimer’s diseases based on volumetric features from MRI using soft computing algorithms","year":2025,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Soft computing; Artificial intelligence; Algorithm; Artificial neural network","score_opus":0.030048282561218116,"score_gpt":0.32253259293582465,"score_spread":0.29248431037460654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410601944","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034965277,0.00094295223,0.9942818,0.00030027927,0.00006872431,0.00053853437,0.00013332134,0.00017995595,0.00005790053],"genre_scores_gemma":[0.21216457,0.000039137874,0.7870021,0.0005193574,0.00007638545,0.00011090303,0.000062026025,0.000009148419,0.000016421169],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988403,0.000056821176,0.00029330043,0.0003894194,0.00025575617,0.0001644386],"domain_scores_gemma":[0.9980646,0.0011819497,0.00014975047,0.00039918866,0.00009669392,0.00010786212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011701515,0.00013342538,0.00020750861,0.0002345684,0.00018719175,0.00018705394,0.0003621953,0.00005931715,0.000009254051],"category_scores_gemma":[0.00015980219,0.00012715197,0.000052247684,0.0006834828,0.00012508608,0.00021466396,0.00014370015,0.00012285642,0.0000024231526],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002403176,0.0001601909,0.00752174,0.00001801923,0.00002938089,0.0000011216443,0.00010126176,0.00028789463,0.0024102174,0.00030112657,0.00051745074,0.9886492],"study_design_scores_gemma":[0.00058766024,0.00006116024,0.13556503,0.00017643956,0.00009518327,2.9666543e-7,0.000036879934,0.81914526,0.04305968,0.0007630395,0.00027636997,0.00023300778],"about_ca_topic_score_codex":0.00027382828,"about_ca_topic_score_gemma":0.0000014983685,"teacher_disagreement_score":0.9884162,"about_ca_system_score_codex":0.000023615832,"about_ca_system_score_gemma":0.00007064139,"threshold_uncertainty_score":0.5185107},"labels":[],"label_agreement":null},{"id":"W4410730174","doi":"10.1007/978-3-031-91585-7_21","title":"A Bottom-Up Approach to Class-Agnostic Image Segmentation","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Simon Fraser University","funders":"","keywords":"Computer science; Top-down and bottom-up design; Class (philosophy); Segmentation; Image segmentation; Artificial intelligence; Computer vision; Image (mathematics); Programming language","score_opus":0.016145739897171767,"score_gpt":0.2815749654816712,"score_spread":0.26542922558449944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410730174","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000035866155,0.00009554764,0.9781666,0.0009734054,0.0032514308,0.0011089232,0.000008038504,0.00046738863,0.015925124],"genre_scores_gemma":[0.001102765,0.000026526019,0.9887259,0.007605065,0.00047587068,0.00007555145,0.000019685178,0.000026874308,0.0019417326],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949876,0.00007003883,0.00069566956,0.001910201,0.0016607638,0.00067574164],"domain_scores_gemma":[0.99691373,0.0005998717,0.0002577624,0.0014909738,0.0004115913,0.0003260516],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010884175,0.00056352624,0.00054065365,0.001459215,0.00022569892,0.0008825172,0.0038118148,0.00034129925,0.000028199882],"category_scores_gemma":[0.00044066887,0.00053870596,0.00012629094,0.0012440531,0.0005578397,0.00088970014,0.0017620127,0.0010170428,0.00009479848],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005475702,0.000059029626,0.00000440081,0.00011198912,0.0000127848025,0.000030436897,0.0009760301,0.0011414889,0.0024671536,0.012985709,0.003735645,0.97846985],"study_design_scores_gemma":[0.0010034139,0.00054037623,0.00006483757,0.0015841592,0.000045345423,0.00009335451,0.0000016565187,0.7550823,0.08996333,0.14705926,0.0022911143,0.002270865],"about_ca_topic_score_codex":0.000028475557,"about_ca_topic_score_gemma":0.000009713823,"teacher_disagreement_score":0.976199,"about_ca_system_score_codex":0.00056562055,"about_ca_system_score_gemma":0.000811951,"threshold_uncertainty_score":0.99970645},"labels":[],"label_agreement":null},{"id":"W4410893640","doi":"10.1148/rycan.240336","title":"Deep Learning-based Anatomy-Aware Morph Model for Registration of Prostate Whole-Mount Histopathology to MRI","year":2025,"lang":"en","type":"article","venue":"Radiology Imaging Cancer","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"David Geffen School of Medicine, University of California, Los Angeles; National Cancer Institute; National Institutes of Health","keywords":"Histopathology; Prostate; Prostate cancer; Artificial intelligence; Mount; Image registration; Deep learning; Magnetic resonance imaging; Computer science; Medicine; Anatomy; Radiology; Pathology; Cancer; Image (mathematics)","score_opus":0.010241736857838711,"score_gpt":0.32753967438046433,"score_spread":0.31729793752262564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410893640","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027406279,0.00038728028,0.9823603,0.013468436,0.00022869973,0.00053299085,0.000009945499,0.00021530433,0.00005638278],"genre_scores_gemma":[0.6196134,0.000042202577,0.37051335,0.007884588,0.000041708507,0.0008571087,0.000039818176,0.000018055323,0.0009897284],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864036,0.00012053821,0.00036462987,0.00046438733,0.00012857157,0.0002814856],"domain_scores_gemma":[0.9989935,0.000130582,0.00019538138,0.00035822374,0.00025496955,0.00006731991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046870112,0.00013733284,0.00026387197,0.00025948937,0.00009304352,0.000023769473,0.00046968256,0.00006632034,0.000007400245],"category_scores_gemma":[0.00018250791,0.00013913489,0.000058430607,0.00028167904,0.00015384133,0.00017091166,0.00007113028,0.000161592,0.0000018907772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015877352,0.00011572032,0.007923828,0.00037258552,0.000043170417,0.00003049804,0.0021023082,0.7159523,0.068793274,0.009146318,0.048350986,0.14701027],"study_design_scores_gemma":[0.0004876454,0.00006476297,0.00079329516,0.000053316002,0.000015110337,0.0000062768077,0.00001559295,0.9837264,0.010597249,0.0017251463,0.0023891337,0.00012608238],"about_ca_topic_score_codex":0.000076826625,"about_ca_topic_score_gemma":0.000028984288,"teacher_disagreement_score":0.6168728,"about_ca_system_score_codex":0.00034345832,"about_ca_system_score_gemma":0.00035304492,"threshold_uncertainty_score":0.5673756},"labels":[],"label_agreement":null},{"id":"W4411035555","doi":"10.1101/2025.06.02.656812","title":"Accurate spatial localization of Allen Human Brain Atlas gene expression data for human neuroimaging","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Western University; University of Toronto; McGill University; Douglas Mental Health University Institute; University of Calgary","funders":"","keywords":"Neuroimaging; Atlas (anatomy); Human brain; Brain atlas; Neuroscience; Brain mapping; Computer science; Psychology; Biology; Anatomy","score_opus":0.04273646402245679,"score_gpt":0.31338777924629785,"score_spread":0.27065131522384106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411035555","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004047144,0.0001461118,0.99246293,0.00034431607,0.00059691357,0.0012845257,0.00042830917,0.0006830812,0.0000066649222],"genre_scores_gemma":[0.6285649,0.00007047381,0.36958456,0.0008842508,0.0004213382,0.0003334289,0.00003144305,0.00009041533,0.000019180869],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9961144,0.00029776656,0.0009706497,0.001586842,0.00060233887,0.00042804415],"domain_scores_gemma":[0.99437344,0.00017582379,0.00089998444,0.0037146225,0.00064322614,0.00019290035],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011662566,0.0004595218,0.0005716929,0.0004100142,0.0002900929,0.0003524079,0.003833066,0.0003257237,0.000019489857],"category_scores_gemma":[0.0006219293,0.00050605135,0.00010958286,0.00035488015,0.00014368362,0.00066823896,0.004261175,0.00045745622,0.0000030441627],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000069068774,0.00012053394,0.00024466202,0.0006385813,0.00004284694,0.000010486687,0.000020682544,0.00012417996,0.9927362,0.0005890937,0.005421829,0.00004399833],"study_design_scores_gemma":[0.000536026,0.00005756665,0.0013671523,0.00071630575,0.00004585279,7.471088e-9,9.1825484e-7,0.04208629,0.95316315,0.00003809102,0.0015033088,0.00048536266],"about_ca_topic_score_codex":0.00015720956,"about_ca_topic_score_gemma":0.000005653835,"teacher_disagreement_score":0.62451774,"about_ca_system_score_codex":0.000121741075,"about_ca_system_score_gemma":0.00047313768,"threshold_uncertainty_score":0.9997391},"labels":[],"label_agreement":null},{"id":"W4411364539","doi":"10.1007/s11220-025-00619-0","title":"Hybrid Landmark- and Intensity-Based Image Registration","year":2025,"lang":"en","type":"article","venue":"Sensing and Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sunnybrook Hospital; Ontario Tech University","funders":"","keywords":"Landmark; Artificial intelligence; Image registration; Computer vision; Intensity (physics); Computer science; Image (mathematics); Optics; Physics","score_opus":0.006128293081880071,"score_gpt":0.2579056099547264,"score_spread":0.2517773168728463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411364539","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008185783,0.00009965926,0.9849418,0.004937775,0.00009841535,0.00005768474,3.4925637e-7,0.0002006569,0.0014779213],"genre_scores_gemma":[0.5577556,0.000014082596,0.43816957,0.00393479,0.000017063223,4.5371246e-7,0.0000033486128,0.0000034085192,0.00010172685],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994407,0.000036139827,0.0001151642,0.00021678658,0.00008423258,0.00010693038],"domain_scores_gemma":[0.9996331,0.000061093124,0.0000267714,0.00016156412,0.000074953576,0.000042517964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024360698,0.00006829643,0.00008533461,0.00009884456,0.000111791946,0.00027645915,0.000060957456,0.000010041659,7.850895e-7],"category_scores_gemma":[0.00011698735,0.00006505721,0.000011963768,0.00008765249,0.00010540569,0.00026211914,0.000068091205,0.00008256051,9.180005e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000075561893,0.000011857116,0.0010921734,0.000064484244,0.0000073292517,0.000079866295,0.00018595858,3.791126e-7,0.11060573,0.0010520446,0.008515939,0.87837666],"study_design_scores_gemma":[0.00054915564,0.000019891278,0.0029458161,0.00029628287,0.000015486223,0.00011486504,0.000089834735,0.46894926,0.5166328,0.00918938,0.0009816538,0.00021558326],"about_ca_topic_score_codex":0.000070542796,"about_ca_topic_score_gemma":0.0000017473302,"teacher_disagreement_score":0.8781611,"about_ca_system_score_codex":0.00001350067,"about_ca_system_score_gemma":0.000030850813,"threshold_uncertainty_score":0.26659018},"labels":[],"label_agreement":null},{"id":"W4411600325","doi":"10.1109/tbme.2025.3582749","title":"Prompt Learning With Bounding Box Constraints for Medical Image Segmentation","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; École de Technologie Supérieure","funders":"National Research Council Canada","keywords":"Image segmentation; Minimum bounding box; Artificial intelligence; Computer science; Segmentation; Medical imaging; Computer vision; Bounding overwatch; Image (mathematics); Scale-space segmentation; Pattern recognition (psychology)","score_opus":0.006084229846036147,"score_gpt":0.26646006776881476,"score_spread":0.26037583792277863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411600325","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041685387,0.000011579005,0.99622625,0.0013344112,0.00058273476,0.00047150132,0.0000051518314,0.0008353598,0.00011617441],"genre_scores_gemma":[0.2801487,0.000031852174,0.7184029,0.0005796792,0.00005923602,0.00046973763,0.000012935154,0.000026718295,0.0002681994],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981664,0.000037882997,0.0003628208,0.00039881546,0.0006790469,0.00035504534],"domain_scores_gemma":[0.9990016,0.000387186,0.000053437332,0.00020496402,0.00007789697,0.00027488358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005253618,0.00019147007,0.00020595413,0.00042612187,0.0001947343,0.00013476086,0.00041643405,0.00014586461,0.00011218436],"category_scores_gemma":[0.000113065784,0.00016920864,0.000074100404,0.00068208924,0.0002217191,0.00035447394,0.0000050780823,0.00045988802,0.000009647571],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046086057,0.00038288333,0.000004952169,0.00036178998,0.00019722116,0.000060372782,0.000484357,0.001924977,0.100684956,0.0018352158,0.00075129804,0.8932659],"study_design_scores_gemma":[0.0024333727,0.0005776257,0.000021067239,0.0007869139,0.000046755227,0.00005916589,0.00012760497,0.50305355,0.49034542,0.00012533535,0.001993385,0.00042976966],"about_ca_topic_score_codex":0.0000051585102,"about_ca_topic_score_gemma":0.0000011636535,"teacher_disagreement_score":0.8928361,"about_ca_system_score_codex":0.00014085755,"about_ca_system_score_gemma":0.00022015719,"threshold_uncertainty_score":0.6900128},"labels":[],"label_agreement":null},{"id":"W4411668150","doi":"10.28924/2291-8639-23-2025-151","title":"Spinor Formulation of Frenet Normal Spherical Image in Euclidean and Pseudo-Euclidean Spaces","year":2025,"lang":"en","type":"article","venue":"International Journal of Analysis and Applications","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Frenet–Serret formulas; Mathematics; Euclidean geometry; Spinor; Pure mathematics; Euclidean space; Image (mathematics); Mathematical analysis; Geometry; Mathematical physics; Computer vision; Computer science; Curvature","score_opus":0.006215005789313005,"score_gpt":0.3115195926138906,"score_spread":0.3053045868245776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411668150","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07906455,0.000100994504,0.9188777,0.0014707521,0.000025516487,0.00007170505,0.000004061944,0.000008353957,0.0003763734],"genre_scores_gemma":[0.80567676,0.00021914457,0.1938296,0.00018176634,0.00003529458,0.000007631552,0.00000524531,0.0000018805772,0.000042712225],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9988976,0.000037142527,0.0005638989,0.00013961666,0.0002907925,0.0000709549],"domain_scores_gemma":[0.9990014,0.000120279146,0.00036075246,0.00012572581,0.00033472112,0.00005714303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036055356,0.00006954974,0.00020414009,0.0005732031,0.000030131087,0.000113039765,0.0003937953,0.000032890548,0.000023084214],"category_scores_gemma":[0.000059157435,0.000061091574,0.00008434057,0.00070064666,0.00006455214,0.00042435346,0.000120335106,0.00009826715,4.8887864e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005285066,0.0005917837,0.28640628,0.000052508985,0.0014975733,0.000021679516,0.0007830132,0.00046726415,0.052121386,0.0977672,0.0008183205,0.5594201],"study_design_scores_gemma":[0.0019753007,0.00019447367,0.80007726,0.00018140321,0.0005740636,0.00005356609,0.0004854787,0.08646093,0.06870236,0.03861223,0.0023161785,0.00036675297],"about_ca_topic_score_codex":0.00008630651,"about_ca_topic_score_gemma":0.000042646072,"teacher_disagreement_score":0.72661215,"about_ca_system_score_codex":0.00003087776,"about_ca_system_score_gemma":0.000043270793,"threshold_uncertainty_score":0.24912423},"labels":[],"label_agreement":null},{"id":"W4411803105","doi":"10.2139/ssrn.5331280","title":"Generative AI Models for Images with Copyright-Free Training","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Generative grammar; Training (meteorology); Computer science; Artificial intelligence; Generative model; Geography","score_opus":0.02363726053392255,"score_gpt":0.3058448552722538,"score_spread":0.28220759473833124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411803105","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000037595353,0.0015052928,0.9899705,0.005995376,0.00032753934,0.0006863846,0.000032068074,0.00025795828,0.0011872754],"genre_scores_gemma":[0.0115618985,0.002506815,0.97755307,0.0026078457,0.0005334466,0.00035149127,0.00003235707,0.000041484578,0.0048115626],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99605614,0.00017806937,0.0005168391,0.00066902203,0.0006045228,0.0019754209],"domain_scores_gemma":[0.99794835,0.00015066522,0.00042253756,0.00082542666,0.0005006237,0.00015237651],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002004045,0.00038316488,0.00049729383,0.00031552467,0.00030621563,0.0005323258,0.002904439,0.00021439926,0.0000099908975],"category_scores_gemma":[0.00011664762,0.00030076058,0.00020374694,0.00021158606,0.00010632225,0.0007219277,0.0007464239,0.0039442335,0.0000013097675],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009395398,0.00012776296,0.0000051648317,0.00013309269,0.0009285268,0.00002341944,0.0029505,0.004860499,0.00042352363,0.6793357,0.017510185,0.29360765],"study_design_scores_gemma":[0.00084769167,0.00037424633,9.182309e-7,0.00021064494,0.000057611163,0.00013312155,0.00023928541,0.0674167,0.008117834,0.9221147,0.00014523625,0.00034204187],"about_ca_topic_score_codex":0.00002842322,"about_ca_topic_score_gemma":0.000113740556,"teacher_disagreement_score":0.2932656,"about_ca_system_score_codex":0.0010697978,"about_ca_system_score_gemma":0.011177277,"threshold_uncertainty_score":0.99994445},"labels":[],"label_agreement":null},{"id":"W4412152915","doi":"10.1007/s11760-025-04452-6","title":"Deformation field prediction based on a modified loss function with U-Net for non rigid image registration","year":2025,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Deformation (meteorology); Net (polyhedron); Image registration; Function (biology); Artificial intelligence; Field (mathematics); Computer vision; Image (mathematics); Computer science; Mathematics; Geometry; Materials science; Biology; Composite material; Pure mathematics","score_opus":0.01047495091297847,"score_gpt":0.27261262196746355,"score_spread":0.2621376710544851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412152915","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010716886,0.000017129418,0.9950423,0.001011952,0.000054689404,0.0004925469,0.000003666125,0.00023946556,0.0020665468],"genre_scores_gemma":[0.80955446,0.0000043429923,0.18784145,0.0021698603,0.000056104935,0.00017458772,0.000045669374,0.000008155874,0.00014539981],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989139,0.000043235115,0.00027647676,0.0003385403,0.00025518518,0.00017268343],"domain_scores_gemma":[0.9992283,0.00013459472,0.00016235416,0.00017540719,0.00024364065,0.00005571406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004671427,0.00014121953,0.00012450434,0.00018853779,0.0002785848,0.00050726923,0.00014449422,0.00007662834,0.0000060315115],"category_scores_gemma":[0.00009045823,0.00011651946,0.000029422368,0.00030376398,0.00006149131,0.0019834558,0.000023389448,0.00013103725,0.0000013590407],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078857335,0.00016931808,0.00016189158,0.0010335504,0.00002126365,0.000008392103,0.00044822902,0.00021540995,0.107934505,0.0008903644,0.0062357695,0.8820927],"study_design_scores_gemma":[0.0009257271,0.0006468643,0.00031635436,0.00041391797,0.00002868416,0.0000039356273,0.00004445477,0.70443416,0.2900962,0.0028809672,0.00007846386,0.00013031703],"about_ca_topic_score_codex":0.000012956232,"about_ca_topic_score_gemma":0.0000028737277,"teacher_disagreement_score":0.8819624,"about_ca_system_score_codex":0.00004734032,"about_ca_system_score_gemma":0.00014079166,"threshold_uncertainty_score":0.48916087},"labels":[],"label_agreement":null},{"id":"W4412441453","doi":"10.1016/j.bspc.2025.108251","title":"Robust semantic learning for precise medical image segmentation","year":2025,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Computer vision; Image segmentation; Image (mathematics); Pattern recognition (psychology); Machine learning","score_opus":0.012427529040500866,"score_gpt":0.285572493129623,"score_spread":0.27314496408912214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412441453","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028112848,0.0005930004,0.9930056,0.0051245037,0.000093288945,0.00037844892,0.0000014122354,0.0003421341,0.00018053573],"genre_scores_gemma":[0.80829984,0.0000539468,0.18705763,0.0034795494,0.00018842235,0.00029777308,0.000022293832,0.000014262315,0.00058627664],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981332,0.00011931323,0.00039349502,0.00042611445,0.0006249355,0.00030298694],"domain_scores_gemma":[0.9989544,0.0004108105,0.000112768095,0.0001043206,0.0001426887,0.00027501304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010236517,0.00014848134,0.00023784368,0.00016308358,0.00029512594,0.0002879836,0.00040663936,0.00015072015,0.000063666084],"category_scores_gemma":[0.00052878086,0.00012120279,0.000052369644,0.00034955822,0.0002718013,0.00037435626,0.00009403648,0.0002361696,0.0000041550475],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028630699,0.00008475344,0.00006787219,0.00021540179,0.000020671634,0.000009332261,0.00011022183,0.0000016796918,0.01592977,0.00024182229,0.0015526399,0.9817372],"study_design_scores_gemma":[0.0041534416,0.00031468953,0.00023564143,0.00059066975,0.0000624694,0.000018157958,0.00011678963,0.97573674,0.012365291,0.0036085472,0.0025190597,0.00027850224],"about_ca_topic_score_codex":0.000008743574,"about_ca_topic_score_gemma":7.10052e-7,"teacher_disagreement_score":0.9814587,"about_ca_system_score_codex":0.000035721023,"about_ca_system_score_gemma":0.00029326073,"threshold_uncertainty_score":0.49425063},"labels":[],"label_agreement":null},{"id":"W4412464452","doi":"10.1002/mp.17952","title":"Adversarial training with misaligned label correction for carotid segmentation from simultaneous non‐contrast angiography and intraplaque hemorrhage MRI","year":2025,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Contrast (vision); Modality (human–computer interaction); Computer vision; Dilation (metric space); Ground truth; Image registration; Generator (circuit theory); Pattern recognition (psychology); Image (mathematics); Mathematics; Physics","score_opus":0.00874240522177335,"score_gpt":0.2617717151423522,"score_spread":0.25302930992057887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412464452","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043295086,0.000034252185,0.9932164,0.00065347605,0.00074193074,0.00061231654,0.000014016298,0.00024775986,0.00015034473],"genre_scores_gemma":[0.77414167,0.000017672299,0.22292489,0.0022688701,0.0003252178,0.00014690854,0.00011055449,0.000015608643,0.00004862055],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850214,0.000059974285,0.00026797867,0.00043909275,0.00048118172,0.00024962204],"domain_scores_gemma":[0.9985411,0.00084987184,0.00010378964,0.00021258106,0.000104027145,0.00018863223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023710671,0.00017755844,0.00025013793,0.00008420853,0.00014387917,0.00010229004,0.0003082752,0.00011599467,0.000012958345],"category_scores_gemma":[0.00022506449,0.00015607929,0.00006831736,0.00045866825,0.00017336583,0.00030596158,0.00006387884,0.00018326667,0.0000014053202],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000111566,0.00020476461,0.00034891718,0.000059705268,0.00015694613,0.000036958165,0.0022964985,0.000042525455,0.009076839,0.00039291792,0.0027583912,0.984514],"study_design_scores_gemma":[0.009254972,0.0009339032,0.00032924328,0.0004731204,0.0002517696,0.000018596029,0.000763199,0.500343,0.47769603,0.009105923,0.00014342139,0.00068681914],"about_ca_topic_score_codex":0.00013919738,"about_ca_topic_score_gemma":0.000038955633,"teacher_disagreement_score":0.9838272,"about_ca_system_score_codex":0.00005775641,"about_ca_system_score_gemma":0.00017047577,"threshold_uncertainty_score":0.6364729},"labels":[],"label_agreement":null},{"id":"W4412464586","doi":"10.1038/s41598-025-09211-8","title":"Energy-based segmentation methods for images with non-Gaussian noise","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Okanagan University College; University of British Columbia, Okanagan Campus","funders":"Natural Sciences and Engineering Research Council of Canada; National Aeronautics and Space Administration","keywords":"Segmentation; Computer science; Noise (video); Energy (signal processing); Artificial intelligence; Gaussian; Gaussian noise; Pattern recognition (psychology); Computer vision; Image (mathematics); Statistics; Mathematics; Chemistry; Computational chemistry","score_opus":0.012082654661563087,"score_gpt":0.3449839336562043,"score_spread":0.33290127899464117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412464586","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001242479,0.000047594454,0.99404925,0.0008183891,0.0024011768,0.00050064194,0.0000010431219,0.00030108893,0.0017565372],"genre_scores_gemma":[0.013698243,0.0000010122533,0.9801516,0.0007058969,0.000018645303,0.0003530261,0.00004279456,0.000009235151,0.005019568],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99787873,0.000099962635,0.00045177352,0.00088612497,0.00040004018,0.00028339267],"domain_scores_gemma":[0.9980888,0.00014425366,0.0002839611,0.0010572593,0.0003045575,0.00012121685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002074183,0.00015252226,0.00017989596,0.00039877408,0.00032990688,0.0007195861,0.0004171195,0.000051245646,0.000025563708],"category_scores_gemma":[0.00013308342,0.0001213009,0.00007678076,0.0010789854,0.00023047194,0.00056074327,0.00009542249,0.000063105705,0.0000019018053],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011129282,0.00011582433,0.00024281243,0.00008396796,0.000024398045,0.00006815751,0.00015496736,0.000052601088,0.289128,0.0010446904,0.05927628,0.64979714],"study_design_scores_gemma":[0.00024517774,0.000051921237,0.00009688828,0.000067439425,0.000017018805,0.000012962891,0.000022617305,0.017918415,0.9598188,0.013122811,0.0084786,0.00014737553],"about_ca_topic_score_codex":0.000022671156,"about_ca_topic_score_gemma":0.0000046428618,"teacher_disagreement_score":0.6706908,"about_ca_system_score_codex":0.000078082,"about_ca_system_score_gemma":0.0004719584,"threshold_uncertainty_score":0.6938985},"labels":[],"label_agreement":null},{"id":"W4412613749","doi":"10.1145/3721238.3730698","title":"BrepDiff: Single-Stage B-rep Diffusion Model","year":2025,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Stage (stratigraphy); Diffusion; Single stage; Computer science; Physics; Engineering; Geology; Thermodynamics","score_opus":0.021749913078708234,"score_gpt":0.296047407546033,"score_spread":0.27429749446732477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412613749","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011315771,0.000015630918,0.9419742,0.0013860511,0.00010778018,0.00012151774,6.4053455e-7,0.00070913124,0.05455347],"genre_scores_gemma":[0.069919206,0.000010748023,0.85901874,0.008440379,0.000011886531,0.00002045302,0.0000027244662,0.0000049876057,0.06257088],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909616,0.000028769711,0.0001945495,0.0002883375,0.0002338149,0.00015839073],"domain_scores_gemma":[0.9992909,0.00005246143,0.000036728106,0.0005007128,0.00005050182,0.000068740184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015365802,0.000084017774,0.000092797425,0.00011597085,0.00006660126,0.00011577723,0.0006066651,0.000046445028,0.00011517439],"category_scores_gemma":[0.0000695519,0.000069712565,0.000039151728,0.000284876,0.000039839375,0.00032619282,0.00039548686,0.000082162165,0.000032985092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004439425,0.00037432488,0.00016150267,0.000044693697,0.000012739711,0.000016323485,0.00037685467,0.00014010555,0.22090262,0.17498769,0.059617355,0.54336137],"study_design_scores_gemma":[0.0002001485,0.000023900637,0.00007847954,0.000022137358,0.000002149071,8.415172e-7,0.000014622443,0.71937454,0.26993188,0.0091740275,0.0010747063,0.00010258364],"about_ca_topic_score_codex":0.000023598632,"about_ca_topic_score_gemma":0.0000039392053,"teacher_disagreement_score":0.7192344,"about_ca_system_score_codex":0.00004620183,"about_ca_system_score_gemma":0.000049905488,"threshold_uncertainty_score":0.28427958},"labels":[],"label_agreement":null},{"id":"W4412831604","doi":"10.1162/imag.a.116","title":"Robust deep MRI contrast synthesis using a prior-based and task-oriented 3D network","year":2025,"lang":"en","type":"article","venue":"Imaging Neuroscience","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Université de Bordeaux; Ministerio de Ciencia e Innovación; Universitat Politècnica de València; Agence Nationale de la Recherche","keywords":"Contrast (vision); Computer science; Task (project management); Artificial intelligence; Engineering","score_opus":0.017468647834402412,"score_gpt":0.27330910586182183,"score_spread":0.2558404580274194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412831604","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002236961,0.0001311426,0.99462265,0.001477815,0.0006174501,0.00024795628,0.0000015824064,0.0004263805,0.00023807828],"genre_scores_gemma":[0.3242104,0.000018736348,0.6651209,0.01052637,0.000035786583,0.00003274991,3.588805e-7,0.000011558818,0.000043130258],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793684,0.00015595427,0.0002878227,0.0007410857,0.00040093414,0.00047734502],"domain_scores_gemma":[0.9988215,0.00030801626,0.00012268445,0.00049804745,0.000095948155,0.00015379587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056189887,0.00017522083,0.00018554597,0.00019666163,0.0004364124,0.0004677654,0.0008219697,0.000027362772,0.000005146416],"category_scores_gemma":[0.0007896219,0.00017058305,0.00003577278,0.0012897564,0.00050654163,0.00073996274,0.00034523924,0.00017013085,0.000001671683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029976502,0.00033066876,0.0298046,0.00016996035,0.000010783751,0.00036936218,0.00043629794,0.022916768,0.30448332,0.0053595146,0.0037075814,0.63238114],"study_design_scores_gemma":[0.00019309144,0.000013302157,0.0053192265,0.00013145007,0.000011137336,0.000019961497,0.0000084410985,0.973136,0.020341536,0.00016493308,0.00049141067,0.00016949575],"about_ca_topic_score_codex":0.00004030113,"about_ca_topic_score_gemma":0.000002063861,"teacher_disagreement_score":0.9502193,"about_ca_system_score_codex":0.00005824974,"about_ca_system_score_gemma":0.00018215469,"threshold_uncertainty_score":0.6956175},"labels":[],"label_agreement":null},{"id":"W4413010725","doi":"10.1109/tmi.2025.3596247","title":"Collaborative Learning of Augmentation and Disentanglement for Semi-Supervised Domain Generalized Medical Image Segmentation","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; China Scholarship Council; Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China","keywords":"Image segmentation; Segmentation; Artificial intelligence; Computer science; Image (mathematics); Domain (mathematical analysis); Computer vision; Medical imaging; Scale-space segmentation; Pattern recognition (psychology); Mathematics","score_opus":0.007367527850164544,"score_gpt":0.3121428772606518,"score_spread":0.3047753494104873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413010725","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040429626,0.00010770066,0.98771626,0.006557703,0.00042316047,0.0007530817,0.000014536217,0.00020866757,0.00017590511],"genre_scores_gemma":[0.29446986,0.00073453964,0.69765747,0.0057133655,0.00007558699,0.0009190963,0.00005692405,0.000036621535,0.00033651473],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9971709,0.00032245394,0.0006457423,0.00046023517,0.0011297491,0.00027087764],"domain_scores_gemma":[0.9985764,0.0005616935,0.0001487545,0.00021482375,0.00021669663,0.0002816389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010709439,0.00019667411,0.00029061758,0.0003219519,0.00026301912,0.00009862134,0.00038216685,0.00009556148,0.00031016723],"category_scores_gemma":[0.00017277889,0.00018676586,0.000087504966,0.00061709934,0.00029043632,0.0005162077,0.000013742519,0.00030539036,0.0000025899906],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000121318655,0.0004398457,0.00015548164,0.00027377342,0.00014729395,0.000026990227,0.0024247116,0.000089617,0.07486918,0.0021439895,0.002244543,0.91706324],"study_design_scores_gemma":[0.006277434,0.00017903635,0.00008672091,0.0005718654,0.000083759856,0.000014026779,0.0023200042,0.37257853,0.6149814,0.0022346796,0.00034521523,0.00032729498],"about_ca_topic_score_codex":0.000031420313,"about_ca_topic_score_gemma":0.000010780873,"teacher_disagreement_score":0.91673595,"about_ca_system_score_codex":0.00012868264,"about_ca_system_score_gemma":0.00032533656,"threshold_uncertainty_score":0.761609},"labels":[],"label_agreement":null},{"id":"W4413093915","doi":"10.1038/s41467-025-62373-x","title":"DeepISLES: a clinically validated ischemic stroke segmentation model from the ISLES'22 challenge","year":2025,"lang":"en","type":"article","venue":"Nature Communications","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sunnybrook Health Science Centre; University of Toronto","funders":"National Institute of Neurological Disorders and Stroke; Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Vlaamse regering; KU Leuven; Ministry of Trade, Industry and Energy; Schweizerische Herzstiftung; National Research Foundation; Korea Evaluation Institute of Industrial Technology; Pohang University of Science and Technology; National Research Foundation of Korea; National Institutes of Health; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Helmut Horten Stiftung; National Science Foundation","keywords":"Ischemic stroke; Stroke (engine); Segmentation; Computer science; Medicine; Computational biology; Internal medicine; Cardiology; Bioinformatics; Artificial intelligence; Biology; Ischemia; Physics","score_opus":0.03604308001600156,"score_gpt":0.36846601975133053,"score_spread":0.332422939735329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413093915","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005370764,0.003505575,0.95280325,0.029433774,0.000172344,0.00049124466,0.00005323264,0.0005040063,0.012499521],"genre_scores_gemma":[0.3490794,0.0020858357,0.6377391,0.010130893,0.00002826526,0.00020415091,0.00021722728,0.00001121233,0.0005038916],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980997,0.00040022726,0.0005567974,0.00037656218,0.00036588093,0.00020084727],"domain_scores_gemma":[0.9942764,0.0014828937,0.00021589822,0.0036738012,0.0002779314,0.00007309382],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0006787687,0.00016754691,0.00018281277,0.00009631066,0.00041415292,0.00019050293,0.005407947,0.0003116233,0.00002605701],"category_scores_gemma":[0.0005971198,0.00013345722,0.00010823848,0.00056235265,0.00022125215,0.00043646825,0.0014200901,0.0013794041,0.000031991643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003162143,0.0010822869,0.0011196341,0.000027080196,0.00039265334,0.000002715608,0.0047689923,0.00012952197,0.056279477,0.13305332,0.40884084,0.39427185],"study_design_scores_gemma":[0.002901958,0.00014706266,0.002700806,0.0004472687,0.00028912732,0.000004362081,0.001475957,0.791507,0.09169044,0.044258382,0.063472115,0.0011055247],"about_ca_topic_score_codex":0.000034023604,"about_ca_topic_score_gemma":0.00007994757,"teacher_disagreement_score":0.7913775,"about_ca_system_score_codex":0.00009529045,"about_ca_system_score_gemma":0.00021519381,"threshold_uncertainty_score":0.9999733},"labels":[],"label_agreement":null},{"id":"W4413156420","doi":"10.1109/cvpr52734.2025.02597","title":"Shape and Texture: What Influences Reliable Optical Flow Estimation?","year":2025,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Texture (cosmology); Flow (mathematics); Computer science; Estimation; Optical flow; Artificial intelligence; Computer vision; Mathematics; Image (mathematics); Geometry; Engineering","score_opus":0.008567508339398042,"score_gpt":0.29416519082250325,"score_spread":0.2855976824831052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413156420","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004893723,0.00028499356,0.9895817,0.0051604505,0.00013175773,0.000116038005,1.02977154e-7,0.00031665666,0.003918906],"genre_scores_gemma":[0.015043675,0.00014114435,0.9775976,0.005950537,0.000008695383,0.000021379881,0.0000012191698,0.0000019818497,0.0012337909],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992732,0.000017175054,0.00016330746,0.00023511169,0.00018864033,0.00012257337],"domain_scores_gemma":[0.99950796,0.00011817893,0.000021265272,0.00022679762,0.000054468263,0.00007131585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020218793,0.00007046914,0.00008565692,0.00007881624,0.00007228887,0.0005861322,0.00033446727,0.000046986795,0.00012407065],"category_scores_gemma":[0.00013810984,0.00005561205,0.000014014955,0.00029549215,0.00008907161,0.0016561188,0.00020249771,0.000081743216,0.00002851868],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.2798226e-7,0.000016419339,0.00012078434,0.000015144216,0.0000047075364,0.0000028491147,0.00007322132,0.000021459262,0.0005454993,0.0253799,0.009036287,0.9647829],"study_design_scores_gemma":[0.00015930823,0.000036974317,0.0015768816,0.000095682575,0.0000050783397,0.0000051636093,0.000053104264,0.9261009,0.047001798,0.024126465,0.0007311191,0.000107495114],"about_ca_topic_score_codex":0.000007365983,"about_ca_topic_score_gemma":0.0000013214528,"teacher_disagreement_score":0.9646754,"about_ca_system_score_codex":0.00001410363,"about_ca_system_score_gemma":0.00005036583,"threshold_uncertainty_score":0.5652087},"labels":[],"label_agreement":null},{"id":"W4413679324","doi":"10.1109/compsac65507.2025.00210","title":"Grey Wolf Optimizer Enhances Adaptive Atrous Spatial Pyramid Pooling for Efficient Multi-Scale Feature Selection in Medical Image Segmentation","year":2025,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Pooling; Pyramid (geometry); Artificial intelligence; Computer science; Image segmentation; Pattern recognition (psychology); Computer vision; Grey scale; Segmentation; Scale (ratio); Selection (genetic algorithm); Feature selection; Feature (linguistics); Mathematics; Cartography; Geography","score_opus":0.010782092220181871,"score_gpt":0.31049195816336,"score_spread":0.2997098659431781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413679324","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036715737,0.000050621864,0.9926278,0.0014900144,0.00036152868,0.0009916207,0.0000032103276,0.00039303364,0.00041060802],"genre_scores_gemma":[0.08505508,0.000017497878,0.9127009,0.0011788327,0.0000643231,0.00027551223,0.0000145989525,0.000010232302,0.0006830551],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979978,0.00012749847,0.0004024091,0.00057186943,0.0005554063,0.0003449702],"domain_scores_gemma":[0.99914557,0.0002285477,0.00011635023,0.00016701252,0.00022053492,0.00012199921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068404735,0.00018835625,0.00023487105,0.00029389455,0.00013227454,0.00015597425,0.00048873667,0.00017717497,0.00008205338],"category_scores_gemma":[0.00027025875,0.00016700922,0.00008239875,0.00062509347,0.00010110688,0.00040074394,0.00015781398,0.00027805267,0.00000843642],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003473724,0.0012894826,0.00048960955,0.00019582393,0.000101548125,0.00003572202,0.0031161748,0.0035152156,0.22092181,0.0025407868,0.008663815,0.7587826],"study_design_scores_gemma":[0.0011048422,0.00009659973,0.0005190361,0.000075768796,0.000007903678,0.000003512621,0.00014766051,0.6551122,0.3425896,0.00019044321,0.000020340034,0.00013212196],"about_ca_topic_score_codex":0.00029987632,"about_ca_topic_score_gemma":0.0004877489,"teacher_disagreement_score":0.75865054,"about_ca_system_score_codex":0.000235976,"about_ca_system_score_gemma":0.00022414715,"threshold_uncertainty_score":0.68104386},"labels":[],"label_agreement":null},{"id":"W4413814198","doi":"10.1111/cgf.70203","title":"FRIDU: Functional Map Refinement with Guided Image Diffusion","year":2025,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Azrieli Foundation; Israel Science Foundation","keywords":"Computer science; Computer vision; Image (mathematics); Artificial intelligence; Computer graphics (images); Diffusion","score_opus":0.012052636813164777,"score_gpt":0.2582228521678884,"score_spread":0.2461702153547236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413814198","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00053326704,0.00006121692,0.98731524,0.009370814,0.00081577734,0.00028721383,0.0000022208535,0.00058376906,0.0010304675],"genre_scores_gemma":[0.013262133,0.000045425775,0.96763647,0.017614814,0.00012422737,0.00010105019,0.000050485036,0.000019157274,0.0011462464],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819046,0.00006363787,0.0003462911,0.00052690745,0.00052422495,0.00034848807],"domain_scores_gemma":[0.99873596,0.00008049797,0.000104838,0.0007245715,0.00023539936,0.00011875733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026628497,0.00020540025,0.00018817333,0.0003819823,0.00023438045,0.0002314092,0.0008022303,0.0000745987,0.00004818603],"category_scores_gemma":[0.000011541121,0.00016613553,0.000087087865,0.00074327993,0.00014293344,0.00040424976,0.00071177253,0.00022171538,0.000028831351],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001766896,0.00024382719,0.0019188869,0.00007137881,0.00007615106,0.000041509535,0.00009281956,0.000009099524,0.0015259269,0.343505,0.57701516,0.07548255],"study_design_scores_gemma":[0.008061104,0.0017168103,0.04062944,0.0012424244,0.000112606685,0.00012092372,0.000098509125,0.48846972,0.07443177,0.19676691,0.18606593,0.0022838337],"about_ca_topic_score_codex":0.000021358966,"about_ca_topic_score_gemma":0.00001078078,"teacher_disagreement_score":0.48846063,"about_ca_system_score_codex":0.00005071932,"about_ca_system_score_gemma":0.0000979953,"threshold_uncertainty_score":0.67748106},"labels":[],"label_agreement":null},{"id":"W4414120573","doi":"10.1016/j.knosys.2025.114454","title":"Enhancing dual network based semi-supervised medical image segmentation with uncertainty-guided pseudo-labeling","year":2025,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Specific Research Project of Guangxi for Research Bases and Talents; National Natural Science Foundation of China","keywords":"Segmentation; Consistency (knowledge bases); Image segmentation; Weighting; Voxel; Pattern recognition (psychology); Feature (linguistics); Medical imaging; Image (mathematics)","score_opus":0.014850625970144346,"score_gpt":0.29566381608545095,"score_spread":0.2808131901153066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414120573","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029437416,0.0006166805,0.9897199,0.00082952244,0.0014061532,0.0011780906,0.000005199707,0.001201549,0.0020991294],"genre_scores_gemma":[0.516234,0.000017895032,0.47592282,0.0041060043,0.0008867334,0.0011774738,0.0001869082,0.000096879965,0.0013712808],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99489385,0.0009649841,0.0011566414,0.0009489561,0.00127641,0.00075918436],"domain_scores_gemma":[0.9964555,0.0011038793,0.00030600035,0.0009800692,0.00072341843,0.00043114758],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0029157754,0.00045267964,0.0006092736,0.00041313976,0.0004037136,0.00050137873,0.0010920066,0.00026722928,0.00010797015],"category_scores_gemma":[0.00051124184,0.0003864909,0.0001284861,0.001861616,0.00020085985,0.0004954292,0.00018171704,0.00046135715,0.00009751095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052227906,0.0028404845,0.005630704,0.0092484495,0.00093748583,0.0011639243,0.0046719294,0.045544516,0.63152766,0.011047455,0.18299265,0.10387243],"study_design_scores_gemma":[0.0031852815,0.00018238908,0.00003898351,0.0028304919,0.000052325187,0.000015645272,0.00019851267,0.83886796,0.15343639,0.000069732145,0.0006313165,0.0004909991],"about_ca_topic_score_codex":0.00017616106,"about_ca_topic_score_gemma":0.00012896315,"teacher_disagreement_score":0.7933234,"about_ca_system_score_codex":0.00047545452,"about_ca_system_score_gemma":0.002066916,"threshold_uncertainty_score":0.9998587},"labels":[],"label_agreement":null},{"id":"W4414270018","doi":"10.1016/j.micron.2025.103915","title":"Advancing X-ray microcomputed tomography image processing of avian eggshells: An improved registration metric for multiscale 3D images and resolution-enhanced segmentation of eggshell pores using edge-attentive neural networks","year":2025,"lang":"en","type":"article","venue":"Micron","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre; Group for Research in Decision Analysis; McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; McGill University","keywords":"Eggshell; Context (archaeology); Convolutional neural network; Segmentation; Pattern recognition (psychology); Metric (unit); Grayscale; Artificial neural network; Tomography","score_opus":0.00847937867719048,"score_gpt":0.29150799334884436,"score_spread":0.28302861467165386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414270018","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.066797234,0.00079152454,0.9312349,0.00004921751,0.00011465948,0.0008799874,0.000013979707,0.000098484226,0.000020007192],"genre_scores_gemma":[0.39746705,0.000021886504,0.602325,0.00006304792,0.000021815034,0.00002465643,0.000041732776,0.000009424877,0.000025392636],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982763,0.00012763553,0.00064500107,0.00050387246,0.00016528508,0.00028188812],"domain_scores_gemma":[0.99851865,0.00011177151,0.0005702802,0.0002768371,0.00045394604,0.000068537425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046297838,0.00019424295,0.0002997795,0.0005113659,0.00016347307,0.0001284901,0.00032484136,0.00009347238,0.000002027077],"category_scores_gemma":[0.00007171382,0.00020027466,0.00007819396,0.00097182876,0.00019984819,0.0011955687,0.00011341392,0.0001210882,5.744207e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033896613,0.000094116986,0.0001912005,0.0002557877,0.000015906628,5.584505e-7,0.00043852974,0.0005335942,0.8531774,0.0000066426987,0.000066774424,0.14518555],"study_design_scores_gemma":[0.0005255332,0.00012275622,0.0013622855,0.00014284921,0.000029858194,0.0000013716067,0.0001802928,0.4191111,0.5783637,0.000048035443,0.0000026882046,0.000109489345],"about_ca_topic_score_codex":0.00014679473,"about_ca_topic_score_gemma":0.000020331127,"teacher_disagreement_score":0.4185775,"about_ca_system_score_codex":0.00007078316,"about_ca_system_score_gemma":0.00006666075,"threshold_uncertainty_score":0.81669635},"labels":[],"label_agreement":null},{"id":"W4414317214","doi":"10.1088/2057-1976/ae08bb","title":"EigenU-Net: integrating eigenvalue decomposition of the Hessian into U-Net for 3D coronary artery segmentation","year":2025,"lang":"en","type":"article","venue":"Biomedical Physics & Engineering Express","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; Toronto General Hospital; University of Toronto; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hessian matrix; Segmentation; Eigenvalues and eigenvectors; Voxel; Pattern recognition (psychology); Image segmentation; Coronary arteries; Gaussian; Scale-space segmentation","score_opus":0.007582297918239582,"score_gpt":0.2759051442794383,"score_spread":0.26832284636119874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414317214","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049578543,0.000062669584,0.99346095,0.0002683329,0.00057334686,0.00044892638,0.000009774104,0.00019022412,0.000027903307],"genre_scores_gemma":[0.16075185,0.0000055793657,0.8386155,0.00025721392,0.0001163831,0.00017005198,0.000044139822,0.000013026147,0.000026307665],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986946,0.000050558796,0.00038433608,0.0002838907,0.00037422005,0.00021239225],"domain_scores_gemma":[0.9991095,0.00021856432,0.00012214805,0.00039102207,0.00007733331,0.00008141678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002338775,0.00016006787,0.00018970527,0.00009089717,0.0000980204,0.000055468627,0.0006916685,0.00008336005,0.000005507862],"category_scores_gemma":[0.00007737083,0.00012937245,0.00009552911,0.000424311,0.00010476063,0.00029221625,0.00021141586,0.00015839502,0.0000015839184],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000059961963,0.00011427934,0.000036166417,0.00022913453,0.000043958087,0.000001456623,0.00072176015,0.00021229683,0.87167835,0.006627481,0.001833847,0.118495256],"study_design_scores_gemma":[0.0009918687,0.00015363611,0.0013572747,0.0010317003,0.000050853963,0.000005364053,0.00014684038,0.3999682,0.5828606,0.008379434,0.004650035,0.00040422328],"about_ca_topic_score_codex":0.000017256989,"about_ca_topic_score_gemma":2.3614803e-7,"teacher_disagreement_score":0.3997559,"about_ca_system_score_codex":0.00009684297,"about_ca_system_score_gemma":0.00008132566,"threshold_uncertainty_score":0.52756554},"labels":[],"label_agreement":null},{"id":"W4414475609","doi":"10.1007/s44443-025-00200-5","title":"Source-free cross-modality medical image synthesis with diffusion priors","year":2025,"lang":"en","type":"article","venue":"Journal of King Saud University - Computer and Information Sciences","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"National Natural Science Foundation of China","keywords":"Prior probability; Fidelity; Image (mathematics); Scalability; Source code; Solver; Encoding (memory); Medical imaging; Synthetic data; Pattern recognition (psychology)","score_opus":0.005573113904293679,"score_gpt":0.25034571063802585,"score_spread":0.24477259673373217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414475609","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06359661,0.000009362815,0.93202,0.0026652676,0.0001480144,0.00005316537,7.726077e-7,0.000048257247,0.0014585388],"genre_scores_gemma":[0.35615647,0.00017763424,0.6408997,0.0026195527,0.000062916304,2.720307e-7,6.1083693e-7,0.00000212148,0.000080679456],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845016,0.000089241745,0.0003345406,0.000112574926,0.0008762002,0.00013728696],"domain_scores_gemma":[0.9987631,0.00026379462,0.0003612982,0.00016270655,0.00030794917,0.00014117704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014179149,0.000085426815,0.0001618659,0.00050443097,0.00039069683,0.0005193257,0.0013187676,0.00005348537,0.000018062],"category_scores_gemma":[0.0002466241,0.00006273894,0.000046694557,0.0006359495,0.00048564785,0.007009475,0.0005255055,0.00017132804,0.0000013310083],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006511396,0.00009632509,0.020828856,0.00011765265,0.00005292116,0.000034885426,0.0036922593,0.00019841171,0.00012299804,0.01713616,0.0034011246,0.9542533],"study_design_scores_gemma":[0.005610484,0.0014947064,0.29386696,0.0024310607,0.00010052687,0.00075317355,0.0029592598,0.6376879,0.00962358,0.0043811575,0.040113755,0.0009774386],"about_ca_topic_score_codex":0.00003836599,"about_ca_topic_score_gemma":0.0000028233023,"teacher_disagreement_score":0.95327586,"about_ca_system_score_codex":0.000056985147,"about_ca_system_score_gemma":0.0003223287,"threshold_uncertainty_score":0.50817},"labels":[],"label_agreement":null},{"id":"W4414529667","doi":"10.1007/978-981-96-6254-8_5","title":"Gray-Scale Edge Detection Techniques: A Survey and Comparative Analysis","year":2025,"lang":"en","type":"book-chapter","venue":"Smart innovation, systems and technologies","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"PotashCorp (Canada)","funders":"","keywords":"Grayscale; Edge detection; Enhanced Data Rates for GSM Evolution; Benchmark (surveying); Process (computing); Deep learning; Cover (algebra)","score_opus":0.04196049954848944,"score_gpt":0.29068521010269766,"score_spread":0.24872471055420822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414529667","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001521866,0.0014520785,0.9602469,0.0002036995,0.0002391756,0.00094035285,0.00006710931,0.0030135806,0.03368491],"genre_scores_gemma":[0.6077642,0.0046687527,0.18387495,0.0005618048,0.00018556886,0.0016471441,0.0008747048,0.000088162866,0.20033474],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977359,0.00007036413,0.00085807935,0.00077803485,0.00034777462,0.00020984933],"domain_scores_gemma":[0.99748886,0.00020979182,0.00060440955,0.0007731519,0.00089494867,0.000028813152],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010517841,0.00039116954,0.00079858745,0.0026227587,0.00023482183,0.00034437905,0.0005287415,0.00065395114,0.0000043312198],"category_scores_gemma":[0.00016200272,0.0003510109,0.000058957026,0.001995721,0.00041764608,0.0003508231,0.0005129024,0.00046611694,0.0000029217404],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014301031,0.000036183574,0.0031771197,0.00044433246,0.0010361852,0.0000090112035,0.00023421521,5.3637854e-7,0.0007433737,0.60896724,0.010328804,0.3750087],"study_design_scores_gemma":[0.002308991,0.002481552,0.024066169,0.0049177376,0.0020401203,0.00026781246,0.0031197795,0.014347903,0.2492736,0.22779378,0.46030444,0.009078116],"about_ca_topic_score_codex":0.00022795501,"about_ca_topic_score_gemma":0.00027948085,"teacher_disagreement_score":0.77637196,"about_ca_system_score_codex":0.000095147254,"about_ca_system_score_gemma":0.00007465399,"threshold_uncertainty_score":0.9998942},"labels":[],"label_agreement":null},{"id":"W4414802118","doi":"10.1016/j.hpb.2025.07.168","title":"The Use of Artificial Intelligence in Generating 3D Liver Volumetrics","year":2025,"lang":"en","type":"article","venue":"HPB","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto General Hospital","funders":"","keywords":"Component (thermodynamics); Replicate; Software; Key (lock); 3d model","score_opus":0.08002144032001782,"score_gpt":0.32588065242482994,"score_spread":0.24585921210481212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414802118","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015221533,0.00011387856,0.9980601,0.00002441033,0.00012280894,0.00008597443,2.9451095e-7,0.000038162692,0.00003220489],"genre_scores_gemma":[0.00043966516,0.00007103673,0.9988085,0.00040468524,0.000010372541,0.0000084524845,2.9934685e-7,0.0000014634079,0.00025550864],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993218,0.00006855863,0.00023711742,0.00011864722,0.00015640074,0.0000974809],"domain_scores_gemma":[0.9992366,0.0003963497,0.000051780156,0.00023609362,0.00006181606,0.000017394676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033812513,0.00003895685,0.000057481724,0.00012257716,0.000051921066,0.00010119851,0.00037638584,0.000024696596,0.00000895788],"category_scores_gemma":[0.0007359356,0.000029790523,0.000016602089,0.0008317796,0.00005979845,0.00018349547,0.00015923376,0.00008193779,0.000005994669],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.6474513e-7,0.0000144546675,0.00013504642,0.0000040644436,0.0000012572826,0.000002394055,0.00010219308,0.00006738074,0.000942635,0.0017235588,0.000832096,0.9961743],"study_design_scores_gemma":[0.000009400418,0.000010841981,0.0002732892,0.000016298412,8.31452e-7,2.768256e-7,0.000013129457,0.99041444,0.008561642,0.00009032972,0.000581281,0.000028210201],"about_ca_topic_score_codex":0.00006993842,"about_ca_topic_score_gemma":0.000033314027,"teacher_disagreement_score":0.996146,"about_ca_system_score_codex":0.00002351551,"about_ca_system_score_gemma":0.000044629258,"threshold_uncertainty_score":0.12148222},"labels":[],"label_agreement":null},{"id":"W4414951670","doi":"10.1038/s41598-025-19121-4","title":"A hybrid approach for enhancing pseudo-labeling in medical images through pseudo-label refinement","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Robustness (evolution); Deep learning; Image segmentation; Pruning; Medical imaging; Pattern recognition (psychology)","score_opus":0.02196825798798245,"score_gpt":0.3229647532610594,"score_spread":0.3009964952730769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414951670","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004025298,0.00023074703,0.9863125,0.0012265085,0.0034177897,0.0009334307,0.0000016371331,0.00037637388,0.0034756993],"genre_scores_gemma":[0.041509327,0.000022323089,0.95319456,0.0012193912,0.000064003834,0.0005016471,0.000052883563,0.000016223446,0.0034196118],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99501956,0.00011306656,0.0012911719,0.0015523419,0.0013925002,0.000631355],"domain_scores_gemma":[0.9976294,0.00018445404,0.00033803354,0.0013787474,0.00029203165,0.00017730994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060091787,0.00023744667,0.00036844026,0.00042757153,0.00034961104,0.0006202661,0.0009805067,0.00010295602,0.000064604465],"category_scores_gemma":[0.0017407176,0.00021714836,0.00009997918,0.0012015962,0.00029760838,0.0007677472,0.0005814599,0.0002758619,0.0000058030823],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026514967,0.0019519966,0.0007626749,0.0009506588,0.00009987124,0.0017204162,0.002129768,0.00009486513,0.31840298,0.010585189,0.33798975,0.32528532],"study_design_scores_gemma":[0.0010032507,0.000064441956,0.000042499003,0.000493019,0.00002098316,0.00024295546,0.00014892277,0.06529324,0.87609327,0.051735513,0.0043972065,0.00046472694],"about_ca_topic_score_codex":0.000062259984,"about_ca_topic_score_gemma":0.000018132443,"teacher_disagreement_score":0.55769026,"about_ca_system_score_codex":0.00018089615,"about_ca_system_score_gemma":0.000736892,"threshold_uncertainty_score":0.8855053},"labels":[],"label_agreement":null},{"id":"W4415077484","doi":"10.1007/978-3-032-07502-4_11","title":"Pixels Under Pressure: Exploring Fine-Tuning Paradigms for Foundation Models in High-Resolution Medical Imaging","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Fidelity; Classifier (UML); Medical imaging; Pixel; Key (lock); Benchmark (surveying); Downstream (manufacturing); Set (abstract data type)","score_opus":0.044787075907380194,"score_gpt":0.29737202920274663,"score_spread":0.25258495329536645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415077484","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000010819964,0.00043251493,0.9939417,0.002342382,0.0016207776,0.00080843345,0.0000042150245,0.00028941224,0.0005497417],"genre_scores_gemma":[0.050146557,0.00012590877,0.9462347,0.0025349723,0.00040340383,0.00020815153,0.000037580307,0.00003659429,0.00027214873],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954046,0.00007461681,0.00081891246,0.001497233,0.0015187074,0.0006859146],"domain_scores_gemma":[0.99731505,0.0010578745,0.00028894926,0.0008988813,0.00024069681,0.00019854103],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0019490853,0.00044462876,0.00051739154,0.0012699373,0.00023381409,0.0005147991,0.0025970668,0.00027386812,0.00003113709],"category_scores_gemma":[0.00040591418,0.0004460587,0.00010185126,0.0007054184,0.00048399324,0.0020982435,0.0010854519,0.00082151004,0.0000045658694],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000061621704,0.00002470219,0.000011559826,0.00008358521,0.000008527852,0.000020383945,0.00033878686,0.06294531,0.000085006934,0.09218851,0.00006280335,0.8442247],"study_design_scores_gemma":[0.00034088801,0.000031523494,0.000021112512,0.0008529498,0.0000074083214,0.00000905128,2.8041143e-7,0.7191341,0.0011133975,0.27793753,0.00024196181,0.00030982777],"about_ca_topic_score_codex":0.00010271231,"about_ca_topic_score_gemma":0.00011024482,"teacher_disagreement_score":0.84391487,"about_ca_system_score_codex":0.00045596814,"about_ca_system_score_gemma":0.0009189665,"threshold_uncertainty_score":0.99979913},"labels":[],"label_agreement":null},{"id":"W4415288416","doi":"10.1101/2025.10.16.682836","title":"Intracortical microstructure profiling: a versatile method for indexing cortical lamination","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Hospital for Sick Children; Deutsche Forschungsgemeinschaft; Canada Research Chairs; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada","keywords":"Workflow; Laminar flow; Microstructure; Neuroanatomy; Profiling (computer programming); Toolbox","score_opus":0.014755973191619655,"score_gpt":0.28972847485709197,"score_spread":0.2749725016654723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415288416","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004588618,0.000104807776,0.9907928,0.00052422174,0.0012290939,0.0016105606,0.0001404356,0.0010003467,0.000009134253],"genre_scores_gemma":[0.14538987,0.000019004283,0.85323405,0.00077836175,0.00022919443,0.00030144575,0.0000011874359,0.000037552683,0.000009311806],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99650514,0.0002947224,0.0007642402,0.0013064209,0.00055465737,0.0005748467],"domain_scores_gemma":[0.9967686,0.0005060463,0.000384411,0.0012541023,0.00080558175,0.00028127644],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012644138,0.00046866163,0.00055679557,0.00036475895,0.00020185967,0.0004945906,0.0013814555,0.0007199786,0.000018365472],"category_scores_gemma":[0.0018065699,0.000497381,0.0001804789,0.0005931245,0.00012741343,0.00033159586,0.0010726289,0.0011294709,0.0000071820505],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003114309,0.00015694038,0.00041668073,0.0009697691,0.000140735,0.000029767107,0.000055337903,0.00002937155,0.96785146,0.02901705,0.00097089505,0.00033085406],"study_design_scores_gemma":[0.00044827806,0.00006219168,0.003071768,0.00028736945,0.00008486721,2.6483908e-8,0.000002804761,0.06471216,0.9301821,0.00011773295,0.0005120914,0.00051862997],"about_ca_topic_score_codex":0.000012342817,"about_ca_topic_score_gemma":4.9710377e-7,"teacher_disagreement_score":0.14080127,"about_ca_system_score_codex":0.00036638865,"about_ca_system_score_gemma":0.0010244591,"threshold_uncertainty_score":0.99974775},"labels":[],"label_agreement":null},{"id":"W4415357126","doi":"10.3390/make7040124","title":"SemiSeg-CAW: Semi-Supervised Segmentation of Ultrasound Images by Leveraging Class-Level Information and an Adaptive Multi-Loss Function","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Segmentation; Weighting; Scale-space segmentation; Feature (linguistics); Pattern recognition (psychology); Image segmentation; Segmentation-based object categorization; Dependency (UML)","score_opus":0.017657013095751087,"score_gpt":0.31087791044407603,"score_spread":0.29322089734832496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415357126","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02794263,0.0006636325,0.9700331,0.0001368131,0.00018404699,0.00022592142,0.000010447542,0.00023401118,0.0005693604],"genre_scores_gemma":[0.93614805,0.00042143546,0.06216871,0.000117455726,0.000033365362,0.000035264322,0.00019718982,0.000009898691,0.00086862635],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872494,0.00027057712,0.00037498775,0.00029400297,0.00018091075,0.00015459541],"domain_scores_gemma":[0.9991354,0.00023264895,0.00022520113,0.00013449148,0.00018842025,0.00008385019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006168592,0.00017088464,0.00017319049,0.00026705442,0.00027928702,0.00019017536,0.000115374714,0.00010184222,0.000019540217],"category_scores_gemma":[0.00018673103,0.00017179405,0.00002890309,0.00028664948,0.000078671954,0.0026921702,0.00007177938,0.00035246226,0.0000043206105],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007629956,0.00017554426,0.011486905,0.00016648872,0.000044832002,6.6511114e-7,0.002368879,0.00006893636,0.2784661,0.00025990652,0.00098861,0.70589685],"study_design_scores_gemma":[0.0037078192,0.00066763273,0.040415704,0.0003039682,0.00011315634,0.00004384186,0.002948726,0.6423062,0.30492178,0.00062935334,0.0033220588,0.00061975437],"about_ca_topic_score_codex":0.00020513502,"about_ca_topic_score_gemma":0.000016819622,"teacher_disagreement_score":0.90820545,"about_ca_system_score_codex":0.0000715786,"about_ca_system_score_gemma":0.00005025491,"threshold_uncertainty_score":0.7005558},"labels":[],"label_agreement":null},{"id":"W4415422329","doi":"10.1016/j.cviu.2025.104522","title":"MOSAIC: A multi-view 2.5D organ slice selector with cross-attentional reasoning for anatomically-aware CT localization in medical organ segmentation","year":2025,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"King Fahd University of Petroleum and Minerals","keywords":"Segmentation; Pipeline (software); Key (lock); Pattern recognition (psychology); Image segmentation; Medical imaging; Orientation (vector space); Scale-space segmentation","score_opus":0.02414661260178595,"score_gpt":0.343516442457225,"score_spread":0.31936982985543905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415422329","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026332964,0.00006664847,0.995336,0.0009280366,0.00015560085,0.0006020921,0.000004577925,0.00024358208,0.000030146752],"genre_scores_gemma":[0.22263251,0.00011612552,0.77333075,0.0035556152,0.00005604837,0.00005421929,0.00012713917,0.000029325332,0.000098238794],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979113,0.00014282009,0.0004835698,0.0006478513,0.0005019305,0.00031253003],"domain_scores_gemma":[0.99901235,0.0002560661,0.000138697,0.0002225546,0.00018944831,0.00018090296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006879071,0.00022368321,0.00027397092,0.0003664721,0.00031519437,0.00064539665,0.00039582964,0.000081395294,0.00004688652],"category_scores_gemma":[0.000119217904,0.00019167876,0.000044865687,0.0008893671,0.00017029104,0.0010000337,0.0002518403,0.00020570688,0.0000023505286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006170552,0.0021998659,0.046498742,0.0031282126,0.0004482131,0.0006255423,0.0050702062,0.00045245013,0.023656083,0.117640056,0.029811505,0.76985204],"study_design_scores_gemma":[0.0035773744,0.00019377444,0.0011823272,0.001054906,0.000013858682,0.000051053226,0.00018740066,0.9855397,0.0064628515,0.0012055114,0.00024168243,0.00028957348],"about_ca_topic_score_codex":0.000017270075,"about_ca_topic_score_gemma":0.000030243242,"teacher_disagreement_score":0.9850872,"about_ca_system_score_codex":0.00039237132,"about_ca_system_score_gemma":0.00020322371,"threshold_uncertainty_score":0.78164333},"labels":[],"label_agreement":null},{"id":"W4415482185","doi":"10.1109/tsp.2025.3624791","title":"Regularized Top-$ k $: A Bayesian Framework for Gradient Sparsification","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada); University of Toronto","funders":"","keywords":"Scaling; Posterior probability; Convergence (economics); Rate of convergence; Bayesian probability; Prior probability; Generalization; Inverse; Inverse problem","score_opus":0.028707610141174594,"score_gpt":0.3225312797731015,"score_spread":0.29382366963192685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415482185","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006782868,0.00034508997,0.9923266,0.0037893238,0.0010794022,0.0016068586,0.000020359576,0.00048249806,0.00028199703],"genre_scores_gemma":[0.5050623,0.000050141236,0.49184382,0.0015428138,0.00007695707,0.00043172797,0.0000034758139,0.000027402843,0.0009614051],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961289,0.00022831309,0.0010467299,0.0012132283,0.00070263445,0.00068015896],"domain_scores_gemma":[0.9975219,0.00056279317,0.00040355555,0.0007208488,0.0005117601,0.00027915795],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008872173,0.00046435956,0.0004916725,0.00072830275,0.001253732,0.0008907198,0.0009741602,0.000434452,0.00019451293],"category_scores_gemma":[0.000052301017,0.0005018995,0.00032742592,0.0019467124,0.0003171769,0.0009692911,0.0000059215495,0.0008105178,0.000019953863],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020732966,0.00060638937,0.0000012249156,0.0005328947,0.000076303564,0.000003866636,0.0012580381,0.0009091193,0.008170728,0.0029935075,0.00028399736,0.9849566],"study_design_scores_gemma":[0.00088767207,0.00031394826,0.000008677928,0.0020117587,0.00018086356,0.000006035114,0.00018236222,0.4853877,0.47193694,0.038356025,0.00030381692,0.00042419255],"about_ca_topic_score_codex":0.000012033771,"about_ca_topic_score_gemma":0.0000031539723,"teacher_disagreement_score":0.9845324,"about_ca_system_score_codex":0.0003650851,"about_ca_system_score_gemma":0.0007651521,"threshold_uncertainty_score":0.9997433},"labels":[],"label_agreement":null},{"id":"W4415737166","doi":"10.1080/24699322.2025.2580307","title":"Patient-specific functional liver segments based on centerline classification of the hepatic and portal veins","year":2025,"lang":"en","type":"article","venue":"Computer Assisted Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Norges Forskningsråd; Queen's University","keywords":"Segmentation; Hepatic veins; Workflow; Image segmentation; Portal vein","score_opus":0.027200435077165987,"score_gpt":0.24082702794585437,"score_spread":0.21362659286868838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415737166","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037041806,0.00006240422,0.96074533,0.0005499027,0.0010094099,0.00021709633,0.00000563363,0.00011089779,0.00025754157],"genre_scores_gemma":[0.9652795,0.000016607526,0.03250666,0.002036758,0.00003823097,0.000027452143,0.00003152733,0.0000070577803,0.000056197216],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99841326,0.00021362602,0.0004438718,0.00034870458,0.00043147145,0.00014905095],"domain_scores_gemma":[0.99854034,0.0005164715,0.000216211,0.00053447107,0.0001318381,0.000060654176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026042518,0.00013402352,0.0001931228,0.00023686762,0.00010909579,0.00007407492,0.0002590281,0.00005837582,0.00002944751],"category_scores_gemma":[0.000041564854,0.00010338344,0.0001135144,0.0004957721,0.00011018441,0.00016306102,0.00017217985,0.00013035742,0.0000039698893],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029029135,0.00084832235,0.025729997,0.00009762688,0.00004529842,0.000019394576,0.00009274462,0.000052871033,0.0017718234,0.0011306476,0.050697595,0.9194847],"study_design_scores_gemma":[0.00031854847,0.00007447414,0.7490247,0.0003533038,0.000011063539,0.000007068565,0.000008470598,0.24088821,0.008134023,0.0000833774,0.00094624533,0.00015055736],"about_ca_topic_score_codex":0.0000037690957,"about_ca_topic_score_gemma":9.548097e-7,"teacher_disagreement_score":0.92823863,"about_ca_system_score_codex":0.000053532563,"about_ca_system_score_gemma":0.00010678503,"threshold_uncertainty_score":0.42158544},"labels":[],"label_agreement":null},{"id":"W4416438657","doi":"10.3174/ajnr.a9110","title":"Hippocampal Segmentation Performance on 7T MRI: Intensity-Based Accuracy Assessment with Paired 3T–7T Volume Comparison across Multiple Algorithms","year":2025,"lang":"en","type":"article","venue":"American Journal of Neuroradiology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Segmentation; Hippocampal formation; Pattern recognition (psychology); Volume (thermodynamics)","score_opus":0.01771739982801899,"score_gpt":0.33596088687315007,"score_spread":0.31824348704513106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416438657","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33050945,0.000009071997,0.6656147,0.0032410526,0.00035771506,0.00016847588,0.0000025434438,0.00007275027,0.000024200337],"genre_scores_gemma":[0.764643,0.000028235683,0.22873244,0.006497715,0.000051076157,0.000015095645,0.0000062292156,0.000010816385,0.000015402757],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99747485,0.00052465685,0.00075090886,0.0003882316,0.00046322646,0.00039809998],"domain_scores_gemma":[0.9970647,0.0008508152,0.001073967,0.00042260523,0.00041038092,0.00017752676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006329928,0.00024034025,0.0006514813,0.0002839451,0.00017527113,0.00010547973,0.0008938578,0.000049325292,0.000009809441],"category_scores_gemma":[0.00026250567,0.00019174353,0.00009968721,0.0007138468,0.0005480447,0.00045293444,0.00010889034,0.0006381867,0.0000063406137],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066935504,0.00058150635,0.22549334,0.000040392963,0.00015339087,0.00020603744,0.0009919571,0.005159853,0.008018704,0.000063803585,0.007946104,0.75067556],"study_design_scores_gemma":[0.004870033,0.021248462,0.41045082,0.0003227825,0.00007559738,0.0005547138,0.0014253098,0.5019928,0.056537323,0.000111970505,0.0017636764,0.00064649835],"about_ca_topic_score_codex":0.000026910664,"about_ca_topic_score_gemma":0.0000030369863,"teacher_disagreement_score":0.750029,"about_ca_system_score_codex":0.00020111675,"about_ca_system_score_gemma":0.00033085496,"threshold_uncertainty_score":0.78190744},"labels":[],"label_agreement":null},{"id":"W4416981882","doi":"10.48550/arxiv.2504.19930","title":"Accelerated 3D-3D rigid registration of echocardiographic images obtained from apical window using particle filter","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Translation (biology); Image registration; Rotation (mathematics); Filter (signal processing); Image quality; Noise (video); Particle filter; Speedup; Process (computing)","score_opus":0.077824263196939,"score_gpt":0.3288489446352797,"score_spread":0.25102468143834067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416981882","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3635302,0.000116952026,0.6348019,0.000322039,0.0002912018,0.0003432898,0.000033462715,0.00031452632,0.000246394],"genre_scores_gemma":[0.76968205,0.000079815065,0.2294268,0.0004385609,0.000112948954,0.000042784395,0.00008225134,0.000012803741,0.00012197772],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99720585,0.00033164409,0.00078509795,0.0008262361,0.00054440316,0.00030677018],"domain_scores_gemma":[0.9976163,0.00019861343,0.00045445169,0.0012765637,0.00032053614,0.0001335181],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047373335,0.0002995141,0.00050638075,0.00020300738,0.0000936221,0.00020151751,0.0011366693,0.0003043479,0.0000856811],"category_scores_gemma":[0.00027146778,0.00029505108,0.00020144557,0.0006246234,0.00020586523,0.00047447116,0.000921169,0.0005577681,0.000011984806],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001082391,0.0006529151,0.53794056,0.00053554575,0.0008517773,0.00017938289,0.00138764,0.0013067203,0.4053974,0.00039697255,0.0060019363,0.045240927],"study_design_scores_gemma":[0.0006430948,0.00006760543,0.14053605,0.00050891226,0.00012653062,0.0000028297984,0.000024782685,0.05408906,0.8012363,0.0022136092,0.000065068256,0.0004861581],"about_ca_topic_score_codex":0.0006206879,"about_ca_topic_score_gemma":0.0000072580992,"teacher_disagreement_score":0.40615186,"about_ca_system_score_codex":0.000079709,"about_ca_system_score_gemma":0.00027059123,"threshold_uncertainty_score":0.9999502},"labels":[],"label_agreement":null},{"id":"W54451214","doi":"10.1007/978-3-642-15352-5_8","title":"Image Segmentation According to the Movement of Real Objects","year":2010,"lang":"en","type":"book-chapter","venue":"Springer topics in signal processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Segmentation; Artificial intelligence; Computer vision; Scale-space segmentation; Optical flow; Image segmentation; Segmentation-based object categorization; Parametric statistics; Pixel; Minimum spanning tree-based segmentation; Range segmentation; Computer science; Boundary (topology); Parametric model; Flow (mathematics); Pattern recognition (psychology); Mathematics; Image (mathematics); Geometry; Mathematical analysis; Statistics","score_opus":0.022246411008766506,"score_gpt":0.29512030998523786,"score_spread":0.27287389897647135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W54451214","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018623631,0.000103485545,0.9109506,0.00052428554,0.00024052031,0.00056094705,0.000002202221,0.00010647437,0.087325245],"genre_scores_gemma":[0.019573117,0.0001147821,0.9598374,0.0018393538,0.00064970943,0.00008935599,0.000009203862,0.000064718406,0.017822376],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980781,0.00002525668,0.00056547293,0.0004422914,0.0006560186,0.00023284224],"domain_scores_gemma":[0.9989029,0.0000612984,0.0003687042,0.00041634962,0.00017080116,0.00007994442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062175764,0.00023062118,0.0002711542,0.0002499173,0.00010146427,0.00019371415,0.00095033535,0.00017309276,0.00006368837],"category_scores_gemma":[0.00003560905,0.00019187326,0.00006615802,0.00012728346,0.00008835274,0.00037553263,0.0004701089,0.0006213071,0.000012716408],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008490105,0.00003683494,0.00007627925,0.0003410523,0.000017706632,0.000030001576,0.0029066338,0.000019498933,0.06556578,0.010934184,0.00010713475,0.9199564],"study_design_scores_gemma":[0.00082148856,0.0003367469,0.00034722363,0.0031498126,0.000064481734,0.00001010134,0.00049902324,0.010284542,0.8791496,0.09965767,0.004224204,0.0014550762],"about_ca_topic_score_codex":0.000041625568,"about_ca_topic_score_gemma":0.000036378176,"teacher_disagreement_score":0.9185013,"about_ca_system_score_codex":0.000130402,"about_ca_system_score_gemma":0.00017355659,"threshold_uncertainty_score":0.78243643},"labels":[],"label_agreement":null},{"id":"W54985407","doi":"10.1007/978-3-642-41914-0_4","title":"Fully Automated Brain Tumor Segmentation Using Two MRI Modalities","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Segmentation; Computer science; Thresholding; Histogram; Artificial intelligence; Sørensen–Dice coefficient; Magnetic resonance imaging; Volume (thermodynamics); Image segmentation; Computer vision; Pattern recognition (psychology); Image (mathematics); Radiology; Medicine","score_opus":0.02160179779354516,"score_gpt":0.29901388069525586,"score_spread":0.2774120829017107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W54985407","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021240408,0.00012050345,0.9944423,0.0009110932,0.0011207686,0.00080413773,0.0000064518804,0.0013843706,0.0009979998],"genre_scores_gemma":[0.005630824,0.000010055451,0.9876758,0.0057716654,0.0002888256,0.000027778868,0.0000134610045,0.000043613953,0.00053801946],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99542576,0.00010145261,0.0007887805,0.001455565,0.0015152142,0.00071322935],"domain_scores_gemma":[0.99719894,0.000503343,0.0004991785,0.0011608243,0.00037965307,0.00025805246],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0010208776,0.000581396,0.0005351935,0.0010139094,0.00028132697,0.0011051027,0.0027603277,0.0001982899,0.00017311519],"category_scores_gemma":[0.00014179666,0.0005467014,0.00011535513,0.00065046607,0.0008762437,0.0016797883,0.0011528541,0.00064049906,0.00009973382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000069831167,0.00007193155,0.000039467814,0.00017606794,0.000038159622,0.00026472614,0.0030663616,0.03822951,0.020320402,0.013439064,0.0013780354,0.9229693],"study_design_scores_gemma":[0.00030254008,0.00010495146,0.000020912392,0.00035343255,0.0000071103505,0.0001028898,8.072247e-7,0.9178722,0.024208149,0.056347545,0.00008243848,0.000597044],"about_ca_topic_score_codex":0.00018842363,"about_ca_topic_score_gemma":0.000025718382,"teacher_disagreement_score":0.9223722,"about_ca_system_score_codex":0.00062427716,"about_ca_system_score_gemma":0.0007044239,"threshold_uncertainty_score":0.9999319},"labels":[],"label_agreement":null},{"id":"W57713726","doi":"10.20965/jaciii.2009.p0115","title":"Automatic Acquisition of Image Filtering and Object Extraction Procedures from Ground-Truth Samples","year":2009,"lang":"en","type":"article","venue":"Journal of Advanced Computational Intelligence and Intelligent Informatics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Ontario Tech University","funders":"","keywords":"Computer science; Artificial intelligence; Ground truth; Image processing; Sample (material); Object (grammar); Parameterized complexity; Image (mathematics); Computer vision; Digital image processing; Pattern recognition (psychology); Algorithm","score_opus":0.021827763051525475,"score_gpt":0.3156307259324421,"score_spread":0.2938029628809167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W57713726","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.093921654,0.00034326324,0.9052392,0.00013119561,0.00013019894,0.00013322668,0.000004912284,0.00003942569,0.00005687798],"genre_scores_gemma":[0.4647084,0.00067034573,0.53434354,0.00023808629,0.000026998801,0.000001398924,0.0000065441195,0.0000030828771,0.0000016578371],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978526,0.000039984086,0.0013160398,0.00011467631,0.00052733504,0.00014936241],"domain_scores_gemma":[0.9976249,0.0005673705,0.0010571308,0.00012880517,0.0004983671,0.00012347348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003691348,0.00016604856,0.0002987769,0.00029889724,0.000080117956,0.0001809523,0.0003245667,0.00005567953,0.000030500758],"category_scores_gemma":[0.0003016157,0.00014349865,0.00006873076,0.00023390283,0.000105839565,0.00275104,0.00006797425,0.00019279982,0.000002719365],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003839509,0.00012139614,0.000035959445,0.00016463602,0.00004970565,0.0000067637316,0.0053291046,0.01466291,0.0045128516,0.007875795,0.00006523779,0.9671372],"study_design_scores_gemma":[0.0002929802,0.001478574,0.0053499825,0.0010288369,0.000044716846,0.00043185908,0.0042392476,0.47369117,0.22936097,0.28366613,0.000048785856,0.00036674677],"about_ca_topic_score_codex":0.0000048474267,"about_ca_topic_score_gemma":4.0020353e-7,"teacher_disagreement_score":0.96677047,"about_ca_system_score_codex":0.000054740733,"about_ca_system_score_gemma":0.00009229806,"threshold_uncertainty_score":0.5851705},"labels":[],"label_agreement":null},{"id":"W577877354","doi":"10.1007/978-3-319-10404-1_48","title":"TRIC: Trust Region for Invariant Compactness and Its Application to Abdominal Aorta Segmentation","year":2014,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St Joseph's Health Care; Simon Fraser University; McGill University; CARE Canada","funders":"","keywords":"Compact space; Segmentation; Computer science; Invariant (physics); Image segmentation; Graph; Constraint (computer-aided design); Term (time); Algorithm; Artificial intelligence; Mathematical optimization; Theoretical computer science; Mathematics; Pure mathematics","score_opus":0.018258798988364026,"score_gpt":0.30158890660950494,"score_spread":0.2833301076211409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W577877354","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009486742,0.000019557003,0.98693174,0.002251264,0.0002425792,0.0009308915,7.8552875e-7,0.00012899659,0.000007444469],"genre_scores_gemma":[0.49239892,0.0000022815548,0.5055651,0.0018793991,0.000071969356,0.00007568861,0.0000017874444,0.0000041896683,6.8847777e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981256,0.00007790129,0.00030237364,0.00073846505,0.00042496764,0.0003306626],"domain_scores_gemma":[0.9986616,0.00042340867,0.00012794833,0.00042361804,0.00016882528,0.00019461056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011134514,0.000156928,0.00018964523,0.00037210737,0.0001978478,0.00029590973,0.0009892253,0.00005854553,0.0000012792683],"category_scores_gemma":[0.00034693064,0.00014043217,0.0000246981,0.0012787755,0.00011900571,0.00074172835,0.00027183956,0.00011254656,0.00000464901],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009696376,0.000031999272,0.00025210832,0.000023375975,0.0000012273689,0.0000012212996,0.00071190635,0.0018856997,0.020821495,0.0013242284,0.000024608134,0.9749124],"study_design_scores_gemma":[0.000370002,0.00018165173,0.0014367447,0.000031329902,0.0000023731568,0.00002004459,9.356689e-7,0.82562053,0.16599008,0.006151314,0.000035723377,0.00015928248],"about_ca_topic_score_codex":0.000021872373,"about_ca_topic_score_gemma":0.000013455454,"teacher_disagreement_score":0.97475314,"about_ca_system_score_codex":0.000106540385,"about_ca_system_score_gemma":0.00008148692,"threshold_uncertainty_score":0.57266575},"labels":[],"label_agreement":null},{"id":"W626095524","doi":"","title":"Proceedings of the 24th IASTED Asian Conference on Modelling and Simulation, held July 17-19, 2013 in Banff, Canada . Proceedings of the 15th IASTED International Conference on Signal and Image Processing, held July 17-19, 2013 in Banff, Canada","year":2013,"lang":"en","type":"book","venue":"ACTA Press eBooks","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Library science; Computer science; Geography","score_opus":0.035249700793618895,"score_gpt":0.2548151541879338,"score_spread":0.21956545339431494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W626095524","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053315096,0.0004499645,0.021561682,0.011218829,0.0013403541,0.01797075,0.00081767346,0.00048827866,0.89283735],"genre_scores_gemma":[0.9102891,0.0000866657,0.0040468075,0.0016276783,0.00009997852,0.00029169748,0.000025975436,0.00008123516,0.08345084],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957502,0.00005390153,0.0011062218,0.0009472196,0.0016556245,0.00048683537],"domain_scores_gemma":[0.9966253,0.00020876642,0.0013441525,0.0003322704,0.0012243172,0.00026520694],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041686292,0.00059670245,0.00067662925,0.0002723762,0.00015858229,0.00039807978,0.0020000662,0.00031359392,0.000038181817],"category_scores_gemma":[0.000141582,0.00044947283,0.000055375996,0.00014332707,0.00043923227,0.0006382512,0.00063415046,0.001094708,3.1619356e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061689585,0.0005425521,0.0007938737,0.0060122292,0.00045925778,0.000050028524,0.03072758,0.0030666064,0.014312813,0.023908777,0.8881828,0.031326596],"study_design_scores_gemma":[0.003020413,0.0004051944,0.0014066789,0.011344961,0.000109092354,0.000033851866,0.00076623197,0.9255045,0.035527587,0.005969132,0.014034279,0.001878059],"about_ca_topic_score_codex":0.45663047,"about_ca_topic_score_gemma":0.3271275,"teacher_disagreement_score":0.9224379,"about_ca_system_score_codex":0.00049676385,"about_ca_system_score_gemma":0.003564421,"threshold_uncertainty_score":0.9997957},"labels":[],"label_agreement":null},{"id":"W6891726167","doi":"10.48448/78b2-nf48","title":"Targeted Image Data Augmentation Increases Basic Skills Captioning Robustness","year":2023,"lang":"en","type":"other","venue":"Open MIND","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Closed captioning; Robustness (evolution); Image (mathematics); Image manipulation; Image processing","score_opus":0.05292065409838276,"score_gpt":0.35919617793720604,"score_spread":0.3062755238388233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6891726167","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000021110653,0.000086142136,0.92085755,0.00014592234,0.0004316002,0.001004414,0.00031789573,0.0002536372,0.076881714],"genre_scores_gemma":[0.000014328919,0.00009002842,0.75376046,0.000105826766,0.00015492269,0.00006580908,0.0013054217,0.00026743495,0.24423574],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977765,0.00016986097,0.00034740975,0.00094596914,0.0004966465,0.0002636199],"domain_scores_gemma":[0.99765617,0.00013454595,0.0003623299,0.0016499872,0.000053539334,0.00014344938],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006727481,0.00025079935,0.00030516583,0.00028588742,0.00008867446,0.00089099025,0.004208419,0.00015940976,0.01248488],"category_scores_gemma":[0.00030005563,0.00024929256,0.000032186064,0.00048318037,0.000090380214,0.0014584862,0.0025156997,0.00018507452,0.0016940344],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017368295,0.00008868329,0.000012134015,0.000018374061,0.00004657133,0.000074496355,0.000095770054,0.0000038085325,0.00042050498,0.000011068676,0.61554855,0.38367832],"study_design_scores_gemma":[0.0067744,0.00039112943,0.00092816184,0.0069312085,0.00054020673,0.00013407372,0.0008879392,0.061960686,0.08916122,0.00041537534,0.8260111,0.005864505],"about_ca_topic_score_codex":0.0014843941,"about_ca_topic_score_gemma":0.0006359947,"teacher_disagreement_score":0.37781382,"about_ca_system_score_codex":0.00007724793,"about_ca_system_score_gemma":0.00020091102,"threshold_uncertainty_score":0.99999595},"labels":[],"label_agreement":null},{"id":"W6894110373","doi":"10.5281/zenodo.7930158","title":"Hammers Atlas Registered to ICBM","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Atlas (anatomy); Hammer; Hyperintensity; Nonlinear system","score_opus":0.0692075806079426,"score_gpt":0.3009716558456124,"score_spread":0.23176407523766981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6894110373","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002413012,0.000013130644,0.9227042,0.006431447,0.00018248396,0.0005373222,0.000032206237,0.006026803,0.061659403],"genre_scores_gemma":[0.66139245,0.0005030218,0.24869615,0.014311738,0.0011248533,0.0000016142702,0.004640602,0.00777512,0.061554447],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99820507,0.0002313067,0.00022553257,0.00045964168,0.0005112721,0.00036715408],"domain_scores_gemma":[0.99859273,0.000030690044,0.00006871526,0.0007144939,0.00029559823,0.00029776277],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008423561,0.00010394763,0.00010480412,0.00037407363,0.0009805551,0.00094155385,0.0020285766,0.000040058985,0.002810652],"category_scores_gemma":[0.00095248886,0.00010979329,0.000038316146,0.0016236647,0.00008379198,0.0003976886,0.002148196,0.00015764919,0.03362588],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006217129,0.000023500746,9.85327e-7,0.00001548653,0.000008265628,0.000019379153,0.0010259415,0.000008497741,0.009347836,0.003062102,0.66750264,0.3189791],"study_design_scores_gemma":[0.00022734451,0.00017327354,0.00052305957,0.000024893554,0.0000024533983,0.00003607766,0.00012994798,0.0014141238,0.009173222,0.00038743182,0.9877423,0.0001658389],"about_ca_topic_score_codex":0.000011074381,"about_ca_topic_score_gemma":1.2111026e-7,"teacher_disagreement_score":0.674008,"about_ca_system_score_codex":0.00009933147,"about_ca_system_score_gemma":0.0000037409907,"threshold_uncertainty_score":0.99810094},"labels":[],"label_agreement":null},{"id":"W6894252634","doi":"10.5683/sp3/a1dvy8","title":"Seasonal shoreline change in northeastern Haida Gwaii dataset","year":2025,"lang":"en","type":"dataset","venue":"Open MIND","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; Queen's University","funders":"","keywords":"Shore; Transect; Shapefile; Baseline (sea); Erosion; Period (music); Longitude","score_opus":0.06499778325112651,"score_gpt":0.3805056887053528,"score_spread":0.31550790545422625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6894252634","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000032785408,0.00010408306,0.01562714,0.0007960965,0.00031671042,0.0009116384,0.9821101,0.0000054253496,0.00012555053],"genre_scores_gemma":[0.0000016817537,0.000095923126,0.059100803,0.0018761918,0.00012111145,0.00018120506,0.9383058,0.0000048068136,0.0003124568],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99760836,0.00016943632,0.00048392316,0.000867099,0.0005359849,0.00033520884],"domain_scores_gemma":[0.9979271,0.0001072468,0.00018762347,0.001556672,0.00005274938,0.00016859874],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00069808617,0.0002908928,0.00041287483,0.0002741452,0.000050591963,0.0005878514,0.0053248005,0.00021578949,0.003408225],"category_scores_gemma":[0.0001072523,0.0002771533,0.000040986244,0.0004928039,0.0000650748,0.00086369814,0.0038559583,0.0005023024,0.00034582295],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046675154,0.00008150498,0.000010546173,0.00002223221,0.00000784864,0.00013128469,0.000029448756,1.0651551e-7,0.0000012048905,0.0000010847452,0.70655835,0.2931517],"study_design_scores_gemma":[0.00032625784,0.000049679293,0.000032088912,0.0003258915,0.000014303052,0.000008426183,0.0000058164283,0.0008481285,0.0001355636,0.000012043866,0.99796957,0.0002722361],"about_ca_topic_score_codex":0.0017395183,"about_ca_topic_score_gemma":0.0040434166,"teacher_disagreement_score":0.29287946,"about_ca_system_score_codex":0.00007662936,"about_ca_system_score_gemma":0.00032697854,"threshold_uncertainty_score":0.99996805},"labels":[],"label_agreement":null},{"id":"W6901709699","doi":"10.60692/3hpzd-wqp31","title":"Fast two-step segmentation of natural color scenes using hierarchical region-growing and a Color-Gradient Network","year":2008,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Segmentation; Image segmentation; Ground truth; Focus (optics); Scale-space segmentation; Pattern recognition (psychology); Segmentation-based object categorization","score_opus":0.03519539603815792,"score_gpt":0.24414810604221945,"score_spread":0.20895271000406154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901709699","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43281648,0.0000065298,0.5664073,0.000032784807,0.00017558105,0.0003064881,0.000002442575,0.00015820777,0.00009413651],"genre_scores_gemma":[0.8545121,4.928519e-7,0.14514212,0.00022712699,0.000053665746,0.000032357788,0.0000071211102,0.000005541539,0.000019447645],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819684,0.00014515419,0.0007307018,0.000195912,0.00047083077,0.00026058705],"domain_scores_gemma":[0.99893564,0.000025235022,0.0004463576,0.0002735527,0.00019505335,0.00012413894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034750748,0.00016688493,0.0002614593,0.0002300081,0.00026130144,0.00013510733,0.0002723149,0.00006909961,0.0000018840809],"category_scores_gemma":[0.000024176226,0.00014326515,0.00005689888,0.00041504446,0.0001307373,0.0021746904,0.00016527904,0.00012966375,0.000010932697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007651773,0.00007536403,0.31059182,0.0039210618,0.0006068383,0.0003185374,0.56248194,0.0117497,0.0071183103,0.037498195,0.0023788102,0.062494252],"study_design_scores_gemma":[0.0028276087,0.0002326235,0.017544823,0.0008810889,0.000037220576,0.0010775813,0.005976458,0.95145786,0.019364785,0.000017854933,0.000032552023,0.0005495346],"about_ca_topic_score_codex":0.00001791054,"about_ca_topic_score_gemma":2.2796227e-7,"teacher_disagreement_score":0.9397082,"about_ca_system_score_codex":0.00013156996,"about_ca_system_score_gemma":0.00006563941,"threshold_uncertainty_score":0.5842183},"labels":[],"label_agreement":null},{"id":"W6906175771","doi":"10.1594/ecr2013/c-2424","title":"Accuracy of a semi-automated liver segmentation method using MR imaging","year":2013,"lang":"en","type":"article","venue":"Espace ÉTS (ETS)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre de Santé et de Services Sociaux Cavendish","funders":"","keywords":"Segmentation; Image segmentation; Medical imaging; Magnetic resonance imaging; Pattern recognition (psychology)","score_opus":0.02219967046047408,"score_gpt":0.3401442352558418,"score_spread":0.3179445647953677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6906175771","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018071529,0.00013355931,0.9795276,0.0005992142,0.00014091036,0.00045913225,0.00000161593,0.0008214895,0.00024498356],"genre_scores_gemma":[0.11366666,0.000026004222,0.8853355,0.00074181875,0.000031377207,0.000034629884,0.0000047417875,0.000016339161,0.0001429326],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982946,0.00025940032,0.00034918787,0.00035938967,0.00046218137,0.00027526438],"domain_scores_gemma":[0.9985175,0.0003160737,0.00033984642,0.00046615695,0.00022954518,0.00013083618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048079743,0.00016279025,0.00020545647,0.00021417887,0.00007918137,0.0001859762,0.00053565274,0.00005203003,0.00031328335],"category_scores_gemma":[0.00026987083,0.00015370961,0.00006226065,0.0005341931,0.00006393992,0.0017599257,0.00025785292,0.00012928025,0.00009863077],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034551192,0.00007574997,0.0004972973,0.000067758316,0.000025716283,0.000017640965,0.00266175,0.00017849832,0.75819147,0.00034443795,0.008433373,0.22950286],"study_design_scores_gemma":[0.0002181786,0.00001631401,0.0007337323,0.0000582893,0.000009412465,0.000018833047,0.00014046482,0.5694672,0.42893538,0.00018651952,0.00008428253,0.00013141711],"about_ca_topic_score_codex":0.00052128674,"about_ca_topic_score_gemma":0.0000030843144,"teacher_disagreement_score":0.5692887,"about_ca_system_score_codex":0.000094075804,"about_ca_system_score_gemma":0.00008318562,"threshold_uncertainty_score":0.6268096},"labels":[],"label_agreement":null},{"id":"W6929528939","doi":"10.48660/12120025","title":"Adventures with Monte Carlo Simulations of the Self-Avoiding Walk","year":2012,"lang":"en","type":"other","venue":"PIRSA","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Monte Carlo method; Monte Carlo method in statistical physics; Monte Carlo molecular modeling; Markov chain Monte Carlo","score_opus":0.011035015513393595,"score_gpt":0.2536185492202541,"score_spread":0.24258353370686053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6929528939","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007936224,0.0011697282,0.809719,0.0004475327,0.0005855053,0.00086241076,0.000028399245,0.0011019842,0.18600604],"genre_scores_gemma":[0.2730272,0.0001196204,0.47533166,0.0009935191,0.00061542774,0.000079536425,0.0000083361265,0.00035518676,0.24946947],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99904954,0.0000937847,0.0001500919,0.00018058387,0.00037383306,0.00015218598],"domain_scores_gemma":[0.9989876,0.00007612916,0.00023967649,0.00060199806,0.000036756788,0.000057824018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000119146775,0.00013238085,0.00015755596,0.00009387781,0.0000516889,0.00002609599,0.00072203134,0.00009206823,0.00027057703],"category_scores_gemma":[0.000034342298,0.000080081285,0.00005115021,0.00021355995,0.000053098447,0.00010331416,0.00017378485,0.00018545418,0.00001102835],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000055625155,0.0004687419,0.0030559625,0.00056130486,0.00046033677,0.000010828949,0.006163365,0.00021548383,0.0011884769,0.008459382,0.9012673,0.07814326],"study_design_scores_gemma":[0.0010969831,0.00017540448,0.0021385932,0.0018605428,0.00029153304,0.00002181465,0.00012441848,0.012277038,0.038800858,0.0006979379,0.9413133,0.0012015478],"about_ca_topic_score_codex":0.0001356774,"about_ca_topic_score_gemma":0.000057154157,"teacher_disagreement_score":0.33438736,"about_ca_system_score_codex":0.000030241998,"about_ca_system_score_gemma":0.00005990997,"threshold_uncertainty_score":0.326562},"labels":[],"label_agreement":null},{"id":"W6929937843","doi":"10.5281/zenodo.11283830","title":"IN Al Maşţabah/Laytown +2778915305 SSD Chemical Solutions activation powder Zrenjanin","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Counterfeit; Counterfeit Drugs; Quality (philosophy); Electronic money; Banknote","score_opus":0.036578636640508755,"score_gpt":0.28451548872024374,"score_spread":0.24793685207973498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6929937843","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000013504187,0.00016875502,0.36333922,0.0041402765,0.0003014649,0.0007242465,0.000115665,0.0038448381,0.62735206],"genre_scores_gemma":[0.010322744,0.00086543494,0.0853189,0.007725856,0.0019530625,0.0000029149282,0.005525361,0.028198248,0.86008745],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974159,0.00027320907,0.00036593265,0.00080578175,0.0006578939,0.00048130148],"domain_scores_gemma":[0.99872726,0.000023315635,0.00015370981,0.0007264052,0.0001716894,0.0001976219],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006230607,0.0002560527,0.00023085481,0.00082549785,0.00029710532,0.00095287163,0.0018907976,0.00021951094,0.029979449],"category_scores_gemma":[0.0004190104,0.00027405866,0.000072543924,0.0010712926,0.00018473767,0.00044237403,0.0021866069,0.0006833085,0.018773036],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000040526415,0.00009187668,6.236712e-8,0.0000681526,0.000025069598,0.000025004061,0.00030046303,5.8595276e-7,0.009000105,0.0050155073,0.9501274,0.035341702],"study_design_scores_gemma":[0.00026209606,0.000058895203,0.0000055083988,0.0002263112,0.0000078236035,0.000054286527,0.00003714381,0.0004771745,0.0039919103,0.00075694936,0.993839,0.00028290044],"about_ca_topic_score_codex":0.00006899869,"about_ca_topic_score_gemma":6.8320406e-7,"teacher_disagreement_score":0.27802032,"about_ca_system_score_codex":0.0003229326,"about_ca_system_score_gemma":0.000013254114,"threshold_uncertainty_score":0.99997115},"labels":[],"label_agreement":null},{"id":"W6930202172","doi":"10.5281/zenodo.12184672","title":"go transit map pdf","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Articular cartilage damage; Intersection (aeronautics); Work (physics); Limiting; Nucleofection","score_opus":0.02539460026784567,"score_gpt":0.26205037772102746,"score_spread":0.23665577745318178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930202172","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.1137408e-7,0.00022797073,0.41005993,0.00068965857,0.00020361501,0.00027816716,0.000058282723,0.0033672706,0.58511496],"genre_scores_gemma":[0.00006238144,0.00019794863,0.022747664,0.0005510813,0.0003450617,9.9694496e-8,0.00079124706,0.006856954,0.96844757],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979022,0.000246501,0.00024806394,0.00067392795,0.00059364416,0.00033562636],"domain_scores_gemma":[0.9987197,0.000010834814,0.00010415535,0.0007942868,0.00015150863,0.00021951625],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004625521,0.00021419044,0.00019579545,0.0005099512,0.00034321347,0.0014169601,0.0024693722,0.00015739245,0.14579839],"category_scores_gemma":[0.0001253228,0.00021867875,0.000077962795,0.0005665169,0.00015362028,0.0001987115,0.0013134998,0.00043572948,0.3063977],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016872856,0.000033873534,9.084562e-9,0.0001713384,0.000033644803,0.000043858865,0.00028970416,1.9236207e-7,0.00032706145,0.0034122271,0.8956415,0.100044906],"study_design_scores_gemma":[0.00014101944,0.00008935884,8.827023e-7,0.00017885174,0.000012934554,0.000051916715,0.000024476025,0.0001544602,0.0005722185,0.0005780059,0.99797463,0.00022122377],"about_ca_topic_score_codex":0.000014336684,"about_ca_topic_score_gemma":2.8923802e-7,"teacher_disagreement_score":0.38731226,"about_ca_system_score_codex":0.0001043987,"about_ca_system_score_gemma":0.0000047129956,"threshold_uncertainty_score":0.99961966},"labels":[],"label_agreement":null},{"id":"W6930485405","doi":"10.5281/zenodo.12008444","title":"Black hat pdf","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Subpoena; Scapegoat; Limiting; Headline","score_opus":0.02903250662384941,"score_gpt":0.2703310760270744,"score_spread":0.241298569403225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930485405","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.4127394e-7,0.0001563281,0.3569104,0.00048457625,0.00017114241,0.00027873646,0.000042976677,0.0035887735,0.6383668],"genre_scores_gemma":[0.00006269137,0.000282575,0.018294288,0.0005401828,0.0003417933,6.1518534e-8,0.0006472821,0.006702447,0.9731287],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99791723,0.00024115352,0.00023730272,0.00067458977,0.000597204,0.00033249473],"domain_scores_gemma":[0.9986042,0.000011923413,0.00012508966,0.00087170844,0.00016869235,0.0002184162],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004730816,0.0002088625,0.00018674515,0.00052931823,0.00031914897,0.0017166066,0.0025974945,0.0001524264,0.14145726],"category_scores_gemma":[0.0002595809,0.00021076591,0.00006956759,0.0006512653,0.0002264531,0.00021141156,0.002431156,0.00041530197,0.3425693],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011852511,0.00003040217,1.2287738e-8,0.00012282592,0.000029580686,0.000035142744,0.0002555918,2.1741648e-7,0.00021525117,0.0038467566,0.8862688,0.10919422],"study_design_scores_gemma":[0.000120408695,0.00008119947,0.0000010585901,0.0001735934,0.00001035045,0.000044146313,0.000030424315,0.00029156945,0.0005327372,0.00067105115,0.9978294,0.0002141021],"about_ca_topic_score_codex":0.000013701815,"about_ca_topic_score_gemma":1.9472053e-7,"teacher_disagreement_score":0.33861613,"about_ca_system_score_codex":0.00011685119,"about_ca_system_score_gemma":0.0000051480315,"threshold_uncertainty_score":0.99931973},"labels":[],"label_agreement":null},{"id":"W6930587807","doi":"10.5281/zenodo.14597274","title":"Porcepicus Huber 2022","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Sulcus; Seta; Dorsum; Coronal plane; Occiput","score_opus":0.027859753934464684,"score_gpt":0.2737853329911047,"score_spread":0.24592557905664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930587807","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002424722,0.00020425899,0.8876584,0.0018748866,0.00022151027,0.00022655269,0.00002297522,0.004199251,0.10534967],"genre_scores_gemma":[0.77545613,0.0011217375,0.16638173,0.00653043,0.0016281706,6.8483456e-7,0.0029738736,0.0067515993,0.03915564],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99842656,0.00021539847,0.00019899613,0.0004343087,0.00045731163,0.0002674159],"domain_scores_gemma":[0.999072,0.000028855353,0.000033687083,0.00048340062,0.00021253573,0.00016950103],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006993262,0.00009952471,0.00008477317,0.0002460787,0.00076199195,0.0018186511,0.0015121578,0.000043213233,0.015638849],"category_scores_gemma":[0.0002871101,0.000097195865,0.0000445849,0.0008075995,0.000100188154,0.0006433245,0.0013598925,0.00025485127,0.013400247],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018740776,0.000027910695,1.6944739e-7,0.000031126205,0.000012775593,0.000049036902,0.00058152905,0.0000016695128,0.0065914667,0.022266693,0.43578666,0.5346491],"study_design_scores_gemma":[0.00010258497,0.00010594935,0.000044142933,0.000033744956,0.000004088675,0.00014299885,0.00004417797,0.0037315479,0.0074743708,0.001429449,0.98675394,0.00013301286],"about_ca_topic_score_codex":0.0000056236695,"about_ca_topic_score_gemma":3.530786e-8,"teacher_disagreement_score":0.77521366,"about_ca_system_score_codex":0.00010695512,"about_ca_system_score_gemma":0.0000049067125,"threshold_uncertainty_score":0.99921757},"labels":[],"label_agreement":null},{"id":"W6930674688","doi":"10.5281/zenodo.14980894","title":"Fig. 17 in A revision of the genus Sclerocoelus Marshall (Diptera: Sphaeroceridae)","year":2025,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Paratype; Aedeagus; Holotype; Genus; Scale (ratio)","score_opus":0.024283863886786498,"score_gpt":0.26167839425316336,"score_spread":0.23739453036637687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930674688","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00001156246,0.00035821827,0.26033172,0.00069645216,0.00016016029,0.0009111365,0.00009802273,0.000912855,0.7365199],"genre_scores_gemma":[0.010149342,0.0031109906,0.06856186,0.0031940308,0.0004831545,4.9688634e-7,0.0010741928,0.008767053,0.90465885],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978808,0.00042358864,0.00037144992,0.0004944822,0.00056258374,0.00026710518],"domain_scores_gemma":[0.99840325,0.000022768165,0.0002722416,0.0009788102,0.00023300399,0.000089900976],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005729189,0.00018005662,0.0002408742,0.00044263227,0.0002957988,0.00032406856,0.0032443076,0.00013707223,0.015443208],"category_scores_gemma":[0.00057124184,0.00015520019,0.00007345115,0.0009686791,0.00015301284,0.00014590188,0.0030966825,0.00036923628,0.0009266476],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003828006,0.000045564222,7.901612e-7,0.00012226125,0.0000110692845,0.000006048483,0.00016270149,0.0000011330891,0.00018123572,0.0008665456,0.6671285,0.33147034],"study_design_scores_gemma":[0.00028502598,0.000053367956,0.000107724016,0.0006969817,0.0000062315817,0.000011140712,0.00001575722,0.00021147574,0.0006348094,0.00007984702,0.997761,0.00013665557],"about_ca_topic_score_codex":0.000060413753,"about_ca_topic_score_gemma":0.0000013931932,"teacher_disagreement_score":0.3313337,"about_ca_system_score_codex":0.00013269376,"about_ca_system_score_gemma":0.00001609594,"threshold_uncertainty_score":0.9998512},"labels":[],"label_agreement":null},{"id":"W6930865491","doi":"10.5281/zenodo.14596711","title":"FIGURES 75–77. Cleruchoides noackae Lin & Huber. 75a in Illustrated key to the genera and catalogue of Mymaridae (Hymenoptera) in the Neotropical region","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Key (lock); Head (geology); New guinea; Taxonomy (biology)","score_opus":0.0342883002249399,"score_gpt":0.26703413487426364,"score_spread":0.23274583464932375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930865491","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033164786,0.0034165853,0.32407373,0.021972736,0.00066338485,0.0059831683,0.0005105548,0.0032025336,0.63686085],"genre_scores_gemma":[0.51748663,0.009123064,0.06721251,0.01922405,0.0032940628,0.000013368659,0.007632647,0.02188226,0.3541314],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977249,0.00066226145,0.00035417514,0.00051545346,0.00046673455,0.00027646357],"domain_scores_gemma":[0.9989313,0.000048714235,0.000119314565,0.0006936214,0.00011224658,0.0000948413],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007261013,0.00018453012,0.00020334062,0.00046667864,0.0001838417,0.00069776794,0.0020249165,0.00012601742,0.0008254896],"category_scores_gemma":[0.0003870693,0.00013010684,0.00003117578,0.001003894,0.00022051629,0.00013295888,0.0012005308,0.0005135052,0.0008305777],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010407623,0.00005798227,0.0000026849057,0.00010823962,0.000015933,0.00004774295,0.0033807328,0.000010328094,0.00041043494,0.0020374665,0.92722183,0.06669623],"study_design_scores_gemma":[0.0002611872,0.00021510465,0.00047943046,0.0003459073,0.000008895658,0.000073563846,0.00022300192,0.0007168577,0.00060648366,0.00028598323,0.99658394,0.00019965514],"about_ca_topic_score_codex":0.0004834158,"about_ca_topic_score_gemma":0.00004765345,"teacher_disagreement_score":0.51417017,"about_ca_system_score_codex":0.000077172204,"about_ca_system_score_gemma":0.000008601841,"threshold_uncertainty_score":0.99994737},"labels":[],"label_agreement":null},{"id":"W6930875857","doi":"10.5281/zenodo.4003066","title":"chakravala/Grassmann.jl v0.6.0","year":2020,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Perimeter Institute","funders":"","keywords":"Nucleofection; Gestational period; TSG101; Dysgeusia; Diafiltration; Liquation; Emperipolesis; Triacetin; Fusible alloy","score_opus":0.03451991039160572,"score_gpt":0.2618134048982894,"score_spread":0.22729349450668368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930875857","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.7904764e-7,0.00009339278,0.4782632,0.0010330821,0.00012729327,0.00036002355,0.00006883969,0.004553413,0.5155005],"genre_scores_gemma":[0.0008354616,0.0015148403,0.17110401,0.00565698,0.0021189603,5.198264e-7,0.0059455675,0.031459454,0.7813642],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99740607,0.0003741205,0.00029470626,0.00077068777,0.00075594365,0.00039847687],"domain_scores_gemma":[0.99825925,0.000018751683,0.00022859946,0.00092546653,0.00020273548,0.0003652137],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00041367338,0.00025940122,0.00026374942,0.00041779364,0.0006453853,0.0011685851,0.0036013303,0.00017110097,0.04239946],"category_scores_gemma":[0.00050309615,0.0002735889,0.00008121364,0.00077250577,0.00017902629,0.00023686339,0.0027463099,0.00047175825,0.026164541],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024078229,0.000037580452,6.76902e-8,0.000062066734,0.000026003514,0.00003008517,0.00023869397,2.2093762e-7,0.000473609,0.0030219255,0.80122805,0.19487928],"study_design_scores_gemma":[0.00022960147,0.00014907059,0.0000065962427,0.00007864449,0.000009156869,0.00004624523,0.000026109057,0.00031189056,0.0009439894,0.00017384216,0.997753,0.00027185687],"about_ca_topic_score_codex":0.000024590952,"about_ca_topic_score_gemma":2.2732797e-7,"teacher_disagreement_score":0.3071592,"about_ca_system_score_codex":0.0001011502,"about_ca_system_score_gemma":0.000007659442,"threshold_uncertainty_score":0.9999716},"labels":[],"label_agreement":null},{"id":"W6930910287","doi":"10.5281/zenodo.15557691","title":"Mirror Visionwear Artist Talk","year":2019,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sculpture; Materiality (auditing); Photography; Contemporary art; Cliché; Mondrian; Carving; Optical illusion; Perception","score_opus":0.02362807526715091,"score_gpt":0.26254670708909883,"score_spread":0.23891863182194792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930910287","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008808166,0.000041382576,0.86122745,0.0017445546,0.00024173532,0.0007067685,0.000031549698,0.0032750631,0.12392333],"genre_scores_gemma":[0.83783203,0.00010728496,0.14488447,0.0028841004,0.00012974044,1.7560322e-7,0.0008603335,0.002024992,0.011276897],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99828625,0.00023365096,0.00022719274,0.0004378699,0.00051677023,0.00029825032],"domain_scores_gemma":[0.9986654,0.000026088544,0.00008739809,0.0007003829,0.00032304687,0.00019766252],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006111369,0.000107995125,0.00011506308,0.00018380054,0.0007142518,0.0009481936,0.0018270101,0.000048122332,0.017966894],"category_scores_gemma":[0.00032193252,0.00010865237,0.000043416123,0.00055459933,0.00009048148,0.00063741556,0.0015157366,0.00020396199,0.030382931],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018715744,0.00019324456,0.0000070686538,0.00006518502,0.00002479667,0.000038788654,0.001060869,0.0000031818633,0.094613284,0.018127616,0.22163281,0.66421443],"study_design_scores_gemma":[0.00039735416,0.0002581154,0.00057539257,0.000031111525,0.0000028690304,0.0000986796,0.00007545965,0.0021999283,0.01876304,0.0005945538,0.9768124,0.00019107765],"about_ca_topic_score_codex":0.000013477909,"about_ca_topic_score_gemma":7.836895e-8,"teacher_disagreement_score":0.82902384,"about_ca_system_score_codex":0.00008594222,"about_ca_system_score_gemma":0.000003594356,"threshold_uncertainty_score":0.98293084},"labels":[],"label_agreement":null},{"id":"W6931290855","doi":"10.5281/zenodo.8094905","title":"OpenAIRE interoperability metadata guidelines","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Metadata; Interoperability; Semantic interoperability; Presentation (obstetrics); Geospatial metadata; Metadata modeling","score_opus":0.1287245877516312,"score_gpt":0.34426110038876473,"score_spread":0.21553651263713353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931290855","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016632611,0.000034335335,0.96863556,0.0060284566,0.00016868819,0.00042978956,0.000057058925,0.0054137046,0.017569127],"genre_scores_gemma":[0.53606397,0.0012926108,0.4051943,0.012440195,0.0011251633,0.0000013699882,0.009495134,0.0061610257,0.02822626],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979797,0.0003715921,0.00034231017,0.0005091081,0.00049007835,0.0003071899],"domain_scores_gemma":[0.9980161,0.0000306537,0.000075755845,0.0009336087,0.0007600408,0.00018383509],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0017061571,0.00011476052,0.0001312548,0.00023482926,0.0010695035,0.0016818186,0.0030536626,0.000040356223,0.003444745],"category_scores_gemma":[0.002193885,0.000106751075,0.000045071087,0.0011758753,0.00013566516,0.0013143208,0.00407622,0.00018537833,0.010165046],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000040329705,0.000032655193,0.0000010297485,0.000020600426,0.000013137985,0.000013770948,0.00038583155,0.0000053268345,0.004417538,0.00309292,0.55408365,0.4379295],"study_design_scores_gemma":[0.00025230768,0.00012491719,0.00028161702,0.000031494077,0.0000046024447,0.000049624596,0.0001519751,0.0052767307,0.008525626,0.0008104738,0.984315,0.00017568092],"about_ca_topic_score_codex":0.00001693644,"about_ca_topic_score_gemma":1.8135613e-7,"teacher_disagreement_score":0.5634413,"about_ca_system_score_codex":0.00008154365,"about_ca_system_score_gemma":0.0000048710126,"threshold_uncertainty_score":0.99935454},"labels":[],"label_agreement":null},{"id":"W6931415231","doi":"10.5281/zenodo.6147206","title":"Arostrilepis rauschorum Makarikov, Galbreath & Hoberg, 2013, sp. n.","year":2013,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Type locality; Microtus; Holotype; Host (biology); Arctic","score_opus":0.02325763864287473,"score_gpt":0.24364593031311083,"score_spread":0.2203882916702361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931415231","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018702007,0.00010871137,0.91020846,0.005278649,0.00019896343,0.0008829199,0.000035549558,0.0033667623,0.07804976],"genre_scores_gemma":[0.65897185,0.0017070255,0.29391155,0.009408464,0.0012165937,0.0000027504082,0.0031624648,0.0058844797,0.02573482],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99765503,0.00033107385,0.00033099612,0.0005605968,0.0006168427,0.00050543965],"domain_scores_gemma":[0.99798316,0.000035735724,0.00012953261,0.0008732221,0.00061454636,0.00036378895],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00062819966,0.00017679871,0.00016418609,0.00026187437,0.0012340416,0.0019486131,0.0023833686,0.00007963017,0.026841702],"category_scores_gemma":[0.00044648137,0.00017512843,0.00006112984,0.0007192438,0.0001725654,0.0012418566,0.0020062397,0.00031903893,0.031608447],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000034830118,0.00007719242,0.0000043190976,0.000015697802,0.0000139771755,0.000009895827,0.00028722367,0.0000023265711,0.0068747317,0.002709558,0.7150587,0.27494293],"study_design_scores_gemma":[0.0005111124,0.00027788786,0.001273331,0.00003796933,0.0000072809294,0.00013887009,0.000107198284,0.0025134443,0.012057761,0.0017159877,0.98102623,0.00033293298],"about_ca_topic_score_codex":0.0000947319,"about_ca_topic_score_gemma":1.8917575e-7,"teacher_disagreement_score":0.65710163,"about_ca_system_score_codex":0.00013113298,"about_ca_system_score_gemma":0.000005913355,"threshold_uncertainty_score":0.99908745},"labels":[],"label_agreement":null},{"id":"W6931463064","doi":"10.5281/zenodo.4680872","title":"FIG. 1 in New material of the frog Hungarobatrachus szukacsi Szentesi &amp; Venczel, 2010, from the Santonian of Hungary, supports its neobatrachian affinities and reveals a Gondwanan influence on the European Late Cretaceous anuran fauna","year":2021,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Tyrrell Museum","funders":"","keywords":"Affinities; Bauxite; Vertebrate; Gondwana; Natural (archaeology); Tetrapod (structure)","score_opus":0.028754016370344852,"score_gpt":0.24518685589661585,"score_spread":0.216432839526271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931463064","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1343384,0.006985957,0.021516677,0.01989919,0.0025508706,0.009807641,0.0063103447,0.0033490767,0.79524183],"genre_scores_gemma":[0.73384494,0.0071986397,0.018858284,0.0061363894,0.0017761594,0.0000023690543,0.005019572,0.011828267,0.2153354],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99571127,0.002095939,0.00058219326,0.00056246127,0.0007057546,0.00034237705],"domain_scores_gemma":[0.9976206,0.00014101034,0.0005881845,0.0013064079,0.00020356337,0.00014027883],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010506251,0.00028729343,0.00034588142,0.00018829752,0.00054189254,0.0006707415,0.0031409168,0.00011536172,0.0074902857],"category_scores_gemma":[0.0009154786,0.0001925444,0.00008764889,0.0005498602,0.00043402181,0.00017253013,0.001824128,0.00053232594,0.00019695355],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053715896,0.00021390434,0.000077457866,0.00025053715,0.0001464367,0.000075635166,0.012353842,0.000017794064,0.025570037,0.001511827,0.8634687,0.096260116],"study_design_scores_gemma":[0.0011959701,0.00044518895,0.02627806,0.0032612486,0.000091869195,0.0001335267,0.00076470274,0.00015867391,0.013685411,0.0009096007,0.95221543,0.0008603261],"about_ca_topic_score_codex":0.0004953033,"about_ca_topic_score_gemma":0.000045793913,"teacher_disagreement_score":0.59950656,"about_ca_system_score_codex":0.000050585142,"about_ca_system_score_gemma":0.000033520526,"threshold_uncertainty_score":0.993417},"labels":[],"label_agreement":null},{"id":"W6931702106","doi":"10.5281/zenodo.8330920","title":"Adverbs with preposition and adjective in Saguenay–Lac-St-Jean (Quebec) based on field research: individual portraits - Project Materials","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Adjective; Adverbial; Portrait; Romance languages; Field (mathematics); Variation (astronomy); Grammar; Competition (biology)","score_opus":0.03774267224843994,"score_gpt":0.2985388237760937,"score_spread":0.2607961515276538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931702106","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06218521,0.000054016826,0.84800965,0.00949767,0.00016004947,0.0041339532,0.00019319278,0.0023939824,0.07337228],"genre_scores_gemma":[0.98699725,0.000028634038,0.009828471,0.0012852974,0.000050474584,0.0000015527518,0.00062198367,0.00034006234,0.0008462647],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99718213,0.0009631499,0.00027402194,0.00057201146,0.00066313095,0.00034556296],"domain_scores_gemma":[0.9987913,0.00017034906,0.00008653922,0.00044756517,0.00040403812,0.00010023946],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0017421076,0.00013626805,0.0001518202,0.00079631293,0.00077635545,0.0010977798,0.00094391184,0.00007906967,0.0007777318],"category_scores_gemma":[0.0010475223,0.00012624917,0.000017023227,0.0012018011,0.00021583316,0.0005196263,0.00087492855,0.00035652984,0.00010640032],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008450227,0.0009479218,0.000072078874,0.00046001284,0.00009768431,0.00026098453,0.0064177876,0.0000766155,0.028916163,0.018065533,0.29749674,0.64634347],"study_design_scores_gemma":[0.008783683,0.01036977,0.03552626,0.003291491,0.00006998825,0.00021880692,0.00256161,0.011939944,0.7357903,0.0042437213,0.1854105,0.0017939373],"about_ca_topic_score_codex":0.00045858856,"about_ca_topic_score_gemma":0.000016703896,"teacher_disagreement_score":0.924812,"about_ca_system_score_codex":0.0001672774,"about_ca_system_score_gemma":0.00003027311,"threshold_uncertainty_score":0.9999392},"labels":[],"label_agreement":null},{"id":"W6931796335","doi":"10.5683/sp3/xavfg0","title":"Data from: The influence of human activity on predator-prey spatiotemporal overlap","year":2023,"lang":"en","type":"dataset","venue":"Open MIND","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Population; Trophic level; Predation; Human studies; Community; Animal behavior; Apex predator","score_opus":0.13964375117587405,"score_gpt":0.41419221994053235,"score_spread":0.2745484687646583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931796335","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058920262,0.0000054460597,0.0025363786,0.00019416928,0.0001582528,0.0007352362,0.9903905,0.000011553447,0.000076448516],"genre_scores_gemma":[0.0004023621,0.000028268481,0.010171757,0.00025350638,0.00008395752,0.00003357274,0.9888007,0.000012132274,0.00021374327],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.997541,0.00028027015,0.00038042731,0.00084462855,0.0007707534,0.00018288416],"domain_scores_gemma":[0.9940417,0.00054132316,0.00054413243,0.0047220285,0.000065454966,0.0000853513],"candidate_categories":["open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009859845,0.0002164623,0.00032656125,0.000066708126,0.000118005875,0.0004607991,0.012979294,0.00017166705,0.0004447771],"category_scores_gemma":[0.00054472045,0.00016250357,0.000034377306,0.00028790184,0.0001591807,0.0011543389,0.0071214894,0.00047491293,0.0015901664],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006148152,0.000065390406,0.00001851234,0.000010020941,0.000027116243,0.000013703182,0.00003626598,0.0000015168722,0.00033665448,0.0000014634309,0.9831803,0.016302895],"study_design_scores_gemma":[0.00077648595,0.0005096348,0.012385795,0.0011932981,0.00010471338,0.000002279716,0.000034215096,0.00067492214,0.04056848,0.0004304329,0.9424393,0.0008804384],"about_ca_topic_score_codex":0.006124448,"about_ca_topic_score_gemma":0.0019917893,"teacher_disagreement_score":0.040741008,"about_ca_system_score_codex":0.000040668667,"about_ca_system_score_gemma":0.00026270834,"threshold_uncertainty_score":0.99918723},"labels":[],"label_agreement":null},{"id":"W6931995245","doi":"10.5285/a963d9415bb74247830f8704f825aa90","title":"ESA Sea Surface Temperature Climate Change Initiative (SST_cci): GHRSST Multi-Product ensemble (GMPE), v2.0","year":2020,"lang":"en","type":"dataset","venue":"NERC Environmental Data Service","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sea surface temperature; Advanced very-high-resolution radiometer; Satellite; Radiometer; Sea ice; Climate change; Downscaling; Climate model","score_opus":0.07833790267036615,"score_gpt":0.2997334305902876,"score_spread":0.22139552791992145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931995245","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000065157765,0.0006527751,0.004101,0.0024606884,0.0008123074,0.0014981816,0.98995227,0.00043177305,0.00002582424],"genre_scores_gemma":[0.000087144064,0.005455393,0.060356345,0.026076645,0.0006006482,0.00011560849,0.9072081,0.00007956273,0.000020587251],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9933606,0.00054908305,0.000841365,0.0028914248,0.0014305464,0.0009269776],"domain_scores_gemma":[0.9931329,0.0001574271,0.00060227467,0.005472445,0.00003571254,0.0005992385],"candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":["open_science"],"category_scores_codex":[0.00061951205,0.0010136615,0.0008411424,0.00013618721,0.0003527528,0.00048046527,0.0079486845,0.0005295321,0.00055391266],"category_scores_gemma":[0.00008782637,0.001009958,0.00010718041,0.0007581598,0.00017773757,0.0032085374,0.010548744,0.001630204,0.00459311],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018443377,0.00029982388,0.000025389643,0.00039392247,0.00008154222,0.00023290381,0.00042055768,0.000002252593,0.0067148055,0.000002165334,0.98804545,0.003762766],"study_design_scores_gemma":[0.0012775799,0.00021391925,0.0009909827,0.00035526225,0.00017734812,0.00008638797,0.0004144194,0.0034868591,0.013459655,0.000011779717,0.97765577,0.0018700347],"about_ca_topic_score_codex":0.00064337,"about_ca_topic_score_gemma":0.00030628752,"teacher_disagreement_score":0.082744226,"about_ca_system_score_codex":0.00028385228,"about_ca_system_score_gemma":0.000103944716,"threshold_uncertainty_score":0.9992351},"labels":[],"label_agreement":null},{"id":"W6957735100","doi":"10.60692/gct91-rws13","title":"Fast two-step segmentation of natural color scenes using hierarchical region-growing and a Color-Gradient Network","year":2008,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Segmentation; Image segmentation; Ground truth; Focus (optics); Scale-space segmentation; Pattern recognition (psychology); Segmentation-based object categorization","score_opus":0.03519539603815792,"score_gpt":0.24414810604221945,"score_spread":0.20895271000406154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6957735100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43281648,0.0000065298,0.5664073,0.000032784807,0.00017558105,0.0003064881,0.000002442575,0.00015820777,0.00009413651],"genre_scores_gemma":[0.8545121,4.928519e-7,0.14514212,0.00022712699,0.000053665746,0.000032357788,0.0000071211102,0.000005541539,0.000019447645],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819684,0.00014515419,0.0007307018,0.000195912,0.00047083077,0.00026058705],"domain_scores_gemma":[0.99893564,0.000025235022,0.0004463576,0.0002735527,0.00019505335,0.00012413894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034750748,0.00016688493,0.0002614593,0.0002300081,0.00026130144,0.00013510733,0.0002723149,0.00006909961,0.0000018840809],"category_scores_gemma":[0.000024176226,0.00014326515,0.00005689888,0.00041504446,0.0001307373,0.0021746904,0.00016527904,0.00012966375,0.000010932697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007651773,0.00007536403,0.31059182,0.0039210618,0.0006068383,0.0003185374,0.56248194,0.0117497,0.0071183103,0.037498195,0.0023788102,0.062494252],"study_design_scores_gemma":[0.0028276087,0.0002326235,0.017544823,0.0008810889,0.000037220576,0.0010775813,0.005976458,0.95145786,0.019364785,0.000017854933,0.000032552023,0.0005495346],"about_ca_topic_score_codex":0.00001791054,"about_ca_topic_score_gemma":2.2796227e-7,"teacher_disagreement_score":0.9397082,"about_ca_system_score_codex":0.00013156996,"about_ca_system_score_gemma":0.00006563941,"threshold_uncertainty_score":0.5842183},"labels":[],"label_agreement":null},{"id":"W6958001821","doi":"10.60692/w78cy-m6778","title":"On the ternary spatial relation \"Between\"","year":2006,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Object (grammar); Relation (database); Fuzzy logic; Space (punctuation); Spatial relation; Fuzzy set","score_opus":0.025097121901958556,"score_gpt":0.21822395166504033,"score_spread":0.19312682976308176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958001821","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07015718,3.281293e-7,0.9185876,0.00029757907,0.00017068423,0.00029253814,0.000008326315,0.0005339447,0.0099517815],"genre_scores_gemma":[0.99319535,8.175521e-9,0.006067217,0.000394196,0.00010786226,0.0000502435,0.000012401035,0.0000041094318,0.00016858608],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99864626,0.00010158259,0.00048168498,0.00012075432,0.0004977843,0.0001519426],"domain_scores_gemma":[0.99912566,0.000029953795,0.000271378,0.000448028,0.00008203993,0.000042956006],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00042465175,0.00011311635,0.000101454316,0.00014776757,0.00015339948,0.00030243033,0.00044275817,0.000066233515,0.000023930545],"category_scores_gemma":[0.000025262436,0.000072944385,0.000045157904,0.00020405737,0.00002676609,0.0010286442,0.0000749932,0.00010961826,0.001127641],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000097427284,0.000033041386,0.27540946,0.00073977717,0.00016397143,0.00004637359,0.10494474,0.0008854849,0.00020985126,0.48810166,0.0422869,0.08708133],"study_design_scores_gemma":[0.002463435,0.00037175708,0.77810794,0.0007761346,0.00003732442,0.00006392556,0.0022683975,0.1298024,0.08266847,0.0013066445,0.0009878721,0.0011456641],"about_ca_topic_score_codex":0.000028006645,"about_ca_topic_score_gemma":7.491318e-8,"teacher_disagreement_score":0.9230382,"about_ca_system_score_codex":0.000089224304,"about_ca_system_score_gemma":0.000018882756,"threshold_uncertainty_score":0.9996501},"labels":[],"label_agreement":null},{"id":"W6976522159","doi":"10.60692/214k8-bh645","title":"On the ternary spatial relation \"Between\"","year":2006,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Object (grammar); Relation (database); Fuzzy logic; Space (punctuation); Spatial relation; Fuzzy set","score_opus":0.025097121901958556,"score_gpt":0.21822395166504033,"score_spread":0.19312682976308176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976522159","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07015718,3.281293e-7,0.9185876,0.00029757907,0.00017068423,0.00029253814,0.000008326315,0.0005339447,0.0099517815],"genre_scores_gemma":[0.99319535,8.175521e-9,0.006067217,0.000394196,0.00010786226,0.0000502435,0.000012401035,0.0000041094318,0.00016858608],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99864626,0.00010158259,0.00048168498,0.00012075432,0.0004977843,0.0001519426],"domain_scores_gemma":[0.99912566,0.000029953795,0.000271378,0.000448028,0.00008203993,0.000042956006],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00042465175,0.00011311635,0.000101454316,0.00014776757,0.00015339948,0.00030243033,0.00044275817,0.000066233515,0.000023930545],"category_scores_gemma":[0.000025262436,0.000072944385,0.000045157904,0.00020405737,0.00002676609,0.0010286442,0.0000749932,0.00010961826,0.001127641],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000097427284,0.000033041386,0.27540946,0.00073977717,0.00016397143,0.00004637359,0.10494474,0.0008854849,0.00020985126,0.48810166,0.0422869,0.08708133],"study_design_scores_gemma":[0.002463435,0.00037175708,0.77810794,0.0007761346,0.00003732442,0.00006392556,0.0022683975,0.1298024,0.08266847,0.0013066445,0.0009878721,0.0011456641],"about_ca_topic_score_codex":0.000028006645,"about_ca_topic_score_gemma":7.491318e-8,"teacher_disagreement_score":0.9230382,"about_ca_system_score_codex":0.000089224304,"about_ca_system_score_gemma":0.000018882756,"threshold_uncertainty_score":0.9996501},"labels":[],"label_agreement":null},{"id":"W6979747766","doi":"","title":"Adaptive triangulations","year":2014,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wavelet; Piecewise; Regular polygon; Simple (philosophy); Polynomial; Embedding; Hierarchy; Unit square; Centroid","score_opus":0.01876855992814453,"score_gpt":0.23580907863774686,"score_spread":0.21704051870960234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6979747766","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031241726,0.000047999514,0.9789151,0.00037383693,0.00034447675,0.00027378532,0.0000070058263,0.00025642116,0.016657233],"genre_scores_gemma":[0.14420012,0.00016026846,0.83367485,0.00021688925,0.00017689174,0.0000029755645,0.0005248751,0.000049805127,0.02099334],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9988779,0.00010488528,0.00011563669,0.00032912262,0.00041982863,0.00015264325],"domain_scores_gemma":[0.99879867,0.000088864625,0.00034128866,0.0004562792,0.00021878754,0.000096134645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018281079,0.00014990498,0.00026931224,0.00029033623,0.00014991837,0.00003021345,0.0009689327,0.0001954853,0.000029195477],"category_scores_gemma":[0.00004528847,0.0001975603,0.0001304294,0.00030544595,0.000061583385,0.00036094556,0.0000986147,0.00020730335,0.00010333992],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036217153,0.0006654976,0.0015953885,0.00089521374,0.0006841779,0.00024122937,0.008998735,0.00008894632,0.00766793,0.099819705,0.30029404,0.57868695],"study_design_scores_gemma":[0.010785614,0.00322024,0.5730687,0.003327539,0.0014025945,0.000034222638,0.121056065,0.102853805,0.07570557,0.03362871,0.06824686,0.006670116],"about_ca_topic_score_codex":0.00061982154,"about_ca_topic_score_gemma":0.045584172,"teacher_disagreement_score":0.57201684,"about_ca_system_score_codex":0.00008102003,"about_ca_system_score_gemma":0.000110921545,"threshold_uncertainty_score":0.97183144},"labels":[],"label_agreement":null},{"id":"W6987860294","doi":"","title":"Using active contours for segmentation of middle-ear images","year":2003,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; University of North Carolina at Chapel Hill","keywords":"Image segmentation; Segmentation; Active contour model; Scale-space segmentation; Image (mathematics); Boundary (topology)","score_opus":0.046538434801197036,"score_gpt":0.3124502119314073,"score_spread":0.2659117771302103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6987860294","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7880856,0.0013315465,0.09212628,0.00008270106,0.009756383,0.015079515,0.006550895,0.0026209862,0.08436608],"genre_scores_gemma":[0.11812877,0.00027245344,0.8692867,0.00076925854,0.00007519712,0.00077231944,0.0016281374,0.00031480225,0.008752341],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9956203,0.00043822412,0.0011370343,0.0011240379,0.0010640086,0.00061638054],"domain_scores_gemma":[0.9960003,0.00041557726,0.0014155132,0.00081432646,0.0010630859,0.00029122183],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009763184,0.0006375276,0.00080734293,0.00056562125,0.00056791207,0.00012397517,0.0011917584,0.00057336374,0.0001051118],"category_scores_gemma":[0.0009741068,0.0007017464,0.00039391383,0.00065683393,0.000098635384,0.0019794183,0.00011239703,0.0006863906,0.000020073892],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012830939,0.00024137013,0.0000044618446,0.0006675675,0.00021975591,0.000016032796,0.000048190337,0.000013216488,0.6238145,0.024035543,0.000028785553,0.35078225],"study_design_scores_gemma":[0.0010105947,0.0002219334,0.000065013264,0.0004804935,0.00015229081,0.000011446251,0.00048480718,0.00020244617,0.9824403,0.013629017,0.0006421931,0.00065949553],"about_ca_topic_score_codex":0.000145914,"about_ca_topic_score_gemma":0.000061349885,"teacher_disagreement_score":0.77716047,"about_ca_system_score_codex":0.0006775274,"about_ca_system_score_gemma":0.00015408744,"threshold_uncertainty_score":0.99954337},"labels":[],"label_agreement":null},{"id":"W6992564483","doi":"","title":"Mathematical methods for 2D-3D cardiac image registration","year":2017,"lang":"en","type":"dissertation","venue":"e-scholar@UOIT (University of Ontario Institute of Technology)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Sunnybrook Research Institute; University of Ontario Institute of Technology","keywords":"Image registration; Affine transformation; Parametric statistics; Dice; Visualization; Similarity (geometry); Regularization (linguistics); Active appearance model; Medical imaging","score_opus":0.02246899267796868,"score_gpt":0.3157350327271643,"score_spread":0.2932660400491956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6992564483","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04163445,0.00016597347,0.9350694,0.0003495784,0.0010537413,0.0012031683,0.000042800417,0.00040815468,0.02007275],"genre_scores_gemma":[0.0000981483,0.00007304277,0.9864049,0.000016340195,0.000020117688,0.000011438784,0.000297336,0.000019303096,0.013059407],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99799603,0.000020949921,0.00050026993,0.0007212641,0.00044407026,0.0003174188],"domain_scores_gemma":[0.99619967,0.000011742948,0.001318813,0.0016715085,0.0006827727,0.00011551649],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012969346,0.00034426255,0.000920235,0.00067451224,0.00059361313,0.00010359891,0.0031599952,0.0009876043,0.00014824864],"category_scores_gemma":[0.00070999574,0.00042404435,0.00038743508,0.0003271295,0.00040932267,0.0023421259,0.0002852493,0.0011346807,0.000020561727],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006445905,0.00016965074,0.000005868387,0.000553866,0.00025228772,0.000030295565,0.0018612603,4.8641283e-7,0.029866885,0.017874526,0.0032440876,0.94607633],"study_design_scores_gemma":[0.00087457354,0.00032920478,0.00045059744,0.0005859307,0.0003406588,0.000010488579,0.0005242066,0.00021701827,0.13035277,0.023870531,0.8418622,0.0005818349],"about_ca_topic_score_codex":0.0019967835,"about_ca_topic_score_gemma":0.0027300178,"teacher_disagreement_score":0.9454945,"about_ca_system_score_codex":0.00049431185,"about_ca_system_score_gemma":0.0013317209,"threshold_uncertainty_score":0.9998211},"labels":[],"label_agreement":null},{"id":"W6996188219","doi":"","title":"Reconstruction of gamma-ray direction using boosted decision trees and the disp parameter","year":2020,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Decision tree; Decision tree learning; Decision theory; Tree (set theory); Decision process","score_opus":0.019839622119455814,"score_gpt":0.2665675488814946,"score_spread":0.24672792676203878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6996188219","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97299737,0.00066087855,0.013513515,0.000085733576,0.0031830992,0.002022917,0.000142561,0.000814091,0.006579855],"genre_scores_gemma":[0.79047173,0.00079241174,0.20741796,0.0003570818,0.00006147069,0.00013259468,0.00015191482,0.000106936,0.0005078938],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9959783,0.0006824181,0.0010990504,0.0009730755,0.0009331732,0.00033397804],"domain_scores_gemma":[0.99668896,0.0009902349,0.0010077583,0.0007225033,0.00036228893,0.00022827278],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001089438,0.00048212858,0.00072325586,0.00036416593,0.00061605766,0.00019895268,0.00083390216,0.00045373305,0.00005173183],"category_scores_gemma":[0.0021710643,0.00038209674,0.0002681678,0.00081963977,0.00022012097,0.001375489,0.00022533155,0.000907881,0.000013207883],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023832472,0.000039566716,0.0000149001435,0.000110169494,0.000083924526,0.0000089776195,0.000036096775,0.0000073927895,0.056660168,0.00716271,0.0000039939728,0.9356338],"study_design_scores_gemma":[0.003865328,0.000393379,0.0026973537,0.0020454177,0.00048679006,0.00018375418,0.0004540741,0.016837107,0.8993701,0.069837146,0.0023578014,0.0014717722],"about_ca_topic_score_codex":0.00021084191,"about_ca_topic_score_gemma":0.00016842765,"teacher_disagreement_score":0.934162,"about_ca_system_score_codex":0.00023624294,"about_ca_system_score_gemma":0.000049114726,"threshold_uncertainty_score":0.9998631},"labels":[],"label_agreement":null},{"id":"W6997145999","doi":"","title":"Using Colour Image Segmentation with Magnetic Resonance Images for Computed Tomography Synthesis","year":2024,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Siemens Healthineers; McGill University Health Centre; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Computed tomography; Segmentation; Magnetic resonance imaging; Image segmentation; Image (mathematics); Image processing","score_opus":0.018589757967635637,"score_gpt":0.276826971114569,"score_spread":0.2582372131469334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6997145999","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5853029,0.025736459,0.21915232,0.00074118696,0.017738512,0.041477792,0.016922258,0.025050437,0.06787815],"genre_scores_gemma":[0.04676411,0.00021741237,0.94732416,0.00052228867,0.00008468579,0.0016155338,0.0006572195,0.0003397277,0.002474847],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9944383,0.000400983,0.001086236,0.0018903359,0.0013207742,0.00086335884],"domain_scores_gemma":[0.9963439,0.00070426636,0.0006533281,0.0010416127,0.00088138366,0.000375478],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010138239,0.00094368664,0.00085647526,0.0010334384,0.0009024975,0.00072745,0.0016354573,0.0005287696,0.00009665352],"category_scores_gemma":[0.00044162426,0.00092017505,0.00039887178,0.001779446,0.00015890886,0.0018967096,0.00023077917,0.0009818369,0.000073221796],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022576706,0.00026119783,0.000009035432,0.0016488387,0.00020239681,0.00022144419,0.00004160016,0.000012873976,0.27158976,0.011057486,0.0002305278,0.71449906],"study_design_scores_gemma":[0.0009316377,0.00059280643,0.0003507599,0.0022824989,0.0004879933,0.000073333955,0.0002498657,0.0034091761,0.9744781,0.011992746,0.003584827,0.0015662024],"about_ca_topic_score_codex":0.0001291166,"about_ca_topic_score_gemma":0.00010283395,"teacher_disagreement_score":0.7281719,"about_ca_system_score_codex":0.0005915698,"about_ca_system_score_gemma":0.0001556147,"threshold_uncertainty_score":0.99932486},"labels":[],"label_agreement":null},{"id":"W7000046512","doi":"","title":"A dynamic brain atlas","year":2002,"lang":"en","type":"other","venue":"Research Portal (King's College London)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Atlas (anatomy); Affine transformation; Brain atlas; Pattern recognition (psychology); Set (abstract data type); Image registration; White matter","score_opus":0.03356699311593534,"score_gpt":0.3604131359141491,"score_spread":0.3268461427982137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7000046512","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000005524777,0.0017083866,0.15381896,0.0027011167,0.0005012284,0.0019819106,0.00017833587,0.001848848,0.83725566],"genre_scores_gemma":[0.0005301764,0.0007096487,0.072077565,0.0006709997,0.00025331628,0.00032653907,0.000062846884,0.00043265047,0.92493623],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99241877,0.000725323,0.0006439224,0.0013980403,0.0034113198,0.0014026107],"domain_scores_gemma":[0.9962766,0.00058513804,0.00030261945,0.0019093681,0.00028155153,0.0006447343],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0020086996,0.00051550753,0.0006651018,0.0019084762,0.0002510176,0.0003858983,0.003355055,0.00060310913,0.01584965],"category_scores_gemma":[0.0008715815,0.00049761945,0.00023216598,0.00229778,0.00056091597,0.00039964737,0.0012779828,0.001630137,0.0022897404],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000414048,0.00013524183,0.0000108137,0.00014295873,0.000056072786,0.0019323599,0.00006979488,2.5340108e-7,0.0001884169,0.0051644733,0.9655659,0.026729546],"study_design_scores_gemma":[0.000936587,0.00035944203,0.000039723585,0.00090707163,0.0000107693795,0.00017203834,0.00006269636,0.0068135583,0.00085099274,0.0016816485,0.9872877,0.000877745],"about_ca_topic_score_codex":0.00044450114,"about_ca_topic_score_gemma":0.0005336277,"teacher_disagreement_score":0.08768058,"about_ca_system_score_codex":0.00022220793,"about_ca_system_score_gemma":0.0005055335,"threshold_uncertainty_score":0.9997475},"labels":[],"label_agreement":null},{"id":"W7007826998","doi":"","title":"Affine registration: A comparison of several methods programs","year":2004,"lang":"en","type":"article","venue":"NPARC","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Affine transformation; Set (abstract data type); Algebra over a field; Calculus (dental)","score_opus":0.053172505948702764,"score_gpt":0.4018051868715449,"score_spread":0.3486326809228421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7007826998","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00057844096,0.000015380678,0.98719996,0.0013997011,0.00007590394,0.0001641669,2.4352445e-7,0.00022327816,0.010342895],"genre_scores_gemma":[0.164539,0.0000017732262,0.8351351,0.00014028214,0.000022984259,0.000018617733,0.000002947725,0.0000029285832,0.00013635641],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99908054,0.00007346065,0.00027281558,0.00017394565,0.00027832456,0.00012092883],"domain_scores_gemma":[0.99938107,0.00003395903,0.00012380176,0.00031800213,0.000076852826,0.00006632808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038003645,0.00006639752,0.00014127424,0.000043085096,0.000030422761,0.000044911045,0.0004183583,0.0000381745,0.00006986658],"category_scores_gemma":[0.00007179525,0.0000596893,0.000037484348,0.0002767989,0.00007803619,0.00023599235,0.00007608521,0.000088815425,0.0000068618037],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037682364,0.00023205773,0.000086790205,0.000023585622,0.00000878035,0.0000040990158,0.0011016255,0.000028539,0.0635997,0.051512953,0.0016330465,0.88176507],"study_design_scores_gemma":[0.00056239107,0.0003974565,0.00029358073,0.000053807675,0.0000063330663,0.000013668133,0.00005735185,0.011428747,0.9150398,0.0704454,0.0015488777,0.00015261193],"about_ca_topic_score_codex":0.00001403097,"about_ca_topic_score_gemma":0.0000034734926,"teacher_disagreement_score":0.8816124,"about_ca_system_score_codex":0.000032016116,"about_ca_system_score_gemma":0.00006978327,"threshold_uncertainty_score":0.24340591},"labels":[],"label_agreement":null},{"id":"W7019630503","doi":"","title":"Image-Based Geometric Modeling","year":2003,"lang":"en","type":"article","venue":"NPARC","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"","score_opus":0.024866198626176315,"score_gpt":0.28019271081785224,"score_spread":0.2553265121916759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7019630503","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006841057,0.000017314158,0.97384024,0.0003043194,0.000085018706,0.00008279564,3.0794757e-7,0.00032102823,0.02466487],"genre_scores_gemma":[0.21523556,0.0000033103984,0.78372717,0.000923276,0.000009503856,0.000013666643,7.0313473e-7,0.0000047039707,0.00008208355],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999089,0.00006333764,0.00014679637,0.00021096616,0.0003080616,0.00018182705],"domain_scores_gemma":[0.9993913,0.000059516224,0.000031788957,0.00034109972,0.00007650191,0.00009979445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036599865,0.000068853326,0.00007685876,0.00021021422,0.000052142328,0.000092512724,0.00039188314,0.000030128444,0.00039306216],"category_scores_gemma":[0.00032208124,0.000064216714,0.000034512363,0.0007242245,0.000024340798,0.00028143052,0.000036241738,0.00009108068,0.00010552099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000052361543,0.00039143278,0.00021497779,0.00007958256,0.00002201088,0.000102244056,0.00035121205,0.00075460784,0.3095272,0.05784212,0.034170613,0.5965388],"study_design_scores_gemma":[0.000254154,0.00003479958,0.000008713252,0.0000090611675,0.0000022140969,0.00000409268,0.0000050080694,0.7166131,0.25749305,0.025067396,0.0003799397,0.00012845428],"about_ca_topic_score_codex":0.0000025932118,"about_ca_topic_score_gemma":1.1651507e-7,"teacher_disagreement_score":0.7158585,"about_ca_system_score_codex":0.00003189254,"about_ca_system_score_gemma":0.000068710644,"threshold_uncertainty_score":0.43037552},"labels":[],"label_agreement":null},{"id":"W7024553455","doi":"","title":"Search for <i>tt </i>resonances in the lepton plus jets final state in <i>pp</i> collisions at √s = 1.96 TeV","year":2008,"lang":"en","type":"article","venue":"Lincoln (University of Nebraska)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institut National de Physique Nucléaire et de Physique des Particules; Science and Technology Facilities Council; Consejo Nacional de Investigaciones Científicas y Técnicas; Natural Sciences and Engineering Research Council of Canada; Fundação para o Desenvolvimento da UNESP; Centre National de la Recherche Scientifique; U.S. Department of Energy; Vetenskapsrådet; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Bundesministerium für Bildung und Forschung; Russian Foundation for Basic Research; Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; Western Canada Research Grid; Korea Science and Engineering Foundation; Department of Science and Technology, Ministry of Science and Technology, India; Grantová Agentura České Republiky; Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS); Federal Agency for Science and Innovation; Deutsche Forschungsgemeinschaft; Alexander von Humboldt-Stiftung; Fundação de Amparo à Pesquisa do Estado de São Paulo; Fermilab; National Science Foundation; Science Foundation Ireland; Secretaría de Ciencia y Técnica, Universidad de Buenos Aires","keywords":"Tevatron; Lepton; Resonance (particle physics); Invariant mass; Fermilab; Top quark; Boson; Electroweak interaction","score_opus":0.052261906976085376,"score_gpt":0.27538493782388873,"score_spread":0.22312303084780336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024553455","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62706923,0.00018126906,0.36871377,0.0026677535,0.00006232667,0.00069786434,0.00003059105,0.00006359718,0.0005135589],"genre_scores_gemma":[0.82134736,0.00085929217,0.17470145,0.001011527,0.000032133506,0.00001172507,0.000022749176,0.000011866077,0.0020018804],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9985531,0.0001708181,0.00017662963,0.00032744466,0.000491897,0.00028012152],"domain_scores_gemma":[0.99895924,0.00045889977,0.00009607938,0.0003237464,0.00008568211,0.000076328695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005826896,0.00010368958,0.0002025232,0.00019315444,0.0002548403,0.000020114538,0.0011952803,0.00006300259,0.00003212315],"category_scores_gemma":[0.00003227298,0.000101498634,0.00007294511,0.00080963643,0.00025873925,0.0004975123,0.0003577351,0.00018633872,0.000013954775],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014467624,0.0030157831,0.17965706,0.0005742888,0.000091130496,0.003196358,0.1700389,0.0036538383,0.021873046,0.008812809,0.16599548,0.44164455],"study_design_scores_gemma":[0.024868686,0.0033540616,0.41777223,0.0008478392,0.000056581932,0.00024392332,0.013031923,0.22731096,0.21562771,0.0075740563,0.08705298,0.0022590526],"about_ca_topic_score_codex":0.0009276013,"about_ca_topic_score_gemma":0.0010385993,"teacher_disagreement_score":0.4393855,"about_ca_system_score_codex":0.00015916406,"about_ca_system_score_gemma":0.00023910787,"threshold_uncertainty_score":0.41389942},"labels":[],"label_agreement":null},{"id":"W7024658024","doi":"","title":"Segmentation of Magnetic Resonance Brain Images Using Watershed Algorithm&#13;\\n","year":2004,"lang":"en","type":"dissertation","venue":"Universiti Putra Malaysia Institutional Repository (Universiti Putra Malaysia)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Segmentation; Scale-space segmentation; Image segmentation; Watershed; Scanner; Noise (video); Magnetic resonance imaging; Pattern recognition (psychology); Segmentation-based object categorization","score_opus":0.009286726886478294,"score_gpt":0.2323914340077816,"score_spread":0.2231047071213033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024658024","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25442982,0.0012668085,0.7219772,0.0005052458,0.0020087503,0.0015429005,0.00027554462,0.0007686569,0.017225107],"genre_scores_gemma":[0.28365135,0.00051695603,0.68250585,0.00082203286,0.0007487083,0.000049086197,0.004244364,0.00022031098,0.027241362],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99434644,0.00033761922,0.0009787434,0.0016254872,0.0018583273,0.0008534076],"domain_scores_gemma":[0.9963434,0.00016799942,0.0010147771,0.0010398796,0.0009602767,0.00047368233],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003914727,0.0009113478,0.00092158053,0.001704653,0.00084435655,0.00030594342,0.0023000136,0.0007550667,0.00029662478],"category_scores_gemma":[0.000040938685,0.0011550793,0.0005764476,0.0015855063,0.0007780145,0.003944579,0.0003547486,0.00085675804,0.00004108716],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001031567,0.0025679632,0.00042744898,0.003079499,0.0012735059,0.020917216,0.00990257,0.1176964,0.6021807,0.06397323,0.009549859,0.1674],"study_design_scores_gemma":[0.009931183,0.0019353065,0.0039464575,0.0036441165,0.0015669437,0.0023809804,0.010591185,0.27095106,0.681588,0.0041047093,0.0038119422,0.005548133],"about_ca_topic_score_codex":0.00072710315,"about_ca_topic_score_gemma":0.0000058602936,"teacher_disagreement_score":0.16185187,"about_ca_system_score_codex":0.0026815364,"about_ca_system_score_gemma":0.002868401,"threshold_uncertainty_score":0.9990899},"labels":[],"label_agreement":null},{"id":"W7031954919","doi":"","title":"REPLICAS","year":2016,"lang":"en","type":"other","venue":"TeesRep (Teesside University)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Dance; Personality; Pavilion; Big Five personality traits; Work (physics); Popularity","score_opus":0.007855905627403698,"score_gpt":0.2357337398229759,"score_spread":0.2278778341955722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7031954919","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.3425824e-7,0.00003493676,0.46958092,0.0002497971,0.00015132404,0.00013073857,0.000006489863,0.0015412568,0.5283043],"genre_scores_gemma":[0.000012394256,0.00016847039,0.091626324,0.0003534208,0.00013735278,0.0000013749377,0.000005741541,0.00011197869,0.90758294],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984002,0.00010497642,0.00015077005,0.00068680814,0.00035737085,0.00029983523],"domain_scores_gemma":[0.99811137,0.00006785923,0.00026902952,0.0013018075,0.000046600733,0.00020331058],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00012218674,0.00024679865,0.00027637888,0.0007884646,0.000063296386,0.00005413873,0.0017370364,0.00026498342,0.0062478813],"category_scores_gemma":[0.000047076104,0.00021911324,0.00010913439,0.0003925678,0.00015546111,0.00027308412,0.0005908288,0.00019230614,0.0017436224],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017924581,0.000022428147,0.00001175874,0.00001921456,0.00003122349,0.0003523902,0.000023299488,1.43151e-8,0.00024946293,0.02293558,0.9337797,0.04257312],"study_design_scores_gemma":[0.00029761426,0.00002401589,0.000006159964,0.00019465078,0.0000148939835,0.000011566205,0.000010448455,0.000014290125,0.0022437177,0.00044996486,0.9964162,0.0003164779],"about_ca_topic_score_codex":0.00008859835,"about_ca_topic_score_gemma":0.000019232473,"teacher_disagreement_score":0.37927866,"about_ca_system_score_codex":0.00013966676,"about_ca_system_score_gemma":0.00013636643,"threshold_uncertainty_score":0.99903363},"labels":[],"label_agreement":null},{"id":"W7036491307","doi":"","title":"Can Tocqueville karaoke?","year":2014,"lang":"en","type":"other","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Politics; Democracy; Context (archaeology); Marketing buzz; Citizen journalism; Legitimacy; The arts; State (computer science)","score_opus":0.00899931091690829,"score_gpt":0.2614095379954103,"score_spread":0.25241022707850197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036491307","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[6.223998e-9,0.00003482685,0.5062709,0.0004203743,0.00013418472,0.00009796285,8.661394e-7,0.0010102347,0.49203065],"genre_scores_gemma":[0.000005655692,0.000025977663,0.32875165,0.0027095685,0.00011492576,0.000020999641,0.0000048815123,0.00007119649,0.66829515],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990797,0.000044661265,0.00013242854,0.00030852153,0.00027617527,0.0001585191],"domain_scores_gemma":[0.9990945,0.000023525336,0.00009258654,0.0006606734,0.000016263406,0.000112479],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00011179068,0.00013928313,0.0001724602,0.00015238751,0.000014738805,0.000068400346,0.00089575944,0.00013857066,0.009188591],"category_scores_gemma":[0.00003065762,0.00011205895,0.000041406576,0.00011304378,0.000039531846,0.000036130907,0.000182308,0.00010411921,0.000876711],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.2295582e-8,0.000006078874,0.0000013418279,0.0000126268715,0.000005087136,0.000002898654,0.000009502001,8.926489e-9,0.00002040928,0.009732447,0.9312367,0.05897287],"study_design_scores_gemma":[0.0000784839,0.000028514898,0.000004161033,0.00006060356,0.0000025901882,0.00000316646,0.0000013727664,0.00023491366,0.0020162438,0.0008690099,0.99650043,0.00020050992],"about_ca_topic_score_codex":0.00031851183,"about_ca_topic_score_gemma":0.000118516116,"teacher_disagreement_score":0.17751923,"about_ca_system_score_codex":0.000021062075,"about_ca_system_score_gemma":0.000042955875,"threshold_uncertainty_score":0.99990124},"labels":[],"label_agreement":null},{"id":"W7036528418","doi":"","title":"Caso de aproximación a trombocitopenia inmunomediada (TIM)","year":2022,"lang":"es","type":"report","venue":"LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Research methodology; Population; Context (archaeology)","score_opus":0.030930853614107863,"score_gpt":0.2834536563255615,"score_spread":0.25252280271145366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036528418","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009987724,0.0056471936,0.51511097,0.0356809,0.016885435,0.0075585875,0.0012335952,0.005944495,0.40195113],"genre_scores_gemma":[0.8343337,0.0054363217,0.14003375,0.0023457503,0.004632447,0.004222764,0.0021618942,0.00051265885,0.006320681],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9828692,0.0025344407,0.003109263,0.0032318293,0.005559088,0.0026961896],"domain_scores_gemma":[0.98941636,0.0011310264,0.0026167566,0.002954002,0.0016892442,0.0021925806],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.0048203426,0.0018850446,0.0022109475,0.0019654194,0.0022264814,0.0018114394,0.005543849,0.0019489733,0.0014718993],"category_scores_gemma":[0.0034532372,0.002017859,0.001120212,0.0028693792,0.00016413079,0.0017547953,0.0032818196,0.004709089,0.00008693025],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.00073938764,0.0058314563,0.001721195,0.0031866815,0.002648123,0.014031496,0.01401827,0.00019238763,0.023808187,0.7029475,0.08745138,0.14342391],"study_design_scores_gemma":[0.0017573108,0.0004197373,0.0026229885,0.0006495833,0.00035330286,0.0100422,0.00048508405,0.001663592,0.004505385,0.00072401867,0.9747058,0.0020709606],"about_ca_topic_score_codex":0.00003394071,"about_ca_topic_score_gemma":0.00012499744,"teacher_disagreement_score":0.8872545,"about_ca_system_score_codex":0.0076071923,"about_ca_system_score_gemma":0.011347337,"threshold_uncertainty_score":0.9998366},"labels":[],"label_agreement":null},{"id":"W7036906859","doi":"","title":"Concepts of \"community\" in Community Economic Development : the social dynamics of community-based development in Winnipeg's inner city","year":2006,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Nucleofection; TSG101; Hyporeflexia; Gestational period; Diafiltration; Fusible alloy; Articular cartilage damage; Proteogenomics","score_opus":0.02613090358286551,"score_gpt":0.2684768006763935,"score_spread":0.242345897093528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036906859","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9670349,0.000014262444,0.030603003,0.00035314672,0.00010252457,0.00044991152,0.000022722816,0.000049836253,0.0013697206],"genre_scores_gemma":[0.97031295,0.0000075369408,0.028501257,0.00008690637,0.0000053937497,0.0000041541357,0.0009367576,0.00001647139,0.00012855377],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99606574,0.0024648388,0.00059848616,0.00014641105,0.0004564054,0.00026813234],"domain_scores_gemma":[0.99729794,0.0006081432,0.0010041888,0.00080046727,0.00023908114,0.000050153038],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0024898206,0.00028650858,0.0006642116,0.0006303111,0.000757887,0.000024071724,0.0031842964,0.00031984053,0.000008790418],"category_scores_gemma":[0.00005031234,0.00033926257,0.00010967578,0.0005358405,0.00047907332,0.0002911369,0.0006798921,0.0019755121,0.0000029939017],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008451009,0.009896215,0.13150018,0.007912115,0.000656736,0.00004602419,0.5648783,0.0017469533,0.0037280458,0.0061537344,0.008609114,0.2640275],"study_design_scores_gemma":[0.0017328123,0.00013478364,0.82686466,0.00060736254,0.000029334851,7.159454e-7,0.1472562,0.0011783332,0.02078558,0.00053121505,0.00037064296,0.00050835824],"about_ca_topic_score_codex":0.046632573,"about_ca_topic_score_gemma":0.8538674,"teacher_disagreement_score":0.8072349,"about_ca_system_score_codex":0.0011268761,"about_ca_system_score_gemma":0.0009421847,"threshold_uncertainty_score":0.99990594},"labels":[],"label_agreement":null},{"id":"W7065901859","doi":"","title":"Flux maximizing geometric flows for 2D and 3D blood vessel segmentation","year":2001,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McGill University","keywords":"Segmentation; Visualization; Intensity (physics); Image segmentation; Flux (metallurgy); Contrast (vision); Field (mathematics); Vector flow; Interpretation (philosophy); Flow (mathematics)","score_opus":0.01906327995101423,"score_gpt":0.2676302346135547,"score_spread":0.24856695466254045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7065901859","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7613268,0.004698634,0.1207625,0.00021650153,0.00986147,0.01504159,0.0022427046,0.007128825,0.078720994],"genre_scores_gemma":[0.07117919,0.0021643343,0.89883894,0.0012035497,0.00021708166,0.0020425948,0.003945409,0.0004113541,0.019997578],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9947922,0.00027492084,0.0011822842,0.0016639634,0.0012225603,0.00086403516],"domain_scores_gemma":[0.99656695,0.0006001308,0.0008281165,0.00094183156,0.000562619,0.000500328],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013665789,0.0008063732,0.00078509684,0.0014319972,0.001096375,0.0004818935,0.001384501,0.00072345516,0.00016499455],"category_scores_gemma":[0.0011839349,0.0008866103,0.00026126552,0.0016907939,0.000054752083,0.0022576759,0.00026619495,0.000978752,0.00007385734],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004696693,0.00023558442,0.000006919604,0.00048679282,0.00017498081,0.00008240568,0.000022760472,0.000007693137,0.064903796,0.006323224,0.00005927459,0.9276496],"study_design_scores_gemma":[0.0033018577,0.0006647967,0.00025896193,0.0006657142,0.00054471596,0.00020813121,0.0002831761,0.001661548,0.95506144,0.024279302,0.011028108,0.002042236],"about_ca_topic_score_codex":0.000077842284,"about_ca_topic_score_gemma":0.000059405247,"teacher_disagreement_score":0.9256074,"about_ca_system_score_codex":0.00038152534,"about_ca_system_score_gemma":0.00008033827,"threshold_uncertainty_score":0.9993585},"labels":[],"label_agreement":null},{"id":"W7071339648","doi":"","title":"Solving variational problems and partial differential equations mapping between manifolds via the closest point method","year":2015,"lang":"en","type":"dissertation","venue":"Summit (Simon Fraser University)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nucleofection; Gestational period; Articular cartilage damage; Diafiltration; Liquation; Tubulopathy","score_opus":0.031205393875068224,"score_gpt":0.27084418959021844,"score_spread":0.23963879571515023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7071339648","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016016506,0.000052337025,0.9952277,0.00034479206,0.00042260013,0.0005494437,0.000030436791,0.000253408,0.0015176511],"genre_scores_gemma":[0.58942497,0.0001955507,0.38519982,0.00055615645,0.0015403404,0.000066527595,0.0051524425,0.00016215733,0.017702026],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975497,0.00040256104,0.00036937775,0.00063390756,0.00072256685,0.00032186293],"domain_scores_gemma":[0.9981469,0.00044411345,0.0004014378,0.00046938533,0.00031559545,0.00022255043],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005597003,0.0003074319,0.00032645944,0.00047530996,0.0004408779,0.0002844877,0.0010797925,0.0002914201,0.00010561628],"category_scores_gemma":[0.00012613846,0.00028606647,0.000112231966,0.00066532975,0.00006764086,0.00081666245,0.000303,0.0004921778,0.000021688486],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020909139,0.0010462245,0.04306835,0.0014658233,0.0027900634,0.0004505265,0.011873721,0.00044988308,0.0019189061,0.16797297,0.07117014,0.69758433],"study_design_scores_gemma":[0.012761909,0.0014945272,0.03406991,0.0026051623,0.0038468295,6.737382e-7,0.067063116,0.58541536,0.040866025,0.10913292,0.13342597,0.00931759],"about_ca_topic_score_codex":0.0002948797,"about_ca_topic_score_gemma":0.0025357984,"teacher_disagreement_score":0.6882667,"about_ca_system_score_codex":0.00019158685,"about_ca_system_score_gemma":0.00024636695,"threshold_uncertainty_score":0.9999592},"labels":[],"label_agreement":null},{"id":"W7096187789","doi":"","title":"University of Alberta Shape-Guided Interactive Image Segmentation","year":2016,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Permission; Key (lock); Image (mathematics); Segmentation; Image segmentation; Image manipulation","score_opus":0.01464868508642849,"score_gpt":0.2742070261304195,"score_spread":0.259558341043991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096187789","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040522576,0.0000010974165,0.98168844,0.001296757,0.000045866993,0.00010120727,9.523673e-7,0.00009604562,0.012717353],"genre_scores_gemma":[0.18592069,0.00001617939,0.80489975,0.00038503634,0.000010073397,0.0000012086484,0.0000019716842,0.00000439261,0.008760715],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994235,0.000052792104,0.00011327759,0.00017025368,0.0001555498,0.0000846341],"domain_scores_gemma":[0.9993638,0.0001852063,0.00008749083,0.00020644732,0.00010209215,0.000055001303],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010072748,0.00005270172,0.000072825205,0.000059796555,0.00002397856,0.000014012022,0.00038097918,0.000021397384,0.0015423159],"category_scores_gemma":[0.000065096,0.00003808296,0.000031204345,0.00011780561,0.00007083989,0.0009953834,0.0001517064,0.000025269623,0.0001251745],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009906452,0.000065629254,0.0002701477,0.000008564412,0.00002013366,0.000007545429,0.0013056493,6.66321e-8,0.6395903,0.0045208577,0.024262318,0.32993886],"study_design_scores_gemma":[0.00051184726,0.000061178725,0.00092052476,0.00003229289,0.000004094327,0.0000038930457,0.00017598392,0.0017023073,0.9951791,0.0009575202,0.00035900626,0.00009220612],"about_ca_topic_score_codex":0.00025427822,"about_ca_topic_score_gemma":0.00003719975,"teacher_disagreement_score":0.35558882,"about_ca_system_score_codex":0.00004963363,"about_ca_system_score_gemma":0.000028522467,"threshold_uncertainty_score":0.9993704},"labels":[],"label_agreement":null},{"id":"W7096658313","doi":"","title":"Rendering falling snow using an inverse Fourier transform","year":2004,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Fourier transform; Inverse; Constant (computer programming); Fourier analysis; Function (biology); Power function; Rendering (computer graphics); Opacity","score_opus":0.05915948829586876,"score_gpt":0.3137942944452048,"score_spread":0.25463480614933603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096658313","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0111729195,0.0000048639754,0.98567903,0.00034871817,0.0000862205,0.00010116446,1.8767273e-7,0.0005126291,0.002094255],"genre_scores_gemma":[0.086575024,0.0000066808584,0.9122796,0.0010309849,0.000033938755,0.0000036907006,9.895825e-7,0.000006905824,0.000062161074],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991177,0.000017429353,0.00017125349,0.00023225708,0.0002663097,0.00019507248],"domain_scores_gemma":[0.99949473,0.000014030719,0.000029770892,0.00027940792,0.000033268218,0.00014877862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021856799,0.00008431234,0.000079962745,0.00008003528,0.00008687725,0.000115648276,0.00039322092,0.000041476334,0.00006281217],"category_scores_gemma":[0.000021683445,0.000077013705,0.000032477543,0.0002036106,0.000035269393,0.0013177218,0.000048914837,0.00009543166,0.000011852952],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003992901,0.00019455588,0.00004618324,0.00003145878,0.00002143023,0.000095753836,0.006057477,0.0028392586,0.08126297,0.015601832,0.00014075496,0.89370435],"study_design_scores_gemma":[0.00051909854,0.00008504937,0.00002270633,0.0000557108,0.000005386663,0.00003178833,0.00021983485,0.17335874,0.8079181,0.017393515,0.00013426297,0.00025583454],"about_ca_topic_score_codex":0.0001636237,"about_ca_topic_score_gemma":0.000052999774,"teacher_disagreement_score":0.8934485,"about_ca_system_score_codex":0.00008286067,"about_ca_system_score_gemma":0.00006877349,"threshold_uncertainty_score":0.31405276},"labels":[],"label_agreement":null},{"id":"W7097055603","doi":"","title":"Author manuscript, published in &amp;quot;International Conference on Image Analysis and Recognition, Canada (2007)&amp;quot; Automatic closed edge detection using level lines","year":2009,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Edge detection; Enhanced Data Rates for GSM Evolution; Image (mathematics); Line (geometry); Pattern recognition (psychology); Probabilistic logic; Set (abstract data type); Level set (data structures); Flexibility (engineering)","score_opus":0.09808730649235839,"score_gpt":0.3300848210611581,"score_spread":0.23199751456879972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097055603","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09438186,0.0000078525345,0.90055233,0.0029073479,0.00041792082,0.0002502974,0.000019326844,0.00026711507,0.0011959719],"genre_scores_gemma":[0.40380365,0.000011043083,0.5913088,0.0026295078,0.000096689655,0.0000233475,0.00010491838,0.000010786882,0.002011284],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99754846,0.00017019326,0.00064684806,0.0006113416,0.0007040289,0.00031910418],"domain_scores_gemma":[0.99856365,0.00010693449,0.00023617668,0.00043076195,0.0004566498,0.00020584105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064993306,0.0002310967,0.0003101875,0.0008342518,0.00011709393,0.0006479625,0.00054823654,0.000099476085,0.0009093531],"category_scores_gemma":[0.00059345324,0.00021789856,0.00006260989,0.0012062745,0.00006033156,0.0013016443,0.00009025886,0.00024368802,0.000013499275],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046535864,0.0005485572,0.0019230882,0.00007306686,0.0003295504,0.0000854466,0.0012637754,0.0000487153,0.09240246,0.00088565686,0.030740155,0.871653],"study_design_scores_gemma":[0.0018282345,0.00017791592,0.09686523,0.00022700825,0.00019846257,0.00007474381,0.00026392512,0.78679925,0.09643041,0.01500896,0.0008369143,0.0012889202],"about_ca_topic_score_codex":0.066052556,"about_ca_topic_score_gemma":0.23363218,"teacher_disagreement_score":0.87036407,"about_ca_system_score_codex":0.00034636274,"about_ca_system_score_gemma":0.00029703326,"threshold_uncertainty_score":0.9956779},"labels":[],"label_agreement":null},{"id":"W7097110839","doi":"","title":"Region Tracking Via Local Statistics And Level Set Pdes","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Tracking (education); Divergence (linguistics); Image (mathematics); Set (abstract data type); Noise (video); Bayesian probability; Level set (data structures); White noise; Domain (mathematical analysis); Prior probability","score_opus":0.09805672465228576,"score_gpt":0.3026824376718559,"score_spread":0.2046257130195701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097110839","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000102190934,0.000036578684,0.9983882,0.00045189378,0.0000397839,0.000060718063,0.0000016321337,0.00017121746,0.00074778893],"genre_scores_gemma":[0.22319989,0.000046452056,0.7745395,0.0011994527,0.0000165659,0.0000035553085,0.0000016651642,0.000003958253,0.0009889746],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938804,0.000031505948,0.00012236978,0.00017042924,0.00017471053,0.00011296827],"domain_scores_gemma":[0.99962103,0.00006603524,0.000031605552,0.00016178229,0.000039246675,0.00008031634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009230322,0.00005940966,0.0000616935,0.000040580122,0.00004778401,0.00008637296,0.0001798326,0.000028641538,0.000098408505],"category_scores_gemma":[0.000037985283,0.0000516285,0.000008007406,0.00008886787,0.00008311906,0.00023550297,0.00007464864,0.000060064653,0.000024196941],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.2001344e-7,0.0000139715585,0.00009192648,0.000007733774,0.0000021718379,0.000022094406,0.00031421354,7.647882e-7,0.00029422896,0.004836805,0.041022636,0.95339316],"study_design_scores_gemma":[0.0006340563,0.00029577472,0.0038061978,0.000051102248,0.000009597023,0.00028427466,0.00014639086,0.88098264,0.08944763,0.02120991,0.0026418783,0.0004905588],"about_ca_topic_score_codex":0.0000226619,"about_ca_topic_score_gemma":0.0000065550917,"teacher_disagreement_score":0.95290256,"about_ca_system_score_codex":0.000013846238,"about_ca_system_score_gemma":0.000004758911,"threshold_uncertainty_score":0.21053492},"labels":[],"label_agreement":null},{"id":"W7097625345","doi":"","title":"A Dynamic Brain Atlas","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Atlas (anatomy); Brain atlas; Pattern recognition (psychology); White matter; Medical imaging; Brain tissue","score_opus":0.015034441437724247,"score_gpt":0.27241906802066035,"score_spread":0.2573846265829361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097625345","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001578421,0.000024511384,0.9650462,0.0070823897,0.00005040206,0.000057104164,1.5989173e-7,0.0005545618,0.027026825],"genre_scores_gemma":[0.05939614,0.000013030538,0.90834504,0.008128893,0.000008494547,0.000011176669,4.4199695e-7,0.0000039331985,0.024092844],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994678,0.000023346363,0.000090890884,0.00014541102,0.00016358942,0.00010895473],"domain_scores_gemma":[0.9995964,0.000053604024,0.000017932329,0.00025102973,0.00001626736,0.00006477372],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008802375,0.000043329044,0.00004448627,0.000043425975,0.000025909174,0.00005933634,0.00040067424,0.000019768588,0.0018286612],"category_scores_gemma":[0.000053501062,0.000035664838,0.000019755676,0.00016770874,0.000023774353,0.000264007,0.00008743677,0.000047500955,0.00071206817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.2530172e-7,0.000039169197,0.000014124607,0.0000033052681,0.0000029492348,0.000015847254,0.00024860975,3.9279547e-7,0.0056007225,0.010286887,0.19915785,0.78463],"study_design_scores_gemma":[0.0004039283,0.00013469583,0.00055754744,0.000015049514,0.0000018067717,0.000043650198,0.00002301333,0.92081994,0.053265944,0.008326386,0.016103018,0.0003050125],"about_ca_topic_score_codex":0.000005252359,"about_ca_topic_score_gemma":0.0000025678803,"teacher_disagreement_score":0.9208196,"about_ca_system_score_codex":0.000016875503,"about_ca_system_score_gemma":0.000003535391,"threshold_uncertainty_score":0.9990838},"labels":[],"label_agreement":null},{"id":"W7100347848","doi":"","title":"Intra-subject Elastic Registration of 3D Ultrasound Images","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"3D ultrasound; Voxel; Image registration; Ultrasound; Speckle pattern; Process (computing); Medical imaging","score_opus":0.018361226330613708,"score_gpt":0.2700829589842802,"score_spread":0.2517217326536665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100347848","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032827198,0.000023086503,0.9821569,0.00014919907,0.00007121246,0.00009639245,7.760136e-7,0.00024535877,0.013974386],"genre_scores_gemma":[0.4535587,0.00004684097,0.5451375,0.0002513667,0.000022541899,0.000007179567,0.000002551303,0.0000033980061,0.0009698971],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990885,0.000045379344,0.00025543556,0.00018088192,0.00031563503,0.00011414473],"domain_scores_gemma":[0.9992449,0.00021052793,0.00009979535,0.00030237559,0.00008632799,0.00005604698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018974503,0.000069854876,0.000098450706,0.00006519809,0.000047884252,0.000026037886,0.00035380264,0.00003279974,0.000162761],"category_scores_gemma":[0.0003175116,0.000059365633,0.00002582336,0.0002312012,0.00014368769,0.0004509944,0.00003634213,0.00006643805,0.000027316375],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007866223,0.00021824185,0.001359836,0.00005558962,0.000024113222,0.00005364604,0.0010729278,0.000012940653,0.87396544,0.011088512,0.067088686,0.045052223],"study_design_scores_gemma":[0.0001443471,0.00010804649,0.0046459744,0.000012288678,0.0000027580622,0.00009305314,0.000014647083,0.0004338624,0.9932894,0.001109557,0.00005074621,0.00009532509],"about_ca_topic_score_codex":0.00004924182,"about_ca_topic_score_gemma":0.0000030398842,"teacher_disagreement_score":0.45027602,"about_ca_system_score_codex":0.000017803428,"about_ca_system_score_gemma":0.00006576857,"threshold_uncertainty_score":0.24208602},"labels":[],"label_agreement":null},{"id":"W7100511406","doi":"","title":"UNIVERSITY OF CALGARY Accelerated Medical Image Registration using the Graphics Processing Unit","year":2011,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Image registration; Graphics; Graphics processing unit; Image processing; Task (project management); Computer graphics","score_opus":0.09945154446801543,"score_gpt":0.30649500889774434,"score_spread":0.2070434644297289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100511406","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045605567,0.0000115159,0.98899513,0.00020492842,0.000023451246,0.00009288148,1.5573826e-7,0.00015145418,0.005959935],"genre_scores_gemma":[0.24624431,0.000028563862,0.7529892,0.000551242,0.000010749401,4.4445662e-7,0.0000023971336,0.0000047996045,0.00016830782],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991235,0.00009724038,0.00014923303,0.00014231644,0.00038851684,0.00009917259],"domain_scores_gemma":[0.999358,0.000031092393,0.000116754185,0.00023252607,0.00018625177,0.000075406984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046925026,0.000056661484,0.00007039377,0.000054028784,0.00012229483,0.00003096247,0.00069579517,0.000059961338,0.00029602338],"category_scores_gemma":[0.00007120779,0.000041568783,0.000023446071,0.0004611373,0.00027687242,0.0005802985,0.00013252744,0.00013117529,0.0000019226336],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007117388,0.0007675574,0.0036115053,0.00026922455,0.00008887771,0.0004141767,0.016677434,0.0000016244373,0.08724564,0.1717331,0.0057068244,0.7134129],"study_design_scores_gemma":[0.0006732876,0.00013426835,0.006148798,0.00015948112,0.00003305901,0.00007102416,0.0010582017,0.6659703,0.31938007,0.0058289054,0.00020757843,0.00033500593],"about_ca_topic_score_codex":0.000730246,"about_ca_topic_score_gemma":0.000050258775,"teacher_disagreement_score":0.71307784,"about_ca_system_score_codex":0.00001133177,"about_ca_system_score_gemma":0.00022368772,"threshold_uncertainty_score":0.32412484},"labels":[],"label_agreement":null},{"id":"W7106037383","doi":"10.7939/83357","title":"3D-3D rigid registration of echocardiographic images obtained from apical window","year":2025,"lang":"en","type":"dissertation","venue":"University of Alberta Library","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Image registration; Rotation (mathematics); Translation (biology); Noise (video); Speedup; Robustness (evolution); Image quality; Process (computing); Frame rate","score_opus":0.005727189742428707,"score_gpt":0.20344559200879964,"score_spread":0.19771840226637094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7106037383","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050738562,0.0007413592,0.67596185,0.0017214526,0.0009657118,0.0012364285,0.00013616742,0.00071410736,0.2677844],"genre_scores_gemma":[0.076658346,0.0016145337,0.8143188,0.0003889263,0.00012557178,0.0000023799846,0.0074028685,0.000048145503,0.09944046],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983996,0.0001547781,0.00035418928,0.00049099,0.0004446488,0.00015577339],"domain_scores_gemma":[0.9982088,0.00041254144,0.000504748,0.0006714972,0.00010182198,0.00010054978],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000078245655,0.00021801863,0.00047011586,0.00051059003,0.00007915716,0.000050600065,0.0013679223,0.00032517497,0.00031001327],"category_scores_gemma":[0.00007733133,0.0002598328,0.00024479194,0.00064210553,0.00013102204,0.0014306412,0.00018061697,0.0002731398,0.000006706193],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021199672,0.0019789096,0.019647075,0.004297606,0.003653134,0.00051125244,0.03475645,0.00007133831,0.02783314,0.04406834,0.5328512,0.32821155],"study_design_scores_gemma":[0.006929739,0.0012702603,0.13048775,0.006513283,0.0015885972,0.00000834399,0.0064050606,0.008079965,0.77822685,0.030414362,0.02645723,0.0036185407],"about_ca_topic_score_codex":0.00239835,"about_ca_topic_score_gemma":0.00017042033,"teacher_disagreement_score":0.75039375,"about_ca_system_score_codex":0.00001902369,"about_ca_system_score_gemma":0.00039259644,"threshold_uncertainty_score":0.9999854},"labels":[],"label_agreement":null},{"id":"W7113101162","doi":"","title":"DEEP LEARNING-BASED CONE-BEAM COMPUTED TOMOGRAPHY CORRECTION FOR DOSE MONITORING AND ADAPTATION IN PROTON THERAPY","year":2025,"lang":"","type":"dissertation","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute of Particle Physics","funders":"","keywords":"Computed tomography; Adaptation (eye); Proton; Proton therapy; Radiation dose","score_opus":0.02810764558747359,"score_gpt":0.31475385222714447,"score_spread":0.2866462066396709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7113101162","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014004615,0.0008233927,0.9731204,0.000082613784,0.0029492842,0.008299656,0.0000020442594,0.0005611075,0.00015689826],"genre_scores_gemma":[0.6014357,0.0016598303,0.38276368,0.00041994193,0.00030178463,0.009888329,0.0012573858,0.00011198862,0.0021613264],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956527,0.00042759013,0.0013405676,0.0013464954,0.0006977159,0.0005349458],"domain_scores_gemma":[0.9966463,0.0010462734,0.00083102734,0.0003904791,0.0008895876,0.00019634493],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011076308,0.00064625684,0.00068811845,0.0017075145,0.0004356715,0.0006309723,0.0005492701,0.0005852478,0.00003499465],"category_scores_gemma":[0.00036305966,0.0006900637,0.00021299948,0.0018972823,0.000102813916,0.00077583105,0.00005732019,0.00083095324,0.0000019617744],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071175944,0.0004289805,0.004434632,0.00081651745,0.000069980284,0.000004441691,0.0039288113,0.006526259,0.003926152,0.000051457588,0.000055703746,0.97904533],"study_design_scores_gemma":[0.00283304,0.0010479322,0.0078051975,0.0011296005,0.00002558067,0.0000011194124,0.0013261324,0.7489627,0.23610285,0.00014384004,0.00011246947,0.00050952676],"about_ca_topic_score_codex":0.00042473452,"about_ca_topic_score_gemma":0.00012828942,"teacher_disagreement_score":0.9785358,"about_ca_system_score_codex":0.00019019248,"about_ca_system_score_gemma":0.0004304688,"threshold_uncertainty_score":0.99955505},"labels":[],"label_agreement":null},{"id":"W7120252894","doi":"10.1002/alz70856_107469","title":"Automating SPECT Reconstruction for Dementia Research Initiatives","year":2025,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sunnybrook Health Science Centre; Ontario Brain Institute; University of Toronto; University of Waterloo; Sunnybrook Hospital","funders":"","keywords":"Dementia; Pipeline (software); Process (computing); Perfusion; Perfusion scanning; Cognition","score_opus":0.07672752943599624,"score_gpt":0.3971927127299618,"score_spread":0.32046518329396556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7120252894","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005665933,0.010597229,0.97956437,0.0008639876,0.00061141525,0.0007199702,0.0000024185995,0.00044652302,0.006627472],"genre_scores_gemma":[0.49287012,0.00005008818,0.5063076,0.00042372584,0.000058847134,0.00026046424,0.00000739561,0.0000092503105,0.000012514739],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980276,0.0002515842,0.0004333817,0.00048252105,0.0003911393,0.00041374963],"domain_scores_gemma":[0.9987804,0.00029232033,0.000120981305,0.00044611839,0.00028420668,0.000075935386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015126301,0.00013479454,0.00015529714,0.00038458282,0.00042043152,0.00027167262,0.0007328613,0.00005627944,0.00011640702],"category_scores_gemma":[0.00022242306,0.00013792406,0.00007431568,0.000714481,0.00015163938,0.00095891894,0.00034424142,0.00019065908,0.00003672867],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000072349662,0.000056551755,0.0009082283,0.000016820677,0.0028804124,0.0000025815734,0.00019031501,7.7160047e-7,0.004160364,0.037146132,0.0077932663,0.9468373],"study_design_scores_gemma":[0.0009694111,0.00028157694,0.0075512626,0.0002199623,0.0018478172,0.000010525392,0.0002047357,0.012709348,0.93244666,0.04039208,0.0029759533,0.00039069258],"about_ca_topic_score_codex":0.000030922798,"about_ca_topic_score_gemma":0.000009156997,"teacher_disagreement_score":0.94644666,"about_ca_system_score_codex":0.000015846821,"about_ca_system_score_gemma":0.0001493618,"threshold_uncertainty_score":0.56243795},"labels":[],"label_agreement":null},{"id":"W7128528570","doi":"10.70102/afts.2025.1834.698","title":"OTSU AND KAPUR ENTROPY BASED OPTIMAL MULTILEVEL IMAGE THRESHOLDING USING JAYA AND STOCHASTIC FRACTAL SEARCH ALGORITHMS FOR ENHANCED IMAGE SEGMENTATION","year":2025,"lang":"","type":"article","venue":"Archives for Technical Sciences","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Thresholding; Otsu's method; Image segmentation; Entropy (arrow of time); Segmentation; Pattern recognition (psychology); Image (mathematics); Image processing","score_opus":0.04195488121884374,"score_gpt":0.380770151292119,"score_spread":0.3388152700732753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7128528570","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023744825,0.00024167578,0.97044057,0.0013952667,0.00029731638,0.0034503369,0.00012298646,0.00022176634,0.000085287924],"genre_scores_gemma":[0.28193238,0.000055125412,0.7171658,0.0004047227,0.00006168038,0.00030471175,0.000012863462,0.000017380176,0.0000453288],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952525,0.00019735681,0.0009115331,0.001744909,0.0007831524,0.0011105359],"domain_scores_gemma":[0.99518526,0.003457752,0.0003128588,0.00039866223,0.00016868101,0.00047679705],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":["sts"],"category_scores_codex":[0.0017280662,0.0004873126,0.00056640693,0.00065913086,0.0016767061,0.001234379,0.0012325576,0.00016136792,0.000021578073],"category_scores_gemma":[0.0014596495,0.0004490964,0.00019656819,0.0007715039,0.0047907573,0.0016942859,0.0009003116,0.00039669863,8.7168763e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011506696,0.00017492843,0.00002634034,0.00023550086,0.00002232074,0.0000024315727,0.00047051936,0.00029817194,0.74465364,0.0024669014,0.000059299975,0.25147486],"study_design_scores_gemma":[0.001402255,0.0007192267,0.00030330077,0.00036932286,0.000058116628,0.0000073896967,0.0002483549,0.6642315,0.32657805,0.005741796,0.000007774245,0.00033293143],"about_ca_topic_score_codex":0.000044682783,"about_ca_topic_score_gemma":0.0000031474192,"teacher_disagreement_score":0.66393334,"about_ca_system_score_codex":0.000101535436,"about_ca_system_score_gemma":0.0006281283,"threshold_uncertainty_score":0.9998024},"labels":[],"label_agreement":null},{"id":"W7131066419","doi":"10.1109/iccvw69036.2025.00077","title":"HAPPI: Hyperbolic Hierarchical Part Prototypes for Image Recognition","year":2025,"lang":"","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Discriminative model; Hierarchy; Aggregate (composite); Euclidean geometry; Image (mathematics); Feature (linguistics); Key (lock); Convolutional neural network; Pattern recognition (psychology)","score_opus":0.03243677614771632,"score_gpt":0.3277537902724389,"score_spread":0.29531701412472255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131066419","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018863121,0.0001291701,0.9620631,0.009022866,0.00083584693,0.0047231596,0.000030351028,0.0005940401,0.022412878],"genre_scores_gemma":[0.0027064716,0.00025045685,0.9632645,0.0097279735,0.00034728748,0.003420918,0.000063114814,0.00002743275,0.020191798],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9968925,0.00025303717,0.0008461813,0.00094891357,0.00044313053,0.0006161992],"domain_scores_gemma":[0.99793184,0.00048332295,0.00015319363,0.0006826482,0.00049112807,0.0002578841],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009682128,0.00032087642,0.00039225852,0.000334537,0.00031316135,0.00056731683,0.0009396117,0.00023047952,0.0018293584],"category_scores_gemma":[0.00087401835,0.00030117607,0.00021394914,0.00084474793,0.0004263701,0.000852112,0.0003680886,0.00036745716,0.00024739717],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052894768,0.00029133324,0.000010402827,0.00030497144,0.000046833007,0.000006148531,0.0002166656,9.2164434e-8,0.004712475,0.00750347,0.050546747,0.93630797],"study_design_scores_gemma":[0.002556795,0.00090143445,0.0002597889,0.0009487154,0.00012557869,0.00001656443,0.00008517421,0.046138156,0.71381384,0.15279485,0.08149332,0.0008657692],"about_ca_topic_score_codex":0.00003902606,"about_ca_topic_score_gemma":0.000008639291,"teacher_disagreement_score":0.9354422,"about_ca_system_score_codex":0.000091500544,"about_ca_system_score_gemma":0.00048473218,"threshold_uncertainty_score":0.99994403},"labels":[],"label_agreement":null},{"id":"W7131114856","doi":"10.1109/iccvw69036.2025.00686","title":"Fitting Image Diffusion Models on Video Datasets","year":2025,"lang":"","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Regularization (linguistics); Generative grammar; Coherence (philosophical gambling strategy); Pattern recognition (psychology); Generative model; Convergence (economics); Temporal resolution; Clutter; Task (project management)","score_opus":0.02145863928133704,"score_gpt":0.3227675969325877,"score_spread":0.30130895765125065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131114856","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022410881,0.00010209093,0.9487705,0.0047642407,0.0006891259,0.0005698783,0.000057766254,0.000516614,0.04430568],"genre_scores_gemma":[0.0663439,0.00042173322,0.88666064,0.03286325,0.00013517207,0.00007554158,0.00016982306,0.000029255974,0.013300673],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99639064,0.00027685598,0.0008215986,0.001117739,0.00083051727,0.00056262757],"domain_scores_gemma":[0.9972879,0.00048023005,0.00019533864,0.001636312,0.00014014958,0.00026002218],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009549393,0.0003524587,0.00034229684,0.00038770045,0.00037283977,0.0007475032,0.0018007627,0.0001637364,0.00096566754],"category_scores_gemma":[0.00045663185,0.0003181412,0.0001166052,0.000923692,0.00020958016,0.0018076063,0.0018151033,0.00047063752,0.00029323396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016807053,0.00036850755,0.000007727702,0.000106913554,0.000022742237,0.000046521087,0.00028228396,0.000029341665,0.013098162,0.031182684,0.25765952,0.6971788],"study_design_scores_gemma":[0.0006396619,0.00014960367,0.000060411392,0.0006439905,0.000021949507,0.0000027440476,0.00006241356,0.77259195,0.20702216,0.016915591,0.0015550718,0.0003344375],"about_ca_topic_score_codex":0.00013652326,"about_ca_topic_score_gemma":0.0000044916483,"teacher_disagreement_score":0.7725626,"about_ca_system_score_codex":0.00014083728,"about_ca_system_score_gemma":0.00020452266,"threshold_uncertainty_score":0.9999476},"labels":[],"label_agreement":null},{"id":"W7160022158","doi":"10.1109/iccv51701.2025.01851","title":"MRGen: Segmentation Data Engine for Underrepresented MRI Modalities","year":2025,"lang":"","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Key Research and Development Program of China","keywords":"Segmentation; Modalities; Pattern recognition (psychology); Modality (human–computer interaction); Image segmentation","score_opus":0.07522969786579645,"score_gpt":0.39261607441300106,"score_spread":0.3173863765472046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7160022158","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000017027753,0.0005743097,0.9736793,0.0131065985,0.0016058094,0.0019272385,0.00015020477,0.00056652015,0.008373013],"genre_scores_gemma":[0.015159211,0.0005797525,0.941351,0.005590961,0.00018852467,0.00028144717,0.0008035224,0.000025570494,0.03602002],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996462,0.00020297112,0.00092385453,0.0012700933,0.0006013915,0.0005397081],"domain_scores_gemma":[0.9960148,0.0006381204,0.00020175196,0.0026085137,0.00035231828,0.0001845256],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001148437,0.0003299962,0.00036636664,0.00037629224,0.00028384087,0.00062430644,0.0029214995,0.00015919161,0.00068041816],"category_scores_gemma":[0.00051018724,0.00033166935,0.00010139456,0.0009010144,0.0002184026,0.0020197707,0.0014276005,0.00019508228,0.000032523592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041690277,0.0003202197,0.000081977756,0.0005005786,0.00026809503,0.0000045929874,0.0006499711,0.0001446617,0.0038056956,0.031004472,0.3410872,0.6220908],"study_design_scores_gemma":[0.0011561406,0.00013492294,0.000062120795,0.0001461418,0.00008373686,0.0000022031945,0.00078909623,0.7877402,0.19449243,0.011234908,0.003839686,0.00031843106],"about_ca_topic_score_codex":0.00019460492,"about_ca_topic_score_gemma":0.000028157436,"teacher_disagreement_score":0.7875955,"about_ca_system_score_codex":0.00015487974,"about_ca_system_score_gemma":0.00049683097,"threshold_uncertainty_score":0.9999135},"labels":[],"label_agreement":null},{"id":"W7160027501","doi":"10.1109/iccv51701.2025.00226","title":"Staining and Locking Computer Vision Models Without Retraining","year":2025,"lang":"","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Retraining; Key (lock); Pattern recognition (psychology); Window (computing)","score_opus":0.024030127625211856,"score_gpt":0.3322091208412337,"score_spread":0.3081789932160219,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7160027501","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012411531,0.00043475596,0.9783696,0.0014808411,0.0006006186,0.0004252937,8.794545e-7,0.00053111784,0.016915772],"genre_scores_gemma":[0.48561943,0.00010546201,0.5095427,0.0037101277,0.00005963457,0.0000072814128,0.0000012224482,0.000010731035,0.0009434369],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967543,0.00025074332,0.00077570556,0.001039316,0.0006147506,0.0005652213],"domain_scores_gemma":[0.9983741,0.00029781403,0.00019550383,0.0006363712,0.0002424686,0.00025376072],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0014835579,0.00034151765,0.0004402848,0.00044071698,0.00041502842,0.0011698712,0.0007421923,0.00019998923,0.00012384409],"category_scores_gemma":[0.000079697944,0.00032953397,0.00006997276,0.00078721077,0.00031854075,0.0018461576,0.00153664,0.0004959587,0.000007110639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012792368,0.000044461332,0.00019221015,0.00011473526,0.000037588627,0.000023953002,0.0036167607,0.00028204414,0.0004753176,0.018293358,0.003117196,0.9737896],"study_design_scores_gemma":[0.000608219,0.00025735976,0.00018554849,0.0011201892,0.00002113164,0.000013714998,0.0002427855,0.98258764,0.004657862,0.009822494,0.00016993353,0.00031314377],"about_ca_topic_score_codex":0.000046072957,"about_ca_topic_score_gemma":0.0000026244102,"teacher_disagreement_score":0.9823056,"about_ca_system_score_codex":0.00010378801,"about_ca_system_score_gemma":0.0002344229,"threshold_uncertainty_score":0.99991566},"labels":[],"label_agreement":null},{"id":"W73503437","doi":"10.1007/978-3-642-23623-5_30","title":"Towards Real-Time 3D US to CT Bone Image Registration Using Phase and Curvature Feature Based GMM Matching","year":2011,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Matching (statistics); Artificial intelligence; Curvature; Feature (linguistics); Image registration; Computer vision; Image matching; Image (mathematics); Pattern recognition (psychology); Mathematics; Geometry","score_opus":0.0236530072180999,"score_gpt":0.3108853164957595,"score_spread":0.28723230927765964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W73503437","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037007432,0.00003593163,0.9610052,0.0009255522,0.00028703737,0.00038063523,0.0000036107274,0.0002890428,0.00006552069],"genre_scores_gemma":[0.19301243,0.0000036249116,0.8045377,0.0023458481,0.00007764883,0.00000794848,0.0000026644177,0.000010413935,0.0000016897752],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997382,0.000120151766,0.0003206994,0.00095383904,0.0007282297,0.00049507665],"domain_scores_gemma":[0.9984912,0.0001245423,0.00016645243,0.0007627787,0.00017328377,0.00028171187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011551103,0.00026580266,0.00026784313,0.0004398142,0.00024169125,0.0005518886,0.0011847296,0.00008441357,0.00002198559],"category_scores_gemma":[0.00022760665,0.00023519494,0.000040459418,0.0017370774,0.00033398502,0.0013891011,0.00048078108,0.00036652444,0.0000058455216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026287433,0.00018178519,0.00016325104,0.000052692543,0.0000050580375,0.00032334778,0.0034884792,0.0015687761,0.350516,0.00017386727,0.00013790427,0.6433625],"study_design_scores_gemma":[0.00047524046,0.00022603571,0.0010157616,0.00015894411,0.0000057893208,0.00012881539,9.088315e-7,0.73731416,0.2556736,0.004642641,0.000015492411,0.0003426357],"about_ca_topic_score_codex":0.0003284328,"about_ca_topic_score_gemma":0.000023228265,"teacher_disagreement_score":0.7357454,"about_ca_system_score_codex":0.00014561522,"about_ca_system_score_gemma":0.0003018057,"threshold_uncertainty_score":0.9590971},"labels":[],"label_agreement":null},{"id":"W766160502","doi":"10.1016/j.dam.2015.06.002","title":"A new measure for comparing biomedical regions of interest in segmentation of digital images","year":2015,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Segmentation; Region of interest; Computer science; Hausdorff distance; Artificial intelligence; Measure (data warehouse); Feature (linguistics); Estimator; Pattern recognition (psychology); Image segmentation; Digital image; Computer vision; Data mining; Mathematics; Image (mathematics); Image processing; Statistics","score_opus":0.12608197161782891,"score_gpt":0.3325182755227295,"score_spread":0.2064363039049006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W766160502","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033779945,0.000014743735,0.9946689,0.00014422074,0.000030334662,0.0004354406,0.00000695734,0.000060017235,0.0012613613],"genre_scores_gemma":[0.39677387,0.0000010846625,0.6031354,0.000011118241,0.000010043384,0.000030434716,0.000015471232,0.0000067775427,0.00001580787],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989448,0.000008824164,0.0005059124,0.00015554103,0.0002553354,0.0001295556],"domain_scores_gemma":[0.99912393,0.00014579849,0.00025338202,0.00027399155,0.00007674663,0.0001261694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003195851,0.000097333774,0.00026986375,0.00013828771,0.00001047321,0.000040687504,0.00043199307,0.000045329525,0.0000021940734],"category_scores_gemma":[0.00019757818,0.00008450972,0.00004433279,0.00025818672,0.00009164575,0.00023592859,0.00014310899,0.00006446563,0.0000023374268],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000087183245,0.0010210143,0.00045350016,0.0015688414,0.00011738593,0.0000060857574,0.024508031,0.000030403427,0.18860835,0.64063174,0.022715803,0.120251685],"study_design_scores_gemma":[0.0032969106,0.0003121011,0.00007838112,0.0005343739,0.000039896804,0.000009881604,0.004568167,0.018144375,0.59828115,0.37432078,0.000052977728,0.0003610405],"about_ca_topic_score_codex":0.000006503539,"about_ca_topic_score_gemma":0.0000070541882,"teacher_disagreement_score":0.4096728,"about_ca_system_score_codex":0.00003556359,"about_ca_system_score_gemma":0.00009993612,"threshold_uncertainty_score":0.34462062},"labels":[],"label_agreement":null},{"id":"W770492350","doi":"10.1007/978-0-387-09749-7_1","title":"Object Segmentation and Markov Random Fields","year":2015,"lang":"en","type":"book-chapter","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Segmentation; Markov random field; Markov chain; Computer science; Object (grammar); Artificial intelligence; Pattern recognition (psychology); Computer vision; Image segmentation; Machine learning","score_opus":0.027096256049507085,"score_gpt":0.2836107735232669,"score_spread":0.2565145174737598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W770492350","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.548093e-7,0.0002241041,0.5876833,0.00021187254,0.00012285014,0.00021573078,0.0000015819593,0.00020900746,0.41133106],"genre_scores_gemma":[0.0001019012,0.0003446495,0.32304865,0.0015257999,0.000091526694,0.00002100377,0.000025954683,0.000017854734,0.6748227],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99878144,0.000026635089,0.0002669165,0.00036056773,0.00044229958,0.00012212161],"domain_scores_gemma":[0.99916184,0.00010808585,0.000121121586,0.00035141193,0.00010707485,0.00015046909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000370644,0.00019050944,0.00023343845,0.00012542681,0.000037712172,0.00013553882,0.00033901777,0.00020432458,0.0007918134],"category_scores_gemma":[0.000038185495,0.00016185413,0.000047720056,0.000023872606,0.00006526765,0.00029985516,0.00023038193,0.00020133816,0.00007178512],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014431944,0.00000890835,0.0000027160536,0.000044386274,0.00004133045,0.000034948207,0.0002669545,2.4710738e-7,0.000104811705,0.051268272,0.20864356,0.7395694],"study_design_scores_gemma":[0.011281755,0.0014021795,0.000046234887,0.00076115417,0.00023697299,0.0003082652,0.000104541716,0.008145026,0.031110767,0.7049847,0.2380121,0.0036062764],"about_ca_topic_score_codex":0.00001582616,"about_ca_topic_score_gemma":0.000007748314,"teacher_disagreement_score":0.73596317,"about_ca_system_score_codex":0.000047511803,"about_ca_system_score_gemma":0.000076922726,"threshold_uncertainty_score":0.86698014},"labels":[],"label_agreement":null},{"id":"W818147388","doi":"","title":"Quantitative longitudinal and cross-sectional shape analysis of retina by registration","year":2015,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"","keywords":"Optometry; Retina; Cross-sectional study; Ophthalmology; Medicine; Psychology; Neuroscience; Pathology","score_opus":0.11032368762714706,"score_gpt":0.42248458058803845,"score_spread":0.3121608929608914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W818147388","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9569411,0.000059123868,0.041799944,0.00020149931,0.000100862075,0.00013526448,0.000010028736,0.000056068817,0.00069607754],"genre_scores_gemma":[0.917237,0.0000020195932,0.082544796,0.00010444189,0.000008762846,0.000017192668,0.000011112058,0.000003313694,0.00007140393],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99746406,0.00022235444,0.00044404747,0.0007365145,0.00085859327,0.0002744199],"domain_scores_gemma":[0.9979342,0.00026882836,0.00039707808,0.00025162371,0.00077613007,0.000372091],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0022363085,0.00014800871,0.00026869436,0.00043537613,0.0001977175,0.00016500913,0.00073317054,0.000082414495,0.000041427815],"category_scores_gemma":[0.0020470095,0.00013573215,0.00004661426,0.003089019,0.006980427,0.0013748943,0.000303221,0.00015796834,0.0000060084676],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019580406,0.00009358186,0.5699187,0.000006775034,0.000072869545,0.000029927778,0.0010557771,0.000026300439,0.42237157,0.00588032,0.0001850247,0.0003395629],"study_design_scores_gemma":[0.00017680768,0.00096096884,0.6683794,0.000009045394,0.000028151637,0.000065131964,0.00010271867,0.04325394,0.27880442,0.008052638,0.0000023260632,0.00016443267],"about_ca_topic_score_codex":0.00017205492,"about_ca_topic_score_gemma":0.0000025806962,"teacher_disagreement_score":0.14356713,"about_ca_system_score_codex":0.00010430429,"about_ca_system_score_gemma":0.00039538738,"threshold_uncertainty_score":0.995722},"labels":[],"label_agreement":null},{"id":"W84819170","doi":"10.1007/978-3-642-36620-8_2","title":"Groupwise Spectral Log-Demons Framework for Atlas Construction","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Atlas (anatomy); Computer science; Construct (python library); Invariant (physics); Isometry (Riemannian geometry); Artificial intelligence; Algorithm; Computer vision; Mathematics; Pure mathematics; Geology","score_opus":0.018014205433776,"score_gpt":0.27638672564833633,"score_spread":0.2583725202145603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W84819170","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000019593985,0.00014633407,0.99326754,0.0013008211,0.0024264765,0.0010573494,0.0000073936144,0.00045073577,0.0013237428],"genre_scores_gemma":[0.0025566989,0.00004650775,0.9943286,0.0019878524,0.0006945553,0.0000596915,0.000008839007,0.00003464376,0.00028258294],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99615043,0.000035168767,0.0006276624,0.0015188644,0.0009441872,0.0007236714],"domain_scores_gemma":[0.9968473,0.0009348856,0.0003860991,0.0012457543,0.00030279948,0.0002831897],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006378489,0.00050936337,0.0005243081,0.0006759703,0.00026134806,0.0006769656,0.0028650335,0.0005127102,0.00015268699],"category_scores_gemma":[0.00032514997,0.0004690311,0.00018092235,0.00047853772,0.0013322379,0.00089700334,0.0007374314,0.0009625985,0.00010157705],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000033781855,0.0000203215,0.000014135162,0.000039307324,0.00001008888,0.000017951286,0.00033339087,0.00025968094,0.00025113145,0.17521234,0.0002737303,0.8235645],"study_design_scores_gemma":[0.00020262,0.00022828799,0.0000364384,0.00035458512,0.000009665713,0.0000802576,3.2737918e-7,0.10329343,0.011776351,0.88287944,0.00055697013,0.0005816389],"about_ca_topic_score_codex":0.000018133716,"about_ca_topic_score_gemma":0.000010394801,"teacher_disagreement_score":0.8229829,"about_ca_system_score_codex":0.0003648893,"about_ca_system_score_gemma":0.00041663792,"threshold_uncertainty_score":0.9997761},"labels":[],"label_agreement":null},{"id":"W87933458","doi":"10.1007/978-3-642-23960-1_33","title":"Cascaded Window Memoization for Medical Imaging","year":2011,"lang":"en","type":"book-chapter","venue":"IFIP International Federation for Information Processing/IFIP","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Memoization; Computer science; Redundancy (engineering); Data redundancy; Speedup; Computation; Window (computing); Computer vision; Artificial intelligence; Algorithm; Parallel computing","score_opus":0.02020875297699608,"score_gpt":0.29094482754906936,"score_spread":0.27073607457207327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W87933458","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[6.4541575e-7,0.000082463215,0.8424345,0.0026824004,0.0015668984,0.0015076513,0.00012836473,0.00062071584,0.15097632],"genre_scores_gemma":[0.01914756,0.00038881708,0.6224511,0.034236506,0.0040841256,0.0041468986,0.023235904,0.00039204568,0.291917],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99540275,0.000025603666,0.0017961276,0.00058771693,0.0018097033,0.00037810224],"domain_scores_gemma":[0.99459255,0.00023053626,0.0015347303,0.00040545608,0.0029652382,0.00027150547],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0011572706,0.00052669353,0.00042472413,0.0008041209,0.0006309125,0.0018360533,0.0014753733,0.00054461573,0.0006173411],"category_scores_gemma":[0.0010401261,0.0005412768,0.00027148385,0.00010744164,0.00014324683,0.0072469832,0.00020932002,0.00039562836,0.0001596667],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007084385,0.000041965293,0.000004660193,0.00027885323,0.0000797699,0.000002268335,0.001116559,0.000017333812,0.00007151496,0.32187176,0.04066119,0.63578326],"study_design_scores_gemma":[0.0030989402,0.0001712909,0.00002168696,0.0010297953,0.000090717665,0.0001393265,0.00008949849,0.21969475,0.019167371,0.123094276,0.63191485,0.0014875148],"about_ca_topic_score_codex":0.000010512082,"about_ca_topic_score_gemma":0.000009403605,"teacher_disagreement_score":0.63429576,"about_ca_system_score_codex":0.0003913592,"about_ca_system_score_gemma":0.0008539156,"threshold_uncertainty_score":0.9997039},"labels":[],"label_agreement":null},{"id":"W88103190","doi":"10.1007/978-3-642-30618-1_7","title":"Non-iterative Multi-modal Partial View to Full View Image Registration Using Local Phase-Based Image Projections","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Image registration; Artificial intelligence; Computer vision; Imaging phantom; Image (mathematics); Nuclear medicine; Medicine","score_opus":0.04260968072371878,"score_gpt":0.349371015160372,"score_spread":0.3067613344366532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W88103190","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000024444478,0.00018042423,0.9957507,0.00079918094,0.0010394878,0.0016519442,0.000015334348,0.00032015052,0.00021828622],"genre_scores_gemma":[0.015272333,0.0000162006,0.9814774,0.0025419348,0.00048260836,0.00007980878,0.000023417731,0.000044319873,0.00006200636],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951186,0.00011826478,0.0009344873,0.0016662881,0.0013098874,0.0008524726],"domain_scores_gemma":[0.9969118,0.00023734945,0.0004912811,0.0012959385,0.0005976807,0.00046590448],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012918082,0.00068616314,0.0006638038,0.0008790364,0.00042972466,0.0009827012,0.0020552129,0.00030690234,0.000074937816],"category_scores_gemma":[0.00014609721,0.0006384857,0.00017994792,0.0010344475,0.0011719252,0.0018753231,0.00069573283,0.0008731336,0.00008604187],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021285749,0.00026501258,0.0000031088312,0.00014262619,0.000014101016,0.00009189992,0.001123117,0.0026498162,0.04185479,0.00052534696,0.00008368306,0.9532252],"study_design_scores_gemma":[0.00072154315,0.0004930944,0.000006967166,0.0008408479,0.000024514797,0.00007299648,5.1632827e-7,0.8504076,0.14460075,0.0014672239,0.0005699448,0.0007940283],"about_ca_topic_score_codex":0.000067205394,"about_ca_topic_score_gemma":0.00006406881,"teacher_disagreement_score":0.9524312,"about_ca_system_score_codex":0.0007051508,"about_ca_system_score_gemma":0.00113279,"threshold_uncertainty_score":0.99960667},"labels":[],"label_agreement":null},{"id":"W898114428","doi":"10.1016/j.media.2015.06.004","title":"Adaptive multi-level conditional random fields for detection and segmentation of small enhanced pathology in medical images","year":2015,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"NeuroRx Research (Canada); McGill University; McGill University Health Centre","funders":"","keywords":"Segmentation; Artificial intelligence; Pattern recognition (psychology); Voxel; Conditional random field; Computer science; Image segmentation; Probabilistic logic; Context (archaeology); Graphical model; Computer vision","score_opus":0.0446848011939909,"score_gpt":0.3367311574336902,"score_spread":0.2920463562396993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W898114428","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009205055,0.00007858218,0.9893856,0.00085827877,0.000066485634,0.0002851909,0.000017441402,0.00005758593,0.000045748737],"genre_scores_gemma":[0.5860499,0.0000595135,0.41280803,0.0007664427,0.00003889586,0.00016606657,0.0000590479,0.000006072433,0.000045979694],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975832,0.00032643863,0.00060395105,0.00040960504,0.00086165697,0.00021513186],"domain_scores_gemma":[0.99825346,0.00066742924,0.00019530856,0.00020550929,0.00032131595,0.00035696157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020060756,0.00013622118,0.0004417815,0.00044767105,0.000039422597,0.00003128897,0.0003759855,0.0002137874,0.00016353982],"category_scores_gemma":[0.0034879402,0.00011969706,0.00012457563,0.00072297576,0.00033369692,0.00032192786,0.00014395136,0.00021933409,0.0000030036567],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003207419,0.00078125956,0.0009419683,0.00009648836,0.0006414049,0.00029363675,0.0028218625,0.0000665308,0.036414277,0.0002722089,0.0007711006,0.9565785],"study_design_scores_gemma":[0.00972088,0.00043425645,0.006083446,0.00005553778,0.00028233713,0.000035409485,0.0005129227,0.6201459,0.35927457,0.0031446188,0.000008447206,0.00030163242],"about_ca_topic_score_codex":0.00012955512,"about_ca_topic_score_gemma":0.000434973,"teacher_disagreement_score":0.9562769,"about_ca_system_score_codex":0.00005026993,"about_ca_system_score_gemma":0.00017787382,"threshold_uncertainty_score":0.48811045},"labels":[],"label_agreement":null},{"id":"W95146287","doi":"10.1007/978-3-642-33418-4_47","title":"Hierarchical Conditional Random Fields for Detection of Gad-Enhancing Lesions in Multiple Sclerosis","year":2012,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; NeuroRx Research (Canada); McGill University","funders":"","keywords":"Conditional random field; Voxel; Pattern recognition (psychology); Multiple sclerosis; Lesion; Artificial intelligence; Computer science; Probabilistic logic; Feature (linguistics); Medicine; Pathology","score_opus":0.03657310260315619,"score_gpt":0.291024444011149,"score_spread":0.25445134140799286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W95146287","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053778667,0.00004838572,0.94505244,0.00030259887,0.00042667348,0.00032599762,0.0000020280343,0.000060540107,0.0000026538175],"genre_scores_gemma":[0.5774403,0.000002755555,0.4221013,0.0003583993,0.0000606783,0.00003277168,0.0000011710887,0.0000024142782,1.6684224e-7],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984198,0.00008696566,0.00035526653,0.0003436716,0.00040968746,0.0003845847],"domain_scores_gemma":[0.9978998,0.0015196337,0.000093679824,0.00027869176,0.000095742675,0.0001124702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012827987,0.00010812456,0.00018437617,0.0003911159,0.00012534297,0.00006257004,0.0006679048,0.000081825456,0.000006060765],"category_scores_gemma":[0.0008671864,0.000097104334,0.000052674655,0.0010253883,0.00028328566,0.00076561404,0.00022507083,0.00021822628,0.000001545919],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021317854,0.00014771764,0.0022156343,0.000028038043,0.0000022719823,0.0000010291849,0.0015453518,0.00425844,0.21146207,0.00025196275,0.000006888781,0.7800593],"study_design_scores_gemma":[0.000664149,0.00007136,0.009491863,0.000061865765,0.0000010232028,0.000004779333,9.1251314e-7,0.33388662,0.6500598,0.0056620515,0.000002943261,0.0000925899],"about_ca_topic_score_codex":0.0000421082,"about_ca_topic_score_gemma":0.000088127636,"teacher_disagreement_score":0.7799667,"about_ca_system_score_codex":0.00008732542,"about_ca_system_score_gemma":0.00010163203,"threshold_uncertainty_score":0.39598},"labels":[],"label_agreement":null},{"id":"W95913851","doi":"10.1007/978-3-642-40811-3_25","title":"Efficient Convex Optimization Approach to 3D Non-rigid MR-TRUS Registration","year":2013,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"Canadian Institutes of Health Research; Canada Research Chairs","keywords":"Fiducial marker; Artificial intelligence; Image registration; Prostate biopsy; Iterative closest point; Computer science; Computer vision; Similarity (geometry); Medicine; Pattern recognition (psychology); Prostate; Point cloud; Image (mathematics)","score_opus":0.01217857219525953,"score_gpt":0.25951894885892285,"score_spread":0.24734037666366332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W95913851","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028334013,0.000011550292,0.9942004,0.0010391743,0.00050997775,0.00086407067,4.2806954e-7,0.00026752093,0.00027345648],"genre_scores_gemma":[0.3458034,0.0000011154204,0.6517741,0.0022821166,0.00006658388,0.00006227807,0.0000020910054,0.000005923961,0.0000023729772],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997134,0.000075736796,0.00041035612,0.00095735246,0.00091906637,0.0005035455],"domain_scores_gemma":[0.99830425,0.00015239167,0.00013619356,0.0008473873,0.0002991666,0.00026063054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083130616,0.00021262691,0.00020800534,0.00045519482,0.00019706986,0.0007799518,0.0018337754,0.000089068526,0.000024049801],"category_scores_gemma":[0.00024709493,0.00018570358,0.00003539259,0.0025143556,0.00029073266,0.0006721972,0.0004900227,0.00022577995,0.000049135055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.804518e-7,0.000076365286,0.000059306454,0.000009696459,0.0000011175885,0.0000020225752,0.00087852357,0.78208596,0.00337855,0.0001299232,0.00007269641,0.21330486],"study_design_scores_gemma":[0.00015985213,0.00009591785,0.000548669,0.00003251921,0.0000011197507,0.000017861872,0.0000011676674,0.97189754,0.026515571,0.0005058158,0.000004671115,0.00021926964],"about_ca_topic_score_codex":0.000105754894,"about_ca_topic_score_gemma":0.000002629368,"teacher_disagreement_score":0.34297,"about_ca_system_score_codex":0.00018500647,"about_ca_system_score_gemma":0.00020429675,"threshold_uncertainty_score":0.75727725},"labels":[],"label_agreement":null},{"id":"W959165447","doi":"10.1007/978-1-4471-5195-1_3","title":"Flux Graphs for 2D Shape Analysis","year":2013,"lang":"en","type":"book-chapter","venue":"Advances in computer vision and pattern recognition","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Graph; Inscribed figure; Mathematics; Combinatorics; Uniqueness; Computer science; Algorithm; Geometry; Mathematical analysis","score_opus":0.024501404154954793,"score_gpt":0.30537194100209797,"score_spread":0.2808705368471432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W959165447","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000050310562,0.00079974014,0.99264383,0.00011786327,0.00042443688,0.0006478268,0.00004269755,0.00017693566,0.0050963303],"genre_scores_gemma":[0.002514177,0.0082328245,0.97170264,0.005867573,0.00039464797,0.00030414056,0.0015819701,0.000076289194,0.00932574],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980251,0.00004597081,0.00056643155,0.0008062516,0.00032599652,0.00023021906],"domain_scores_gemma":[0.9986759,0.00033444085,0.0003195001,0.000367306,0.00017470124,0.0001281869],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025056503,0.0003355232,0.0004938308,0.00077257905,0.00007523364,0.00021998331,0.00042018958,0.00022269682,0.0006408869],"category_scores_gemma":[0.000009399838,0.00030667405,0.00020930523,0.00016647605,0.000080301805,0.0010177805,0.00021574229,0.00025059583,0.000096066055],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003923296,0.000020522604,0.000023590104,0.00008884712,0.00005052201,0.000008433143,0.00004940497,0.0000039228607,0.000006160064,0.0004317861,0.0010759139,0.99823695],"study_design_scores_gemma":[0.0018517016,0.0013611955,0.00060023315,0.0018454076,0.00037241532,0.000036051653,0.0000068009363,0.5388092,0.0007160741,0.40124705,0.051271923,0.0018819508],"about_ca_topic_score_codex":0.000006264274,"about_ca_topic_score_gemma":0.000023089262,"teacher_disagreement_score":0.996355,"about_ca_system_score_codex":0.000033520602,"about_ca_system_score_gemma":0.000014481414,"threshold_uncertainty_score":0.99993855},"labels":[],"label_agreement":null},{"id":"W968716896","doi":"10.1007/s11760-015-0807-z","title":"Editorial of the special issue on Advances in Low-Level Image representations for processing and analysis","year":2015,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; Image processing; Data science; Image (mathematics); Information retrieval; Artificial intelligence","score_opus":0.024259371622725397,"score_gpt":0.3438172899531206,"score_spread":0.3195579183303952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W968716896","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009546853,0.00047217877,0.9960217,0.00045375712,0.0009681064,0.00030497366,0.000009584038,0.00004184063,0.00077318127],"genre_scores_gemma":[0.19368021,0.00015005699,0.7675974,0.0007896666,0.037271146,0.00016390784,0.000019722185,0.000037836395,0.00029004915],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99862653,0.00008249821,0.00034030058,0.0003591323,0.00041371997,0.00017784342],"domain_scores_gemma":[0.99904203,0.0001535927,0.00022289224,0.00016020793,0.00032972833,0.00009152531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006722884,0.00012225888,0.00022186119,0.00019717167,0.00014723667,0.0003065105,0.0002878651,0.000051073806,0.000006744561],"category_scores_gemma":[0.00046458814,0.00009094913,0.000045998462,0.0007767759,0.00022773881,0.0018894072,0.0001273099,0.00012893423,5.628857e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000076705895,0.00010995062,0.0005418038,0.0003089093,0.000018154853,0.0000033707336,0.004229327,0.00002911598,0.012194919,0.000060464557,0.009769458,0.9726578],"study_design_scores_gemma":[0.0058898022,0.00069570553,0.0037084545,0.001748389,0.00045841912,0.000016739876,0.0042114076,0.17086649,0.75749284,0.045517966,0.008148121,0.0012456629],"about_ca_topic_score_codex":0.000017273518,"about_ca_topic_score_gemma":0.000013471303,"teacher_disagreement_score":0.9714122,"about_ca_system_score_codex":0.000023822173,"about_ca_system_score_gemma":0.00015592713,"threshold_uncertainty_score":0.37087977},"labels":[],"label_agreement":null},{"id":"W989272091","doi":"10.1016/j.neuroimage.2015.07.076","title":"An Optimized PatchMatch for multi-scale and multi-feature label fusion","year":2015,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"National Institute on Aging; Ministerio de Economía y Competitividad; National Institutes of Health; Agence Nationale de la Recherche; Dana Foundation; Foundation for the National Institutes of Health","keywords":"Segmentation; Computer science; Artificial intelligence; Pattern recognition (psychology); Computation; Fusion; Feature (linguistics); Sørensen–Dice coefficient; Scale (ratio); Image segmentation; Cartography; Algorithm; Geography","score_opus":0.07967791245572012,"score_gpt":0.35791200459903877,"score_spread":0.2782340921433186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W989272091","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008878136,0.00005641785,0.9889599,0.0007794429,0.00021635942,0.0005429205,0.000012925795,0.0004948322,0.000059069916],"genre_scores_gemma":[0.0034939512,0.00002261336,0.9940787,0.001835345,0.000042947988,0.00006323333,0.000015299236,0.000019450727,0.00042843792],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871135,0.0001245106,0.00017508877,0.00049063104,0.00025836937,0.00024005823],"domain_scores_gemma":[0.99885225,0.00006711966,0.00007875119,0.0005050903,0.00015901898,0.00033778895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038352405,0.00015225264,0.00017872156,0.00006905851,0.000088296125,0.00020221682,0.00052192993,0.00008326096,0.0000056403483],"category_scores_gemma":[0.00017271862,0.00013328981,0.000031132528,0.00015105524,0.00007211781,0.00075248635,0.00020007318,0.00015983733,0.000009862586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013293994,0.0011413071,0.0009165031,0.00009256524,0.00001108392,0.00009704378,0.003661109,0.000016312684,0.6113075,0.00013510694,0.03509668,0.34739187],"study_design_scores_gemma":[0.0047141295,0.0004699581,0.0013393563,0.000016290121,0.000008688289,0.000022239172,0.00006292943,0.93044585,0.061754983,0.00013048324,0.00079377095,0.00024134664],"about_ca_topic_score_codex":0.000027135906,"about_ca_topic_score_gemma":0.000006652022,"teacher_disagreement_score":0.9304295,"about_ca_system_score_codex":0.000018106855,"about_ca_system_score_gemma":0.000040114912,"threshold_uncertainty_score":0.5435401},"labels":[],"label_agreement":null},{"id":"W99298009","doi":"10.1007/978-3-642-40763-5_25","title":"Fast Globally Optimal Segmentation of 3D Prostate MRI with Axial Symmetry Prior","year":2013,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Canadian Institutes of Health Research; Canada Research Chairs","keywords":"Computation; Algorithm; Regular polygon; Convex optimization; Relaxation (psychology); Mathematical optimization; Segmentation; Computer science; Image segmentation; Mathematics; Artificial intelligence; Geometry","score_opus":0.006455328716453375,"score_gpt":0.2531929846017903,"score_spread":0.24673765588533692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W99298009","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03600611,0.000024552754,0.96229315,0.0005912044,0.00022171666,0.00065933686,0.0000014151562,0.00016023361,0.000042274056],"genre_scores_gemma":[0.3690274,0.0000034648062,0.63023883,0.0006645297,0.000033267497,0.000025035259,0.0000013157533,0.000005018088,0.0000011264143],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973054,0.00007545465,0.00039573383,0.0006942326,0.0010757737,0.00045340654],"domain_scores_gemma":[0.9985743,0.00016056132,0.00021340045,0.00056772656,0.00033199138,0.00015199975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058111636,0.0002024018,0.00022428486,0.0003254579,0.00011725303,0.0004257496,0.0017045368,0.000060101927,0.000025227431],"category_scores_gemma":[0.0000822093,0.0001550225,0.000029138106,0.0019997186,0.0005939727,0.0016742863,0.0005323802,0.00020458837,0.000016330685],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007371215,0.000081484046,0.0016823582,0.000024483208,0.0000049050795,0.000010723592,0.0013243717,0.008973784,0.016890757,0.00013459672,0.00002767529,0.9708375],"study_design_scores_gemma":[0.0006612494,0.00062782393,0.0068930187,0.00013255815,0.000003892464,0.00004649581,0.0000051422326,0.565933,0.42309386,0.0022882633,0.0000031034276,0.00031158814],"about_ca_topic_score_codex":0.0001223295,"about_ca_topic_score_gemma":0.000011855438,"teacher_disagreement_score":0.9705259,"about_ca_system_score_codex":0.00012820929,"about_ca_system_score_gemma":0.00029564448,"threshold_uncertainty_score":0.63216335},"labels":[],"label_agreement":null},{"id":"W995666262","doi":"","title":"Shape coefficient as a generic feature for vehicle classification","year":2014,"lang":"en","type":"article","venue":"Logistyka","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Feature (linguistics); Computer science; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.040132198193412676,"score_gpt":0.3087581658879677,"score_spread":0.268625967694555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W995666262","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015051747,0.000048242073,0.99118,0.004283084,0.00018052716,0.00027881243,0.0000016511074,0.00036424992,0.0021582716],"genre_scores_gemma":[0.5276984,0.000014133819,0.46407446,0.0065703276,0.00014748659,0.00017832838,0.000023630628,0.000013061454,0.0012801439],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906194,0.000060660732,0.00014089899,0.00031482833,0.0002329865,0.00018869],"domain_scores_gemma":[0.9991918,0.00012510944,0.00008645139,0.00041121506,0.00009269645,0.00009271523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003377945,0.00008823911,0.00009897717,0.000050780513,0.00009480081,0.00010506685,0.0005519136,0.00006917718,0.000022695163],"category_scores_gemma":[0.00026274906,0.00007649041,0.000047401187,0.00020831435,0.000067007844,0.00014503377,0.00007765311,0.000079231606,0.000097647084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000123544805,0.00013468185,0.000084362255,0.000039240662,0.0000086577775,0.000002832843,0.0002775497,0.00003335206,0.06998031,0.12128726,0.073399976,0.7347394],"study_design_scores_gemma":[0.0005440989,0.00040422575,0.0023790742,0.00001827453,0.000008973257,0.000011639012,0.00003373982,0.81204647,0.093197845,0.007674614,0.08341122,0.000269821],"about_ca_topic_score_codex":0.000006854774,"about_ca_topic_score_gemma":0.0000011894653,"teacher_disagreement_score":0.81201315,"about_ca_system_score_codex":0.000044096018,"about_ca_system_score_gemma":0.000036752223,"threshold_uncertainty_score":0.31191885},"labels":[],"label_agreement":null}]}