{"meta":{"query_hash":"da1b422238c4","filters":{"venue":"Medical Image Analysis"},"cohort_total":366,"direct_labels_cover":0,"predictions_cover":366,"exported":366,"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/da1b422238c4","api":"https://metacan.xera.ac/api/v1/cohort?venue=Medical+Image+Analysis"},"results":[{"id":"W1038736503","doi":"10.1016/j.media.2015.07.003","title":"Multi-scale deep networks and regression forests for direct bi-ventricular volume estimation","year":2015,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":115,"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; CARE Canada; Western University","funders":"Government of Ontario","keywords":"Computer science; Artificial intelligence; Ground truth; Segmentation; Volume (thermodynamics); Regression; Estimation; Scale (ratio); Deep learning; Machine learning; Random forest; Pattern recognition (psychology); Statistics; Mathematics; Engineering","score_opus":0.013897888690403945,"score_gpt":0.2905070853804426,"score_spread":0.2766091966900387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1038736503","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009754851,0.000624246,0.9872701,0.00017858011,0.000062921834,0.00003055717,0.00022472056,0.0012762034,0.0005778218],"genre_scores_gemma":[0.40296176,0.00091801013,0.58706254,0.00028696313,0.00022159434,0.00020874912,0.0011938914,0.0003981783,0.006748421],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996486,0.00008995399,0.000023340064,0.000105133964,0.00007961991,0.000053335425],"domain_scores_gemma":[0.99903274,0.0005010889,0.00009495688,0.00013726522,0.00018805642,0.00004593952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011396734,0.0010863364,0.0009808855,0.0008556894,0.0003629968,0.0010261788,0.0016146624,0.001336446,0.002763096],"category_scores_gemma":[0.004105213,0.0007362566,0.0009684423,0.0011132074,0.00035696063,0.0011739514,0.0013182384,0.0019976925,0.0014080831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022028951,0.00016610944,0.0021275093,0.00016160197,0.00019455199,0.00012866793,0.000066249944,0.3290935,0.011821486,0.0076315715,0.008971605,0.6394169],"study_design_scores_gemma":[0.0000050939034,0.000009959902,0.00030540684,0.000008306766,0.000011525426,0.000024346167,0.000004884326,0.99456894,0.0012564856,0.0033794318,0.00041965113,0.0000059205504],"about_ca_topic_score_codex":0.008921475,"about_ca_topic_score_gemma":0.018915895,"teacher_disagreement_score":0.008921475,"about_ca_system_score_codex":0.000512264,"about_ca_system_score_gemma":0.0008597272,"threshold_uncertainty_score":0.017739117},"labels":[],"label_agreement":null},{"id":"W1627283626","doi":"10.1016/j.media.2015.06.005","title":"Analytic signal phase-based myocardial motion estimation in tagged MRI sequences by a bilinear model and motion compensation","year":2015,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Vision and Imaging","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":"Canadian Nautical Research Society","funders":"Agence Nationale de la Recherche","keywords":"Motion estimation; Bilinear interpolation; Motion field; Estimator; Computer vision; Artificial intelligence; Computer science; Motion compensation; Optical flow; Trajectory; Mathematics; Algorithm; Quarter-pixel motion; Image (mathematics); Physics","score_opus":0.02226197642583755,"score_gpt":0.32333633498314773,"score_spread":0.3010743585573102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1627283626","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018369405,0.00011500334,0.9811529,0.00004598956,0.0000125165225,0.000017596149,0.000019832674,0.0000738957,0.00019275147],"genre_scores_gemma":[0.5456113,0.00065398304,0.45093042,0.00006040581,0.00003563831,0.000100021716,0.00020042973,0.00012159977,0.002286088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998061,0.00007678004,0.000012956643,0.00002870623,0.00005764401,0.000017727847],"domain_scores_gemma":[0.9996941,0.00013470596,0.000046652993,0.000031911146,0.000076236494,0.000016453918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005291324,0.00039866602,0.000354283,0.00033117252,0.00018780028,0.0005102198,0.00047074477,0.00052260025,0.0007259944],"category_scores_gemma":[0.0016132701,0.00040366504,0.0004115402,0.00037936485,0.00026991783,0.00080209924,0.00044861078,0.0005049158,0.00039039427],"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":[0.0007487564,0.00021890822,0.0018749426,0.00041610832,0.00009168939,0.0002170298,0.0002620211,0.46673024,0.2186675,0.016240016,0.000979994,0.29355273],"study_design_scores_gemma":[0.0000073241504,0.000071871036,0.00029473868,0.0000074898335,0.000014848622,0.000109402485,0.000011946323,0.9825716,0.0153462235,0.000977193,0.00057711796,0.00001019212],"about_ca_topic_score_codex":0.0012975471,"about_ca_topic_score_gemma":0.0014280315,"teacher_disagreement_score":0.0012975471,"about_ca_system_score_codex":0.00019663623,"about_ca_system_score_gemma":0.0006087788,"threshold_uncertainty_score":0.0027983189},"labels":[],"label_agreement":null},{"id":"W1884191083","doi":"10.1016/j.media.2016.05.004","title":"Brain tumor segmentation with Deep Neural Networks","year":2016,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":3245,"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; Université de Montréal; Université de Sherbrooke","funders":"","keywords":"Computer science; Convolutional neural network; Exploit; Artificial intelligence; Segmentation; Deep learning; Set (abstract data type); Deep neural networks; Pattern recognition (psychology); Layer (electronics); Machine learning; Architecture; Artificial neural network","score_opus":0.013204008021251752,"score_gpt":0.2671777228647449,"score_spread":0.2539737148434931,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1884191083","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023675684,0.0010817158,0.9697276,0.00043879525,0.00007689104,0.00007161322,0.0003414352,0.0024543956,0.002131879],"genre_scores_gemma":[0.4869321,0.0013990748,0.49798855,0.0004903125,0.00014865842,0.0001614306,0.0012284085,0.0004572513,0.011194205],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981624,0.000024702333,0.000012339988,0.00006055961,0.000051803727,0.00003429442],"domain_scores_gemma":[0.9996799,0.00011712322,0.000048994,0.00004550484,0.000086576394,0.000021946984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047893735,0.0009959874,0.00065097905,0.0012084962,0.00032335264,0.0010987552,0.0008070405,0.001253076,0.0019816284],"category_scores_gemma":[0.0012164263,0.0006310004,0.00076758565,0.000988337,0.00034013946,0.00074734254,0.0008583586,0.0010232272,0.0010416439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003051189,0.00012146774,0.0021016635,0.00019574037,0.00014628796,0.00015867419,0.00006752598,0.20364644,0.03240368,0.0058894963,0.0081811,0.7467827],"study_design_scores_gemma":[0.000004986069,0.000018334478,0.0004919621,0.000015078107,0.000021142218,0.00008029729,0.000009933105,0.98304754,0.010048033,0.004877128,0.0013782263,0.000007455632],"about_ca_topic_score_codex":0.0075340797,"about_ca_topic_score_gemma":0.012877336,"teacher_disagreement_score":0.0075340797,"about_ca_system_score_codex":0.0008736411,"about_ca_system_score_gemma":0.001041078,"threshold_uncertainty_score":0.014980495},"labels":[],"label_agreement":null},{"id":"W1901606657","doi":"10.1016/j.media.2015.06.009","title":"Abdominal multi-organ segmentation from CT images using conditional shape–location and unsupervised intensity priors","year":2015,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":156,"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 Institutes of Health; Ministry of Education, Culture, Sports, Science and Technology; Japan Society for the Promotion of Science; Ontario Council on Graduate Studies, Council of Ontario Universities","keywords":"Prior probability; Segmentation; Artificial intelligence; Pattern recognition (psychology); Computer science; Image segmentation; Computer vision; Bayesian probability","score_opus":0.021190394260791008,"score_gpt":0.2817087070582469,"score_spread":0.2605183127974559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1901606657","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014315346,0.00013086913,0.9843126,0.00011597327,0.000009789813,0.00003276059,0.00009199148,0.00064457586,0.0003461931],"genre_scores_gemma":[0.3656867,0.00045999108,0.62922436,0.00015669613,0.000078533405,0.000168812,0.0010266476,0.00068381644,0.0025143765],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961394,0.00010633783,0.000024021636,0.00010727625,0.000106897154,0.000041463805],"domain_scores_gemma":[0.9989624,0.00047201136,0.00017292977,0.00016824281,0.00016350597,0.000060953505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011434213,0.00079507503,0.00084270333,0.0011789633,0.00040970338,0.001070873,0.0014604103,0.001312067,0.0010559849],"category_scores_gemma":[0.0031185092,0.0012070248,0.001660479,0.0010788086,0.00087114825,0.0009897593,0.0016827247,0.0017494573,0.0007116917],"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":[0.0005923848,0.00016681149,0.0031177301,0.00024599687,0.00023136644,0.00022175492,0.00022698523,0.6519581,0.07482966,0.0112687405,0.0027842137,0.25435627],"study_design_scores_gemma":[0.000013554781,0.000025473259,0.001046966,0.000012896606,0.000027870141,0.00010745384,0.000011365011,0.9862185,0.008117588,0.003786551,0.0006147803,0.000017077864],"about_ca_topic_score_codex":0.004696301,"about_ca_topic_score_gemma":0.00872248,"teacher_disagreement_score":0.004696301,"about_ca_system_score_codex":0.00065229536,"about_ca_system_score_gemma":0.0014803283,"threshold_uncertainty_score":0.009337962},"labels":[],"label_agreement":null},{"id":"W1906917243","doi":"10.1016/j.media.2015.10.006","title":"Population-based prediction of subject-specific prostate deformation for MR-to-ultrasound image registration","year":2015,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":47,"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":"Medical Research Council; University College London Hospitals NHS Foundation Trust; Wellcome Trust; Royal Academy of Engineering; Engineering and Physical Sciences Research Council; Canadian Institutes of Health Research; National Institute for Health and Care Research; NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research; Cancer Research UK","keywords":"Artificial intelligence; Image registration; Percentile; Computer science; Population; Computer vision; Medical imaging; Prostate; Landmark; Magnetic resonance imaging; Ultrasound; Statistical model; Pattern recognition (psychology); Image (mathematics); Mathematics; Statistics; Medicine; Radiology","score_opus":0.014404910519597457,"score_gpt":0.25170295380671487,"score_spread":0.2372980432871174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1906917243","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23445852,0.00024861944,0.7628858,0.00011725786,0.000032437205,0.00012029057,0.00033129993,0.001401191,0.0004045242],"genre_scores_gemma":[0.9080415,0.00018676023,0.08959444,0.000062864834,0.000032294567,0.0002214734,0.00094958436,0.00017195135,0.0007391528],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945396,0.00024684778,0.000024413797,0.00014924418,0.000095825664,0.000029758648],"domain_scores_gemma":[0.9984084,0.0010105731,0.0001678243,0.00022105219,0.00014837076,0.000043755874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022884018,0.0005159333,0.00075006945,0.00067716325,0.00014949433,0.00044607016,0.0006003074,0.0007488814,0.0007761373],"category_scores_gemma":[0.006131501,0.0004913283,0.0010048494,0.00036841715,0.00029808603,0.0003515606,0.00046475188,0.00069235894,0.0005260296],"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":[0.00023276001,0.0001239832,0.015589184,0.00004737487,0.0002059125,0.00007896184,0.00010608366,0.8973897,0.011274329,0.00050189474,0.000632853,0.07381694],"study_design_scores_gemma":[0.00000762363,0.00005925283,0.00449625,0.0000035471364,0.000016736394,0.00005943637,0.000008993974,0.9927423,0.0020431427,0.00035799213,0.00019161134,0.0000131511615],"about_ca_topic_score_codex":0.0036745877,"about_ca_topic_score_gemma":0.005597869,"teacher_disagreement_score":0.0036745877,"about_ca_system_score_codex":0.0004059482,"about_ca_system_score_gemma":0.000843054,"threshold_uncertainty_score":0.0121023655},"labels":[],"label_agreement":null},{"id":"W1967113208","doi":"10.1016/j.media.2010.04.006","title":"Wavelet-based estimation of the hemodynamic responses in diffuse optical imaging","year":2010,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","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":"École de Technologie Supérieure; Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Wavelet; Diffuse optical imaging; Estimator; Computer science; Artificial intelligence; SIGNAL (programming language); Noise (video); Finger tapping; Contrast (vision); Functional near-infrared spectroscopy; Computer vision; Pattern recognition (psychology); Mathematics; Image (mathematics); Neuroscience; Iterative reconstruction; Statistics; Cognition; Psychology; Medicine","score_opus":0.004223334705785716,"score_gpt":0.3080096687362906,"score_spread":0.30378633403050487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967113208","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060251653,0.0003200613,0.93872184,0.00011223643,0.000022923465,0.000008830124,0.000043893106,0.00008063306,0.00043795284],"genre_scores_gemma":[0.60541433,0.0018606813,0.39001852,0.000056386747,0.000094549076,0.000040920837,0.00024883426,0.00009173358,0.0021740543],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99988985,0.00004390007,0.0000055850533,0.000014517339,0.00003535148,0.000010721404],"domain_scores_gemma":[0.999551,0.00028052216,0.000037280275,0.0000291464,0.000079762234,0.00002232751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057145726,0.00030225416,0.0003265522,0.00041326546,0.0000875348,0.00041995986,0.00031727186,0.00048487633,0.00047609047],"category_scores_gemma":[0.0025583727,0.0002614913,0.0002939824,0.0004679153,0.00031420242,0.00073315884,0.00038180276,0.0006455207,0.00020653056],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006115858,0.00013747314,0.0018978096,0.0003834928,0.00009123498,0.00016805486,0.00017931058,0.3348747,0.22425169,0.025861453,0.0015390677,0.4100042],"study_design_scores_gemma":[0.000008078599,0.000030524305,0.00092411437,0.0000075955563,0.000010548032,0.00005427252,0.000011499188,0.988983,0.0071618757,0.0024723662,0.0003271635,0.00000898893],"about_ca_topic_score_codex":0.0006244007,"about_ca_topic_score_gemma":0.0007139222,"teacher_disagreement_score":0.0006244007,"about_ca_system_score_codex":0.0001326543,"about_ca_system_score_gemma":0.00023428768,"threshold_uncertainty_score":0.003022194},"labels":[],"label_agreement":null},{"id":"W1967118256","doi":"10.1016/j.media.2008.07.002","title":"Fusion of optical imaging and MRI for the evaluation and adjustment of macroscopic models of cardiac electrophysiology: A feasibility study","year":2008,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced MRI Techniques and Applications","field":"Medicine","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 Toronto; Health Sciences Centre; Sunnybrook Health Science Centre","funders":"National Heart, Lung, and Blood Institute; National Institutes of Health; University of Alabama; Ontario Innovation Trust","keywords":"Cardiac electrophysiology; Optical mapping; Work (physics); Experimental data; Electrophysiology; Diffuse optical imaging; Contrast (vision); Fusion; Conductivity; Biological system; Physics; Computer science; Artificial intelligence; Mathematics; Statistics; Iterative reconstruction; Neuroscience; Cardiology; Medicine; Thermodynamics","score_opus":0.03505340872948261,"score_gpt":0.39176821624191654,"score_spread":0.35671480751243395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967118256","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52403635,0.0017103938,0.46961448,0.0003716411,0.00009128501,0.000418804,0.00018125406,0.00050285057,0.0030730343],"genre_scores_gemma":[0.85359365,0.000764474,0.14437601,0.00011147512,0.000041783365,0.00012066285,0.000101656,0.0001275419,0.0007626964],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99946374,0.00019418799,0.000020666523,0.000067774614,0.00020139181,0.00005235983],"domain_scores_gemma":[0.9983388,0.0007547172,0.00014065263,0.00029140234,0.00039268116,0.00008164615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022729037,0.00047028167,0.00045598458,0.00091516715,0.00028955674,0.00082246005,0.0005822467,0.0008231531,0.0012212198],"category_scores_gemma":[0.004156147,0.00044635622,0.00037142704,0.00053557707,0.0005349429,0.0012635232,0.00085903733,0.0004990382,0.00028483063],"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":[0.0029508406,0.00082867744,0.014340792,0.00032061053,0.00013878194,0.00073560677,0.00045084592,0.0146535495,0.8096748,0.0018991805,0.00069195824,0.1533144],"study_design_scores_gemma":[0.00037181587,0.00578381,0.046453435,0.00010143015,0.00048915826,0.006729958,0.0005183179,0.30872458,0.6213686,0.002273577,0.0069925464,0.0001927023],"about_ca_topic_score_codex":0.0013727198,"about_ca_topic_score_gemma":0.0012255342,"teacher_disagreement_score":0.0022729037,"about_ca_system_score_codex":0.0003121286,"about_ca_system_score_gemma":0.00065212534,"threshold_uncertainty_score":0.012020409},"labels":[],"label_agreement":null},{"id":"W1968301261","doi":"10.1016/j.media.2013.08.006","title":"Denoising and fast diffusion imaging with physically constrained sparse dictionary learning","year":2013,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":41,"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; Computer science; Noise reduction; Dictionary learning; Diffusion MRI; Pattern recognition (psychology); Gaussian; Diffusion; Sparse approximation; Computer vision; Physics","score_opus":0.012633093816359044,"score_gpt":0.29270872219528005,"score_spread":0.280075628378921,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968301261","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027751988,0.00031601632,0.99588925,0.00023460099,0.000051858362,0.000012649673,0.000032179698,0.00006444523,0.0006238284],"genre_scores_gemma":[0.10435398,0.0015389986,0.88753885,0.00019344885,0.00019424137,0.000079756726,0.00022541915,0.00013316065,0.005742077],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965966,0.00012290101,0.00002161672,0.00006081367,0.00011603067,0.000019017109],"domain_scores_gemma":[0.99890804,0.00060707587,0.0001070003,0.00017840622,0.00016271048,0.00003675793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009868573,0.0006327719,0.00080016983,0.0006943118,0.00025689768,0.00097282144,0.00078984903,0.0014478017,0.0016832302],"category_scores_gemma":[0.0045020473,0.0006122026,0.0006671866,0.00094909954,0.0009772754,0.0017141005,0.0013301242,0.0017270378,0.0005331582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029493787,0.000101404046,0.00071971514,0.00058444636,0.00018714943,0.00022987173,0.00024343714,0.3382022,0.045933425,0.2448675,0.0073140324,0.3613219],"study_design_scores_gemma":[0.000017198065,0.00002965057,0.00016179038,0.000018582743,0.00001596162,0.00015420004,0.000018615843,0.94244885,0.005731,0.047440138,0.0039455146,0.000018515559],"about_ca_topic_score_codex":0.0015896737,"about_ca_topic_score_gemma":0.0018320341,"teacher_disagreement_score":0.0016832302,"about_ca_system_score_codex":0.00031983445,"about_ca_system_score_gemma":0.0006257395,"threshold_uncertainty_score":0.00563097},"labels":[],"label_agreement":null},{"id":"W1969368707","doi":"10.1016/j.media.2008.12.006","title":"Detection and measurement of coverage loss in interleaved multi-acquisition brain MRIs due to motion-induced inter-slice misalignment","year":2009,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced MRI Techniques and Applications","field":"Medicine","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":"Computer science; Artificial intelligence; Data set; Motion (physics); Range (aeronautics); Set (abstract data type); Computer vision; Missing data; Pattern recognition (psychology); Algorithm","score_opus":0.017590773498436654,"score_gpt":0.3237284229383808,"score_spread":0.30613764943994415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969368707","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87550646,0.0012476017,0.12197642,0.000085570326,0.000025367133,0.000031224878,0.000094074596,0.0003047604,0.0007285217],"genre_scores_gemma":[0.98039514,0.00028184403,0.018874085,0.000044799195,0.000012188576,0.000019420477,0.0000957971,0.000043451524,0.00023329267],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99964976,0.00010023616,0.000021159422,0.000048360555,0.0001402409,0.000040114755],"domain_scores_gemma":[0.99709415,0.0015216394,0.0005946824,0.00027150544,0.00037975103,0.00013822067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077369745,0.00033728578,0.00029601384,0.00063923013,0.00021447998,0.00041305358,0.00038653793,0.0005851614,0.0004286673],"category_scores_gemma":[0.0054594325,0.0003327781,0.00011375179,0.0004281217,0.00041239712,0.00067722745,0.0005239103,0.00038939482,0.00011614871],"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":[0.00297039,0.00011013936,0.01766513,0.00016940822,0.000093957045,0.0005147662,0.00036744977,0.011371844,0.9212729,0.0003813394,0.00017247492,0.04491017],"study_design_scores_gemma":[0.00006662868,0.0012737784,0.10804892,0.00004639266,0.00020620856,0.005318509,0.00021723809,0.1163539,0.7665727,0.0007590683,0.0010698639,0.000066828245],"about_ca_topic_score_codex":0.00060338405,"about_ca_topic_score_gemma":0.0006562155,"teacher_disagreement_score":0.00077369745,"about_ca_system_score_codex":0.00025692608,"about_ca_system_score_gemma":0.0002689935,"threshold_uncertainty_score":0.0040917397},"labels":[],"label_agreement":null},{"id":"W1969392314","doi":"10.1016/s1361-8415(02)00087-7","title":"The development and evaluation of a three-dimensional ultrasound-guided breast biopsy apparatus","year":2002,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Breast Lesions and Carcinomas","field":"Medicine","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":"London Health Sciences Centre; Robarts Clinical Trials; Western University","funders":"","keywords":"Biopsy; Imaging phantom; Breast biopsy; Medicine; USB; Radiology; Ultrasound; Needle biopsy; Mammography; Sampling (signal processing); Chicken breast; 3D ultrasound; Medical physics; Breast cancer; Computer science; Computer vision; Cancer","score_opus":0.036347930551477156,"score_gpt":0.29647933319383124,"score_spread":0.26013140264235407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969392314","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13779801,0.0010755587,0.85601306,0.000371773,0.00015926128,0.0007754791,0.00021130183,0.0019968096,0.0015986783],"genre_scores_gemma":[0.29464507,0.0005361615,0.7018939,0.00017945464,0.0000414794,0.0003807019,0.00023314382,0.0001503783,0.00193974],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99895537,0.00017346074,0.0000845356,0.00016716051,0.0005654168,0.00005399345],"domain_scores_gemma":[0.9963063,0.0011038989,0.00026731606,0.0005007461,0.0015573127,0.00026440062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029926156,0.00041668827,0.0004271967,0.0008222947,0.00046658027,0.0008643813,0.0015638742,0.0010702111,0.0016705124],"category_scores_gemma":[0.00399268,0.00035498381,0.00035619692,0.0003193576,0.0006742403,0.0009455222,0.0008864056,0.00052731327,0.0006537193],"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":[0.00037863074,0.00023415718,0.006432074,0.0002917214,0.000046436686,0.0001445061,0.00029702604,0.0032978575,0.87749773,0.0040120496,0.00061204017,0.10675571],"study_design_scores_gemma":[0.0001723663,0.0036031432,0.03485829,0.0001883127,0.0002936804,0.0026446201,0.00027807677,0.09502079,0.82512414,0.0012437946,0.036340088,0.0002327368],"about_ca_topic_score_codex":0.0010957818,"about_ca_topic_score_gemma":0.00078277383,"teacher_disagreement_score":0.0029926156,"about_ca_system_score_codex":0.00070677226,"about_ca_system_score_gemma":0.0015901362,"threshold_uncertainty_score":0.015826643},"labels":[],"label_agreement":null},{"id":"W1969422115","doi":"10.1016/j.media.2008.07.003","title":"3D estimation of soft biological tissue deformation from radio-frequency ultrasound volume acquisitions","year":2008,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Ultrasound Imaging and Elastography","field":"Medicine","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":"Centre Hospitalier de l’Université de Montréal; Hôpital Notre-Dame","funders":"","keywords":"Elastography; Imaging phantom; 3D ultrasound; Ultrasound; Deformation (meteorology); Ultrasonic sensor; Ultrasound elastography; Computer science; Similarity (geometry); Biomedical engineering; Orientation (vector space); Acoustics; Mathematics; Algorithm; Materials science; Artificial intelligence; Physics; Geometry; Optics; Medicine","score_opus":0.01020306237214801,"score_gpt":0.2630836163661459,"score_spread":0.2528805539939979,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969422115","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06722593,0.0005833632,0.9284468,0.00012999185,0.00006113911,0.000071920986,0.0004065259,0.002074258,0.0010000732],"genre_scores_gemma":[0.5523435,0.0011106235,0.44269124,0.00015328363,0.000076420714,0.00016585551,0.0007923125,0.00039816427,0.002268582],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99968565,0.0000526002,0.000018885303,0.000056156798,0.00016144486,0.000025359257],"domain_scores_gemma":[0.99944144,0.00024761472,0.00007050346,0.00007615656,0.00013597988,0.000028280634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055449706,0.00065529183,0.0007260636,0.0017166826,0.00019109412,0.001130452,0.0005125407,0.0012328211,0.0014616234],"category_scores_gemma":[0.0018615794,0.00057894504,0.0007090603,0.0008652366,0.00029093426,0.0006204823,0.0005439296,0.00061730214,0.0009755679],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005669326,0.00015300502,0.008360315,0.00039281472,0.00016847142,0.00062435126,0.0004899937,0.13452461,0.34753007,0.0020314741,0.0032600178,0.50189793],"study_design_scores_gemma":[0.00002848271,0.0001618796,0.018158967,0.000043390708,0.0000919484,0.0021619925,0.000115049006,0.8842138,0.0892485,0.0018655458,0.003802747,0.00010776453],"about_ca_topic_score_codex":0.0013156872,"about_ca_topic_score_gemma":0.0015809248,"teacher_disagreement_score":0.0017166826,"about_ca_system_score_codex":0.00023136723,"about_ca_system_score_gemma":0.00042223875,"threshold_uncertainty_score":0.0048896074},"labels":[],"label_agreement":null},{"id":"W1971405799","doi":"10.1016/j.media.2010.07.001","title":"Multiple q-shell diffusion propagator imaging","year":2010,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":169,"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":"Association France Parkinson","keywords":"Diffusion MRI; Propagator; Computer science; Diffusion; Laplace transform; Fourier transform; SIGNAL (programming language); Diffusion equation; Algorithm; Artificial intelligence; Physics; Computer vision; Mathematical analysis; Mathematics","score_opus":0.01894291456037772,"score_gpt":0.3416434039444404,"score_spread":0.3227004893840627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971405799","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015143542,0.00042462503,0.97843325,0.00060208095,0.000059938768,0.00004506614,0.00007847533,0.0004038547,0.0048091616],"genre_scores_gemma":[0.20760486,0.0010393584,0.7784467,0.00035541292,0.000103400394,0.00010132417,0.0001419971,0.0003787929,0.011828189],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99983716,0.00006186698,0.000013386769,0.000030874944,0.000043201606,0.000013525335],"domain_scores_gemma":[0.99921787,0.0003243219,0.00010707902,0.00013122498,0.0001532947,0.00006618384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088031177,0.0008925688,0.0003490487,0.0006950192,0.0003961658,0.0014291499,0.0006854476,0.0011819083,0.007830753],"category_scores_gemma":[0.0030046643,0.00041621685,0.00040531368,0.0007274672,0.0005906786,0.002446343,0.0010249463,0.0009420145,0.001289991],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076016365,0.00018954271,0.0040449933,0.00102872,0.00023372157,0.0021116333,0.0006708677,0.04357714,0.33454388,0.16312684,0.008119147,0.44159326],"study_design_scores_gemma":[0.00012857396,0.00030353165,0.0037804535,0.00017621521,0.00019854285,0.0066029904,0.0002486123,0.673852,0.16681527,0.12249178,0.025265737,0.00013630187],"about_ca_topic_score_codex":0.000433782,"about_ca_topic_score_gemma":0.0009408439,"teacher_disagreement_score":0.007830753,"about_ca_system_score_codex":0.0002808411,"about_ca_system_score_gemma":0.0006514827,"threshold_uncertainty_score":0.02619642},"labels":[],"label_agreement":null},{"id":"W1985161468","doi":"10.1016/j.media.2011.05.010","title":"An integrated approach to segmentation and nonrigid registration for application in image-guided pelvic radiotherapy","year":2011,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","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":"Princess Margaret Cancer Centre","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health","keywords":"Segmentation; Computer science; Artificial intelligence; Computer vision; Image registration; Image segmentation; Radiation treatment planning; Radiation therapy; Medicine; Image (mathematics); Radiology","score_opus":0.016001706040179614,"score_gpt":0.32771335626662274,"score_spread":0.3117116502264431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985161468","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018321879,0.0000918366,0.99653685,0.000031975826,0.000018041532,0.000041887255,0.000028509596,0.0010917173,0.00032700834],"genre_scores_gemma":[0.028694268,0.00018025338,0.96859956,0.000056580073,0.000029527317,0.000108108456,0.00014495236,0.00052871305,0.001658052],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899584,0.00012548389,0.00006319416,0.00016696907,0.00058825436,0.000060137187],"domain_scores_gemma":[0.9994289,0.00015407022,0.000052309042,0.00010880059,0.00023022038,0.000025652476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010671818,0.0010078541,0.0013875834,0.0016619659,0.00073422433,0.0017672267,0.0021984838,0.0014457515,0.0035530746],"category_scores_gemma":[0.0020129776,0.0012189077,0.0017603986,0.0020624981,0.00048468594,0.0014342859,0.0019268127,0.0012366428,0.0015325726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001980046,0.00025364503,0.00055080646,0.00018496338,0.00024015842,0.000100065,0.00017730916,0.091041096,0.099394046,0.007286111,0.0036649273,0.7969089],"study_design_scores_gemma":[0.000022319718,0.000086385364,0.0011893779,0.00001561087,0.0000841402,0.0002141072,0.000026543998,0.95749754,0.030349776,0.0046800314,0.005788718,0.00004540729],"about_ca_topic_score_codex":0.0049755652,"about_ca_topic_score_gemma":0.010188052,"teacher_disagreement_score":0.0049755652,"about_ca_system_score_codex":0.0006324044,"about_ca_system_score_gemma":0.0016372832,"threshold_uncertainty_score":0.011886179},"labels":[],"label_agreement":null},{"id":"W1987164933","doi":"10.1016/j.media.2006.01.002","title":"Methods for segmenting curved needles in ultrasound images","year":2006,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Image and Object Detection 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":"University of British Columbia","funders":"","keywords":"Hough transform; Computer vision; Artificial intelligence; Segmentation; Curvature; Ultrasound; Computer science; Transformation (genetics); Image (mathematics); Mathematics; Radiology; Medicine; Geometry","score_opus":0.007393570306163159,"score_gpt":0.3360884202962845,"score_spread":0.32869484999012133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987164933","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040366855,0.00032649646,0.9944258,0.000038090373,0.000022226925,0.00005819904,0.000033555098,0.00080207706,0.0002569415],"genre_scores_gemma":[0.03150361,0.00080928335,0.96529025,0.000066407534,0.00006325318,0.00011784726,0.00016859299,0.00029555234,0.0016850933],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988973,0.00016849051,0.00008717639,0.00025449658,0.00049709924,0.00009536344],"domain_scores_gemma":[0.9972318,0.0013674736,0.00032085902,0.00038104877,0.0005867168,0.000112196416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001229451,0.0014957145,0.0012255703,0.0034303295,0.00060824485,0.0015597177,0.0017033508,0.0018091112,0.0033341837],"category_scores_gemma":[0.0033095602,0.0010963336,0.0012024736,0.0023314983,0.0008646952,0.0014127311,0.0010571516,0.0013854832,0.0023890394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018385089,0.00008907226,0.00097077154,0.00034905243,0.000079761056,0.00014315208,0.00018887373,0.010834972,0.11979936,0.002983015,0.001721805,0.8626564],"study_design_scores_gemma":[0.00007844064,0.00034957702,0.006151224,0.00013231563,0.00031484448,0.0025735432,0.00024158074,0.74995065,0.2136474,0.010478489,0.015931025,0.00015098025],"about_ca_topic_score_codex":0.002305632,"about_ca_topic_score_gemma":0.0039681066,"teacher_disagreement_score":0.0034303295,"about_ca_system_score_codex":0.00050644303,"about_ca_system_score_gemma":0.0010042532,"threshold_uncertainty_score":0.011153996},"labels":[],"label_agreement":null},{"id":"W1989986853","doi":"10.1016/j.media.2012.11.007","title":"Regional heart motion abnormality detection: An information theoretic approach","year":2013,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Anomaly Detection Techniques and Applications","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":"CARE Canada; McGill University; London Health Sciences Centre; St Joseph's Health Care; University of Alberta","funders":"","keywords":"Abnormality; Artificial intelligence; Computer science; Motion (physics); Computer vision; Pattern recognition (psychology); Medicine","score_opus":0.008462233525864483,"score_gpt":0.24652221636517618,"score_spread":0.2380599828393117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989986853","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004079941,0.0004889453,0.9941673,0.0002933859,0.000022267606,0.000018519002,0.000050979615,0.00005262503,0.0008260486],"genre_scores_gemma":[0.5498307,0.0031481236,0.44047925,0.00046129394,0.0007770188,0.00024531808,0.00040679725,0.00012362603,0.0045277807],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998738,0.00043147797,0.00008676489,0.00024926584,0.0004236121,0.00007082185],"domain_scores_gemma":[0.99539185,0.0034359375,0.00030901516,0.00029193558,0.00048106207,0.0000902824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026507932,0.001154737,0.0023156758,0.0035110782,0.0006874921,0.0026189997,0.0019800758,0.0015765227,0.0015831408],"category_scores_gemma":[0.007992576,0.000829658,0.0018260761,0.0018952417,0.0022398236,0.0029190872,0.0017272555,0.0014775434,0.00035340895],"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":[0.00024045553,0.00024881156,0.0020678136,0.00043132156,0.0004991217,0.00033338324,0.00018475935,0.5374126,0.010510081,0.20696612,0.0036520618,0.2374535],"study_design_scores_gemma":[0.0000079788815,0.00006038048,0.0004339934,0.000017728262,0.000068137386,0.00010945031,0.000018821886,0.9279597,0.001117835,0.06970354,0.00047805655,0.000024471861],"about_ca_topic_score_codex":0.0018495788,"about_ca_topic_score_gemma":0.0013821819,"teacher_disagreement_score":0.0035110782,"about_ca_system_score_codex":0.0013539322,"about_ca_system_score_gemma":0.0011321504,"threshold_uncertainty_score":0.014018953},"labels":[],"label_agreement":null},{"id":"W1990369221","doi":"10.1016/j.media.2004.11.002","title":"Modelling liver tissue properties using a non-linear visco-elastic model for surgery simulation","year":2004,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Elasticity and Material Modeling","field":"Engineering","cited_by":147,"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":"Linear elasticity; Computation; Elasticity (physics); Computer science; Liver tissue; Linear model; Perforation; Soft tissue; Simulation; Biomedical engineering; Materials science; Algorithm; Surgery; Finite element method; Structural engineering; Engineering; Composite material; Machine learning","score_opus":0.05522292957347681,"score_gpt":0.2745313301477562,"score_spread":0.21930840057427942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990369221","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0779853,0.0005573759,0.9162514,0.00035296415,0.00006215804,0.00007510386,0.00015401348,0.00054237526,0.0040192446],"genre_scores_gemma":[0.88707745,0.0010263711,0.10143488,0.0001279763,0.00003465301,0.00017437505,0.00018122385,0.00022905346,0.0097139515],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998791,0.00003302431,0.000008949007,0.000018686495,0.00004729918,0.000012859537],"domain_scores_gemma":[0.9997329,0.0001431846,0.0000423412,0.000029021665,0.000035132492,0.00001741342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025974278,0.00053770083,0.00049755553,0.00048583193,0.00025413145,0.00085439195,0.00097174133,0.0016692001,0.0017693317],"category_scores_gemma":[0.0012887691,0.0007583173,0.0008579441,0.00044499416,0.00042568284,0.0007455574,0.0005081652,0.0005850448,0.0005847889],"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":[0.00002160732,0.000016532356,0.0002455638,0.000026938304,0.000012010571,0.00006672014,0.000021584026,0.98942286,0.005083031,0.0010331374,0.0000966099,0.003953456],"study_design_scores_gemma":[0.000003544162,0.000008662079,0.00011586882,0.0000025049942,0.0000046790838,0.000036448164,0.0000036403576,0.9984547,0.0007929966,0.00034469206,0.00022794273,0.0000042375896],"about_ca_topic_score_codex":0.004381226,"about_ca_topic_score_gemma":0.0036872537,"teacher_disagreement_score":0.004381226,"about_ca_system_score_codex":0.00051762536,"about_ca_system_score_gemma":0.00065930164,"threshold_uncertainty_score":0.008711457},"labels":[],"label_agreement":null},{"id":"W1991042152","doi":"10.1016/s1361-8415(02)00090-7","title":"Irregularity index: A new border irregularity measure for cutaneous melanocytic lesions","year":2002,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":97,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vancouver Hospital and Health Sciences Centre; University of British Columbia; BC Cancer Agency; Simon Fraser University","funders":"","keywords":"Lesion; Index (typography); Measure (data warehouse); Pattern recognition (psychology); Computer science; Artificial intelligence; Texture (cosmology); Medicine; Mathematics; Pathology; Data mining; Image (mathematics)","score_opus":0.023035226442801503,"score_gpt":0.29739657225210875,"score_spread":0.27436134580930727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991042152","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51158655,0.004573556,0.46961033,0.00034988337,0.00030698613,0.0006443996,0.0033321439,0.0034965873,0.0060995934],"genre_scores_gemma":[0.81514627,0.0012641001,0.1777718,0.00015143486,0.00025017394,0.00034424005,0.0020789132,0.00040771824,0.0025852623],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99932766,0.00007346377,0.00010453174,0.00008301973,0.00037804467,0.000033427274],"domain_scores_gemma":[0.99749017,0.000833322,0.00060314545,0.00014773612,0.0007124386,0.00021333389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001180092,0.00058692874,0.0007189456,0.005898569,0.00031720352,0.0011680168,0.00054755865,0.0005485796,0.0015451257],"category_scores_gemma":[0.0048041637,0.00026015245,0.000608502,0.0020409944,0.00036961463,0.0011645068,0.0007450706,0.00047374683,0.00052769185],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022014421,0.00030429874,0.2629752,0.0006546067,0.0006991183,0.0006892309,0.00031507135,0.016626917,0.07977417,0.0022792064,0.00886357,0.62461734],"study_design_scores_gemma":[0.00023263176,0.0011130213,0.5621401,0.00013380875,0.0008373302,0.008674186,0.00041176903,0.35913646,0.043542616,0.004803875,0.018647125,0.00032702068],"about_ca_topic_score_codex":0.0010835676,"about_ca_topic_score_gemma":0.0013169986,"teacher_disagreement_score":0.005898569,"about_ca_system_score_codex":0.00048520762,"about_ca_system_score_gemma":0.00032949206,"threshold_uncertainty_score":0.0062410235},"labels":[],"label_agreement":null},{"id":"W1992845372","doi":"10.1016/j.media.2013.04.008","title":"The impact of registration accuracy on imaging validation study design: A novel statistical power calculation","year":2013,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","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":"Lawson Health Research Institute; Western University","funders":"","keywords":"Statistical power; Context (archaeology); Modalities; Computer science; Image registration; Medical imaging; Statistical model; Statistical hypothesis testing; Monte Carlo method; Power (physics); Artificial intelligence; Medical physics; Statistics; Mathematics; Image (mathematics); Medicine","score_opus":0.017757023342676096,"score_gpt":0.3701413791481233,"score_spread":0.3523843558054472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992845372","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025151977,0.0013443577,0.9680642,0.0008212022,0.00048961013,0.0016494208,0.00012291945,0.00043355796,0.0019228545],"genre_scores_gemma":[0.6127353,0.00039936794,0.3773842,0.0011886992,0.00046439495,0.005403462,0.00016478862,0.00061205216,0.0016477968],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.6006821,0.3277777,0.021728562,0.021960072,0.026429964,0.0014216617],"domain_scores_gemma":[0.21219975,0.708143,0.016642747,0.0491984,0.013049216,0.0007669552],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.3536951,0.0018246529,0.0032580378,0.002340591,0.0014989768,0.004204974,0.004651523,0.0045605516,0.004098448],"category_scores_gemma":[0.61016315,0.001526686,0.004473136,0.0032717693,0.0057196063,0.004777507,0.0040602745,0.004335135,0.00074146106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.019119944,0.0011711674,0.119908355,0.0043156785,0.022166798,0.001330403,0.004963276,0.04184464,0.018541755,0.09205211,0.009026652,0.66555923],"study_design_scores_gemma":[0.0073900535,0.018762074,0.11673451,0.0017093379,0.026141936,0.0047618854,0.000616742,0.6317981,0.038928457,0.11422788,0.038231146,0.0006979358],"about_ca_topic_score_codex":0.00057789916,"about_ca_topic_score_gemma":0.0006204557,"teacher_disagreement_score":0.6463049,"about_ca_system_score_codex":0.0014134706,"about_ca_system_score_gemma":0.0024238222,"threshold_uncertainty_score":0.797009},"labels":[],"label_agreement":null},{"id":"W1993062536","doi":"10.1016/j.media.2011.07.006","title":"Multi-modal registration of speckle-tracked freehand 3D ultrasound to CT in the lumbar spine","year":2011,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":38,"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; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Artificial intelligence; Computer vision; Computer science; Modal; 3D ultrasound; Lumbar spine; Ultrasound; Radiology; Medicine; Surgery; Materials science","score_opus":0.017139208940812937,"score_gpt":0.2627789060975648,"score_spread":0.24563969715675188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993062536","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52803314,0.0009303832,0.46752718,0.00023565188,0.000045415876,0.00010353451,0.000237245,0.0006342775,0.0022532502],"genre_scores_gemma":[0.8992689,0.00052946695,0.09754796,0.00010873943,0.000040160023,0.00006128575,0.00020957734,0.0001526487,0.002081214],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999726,0.00006874128,0.000020717693,0.000044511566,0.00011371945,0.000026276906],"domain_scores_gemma":[0.999458,0.00023066267,0.00008296218,0.0000722073,0.00012303768,0.000033117605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059301115,0.00017932677,0.0003012016,0.0010011266,0.00025360257,0.0009667579,0.0004092952,0.00080097915,0.0010240669],"category_scores_gemma":[0.0029027974,0.0004193538,0.00038798567,0.0007519069,0.00027833172,0.00062572584,0.0005887479,0.00048635874,0.00030717734],"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":[0.00133101,0.00033866696,0.020716438,0.0006067041,0.00017129652,0.0008167448,0.0011527233,0.09676314,0.5435376,0.0029574644,0.002258332,0.32934994],"study_design_scores_gemma":[0.000049745147,0.00029124002,0.07703546,0.00011275604,0.00014377937,0.0031248948,0.00035338415,0.7552547,0.15740004,0.003017285,0.0030938704,0.00012287246],"about_ca_topic_score_codex":0.0032488233,"about_ca_topic_score_gemma":0.0058166143,"teacher_disagreement_score":0.0032488233,"about_ca_system_score_codex":0.00027821737,"about_ca_system_score_gemma":0.00082745805,"threshold_uncertainty_score":0.0064597726},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26120305,0.0011661231,0.72552955,0.00044419867,0.00011994343,0.00021077624,0.0008512691,0.0044986703,0.0059765745],"genre_scores_gemma":[0.66023415,0.0021258325,0.3301787,0.00013151437,0.00015356054,0.00013638317,0.0007785025,0.00096784753,0.0052934173],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998418,0.000025330924,0.000008870342,0.000022490634,0.00007340505,0.000028105705],"domain_scores_gemma":[0.99929595,0.0003221044,0.00007020552,0.000116798314,0.00015901834,0.000035908255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004734114,0.00048821745,0.00026533628,0.0022068017,0.00030332225,0.0012778054,0.00031322354,0.0005149595,0.0030958639],"category_scores_gemma":[0.0022177768,0.00039134407,0.00046378886,0.0011803545,0.00030752926,0.0005103192,0.0004800601,0.0005611077,0.0006792889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012267685,0.00009662141,0.0029936938,0.0004743859,0.00010028426,0.00085746974,0.0009954115,0.036802914,0.2611721,0.005650472,0.0034115012,0.68621826],"study_design_scores_gemma":[0.00008683667,0.0003797999,0.054782514,0.00018273883,0.00024124356,0.0053605037,0.0006413859,0.46319392,0.44197404,0.012235159,0.020714935,0.00020688707],"about_ca_topic_score_codex":0.0032136769,"about_ca_topic_score_gemma":0.0031572806,"teacher_disagreement_score":0.0032136769,"about_ca_system_score_codex":0.000273082,"about_ca_system_score_gemma":0.00074244844,"threshold_uncertainty_score":0.010356724},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9539692,0.0014374552,0.04094754,0.00015699006,0.000049702452,0.000108462234,0.0003001781,0.0004666506,0.0025637508],"genre_scores_gemma":[0.97438836,0.00093783735,0.022727689,0.000037409598,0.000038413353,0.000027736234,0.000364146,0.00018260686,0.0012957146],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997793,0.00004901411,0.000023489769,0.000038899758,0.00007045812,0.00003888737],"domain_scores_gemma":[0.99928254,0.0002914651,0.00009721179,0.000056574365,0.00021776724,0.0000544321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089278736,0.00044644176,0.0004525481,0.0013938454,0.0003711766,0.0007154373,0.00026924905,0.0005687786,0.0017639508],"category_scores_gemma":[0.003479254,0.00026371007,0.00037883307,0.0008477794,0.0005102606,0.0005502756,0.0003478379,0.0002406142,0.00042740567],"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":[0.0036758853,0.00024764615,0.02016548,0.0007638478,0.00035201106,0.0011982674,0.0006639881,0.017267736,0.7417832,0.0008716596,0.0008757048,0.21213457],"study_design_scores_gemma":[0.000116309704,0.0018286782,0.3404718,0.00009036155,0.00077567063,0.011034353,0.0009784695,0.12324949,0.5145305,0.0028040146,0.004033726,0.00008665679],"about_ca_topic_score_codex":0.0039587016,"about_ca_topic_score_gemma":0.0046621677,"teacher_disagreement_score":0.0039587016,"about_ca_system_score_codex":0.00030136906,"about_ca_system_score_gemma":0.0005005138,"threshold_uncertainty_score":0.00787133},"labels":[],"label_agreement":null},{"id":"W1996757269","doi":"10.1016/s1361-8415(01)00049-4","title":"A computational model of postoperative knee kinematics","year":2001,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Total Knee Arthroplasty Outcomes","field":"Medicine","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":"Queen's University","funders":"","keywords":"Kinematics; Knee Joint; Computer science; Total knee replacement; Prosthesis; Surgical planning; Joint (building); Orthodontics; Medicine; Surgery; Artificial intelligence; Engineering; Physics","score_opus":0.01508868387873902,"score_gpt":0.3006531355982514,"score_spread":0.2855644517195124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996757269","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.074530534,0.00043830782,0.9132783,0.0010775721,0.00013188909,0.00008217296,0.00050037116,0.0002678847,0.009692953],"genre_scores_gemma":[0.9231236,0.0006360216,0.06397957,0.0001738894,0.00009656479,0.000260964,0.00033011992,0.00008880207,0.011310382],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983406,0.000038463815,0.000011347914,0.000034454813,0.000057311136,0.00002436397],"domain_scores_gemma":[0.99958986,0.00022073665,0.00005754153,0.000034661185,0.000065891014,0.000031301264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028944743,0.00044529352,0.0006422202,0.00048249107,0.00032043876,0.0012683764,0.0014243394,0.001448334,0.0027896585],"category_scores_gemma":[0.0018022739,0.00067704095,0.000814452,0.0005297612,0.00075181166,0.0006754552,0.0007462295,0.000742573,0.0004978435],"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":[0.00002184038,0.000026108744,0.00057491934,0.000019187082,0.000012084304,0.00007707276,0.000030230518,0.9841882,0.00066567876,0.0095253335,0.00033109877,0.0045283036],"study_design_scores_gemma":[0.0000029729665,0.0000050276362,0.00011796877,0.0000017812341,0.000002518256,0.000016022335,0.0000032759724,0.9982992,0.00006991951,0.0013179935,0.00016119605,0.0000021383862],"about_ca_topic_score_codex":0.01619443,"about_ca_topic_score_gemma":0.008600573,"teacher_disagreement_score":0.01619443,"about_ca_system_score_codex":0.00061424414,"about_ca_system_score_gemma":0.0011427794,"threshold_uncertainty_score":0.032200336},"labels":[],"label_agreement":null},{"id":"W1997823434","doi":"10.1016/s1361-8415(02)00133-0","title":"Robust registration for computer-integrated orthopedic surgery: Laboratory validation and clinical experience","year":2003,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Robotics and Sensor-Based Localization","field":"Engineering","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":"Queen's University","funders":"","keywords":"Outlier; Artificial intelligence; Computer science; Computer vision; Orthopedic surgery; Spurious relationship; Image registration; Process (computing); Image (mathematics); Medicine; Surgery; Machine learning","score_opus":0.02871522958399645,"score_gpt":0.27951944716133803,"score_spread":0.25080421757734156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997823434","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7753196,0.0025419698,0.21776351,0.00035567954,0.00014177724,0.00050374673,0.00027371134,0.001106427,0.0019935146],"genre_scores_gemma":[0.92675704,0.0009136283,0.06980498,0.00013281865,0.00009041186,0.00021850364,0.00046297914,0.00031180313,0.0013078293],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9927214,0.0047669206,0.00044701755,0.0007886285,0.0010803902,0.00019571594],"domain_scores_gemma":[0.98415965,0.007947125,0.0009999728,0.0041488213,0.002372236,0.00037224306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010941311,0.0012134445,0.0010506565,0.0010182579,0.00040503748,0.001032287,0.0018086401,0.0013001157,0.0023494745],"category_scores_gemma":[0.022270938,0.00056962494,0.0006857808,0.0010299525,0.0021226408,0.0013317931,0.0013284739,0.0009331524,0.0009917388],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.010716957,0.009371561,0.029153131,0.001004552,0.00082188734,0.0010014385,0.0041490584,0.041426,0.23940042,0.0022586593,0.0051017934,0.6555946],"study_design_scores_gemma":[0.003555416,0.08358678,0.10886014,0.0002830254,0.001650199,0.012780336,0.0026346426,0.35951033,0.40509835,0.0057677254,0.015651036,0.00062208466],"about_ca_topic_score_codex":0.0009845617,"about_ca_topic_score_gemma":0.00077352865,"teacher_disagreement_score":0.010941311,"about_ca_system_score_codex":0.00039834788,"about_ca_system_score_gemma":0.00069173856,"threshold_uncertainty_score":0.05786389},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28138068,0.0018465825,0.7072347,0.0006275717,0.00011998692,0.0002490843,0.00039819544,0.004958163,0.003185065],"genre_scores_gemma":[0.5713264,0.0013322931,0.42420322,0.00014606645,0.000085817286,0.000082087194,0.00022418109,0.00048648298,0.0021135223],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985456,0.000037265603,0.000013776248,0.000025516827,0.00005471826,0.000014110476],"domain_scores_gemma":[0.99932253,0.00039267374,0.000047840764,0.00006841289,0.00014075323,0.000027732056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052973995,0.0005359895,0.00074552116,0.0011866242,0.00040210134,0.0008783169,0.0006430816,0.0010477977,0.0016420668],"category_scores_gemma":[0.001822582,0.00031473069,0.00058977515,0.0016016877,0.00028939173,0.00029003582,0.0005456585,0.00035974677,0.00040602387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000677631,0.00019753614,0.0038401873,0.00041508762,0.0002064402,0.0012513115,0.00038998705,0.0670308,0.16062318,0.0014028574,0.0029635963,0.7610014],"study_design_scores_gemma":[0.00009322286,0.00024169519,0.0089093335,0.000028780667,0.00016052905,0.0028295445,0.00021343648,0.90877545,0.07263467,0.0024986842,0.0035566115,0.0000581213],"about_ca_topic_score_codex":0.008057581,"about_ca_topic_score_gemma":0.011008254,"teacher_disagreement_score":0.008057581,"about_ca_system_score_codex":0.0003046345,"about_ca_system_score_gemma":0.00069114723,"threshold_uncertainty_score":0.01602137},"labels":[],"label_agreement":null},{"id":"W2001484348","doi":"10.1016/j.media.2004.06.014","title":"Validation of dynamic heart models obtained using non-linear registration for virtual reality training, planning, and guidance of minimally invasive cardiac surgeries","year":2004,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Surgical Simulation and Training","field":"Medicine","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":"Western University; Robarts Clinical Trials","funders":"","keywords":"Virtual reality; Training (meteorology); Computer science; Artificial intelligence; Image registration; Computer vision; Minimally invasive procedures; Medicine; Surgery; Image (mathematics); Geography","score_opus":0.06121609951463408,"score_gpt":0.36113409017281217,"score_spread":0.2999179906581781,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001484348","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67768615,0.0003071116,0.3166457,0.00012397832,0.00009396381,0.00020639549,0.0007489352,0.0018588335,0.0023289018],"genre_scores_gemma":[0.9694305,0.00016081668,0.028836202,0.00002471749,0.000007098753,0.00006289425,0.00067941606,0.00013234843,0.00066617294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991086,0.00028322396,0.000069018504,0.0001707638,0.00030411963,0.00006436319],"domain_scores_gemma":[0.998038,0.00097024033,0.0001710185,0.00038925503,0.0003726533,0.000058953174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015642846,0.00063333666,0.0004620307,0.0009807261,0.00025765563,0.0011297544,0.0008223217,0.0010655462,0.0016717233],"category_scores_gemma":[0.0057449737,0.0005192957,0.00075221295,0.0004449628,0.0005126306,0.00059399457,0.0006941649,0.00050962897,0.00071971817],"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":[0.001903041,0.0007063389,0.013394043,0.0003993825,0.0003525612,0.0003134593,0.00052750204,0.6216712,0.1805586,0.0015713158,0.00096523546,0.17763728],"study_design_scores_gemma":[0.000085431035,0.00051582465,0.017109785,0.00003725456,0.00013949965,0.0005436947,0.00011894918,0.89840937,0.08101719,0.00049951574,0.0014501726,0.00007335008],"about_ca_topic_score_codex":0.0032472105,"about_ca_topic_score_gemma":0.0025520956,"teacher_disagreement_score":0.0032472105,"about_ca_system_score_codex":0.00039884224,"about_ca_system_score_gemma":0.00086937944,"threshold_uncertainty_score":0.008272827},"labels":[],"label_agreement":null},{"id":"W2003074994","doi":"10.1016/j.media.2007.08.003","title":"Validation of a new surgical procedure for percutaneous scaphoid fixation using intra-operative ultrasound","year":2007,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Orthopedic Surgery and Rehabilitation","field":"Medicine","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":"Kingston General Hospital; Queen's University","funders":"","keywords":"Percutaneous; Medicine; Fixation (population genetics); Fluoroscopy; Ultrasound; Scaphoid fracture; Radiology; Wrist","score_opus":0.015285398822714959,"score_gpt":0.33574210147062916,"score_spread":0.3204567026479142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003074994","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79011476,0.0008979016,0.20369267,0.0003495382,0.00032793,0.0004563303,0.0002671985,0.0007187215,0.0031749138],"genre_scores_gemma":[0.88589764,0.0002619818,0.11248915,0.00016853613,0.00008783908,0.00016880751,0.0001853385,0.00009162749,0.00064904196],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9970962,0.0007196351,0.0001988052,0.0004465147,0.0014250842,0.000113737784],"domain_scores_gemma":[0.99384815,0.0028023282,0.0005034019,0.00079071865,0.0018081727,0.00024725398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029024761,0.0006316304,0.00041888093,0.0015549295,0.0004899447,0.00082016375,0.0009960495,0.0013608173,0.0014947391],"category_scores_gemma":[0.008063868,0.00044959478,0.00040629125,0.00036063988,0.0015714765,0.00080296345,0.0007360514,0.00063991465,0.0007259965],"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":[0.0016632127,0.00041939758,0.061316207,0.00032283185,0.00008038212,0.000549409,0.000492115,0.0007785525,0.8533246,0.00043326794,0.00039440955,0.0802257],"study_design_scores_gemma":[0.00033157162,0.007831702,0.3407483,0.00015601811,0.0006313637,0.019206729,0.0007635874,0.031260427,0.5916519,0.0006274208,0.006576238,0.00021468801],"about_ca_topic_score_codex":0.0007388537,"about_ca_topic_score_gemma":0.0012016406,"teacher_disagreement_score":0.0029024761,"about_ca_system_score_codex":0.00030082135,"about_ca_system_score_gemma":0.0009090868,"threshold_uncertainty_score":0.015349925},"labels":[],"label_agreement":null},{"id":"W2003863798","doi":"10.1016/j.media.2010.05.010","title":"Non-local MRI upsampling","year":2010,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":274,"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":"Upsampling; Interpolation (computer graphics); Artificial intelligence; Computer science; Computer vision; Bicubic interpolation; Image scaling; Constraint (computer-aided design); Coherence (philosophical gambling strategy); Nearest-neighbor interpolation; Image (mathematics); Mathematics; Algorithm; Multivariate interpolation; Image processing; Bilinear interpolation; Statistics","score_opus":0.005285407020192026,"score_gpt":0.2967542073361372,"score_spread":0.2914688003159452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003863798","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01599401,0.0008020641,0.9789372,0.00017212499,0.000112674315,0.000046792324,0.0001059667,0.0003966533,0.0034324783],"genre_scores_gemma":[0.27206168,0.002260882,0.7045707,0.0003760513,0.00028388508,0.00014625728,0.0007032364,0.00042857332,0.019168766],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972135,0.000058558588,0.00001773781,0.00004803604,0.00012527242,0.000029070967],"domain_scores_gemma":[0.9995561,0.000117007905,0.0000387464,0.00017067598,0.00009040385,0.000027025755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048272638,0.0006018712,0.0005214161,0.00046927767,0.00027987937,0.0006215029,0.00037796676,0.0004472986,0.0051504257],"category_scores_gemma":[0.0014161761,0.0003578117,0.00043611153,0.00063702883,0.0003352063,0.00066105387,0.000735502,0.00068945123,0.0014386551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049187883,0.00012745433,0.0016250117,0.00047113738,0.00013665092,0.0003180218,0.00017730011,0.017232856,0.3962944,0.01674127,0.005891046,0.56049305],"study_design_scores_gemma":[0.00004924418,0.00028920115,0.010617072,0.00010102313,0.00031852155,0.004840479,0.00015571252,0.4083116,0.4994212,0.014566704,0.06126224,0.000066939],"about_ca_topic_score_codex":0.0008697954,"about_ca_topic_score_gemma":0.0024841027,"teacher_disagreement_score":0.0051504257,"about_ca_system_score_codex":0.00019022913,"about_ca_system_score_gemma":0.00052704546,"threshold_uncertainty_score":0.017229915},"labels":[],"label_agreement":null},{"id":"W2004369235","doi":"10.1016/j.media.2008.09.001","title":"Depth potential function for folding pattern representation, registration and analysis","year":2008,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":123,"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":"Surface (topology); Curvature; Projection (relational algebra); Mathematics; Geometry; Representation (politics); Scalar (mathematics); Plane (geometry); Function (biology); Invariant (physics); Algorithm; Artificial intelligence; Computer science","score_opus":0.0154725849044681,"score_gpt":0.25613802021610516,"score_spread":0.24066543531163706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004369235","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030232728,0.00021144003,0.9954791,0.00007574714,0.000013940384,0.000025182302,0.000076179334,0.00024807485,0.0008470096],"genre_scores_gemma":[0.23433016,0.0010818877,0.7552302,0.00011913047,0.0000489291,0.0002174541,0.00056253985,0.0004955476,0.007914155],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997998,0.000045640776,0.000012878091,0.000024309824,0.0001003638,0.000017083246],"domain_scores_gemma":[0.9996989,0.000098327655,0.000024334646,0.000069064685,0.00008382336,0.000025723279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004849568,0.00046263557,0.00053710054,0.001091492,0.00030390057,0.0010270835,0.0008965044,0.0007674562,0.003871667],"category_scores_gemma":[0.0018673918,0.00027074877,0.00048653147,0.0011438079,0.0004080396,0.0014652594,0.001089114,0.00077785837,0.0012369445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024029528,0.00006314726,0.0007637603,0.00029928258,0.00004673448,0.00020511955,0.00012805693,0.12729134,0.07320283,0.13570756,0.008062663,0.6539892],"study_design_scores_gemma":[0.000011722697,0.000031191346,0.0004425583,0.000014931458,0.000011764965,0.00027640368,0.00002669747,0.95080507,0.015986312,0.025873352,0.0064986628,0.000021291171],"about_ca_topic_score_codex":0.0017727864,"about_ca_topic_score_gemma":0.0015460021,"teacher_disagreement_score":0.003871667,"about_ca_system_score_codex":0.0005456333,"about_ca_system_score_gemma":0.0008362178,"threshold_uncertainty_score":0.01295203},"labels":[],"label_agreement":null},{"id":"W2007421864","doi":"10.1016/j.media.2009.09.005","title":"A nonlinear identification method to study effective connectivity in functional MRI","year":2009,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","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":"Université de Montréal; McGill University","funders":"Canadian Institutes of Health Research; Institut National de la Santé et de la Recherche Médicale","keywords":"Identification (biology); Artificial intelligence; Computer science; Nonlinear system; Machine learning; Pattern recognition (psychology); Biology","score_opus":0.022841829544684056,"score_gpt":0.3477557489552384,"score_spread":0.32491391941055436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007421864","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004796393,0.000070105736,0.9944271,0.000064900734,0.000021833914,0.0000271317,0.00002502521,0.00009432911,0.00047331073],"genre_scores_gemma":[0.15249535,0.00039858514,0.8398169,0.00007019422,0.0000864722,0.00032855014,0.00016152948,0.00017903229,0.006463373],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99983084,0.0000690676,0.000009032833,0.000029026305,0.000052433938,0.00000950947],"domain_scores_gemma":[0.99954,0.00026415836,0.000040231407,0.000055223947,0.00008290618,0.00001742481],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006563932,0.0004680328,0.00036403973,0.00064658065,0.0005307743,0.0004053471,0.00047908642,0.000654642,0.0014697438],"category_scores_gemma":[0.0017580154,0.00031691877,0.00053819286,0.000519519,0.0005003342,0.0008654201,0.000541238,0.0006813484,0.0005337564],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019594989,0.00025142785,0.0017036082,0.00043299145,0.00023239176,0.0003656834,0.0002861312,0.25514296,0.14291961,0.08469959,0.004341528,0.5094281],"study_design_scores_gemma":[0.000011450492,0.00006414247,0.0008319455,0.000008095878,0.00003249405,0.0001644448,0.000020835078,0.97785187,0.007635701,0.010728933,0.0026272668,0.000022813781],"about_ca_topic_score_codex":0.0017989024,"about_ca_topic_score_gemma":0.0028761837,"teacher_disagreement_score":0.0017989024,"about_ca_system_score_codex":0.00026731225,"about_ca_system_score_gemma":0.0006738957,"threshold_uncertainty_score":0.0049167275},"labels":[],"label_agreement":null},{"id":"W2007981068","doi":"10.1016/j.media.2004.06.016","title":"Brain morphometry using 3D moment invariants","year":2004,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":86,"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":"Invariant (physics); Artificial intelligence; Principal component analysis; Pattern recognition (psychology); Computer science; Moment (physics); Scale (ratio); Mathematics; Computer vision; Physics; Cartography; Geography","score_opus":0.04630396967636442,"score_gpt":0.3427405736488299,"score_spread":0.2964366039724655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007981068","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023649326,0.00031884172,0.974282,0.00013189753,0.00003847317,0.000023911121,0.00012668359,0.00048847793,0.00094025757],"genre_scores_gemma":[0.50539804,0.001078603,0.49108925,0.00007273199,0.00013915308,0.00007261716,0.00025900346,0.00029291684,0.0015976786],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997805,0.000038150873,0.00001632434,0.000040880605,0.000104751794,0.000019430317],"domain_scores_gemma":[0.9994887,0.00016743614,0.0001273628,0.000093625895,0.00009477264,0.000028095566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004328295,0.0003832664,0.0004731671,0.002628747,0.00022636895,0.0012003198,0.00048319253,0.00040101973,0.0013362201],"category_scores_gemma":[0.0017223139,0.00034667292,0.00073070667,0.0015021753,0.00053745846,0.0013570336,0.00060529925,0.0006106665,0.00041358016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000193152,0.00005282028,0.0041377316,0.00021148029,0.00015356188,0.0002390369,0.0001943688,0.047505513,0.13740282,0.05411663,0.003093006,0.7526999],"study_design_scores_gemma":[0.000029177716,0.00014338634,0.014958629,0.000046324378,0.00014970473,0.0025207563,0.00013735675,0.82165486,0.06785482,0.08387107,0.008518673,0.000115315066],"about_ca_topic_score_codex":0.00072704576,"about_ca_topic_score_gemma":0.0008570338,"teacher_disagreement_score":0.002628747,"about_ca_system_score_codex":0.00036093296,"about_ca_system_score_gemma":0.00043980204,"threshold_uncertainty_score":0.00447011},"labels":[],"label_agreement":null},{"id":"W2009911194","doi":"10.1016/j.media.2014.07.003","title":"Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study","year":2014,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":158,"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":"National Heart, Lung, and Blood Institute","keywords":"Segmentation; Computer science; Automation; Artificial intelligence; Computed tomography; Algorithm; Data mining; Machine learning; Radiology; Medicine; Engineering","score_opus":0.012072452417376455,"score_gpt":0.32582657637598206,"score_spread":0.3137541239586056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009911194","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90243626,0.0076166578,0.08380914,0.0003089616,0.00026557557,0.00089232274,0.0016568252,0.0013445944,0.0016696398],"genre_scores_gemma":[0.86917573,0.0026099228,0.11873267,0.00034683073,0.0001881619,0.00046765315,0.0042825653,0.00165343,0.0025431379],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.990873,0.0044672317,0.0011217223,0.0014170259,0.0017415195,0.00037961508],"domain_scores_gemma":[0.96389407,0.025969896,0.0015130441,0.0034316948,0.0046800678,0.00051118986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014619248,0.0025981339,0.002784405,0.0065447567,0.0012423085,0.0029895324,0.002382397,0.004252117,0.002398551],"category_scores_gemma":[0.03278248,0.0012879721,0.0036151407,0.0027642064,0.0009646147,0.001990851,0.0016607028,0.001731496,0.0013663808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.04576739,0.003787704,0.16340676,0.004079399,0.020831937,0.000546189,0.002148312,0.057284728,0.0256966,0.0017672543,0.006154644,0.6685291],"study_design_scores_gemma":[0.008729014,0.026469724,0.37000367,0.0007393354,0.019740868,0.005953323,0.0026945702,0.49665427,0.050486475,0.004080234,0.013566757,0.0008818388],"about_ca_topic_score_codex":0.008886344,"about_ca_topic_score_gemma":0.009799151,"teacher_disagreement_score":0.014619248,"about_ca_system_score_codex":0.0013294568,"about_ca_system_score_gemma":0.0015519053,"threshold_uncertainty_score":0.07731491},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029221209,0.00023898298,0.96689737,0.00014741931,0.000043432392,0.00010856063,0.00025358412,0.0017901706,0.0012993695],"genre_scores_gemma":[0.37695783,0.0007893252,0.61613375,0.0001911439,0.000054001423,0.0002580193,0.0010889982,0.00093666505,0.0035901743],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99961036,0.00004519518,0.00002185177,0.000065132466,0.00022175332,0.000035768662],"domain_scores_gemma":[0.9995382,0.00012350242,0.00007615013,0.00012124274,0.00010098311,0.000040040755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000528463,0.00097514695,0.00086518715,0.0011744653,0.00041516713,0.001524004,0.00092902593,0.0014257168,0.0024093462],"category_scores_gemma":[0.0015200835,0.0015688782,0.001570943,0.0009810742,0.00047128895,0.00071811257,0.0013367892,0.0011425149,0.0010780821],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058754947,0.00018064647,0.0038296764,0.0005051327,0.00017288905,0.0009535415,0.000526392,0.51776826,0.25088015,0.0063814735,0.003939713,0.21427454],"study_design_scores_gemma":[0.000030735457,0.00010048258,0.0023771483,0.0000246417,0.000055643315,0.0009479127,0.000068139925,0.9354663,0.05450685,0.0027011028,0.003662248,0.000058751324],"about_ca_topic_score_codex":0.0050014067,"about_ca_topic_score_gemma":0.008518763,"teacher_disagreement_score":0.0050014067,"about_ca_system_score_codex":0.00067057414,"about_ca_system_score_gemma":0.0018488546,"threshold_uncertainty_score":0.009944558},"labels":[],"label_agreement":null},{"id":"W2012048142","doi":"10.1016/j.media.2014.10.005","title":"Quantification of local changes in myocardial motion by diffeomorphic registration via currents: Application to paced hypertrophic obstructive cardiomyopathy in 2D echocardiographic sequences","year":2014,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cardiomyopathy and Myosin Studies","field":"Medicine","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":"Montreal Heart Institute; Université de Montréal","funders":"Seventh Framework Programme; Centro para el Desarrollo Tecnológico Industrial","keywords":"Normalization (sociology); Hypertrophic cardiomyopathy; Artificial intelligence; Ventricular outflow tract; Cardiology; Pattern recognition (psychology); Computer science; Medicine; Internal medicine; Computer vision","score_opus":0.009045251812629227,"score_gpt":0.2643767267392591,"score_spread":0.2553314749266299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012048142","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24730535,0.00092798646,0.74977046,0.00021003517,0.0000385828,0.00018213174,0.00010286175,0.0005282938,0.0009342729],"genre_scores_gemma":[0.6471161,0.001133462,0.35023606,0.000057422185,0.000057358095,0.00012164012,0.00008454017,0.00021388856,0.0009794546],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998591,0.00005349032,0.000010929724,0.000028698227,0.000036961526,0.000010741451],"domain_scores_gemma":[0.99952555,0.0002757068,0.00004692582,0.0000541159,0.000068017296,0.000029682435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097474887,0.00048780808,0.00046606126,0.001330787,0.00029539218,0.0009080531,0.00033890994,0.0006665717,0.00068702403],"category_scores_gemma":[0.0021688144,0.00033634188,0.00041338234,0.0008998026,0.0004926773,0.0005465174,0.0006193382,0.00046071282,0.00018834112],"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":[0.00069224596,0.0001907964,0.007828044,0.00041514265,0.0001137863,0.0006664976,0.00066598173,0.041608796,0.49750206,0.0026898677,0.0004905141,0.44713628],"study_design_scores_gemma":[0.00010313104,0.00093155185,0.05194642,0.000097731194,0.00027235074,0.0033540695,0.0003851141,0.7522669,0.18076119,0.0051706876,0.0045685894,0.00014230455],"about_ca_topic_score_codex":0.00095566595,"about_ca_topic_score_gemma":0.0017270813,"teacher_disagreement_score":0.001330787,"about_ca_system_score_codex":0.00017591273,"about_ca_system_score_gemma":0.00039686376,"threshold_uncertainty_score":0.005155027},"labels":[],"label_agreement":null},{"id":"W2013160622","doi":"10.1016/j.media.2013.03.009","title":"Tractometer: Towards validation of tractography pipelines","year":2013,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":236,"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":"Tractography; Computer science; Artificial intelligence; Seeding; Pattern recognition (psychology); Computer vision; Mathematics; Diffusion MRI; Engineering; Magnetic resonance imaging","score_opus":0.04430028071975608,"score_gpt":0.3782283427137563,"score_spread":0.33392806199400027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013160622","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037336804,0.0002745076,0.92715234,0.00030357327,0.00027415573,0.00019166022,0.0016614253,0.031788968,0.0010166148],"genre_scores_gemma":[0.25039637,0.00020631538,0.73375833,0.00021449171,0.00007126011,0.000387158,0.0071348716,0.005650177,0.0021809875],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9913952,0.0035487006,0.00067645154,0.0019931414,0.0020447513,0.00034180837],"domain_scores_gemma":[0.9692982,0.014512064,0.002134421,0.007130958,0.0062021525,0.0007222289],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.014358205,0.0022415933,0.0013206512,0.0031285672,0.001444544,0.0044967937,0.003763148,0.0037382175,0.005073681],"category_scores_gemma":[0.07169341,0.0012290898,0.0016850622,0.0017015192,0.0018934546,0.0035230669,0.00445503,0.003251757,0.0037300223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002087643,0.0006937502,0.026266644,0.0014713877,0.001517281,0.00052652054,0.0010556082,0.30119777,0.057843424,0.029097077,0.035247214,0.5429957],"study_design_scores_gemma":[0.00012115251,0.00017875458,0.0042104046,0.000119388125,0.00008500544,0.00030517776,0.00008978347,0.9475107,0.028473815,0.0111101335,0.0077191098,0.000076594435],"about_ca_topic_score_codex":0.011065129,"about_ca_topic_score_gemma":0.010859133,"teacher_disagreement_score":0.9856418,"about_ca_system_score_codex":0.0013332721,"about_ca_system_score_gemma":0.0048541683,"threshold_uncertainty_score":0.07593435},"labels":[],"label_agreement":null},{"id":"W2014373879","doi":"10.1016/s1361-8415(02)00083-x","title":"Deformable organisms for automatic medical image analysis","year":2002,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":76,"is_retracted":false,"has_abstract":false,"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; Toronto Metropolitan University","funders":"National Institute of Mental Health; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Stiftelsen för Strategisk Forskning","keywords":"Artificial intelligence; Computer science; Computer vision; Segmentation; Corpus callosum; Process (computing); Perception; Sagittal plane; Pattern recognition (psychology); Anatomy; Biology; Neuroscience","score_opus":0.0057308416164632,"score_gpt":0.27071737069590973,"score_spread":0.26498652907944653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014373879","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009909677,0.0006021198,0.99384755,0.00021242228,0.00009117291,0.00008817293,0.00028937217,0.00246656,0.0014117616],"genre_scores_gemma":[0.028008377,0.0017223228,0.9599213,0.0002563356,0.0000884115,0.00031558395,0.0010983736,0.0008288776,0.00776046],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995726,0.00006518176,0.000035906687,0.000074292824,0.00022727727,0.00002486472],"domain_scores_gemma":[0.9992906,0.000278771,0.00004831893,0.00021541775,0.00013186545,0.000035009543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065306044,0.0007681462,0.00083152065,0.0014264827,0.00036700655,0.0010395359,0.0015699622,0.001237256,0.0130005535],"category_scores_gemma":[0.0025740184,0.00063089887,0.00078666606,0.0012663292,0.0005371486,0.0008326525,0.0014378991,0.0013365205,0.004642262],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074667296,0.000060296854,0.00022547723,0.00042771106,0.000096451375,0.00016975238,0.00009847027,0.028052881,0.08532645,0.038218103,0.023427159,0.82382256],"study_design_scores_gemma":[0.000055442055,0.00006896967,0.0015721148,0.00015507791,0.000088320376,0.0011500801,0.00005783123,0.72708875,0.08860401,0.053259496,0.12781732,0.00008256124],"about_ca_topic_score_codex":0.0020235856,"about_ca_topic_score_gemma":0.0023491178,"teacher_disagreement_score":0.0130005535,"about_ca_system_score_codex":0.0005088991,"about_ca_system_score_gemma":0.0005303288,"threshold_uncertainty_score":0.043491185},"labels":[],"label_agreement":null},{"id":"W2014940628","doi":"10.1016/j.media.2011.04.003","title":"New methods for MRI denoising based on sparseness and self-similarity","year":2011,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":280,"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":"Thresholding; Artificial intelligence; Noise reduction; Pattern recognition (psychology); Similarity (geometry); Image denoising; Computer science; Exploit; Filter (signal processing); Discrete cosine transform; USable; Invariant (physics); Mathematics; Computer vision; Image (mathematics)","score_opus":0.03962570295564427,"score_gpt":0.35447629242740264,"score_spread":0.31485058947175837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014940628","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014016102,0.00045150283,0.9974909,0.00007231627,0.000067853594,0.00001121765,0.0000102765625,0.000071412986,0.00042301457],"genre_scores_gemma":[0.025485888,0.0014651009,0.9678211,0.00013826715,0.00029704618,0.00007097454,0.000091503505,0.00012168055,0.004508467],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993655,0.00012537048,0.000041628446,0.000090114554,0.00035472278,0.0000226358],"domain_scores_gemma":[0.99880517,0.00045683683,0.00011367377,0.00015916623,0.00040340304,0.0000616673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013985358,0.0008000778,0.0009425895,0.0014868432,0.00029650502,0.0007939394,0.0012284396,0.0014041193,0.001632035],"category_scores_gemma":[0.0027776978,0.00055970665,0.0010749917,0.00092124793,0.0008884367,0.0017645638,0.0012070115,0.0017818707,0.00079710473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023223278,0.00021498266,0.0006829845,0.00083702407,0.00033587904,0.00019612887,0.00033582296,0.07602527,0.1361212,0.1425899,0.0067123934,0.6357162],"study_design_scores_gemma":[0.00004138133,0.000103842045,0.000521205,0.00004162836,0.00008090365,0.0006321406,0.000030793868,0.920195,0.027674962,0.0374312,0.013183169,0.000063812164],"about_ca_topic_score_codex":0.00038665344,"about_ca_topic_score_gemma":0.0010979427,"teacher_disagreement_score":0.001632035,"about_ca_system_score_codex":0.00034291224,"about_ca_system_score_gemma":0.00040375703,"threshold_uncertainty_score":0.007396221},"labels":[],"label_agreement":null},{"id":"W2016296285","doi":"10.1016/j.media.2015.03.001","title":"Skin lesion tracking using structured graphical models","year":2015,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","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":"BC Cancer Agency; Vancouver Coastal Health Research Institute; University of British Columbia; Simon Fraser University; Vancouver Coastal Health","funders":"CIHR Skin Research Training Centre; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canadian Dermatology Foundation","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Computer vision; Matching (statistics); Set (abstract data type); Mathematics","score_opus":0.06139986517366262,"score_gpt":0.3265433110588494,"score_spread":0.26514344588518673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016296285","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0069379667,0.00010477335,0.99130857,0.00009720271,0.00001570242,0.000027777138,0.000088055014,0.00080951606,0.0006104307],"genre_scores_gemma":[0.5694766,0.0005340925,0.42389,0.00029563738,0.00007950397,0.0001229124,0.00063188205,0.00044433703,0.0045250123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950147,0.00016415199,0.000018395014,0.000137392,0.00013150933,0.000047123085],"domain_scores_gemma":[0.99883527,0.00065622415,0.00011290986,0.00016922595,0.00016195973,0.00006452888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006317192,0.0006466324,0.00068959576,0.0016998621,0.00026922312,0.0013550764,0.0009901588,0.0011204868,0.0020510657],"category_scores_gemma":[0.0030162756,0.0007090238,0.0017322815,0.0009718233,0.00050703017,0.0010142095,0.0009594456,0.0009829253,0.0010922977],"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":[0.0002722813,0.0001224948,0.0024592783,0.0001777485,0.00019061631,0.00030125456,0.00016238359,0.6864833,0.0316243,0.014169484,0.0034843448,0.26055244],"study_design_scores_gemma":[0.0000050759145,0.000019705782,0.00022327903,0.0000064512246,0.000012520048,0.000056273228,0.0000054602065,0.993411,0.0017022385,0.0041064657,0.0004446657,0.0000068331133],"about_ca_topic_score_codex":0.0047872704,"about_ca_topic_score_gemma":0.00673289,"teacher_disagreement_score":0.0047872704,"about_ca_system_score_codex":0.0005624257,"about_ca_system_score_gemma":0.0005588218,"threshold_uncertainty_score":0.009518802},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029975323,0.0024723203,0.96148956,0.0002848106,0.00009641779,0.00017332162,0.00031887207,0.0034718635,0.0017174198],"genre_scores_gemma":[0.22240904,0.0016149456,0.7702941,0.0003199955,0.0001276007,0.00021053012,0.0006640215,0.0011832493,0.0031765841],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999328,0.00017985328,0.00005811105,0.000094179195,0.00028618186,0.00005370556],"domain_scores_gemma":[0.99868876,0.00066765095,0.00012316198,0.0002106405,0.0002645778,0.000045255725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090095046,0.000729341,0.00084135827,0.0020328504,0.0005758154,0.0013071938,0.0010206967,0.0013950425,0.0028501563],"category_scores_gemma":[0.002972345,0.0009780998,0.0009187344,0.0011831225,0.00044324002,0.0007461484,0.00080851454,0.00078433775,0.0013374762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005922611,0.00012577821,0.001972988,0.0010529403,0.0001969784,0.0003204461,0.00029457881,0.028724035,0.18741561,0.0030879148,0.0062818914,0.76993454],"study_design_scores_gemma":[0.00010135486,0.00037248366,0.012499827,0.0002687985,0.00036662418,0.005103754,0.00018213158,0.7044289,0.23751551,0.011612008,0.027371267,0.00017732069],"about_ca_topic_score_codex":0.0022092967,"about_ca_topic_score_gemma":0.00444055,"teacher_disagreement_score":0.0028501563,"about_ca_system_score_codex":0.00039408036,"about_ca_system_score_gemma":0.0013346081,"threshold_uncertainty_score":0.009534657},"labels":[],"label_agreement":null},{"id":"W2018427609","doi":"10.1016/j.media.2008.04.003","title":"Towards a validation of atlas warping techniques","year":2008,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Neurological disorders and treatments","field":"Medicine","cited_by":96,"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 warping; Pallidotomy; Atlas (anatomy); Brain atlas; Deep brain stimulation; Thalamotomy; Computer science; Magnetic resonance imaging; Artificial intelligence; Brain mapping; Medicine; Radiology; Anatomy; Parkinson's disease; Pathology","score_opus":0.021996961059738394,"score_gpt":0.3125067473191224,"score_spread":0.290509786259384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018427609","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053499162,0.0017814743,0.9366352,0.00050076836,0.00039937923,0.0003088113,0.00078115397,0.0028305124,0.003263547],"genre_scores_gemma":[0.41932467,0.0008797793,0.5723412,0.00038495183,0.00013905269,0.00026960115,0.0031319293,0.0012627911,0.0022660396],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97716606,0.012621044,0.0016460216,0.0033192474,0.0047235577,0.00052417896],"domain_scores_gemma":[0.9236061,0.03635779,0.0033474597,0.016609045,0.019259559,0.0008200409],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04246822,0.0022658643,0.0015071486,0.004965048,0.0017150727,0.0070306086,0.00347974,0.0042129527,0.003726988],"category_scores_gemma":[0.12074085,0.0011024001,0.0014252763,0.0026431344,0.0027601845,0.0038248198,0.0048930524,0.0028537267,0.0028687513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020367748,0.0005564501,0.027237326,0.0013120361,0.0019570433,0.00040211872,0.0011579192,0.124046996,0.0848442,0.026537668,0.011107191,0.7188043],"study_design_scores_gemma":[0.00024196706,0.0007110465,0.016345391,0.0005243458,0.00049904187,0.0011509372,0.00058160606,0.8525267,0.07991214,0.03054384,0.016843839,0.00011902218],"about_ca_topic_score_codex":0.0045296787,"about_ca_topic_score_gemma":0.003218731,"teacher_disagreement_score":0.04246822,"about_ca_system_score_codex":0.0009275776,"about_ca_system_score_gemma":0.0029087232,"threshold_uncertainty_score":0.22459608},"labels":[],"label_agreement":null},{"id":"W2018861380","doi":"10.1016/j.media.2010.07.011","title":"Intra-operative 3D guidance and edema detection in prostate brachytherapy using a non-isocentric C-arm","year":2010,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","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":"Queen's University","funders":"National Cancer Institute","keywords":"Brachytherapy; Prostate brachytherapy; Medicine; Prostate cancer; Radiation treatment planning; Nuclear medicine; Prostate; Dosimetry; Radiology; Biomedical engineering; Radiation therapy; Cancer","score_opus":0.007717011027891792,"score_gpt":0.30838901095483867,"score_spread":0.3006719999269469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018861380","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8789149,0.0043013655,0.11004408,0.00041718996,0.000072343486,0.00012091223,0.0001596617,0.0004768102,0.005492785],"genre_scores_gemma":[0.95530176,0.00059314584,0.04264722,0.000091597765,0.0000292596,0.000049035156,0.000041668096,0.00010721453,0.0011390501],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996172,0.00015866918,0.000023252866,0.000051608225,0.000105946434,0.00004332713],"domain_scores_gemma":[0.9992741,0.00039747707,0.00010224443,0.000057953894,0.000123775,0.000044474476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006559898,0.00036114603,0.00031032605,0.0009881877,0.0003218895,0.00076292653,0.0004944453,0.00089261256,0.0014204084],"category_scores_gemma":[0.0019151297,0.0005347227,0.00033978702,0.00055907323,0.00040552873,0.0006376016,0.00053081225,0.0006257625,0.00018494939],"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":[0.008325447,0.00032336463,0.035816308,0.0005804538,0.00011062656,0.0011512605,0.00050805876,0.0085904375,0.7668779,0.0010063105,0.0014800724,0.17522982],"study_design_scores_gemma":[0.0005189294,0.0036778888,0.29358703,0.00019295824,0.0009115285,0.016905433,0.00045600857,0.16661476,0.50844425,0.001184551,0.007163186,0.00034339182],"about_ca_topic_score_codex":0.0027557479,"about_ca_topic_score_gemma":0.0049382616,"teacher_disagreement_score":0.0027557479,"about_ca_system_score_codex":0.0003904553,"about_ca_system_score_gemma":0.00074822654,"threshold_uncertainty_score":0.005479455},"labels":[],"label_agreement":null},{"id":"W2021204548","doi":"10.1016/j.media.2012.09.004","title":"Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging","year":2012,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":361,"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; Magnetic resonance imaging; Multiple sclerosis; Computer science; Artificial intelligence; Lesion; Image segmentation; Pattern recognition (psychology); Medicine; Radiology; Pathology","score_opus":0.13020247935303603,"score_gpt":0.4478487463707702,"score_spread":0.3176462670177342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021204548","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00038673214,0.99770564,0.0012744374,0.00011697109,0.00009675978,0.00001306647,0.00006656097,0.000022873026,0.0003168954],"genre_scores_gemma":[0.002361788,0.9924183,0.004385702,0.00017067781,0.00018053078,0.000019090749,0.0001697255,0.00001042935,0.0002838358],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994247,0.0000876104,0.00015697295,0.00013143754,0.00017508742,0.000024095572],"domain_scores_gemma":[0.9984226,0.0008947381,0.00019443262,0.000040929168,0.00041391866,0.00003323772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001535961,0.0012007739,0.0024420843,0.005709707,0.00034753513,0.0011561987,0.0016353171,0.0009873192,0.002116376],"category_scores_gemma":[0.002862971,0.0005549108,0.00146111,0.0050303754,0.00051060214,0.0013159498,0.00061158475,0.0006769684,0.0009496621],"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":[0.00010156017,0.000049747476,0.000512289,0.042874455,0.0004684322,0.00014208275,0.00006347065,0.00037703244,0.0019987249,0.0005185909,0.008968365,0.94392526],"study_design_scores_gemma":[0.00017944332,0.0006193695,0.018651558,0.04578966,0.010018712,0.008124229,0.00044941745,0.0038380201,0.013061189,0.005467929,0.8934322,0.00036835996],"about_ca_topic_score_codex":0.0040623555,"about_ca_topic_score_gemma":0.005180617,"teacher_disagreement_score":0.005709707,"about_ca_system_score_codex":0.0004347314,"about_ca_system_score_gemma":0.0018329683,"threshold_uncertainty_score":0.00812304},"labels":[],"label_agreement":null},{"id":"W2022237952","doi":"10.1016/j.media.2014.06.003","title":"Multi-shell diffusion signal recovery from sparse measurements","year":2014,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","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":"University of Waterloo","funders":"National Center for Research Resources; National Institute of Biomedical Imaging and Bioengineering; National Institute of Mental Health; Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; Academy of Finland","keywords":"Algorithm; SIGNAL (programming language); Diffusion; Computer science; Range (aeronautics); Mathematics; Mathematical optimization; Physics","score_opus":0.08600627826693114,"score_gpt":0.35698312701008417,"score_spread":0.27097684874315303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022237952","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006004083,0.0001483795,0.992935,0.00019588336,0.000021631906,0.000016164971,0.000060824652,0.00016356791,0.00045437829],"genre_scores_gemma":[0.17193136,0.0009333005,0.823344,0.00014204618,0.000094821255,0.00008844485,0.0004531921,0.00017115178,0.0028417718],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974316,0.00007710591,0.000020962107,0.00005053701,0.00009336882,0.000014870343],"domain_scores_gemma":[0.99891317,0.0004964495,0.00016202334,0.00022822928,0.00015637142,0.000043792028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006940038,0.000722605,0.0005580611,0.000652448,0.0002527418,0.0007828659,0.00064621255,0.0011132965,0.0012830623],"category_scores_gemma":[0.005170384,0.000570522,0.0005709719,0.00093633716,0.0006356294,0.0017266603,0.0013553629,0.0015460904,0.0007178813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037200615,0.00013261846,0.001526005,0.000738478,0.00021879408,0.00038739704,0.0003744925,0.2509223,0.14738792,0.05829751,0.0059944238,0.5336481],"study_design_scores_gemma":[0.000019260759,0.000062426174,0.0006150675,0.000034181674,0.000036195368,0.00041765056,0.00004336143,0.9406218,0.023966588,0.029855145,0.004297675,0.0000306006],"about_ca_topic_score_codex":0.000851536,"about_ca_topic_score_gemma":0.0013813028,"teacher_disagreement_score":0.0012830623,"about_ca_system_score_codex":0.000183304,"about_ca_system_score_gemma":0.0006512347,"threshold_uncertainty_score":0.0042922497},"labels":[],"label_agreement":null},{"id":"W2022848122","doi":"10.1016/j.media.2004.06.011","title":"Accurate assessment of patellar tracking using fiducial and intensity-based fluoroscopic techniques","year":2004,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","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":"Queen's University","funders":"","keywords":"Fiducial marker; Translation (biology); Kinematics; Orientation (vector space); Fluoroscopy; Rotation (mathematics); Tracking (education); Computer vision; Patella; Cadaver; Artificial intelligence; Intensity (physics); Match moving; Computer science; Medicine; Anatomy; Motion (physics); Mathematics; Radiology; Physics; Geometry; Optics","score_opus":0.019423565391080734,"score_gpt":0.30225480110535924,"score_spread":0.2828312357142785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022848122","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2597296,0.0034762502,0.7327624,0.0001904538,0.00007086012,0.000094345145,0.00013816026,0.0010334683,0.0025045043],"genre_scores_gemma":[0.77185345,0.0009241377,0.22600567,0.00010728107,0.0000316215,0.00004848678,0.00008049199,0.00019824202,0.00075063756],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99894327,0.0003147665,0.00008051319,0.00011726818,0.00047276885,0.00007142911],"domain_scores_gemma":[0.99621946,0.0019774605,0.00036633885,0.00038087618,0.0009455841,0.00011024556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022728126,0.0004372702,0.0005325792,0.0019402095,0.000347079,0.0010686208,0.00063701026,0.0013059207,0.0007363892],"category_scores_gemma":[0.011404154,0.00051560666,0.00026450682,0.0005955918,0.00040633054,0.001469198,0.0006548251,0.0006683647,0.000365814],"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":[0.0014232427,0.00012769802,0.03503873,0.00055114663,0.00011430533,0.00037093533,0.0005695629,0.007368742,0.565825,0.0015779438,0.0010261572,0.38600647],"study_design_scores_gemma":[0.00018554767,0.0013021717,0.17644179,0.0003308909,0.0006515253,0.01799982,0.00041087775,0.28022692,0.51114947,0.0026967912,0.008203276,0.0004009443],"about_ca_topic_score_codex":0.0026325954,"about_ca_topic_score_gemma":0.0028218653,"teacher_disagreement_score":0.0026325954,"about_ca_system_score_codex":0.00030299075,"about_ca_system_score_gemma":0.0007040231,"threshold_uncertainty_score":0.012019932},"labels":[],"label_agreement":null},{"id":"W2023743679","doi":"10.1016/j.media.2008.10.004","title":"Phase unwrapping of MR images using ΦUN – A fast and robust region growing algorithm","year":2008,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced X-ray Imaging Techniques","field":"Physics and Astronomy","cited_by":92,"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":"Friedrich-Schiller-Universität Jena","keywords":"Algorithm; Computer science; Phase unwrapping; Artificial intelligence; Signal-to-noise ratio (imaging); Image resolution; Noise (video); Phase (matter); Imaging phantom; Computer vision; Image (mathematics); Physics; Optics; Interferometry","score_opus":0.022163233765784555,"score_gpt":0.30973673754343667,"score_spread":0.2875735037776521,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023743679","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024033429,0.00015901025,0.9965823,0.000044929755,0.000029569643,0.000021241418,0.000014843812,0.00041744465,0.0003273631],"genre_scores_gemma":[0.024490586,0.00026298565,0.9735605,0.000031078544,0.000024327057,0.00004431801,0.000066854984,0.00027471018,0.0012446037],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994234,0.0001525708,0.000044061282,0.000103283615,0.00024095323,0.000035815796],"domain_scores_gemma":[0.99869543,0.00050343183,0.0001670437,0.0002388837,0.00034946363,0.000045801455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011032699,0.0009173493,0.0007681136,0.0011051922,0.00046932042,0.0010345766,0.001052374,0.0010133277,0.0019061329],"category_scores_gemma":[0.0042828037,0.00076623436,0.00080100587,0.0011858303,0.0005481294,0.001829818,0.0012474385,0.0012218253,0.0015096873],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028318356,0.000064394284,0.0004483592,0.00034269755,0.00009181628,0.00024021216,0.00023733347,0.07870931,0.21052843,0.015898965,0.0028849097,0.69027036],"study_design_scores_gemma":[0.00003114367,0.00011316302,0.0007100161,0.000030142246,0.000054414453,0.0007405099,0.000044453223,0.8100961,0.16030805,0.008023863,0.019781757,0.00006632575],"about_ca_topic_score_codex":0.0010617626,"about_ca_topic_score_gemma":0.0014471359,"teacher_disagreement_score":0.0019061329,"about_ca_system_score_codex":0.00024816595,"about_ca_system_score_gemma":0.0009303432,"threshold_uncertainty_score":0.006376624},"labels":[],"label_agreement":null},{"id":"W2025476726","doi":"10.1016/j.media.2007.06.008","title":"Clinical validation of vessel-based registration for correction of brain-shift","year":2007,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Intracranial Aneurysms: Treatment and Complications","field":"Medicine","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":"McGill University; Montreal Neurological Institute and Hospital","funders":"Norges Forskningsråd","keywords":"Artificial intelligence; Computer science; Computer vision; Image registration; Spline (mechanical); Thin plate spline; Tracking (education); Pattern recognition (psychology); Image (mathematics)","score_opus":0.02600084122234758,"score_gpt":0.3658752603438896,"score_spread":0.33987441912154204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025476726","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8643961,0.0031053005,0.12268716,0.0004930086,0.0004294507,0.000539307,0.00072534324,0.0013733887,0.0062508793],"genre_scores_gemma":[0.9730776,0.00044036086,0.02461055,0.00008637957,0.000064518485,0.000117378666,0.00031869576,0.00044377465,0.00084081345],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99677163,0.0017131096,0.0002630226,0.00050175766,0.0006410364,0.00010929429],"domain_scores_gemma":[0.9880602,0.00561121,0.0006072079,0.0025089942,0.0029837925,0.00022865107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010319271,0.00065841724,0.0007530923,0.0013449768,0.0007322455,0.0014901813,0.0011665517,0.0014292839,0.0026986867],"category_scores_gemma":[0.030601613,0.00060020847,0.00039385745,0.00061480084,0.0011462884,0.0009895652,0.0007864918,0.00074779347,0.0014803451],"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":[0.012889439,0.0010958799,0.16730998,0.0011284952,0.0008511625,0.0016052624,0.0026337751,0.009118773,0.25343084,0.0022790902,0.0064442297,0.54121304],"study_design_scores_gemma":[0.0023058066,0.010887056,0.46572894,0.0003474627,0.0017995064,0.03343804,0.001098339,0.17613779,0.2799993,0.0037065777,0.024088578,0.00046267672],"about_ca_topic_score_codex":0.00096590916,"about_ca_topic_score_gemma":0.0010537971,"teacher_disagreement_score":0.010319271,"about_ca_system_score_codex":0.00031103898,"about_ca_system_score_gemma":0.0008287533,"threshold_uncertainty_score":0.05457419},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01279055,0.000076878256,0.985811,0.000058645743,0.000012597612,0.000013361667,0.000045507568,0.00059091643,0.0006006246],"genre_scores_gemma":[0.34568334,0.00044255258,0.6479442,0.00004277567,0.00005058615,0.00008517032,0.00031056412,0.00083846116,0.004602229],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995541,0.00015412539,0.000024225174,0.00008549083,0.00014507613,0.0000369207],"domain_scores_gemma":[0.99921894,0.00032075786,0.000104257546,0.00017285116,0.00013808839,0.000045143326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013151804,0.00064622116,0.00055290037,0.0012739811,0.0003241026,0.0016029769,0.00068654463,0.0006826909,0.0036119113],"category_scores_gemma":[0.0042721797,0.0004954446,0.000661862,0.0015702238,0.0007769335,0.0014761314,0.0012797688,0.0012441558,0.0012288444],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00074897544,0.00012395979,0.0034186027,0.00017437009,0.00014448442,0.00023039825,0.00035140017,0.2024788,0.17144725,0.09864978,0.0030270685,0.51920485],"study_design_scores_gemma":[0.000025219248,0.00009119684,0.002928834,0.000033715616,0.00005274752,0.0002964685,0.000051401454,0.9198657,0.046298772,0.026351938,0.0039539426,0.000049909497],"about_ca_topic_score_codex":0.0024065846,"about_ca_topic_score_gemma":0.0023963694,"teacher_disagreement_score":0.0036119113,"about_ca_system_score_codex":0.00040595635,"about_ca_system_score_gemma":0.0006954657,"threshold_uncertainty_score":0.012083113},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003145102,0.000090639114,0.996101,0.000041922514,0.000006853185,0.000015840496,0.000042757514,0.0004790613,0.00007679402],"genre_scores_gemma":[0.1799442,0.00065405754,0.81462157,0.00013597257,0.00009121795,0.0002550989,0.0007808609,0.0008589458,0.0026581052],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931216,0.00019754212,0.00004064552,0.00014014634,0.00025638973,0.000053201977],"domain_scores_gemma":[0.9983047,0.00075958774,0.00021414964,0.00039400693,0.00026601605,0.000061594495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014516984,0.00091328437,0.0011482008,0.0012429974,0.00042213147,0.0011916977,0.0020054278,0.0016144596,0.0010467665],"category_scores_gemma":[0.0057308986,0.0011805389,0.0019911693,0.001275212,0.00078395556,0.0015045695,0.0013120914,0.002268564,0.0010783379],"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":[0.000087757864,0.00008371655,0.0007307603,0.000075150165,0.00010745009,0.00008125918,0.00007145246,0.7844077,0.024200872,0.013295772,0.0018040065,0.17505412],"study_design_scores_gemma":[0.0000029270434,0.000007830156,0.00018262689,0.000002328305,0.0000075354974,0.000026801938,0.000003117685,0.9938415,0.0010656089,0.004558274,0.00029612583,0.000005210073],"about_ca_topic_score_codex":0.0064572976,"about_ca_topic_score_gemma":0.010467906,"teacher_disagreement_score":0.0064572976,"about_ca_system_score_codex":0.0009540138,"about_ca_system_score_gemma":0.0013488934,"threshold_uncertainty_score":0.0128394365},"labels":[],"label_agreement":null},{"id":"W2029665903","doi":"10.1016/j.media.2011.01.005","title":"Extracting skeletal muscle fiber fields from noisy diffusion tensor data","year":2011,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","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":"University of British Columbia","funders":"Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; Canada Research Chairs","keywords":"Smoothing; Diffusion MRI; Noise (video); Tensor (intrinsic definition); Noise reduction; Artificial intelligence; Pattern recognition (psychology); Mathematics; Fiber; Synthetic data; SIGNAL (programming language); Computer science; Algorithm; Computer vision; Geometry","score_opus":0.1199979832209549,"score_gpt":0.37935965336528793,"score_spread":0.25936167014433303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029665903","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.068250395,0.0013046041,0.9275492,0.0003792661,0.000088806475,0.000062995925,0.0006114609,0.0011882521,0.000565011],"genre_scores_gemma":[0.3293868,0.002704155,0.6605772,0.00014201537,0.00027743544,0.00009985903,0.0020501208,0.0003852537,0.0043771793],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99981064,0.000028464663,0.000020337637,0.00005283242,0.00006556954,0.00002212817],"domain_scores_gemma":[0.99929273,0.0002589272,0.00013294903,0.000104427956,0.00016401616,0.00004696283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008709697,0.001084978,0.0010800873,0.002052786,0.00035876207,0.00094879075,0.00066611735,0.001589278,0.0010027359],"category_scores_gemma":[0.0028425502,0.0009372499,0.0010799705,0.001491699,0.00046358956,0.0009816558,0.0007303788,0.0010112104,0.0011807503],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000524417,0.00011859077,0.0062957862,0.0006413182,0.00023867673,0.0014206289,0.0002545368,0.10024789,0.30729213,0.0032659196,0.004602508,0.5750975],"study_design_scores_gemma":[0.0000413672,0.00015387437,0.010934958,0.00009522747,0.00020460533,0.001881486,0.00013941326,0.9157869,0.051014993,0.014063325,0.005621533,0.000062309045],"about_ca_topic_score_codex":0.0029847084,"about_ca_topic_score_gemma":0.0050619934,"teacher_disagreement_score":0.0029847084,"about_ca_system_score_codex":0.00025756744,"about_ca_system_score_gemma":0.00093089376,"threshold_uncertainty_score":0.0059346557},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8457381,0.003964246,0.14099847,0.00071524875,0.000086806765,0.00021596265,0.00055220985,0.0012515824,0.006477415],"genre_scores_gemma":[0.9553732,0.0010291769,0.04083361,0.000098303935,0.00004325169,0.000036846683,0.00018953622,0.00020173659,0.002194246],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997888,0.00005850421,0.000022304257,0.000025783853,0.00006646722,0.0000381691],"domain_scores_gemma":[0.99858785,0.0009282003,0.00009780617,0.000053863594,0.00020177604,0.00013054976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009454069,0.00048676576,0.00020689558,0.0020247079,0.00024422724,0.00081171206,0.0002498871,0.0007985929,0.003977902],"category_scores_gemma":[0.0024622246,0.00028200098,0.00017890868,0.00043415328,0.0002889192,0.0005866248,0.0004820622,0.00037842247,0.00029539387],"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":[0.002641012,0.0001194132,0.011183069,0.0010464503,0.00012968386,0.002369476,0.0006830624,0.0059952172,0.8463564,0.001082947,0.0007412834,0.12765199],"study_design_scores_gemma":[0.00021533044,0.0017758368,0.1914639,0.00041151422,0.0005493534,0.021150308,0.0011571002,0.12389261,0.65016454,0.0019201165,0.0070836516,0.00021581746],"about_ca_topic_score_codex":0.0010491577,"about_ca_topic_score_gemma":0.0010081298,"teacher_disagreement_score":0.003977902,"about_ca_system_score_codex":0.00017788602,"about_ca_system_score_gemma":0.00027845197,"threshold_uncertainty_score":0.013307393},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062934294,0.00023710348,0.93503493,0.0001283655,0.000027510714,0.000044821973,0.00004415747,0.0004400065,0.0011088114],"genre_scores_gemma":[0.690631,0.0006349276,0.3050194,0.00009030145,0.000051565345,0.000084017825,0.00021451553,0.00024305626,0.003031217],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996481,0.00009729753,0.000023923007,0.00008794343,0.00011436519,0.000028250248],"domain_scores_gemma":[0.9995171,0.00018895676,0.00010999951,0.000107549175,0.000055872988,0.000020582338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067536,0.0003777749,0.0004862786,0.0012706601,0.00029384534,0.00086806825,0.00050524296,0.0007613189,0.0010722486],"category_scores_gemma":[0.0022455468,0.000514882,0.0006881138,0.0010186378,0.00051211973,0.00087191595,0.00070436136,0.00072319293,0.00040400308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067708775,0.00027318104,0.004330127,0.00020213285,0.0001649416,0.00022845666,0.00027757222,0.2529968,0.19998007,0.013118699,0.0010352603,0.5267157],"study_design_scores_gemma":[0.000013620362,0.00013407721,0.0065389434,0.000024892428,0.000050841216,0.00032471746,0.000052394294,0.9377299,0.046621513,0.0070344354,0.0014319099,0.000042684336],"about_ca_topic_score_codex":0.0008909285,"about_ca_topic_score_gemma":0.0015323354,"teacher_disagreement_score":0.0012706601,"about_ca_system_score_codex":0.0002483231,"about_ca_system_score_gemma":0.00047625593,"threshold_uncertainty_score":0.0035870671},"labels":[],"label_agreement":null},{"id":"W2045105614","doi":"10.1016/j.media.2010.03.001","title":"Robust Rician noise estimation for MR images","year":2010,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":237,"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":"Robustness (evolution); Computer science; Artificial intelligence; Estimator; Gaussian noise; Noise (video); Rician fading; Ghosting; Wavelet; Noise reduction; Pattern recognition (psychology); Computer vision; Mathematics; Algorithm; Statistics; Image (mathematics); Fading","score_opus":0.01805896654669985,"score_gpt":0.3059172455766335,"score_spread":0.28785827902993366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045105614","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031873882,0.00034847873,0.9958401,0.00010782661,0.000016310962,0.0000073037695,0.000022548078,0.00016387399,0.00030622116],"genre_scores_gemma":[0.17062879,0.0017975719,0.8214508,0.0002196451,0.00020153094,0.00007797078,0.00038093046,0.00038541268,0.004857285],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999373,0.00020717895,0.000039897004,0.00010830062,0.00022893593,0.000042579843],"domain_scores_gemma":[0.999027,0.0004340986,0.00012693541,0.00019000986,0.00019641103,0.000025572706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011317396,0.00075752893,0.0007908986,0.0009945598,0.00020527744,0.00068872917,0.0007036311,0.0011681614,0.0012675487],"category_scores_gemma":[0.006092091,0.00053174887,0.00066937663,0.000722301,0.00071662664,0.0010560965,0.00085913506,0.0010434302,0.00081607135],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004940674,0.000087741435,0.00066728046,0.0005030647,0.00022238154,0.00016583539,0.00019395982,0.3451545,0.11981882,0.04812074,0.0064953105,0.4780763],"study_design_scores_gemma":[0.000014947098,0.000046154608,0.00056423293,0.000023664783,0.000053267195,0.00015306455,0.000017793083,0.94761217,0.030258223,0.016906558,0.004320373,0.000029565412],"about_ca_topic_score_codex":0.001632447,"about_ca_topic_score_gemma":0.0019814472,"teacher_disagreement_score":0.001632447,"about_ca_system_score_codex":0.0004181062,"about_ca_system_score_gemma":0.00062886195,"threshold_uncertainty_score":0.0059853196},"labels":[],"label_agreement":null},{"id":"W2045999511","doi":"10.1016/j.media.2007.10.006","title":"Performance evaluation of a medical robotic 3D-ultrasound imaging system","year":2007,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Peripheral Artery Disease Management","field":"Medicine","cited_by":58,"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; Montreal Clinical Research Institute; Université de Montréal","funders":"","keywords":"Imaging phantom; Scanner; Computer science; Repeatability; Computer vision; Artificial intelligence; Stenosis; Ground truth; Robot; Biomedical engineering; Medicine; Nuclear medicine; Radiology; Mathematics","score_opus":0.015040411005570417,"score_gpt":0.3230855610905272,"score_spread":0.3080451500849568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045999511","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86695766,0.0006469319,0.12392698,0.000228621,0.00013686402,0.00023380476,0.0003687186,0.0045684143,0.002931874],"genre_scores_gemma":[0.9502054,0.00011096107,0.046666536,0.00013490142,0.00003833136,0.00008553466,0.00036314497,0.00013395748,0.002261257],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99922717,0.00025097167,0.000057808986,0.00016755078,0.00023972335,0.000056790097],"domain_scores_gemma":[0.9982383,0.0008406064,0.00009001059,0.000114544215,0.0006004837,0.00011611414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012619314,0.0004904313,0.0006333092,0.0006732181,0.00036356167,0.00061758136,0.00066923467,0.0006728772,0.0034993824],"category_scores_gemma":[0.0031401494,0.000280328,0.00025351022,0.00032510032,0.00024343353,0.0004882798,0.00042921072,0.00016360614,0.0010172296],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.009872719,0.0010182451,0.026485486,0.0006864177,0.00046484426,0.00081699697,0.00044925688,0.07148917,0.43829006,0.0009893562,0.0042666094,0.4451707],"study_design_scores_gemma":[0.0003889223,0.005751298,0.059948158,0.000024944933,0.00038795528,0.0016187581,0.00017935106,0.7037449,0.2214322,0.00025577177,0.006120891,0.00014686405],"about_ca_topic_score_codex":0.0037366536,"about_ca_topic_score_gemma":0.0021730866,"teacher_disagreement_score":0.0037366536,"about_ca_system_score_codex":0.00051929546,"about_ca_system_score_gemma":0.0005853656,"threshold_uncertainty_score":0.01170665},"labels":[],"label_agreement":null},{"id":"W2046296065","doi":"10.1016/j.media.2012.06.006","title":"Intervertebral disc segmentation in MR images using anisotropic oriented flux","year":2012,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":67,"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; London Health Sciences Centre; Western University; CARE Canada","funders":"","keywords":"Segmentation; Sagittal plane; Artificial intelligence; Intervertebral disc; Computer vision; Computer science; Tracking (education); Image segmentation; Level set (data structures); Active contour model; Pattern recognition (psychology); Anatomy; Medicine","score_opus":0.009771000929835016,"score_gpt":0.27683422184961465,"score_spread":0.26706322091977963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046296065","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07152301,0.00073341426,0.9251138,0.00020880408,0.000056620458,0.00007627098,0.00008416173,0.00043545244,0.0017684903],"genre_scores_gemma":[0.35310772,0.0011780554,0.6428979,0.000079355945,0.000091721675,0.00007698435,0.0001538534,0.00022952967,0.0021848853],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986327,0.000037028505,0.000012687537,0.000020671609,0.00004989777,0.000016480639],"domain_scores_gemma":[0.9997023,0.00011890247,0.00004391838,0.00003003534,0.00008734721,0.000017409933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007050239,0.0005278086,0.0004718065,0.0018567436,0.00043737152,0.0013262894,0.00034674813,0.00096401374,0.0011800359],"category_scores_gemma":[0.0012641804,0.00037012558,0.00056187896,0.0008664668,0.0004083373,0.00079201895,0.0002905132,0.00041600244,0.00042203892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008325072,0.00017664445,0.004390627,0.0007758411,0.00015330197,0.0007151297,0.00049399905,0.05733216,0.4154689,0.012383327,0.0019185961,0.50535893],"study_design_scores_gemma":[0.00006936817,0.00018881886,0.007485824,0.00012694966,0.0002146731,0.001342704,0.00021866283,0.84043646,0.13286678,0.010022286,0.0069623603,0.00006525573],"about_ca_topic_score_codex":0.0017113106,"about_ca_topic_score_gemma":0.0023597875,"teacher_disagreement_score":0.0018567436,"about_ca_system_score_codex":0.00034084177,"about_ca_system_score_gemma":0.0007362152,"threshold_uncertainty_score":0.003947556},"labels":[],"label_agreement":null},{"id":"W2050978844","doi":"10.1016/j.media.2010.04.007","title":"High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models","year":2010,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":123,"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":"National Cancer Institute; American Cancer Society","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Markov chain; Pixel; Pairwise comparison; Markov model; Probabilistic logic; Markov random field; Markov process; Prior probability; Segmentation; Image segmentation; Bayesian probability; Machine learning; Mathematics","score_opus":0.01819696402735157,"score_gpt":0.3014415382075682,"score_spread":0.28324457418021665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050978844","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13877708,0.00043350953,0.85704,0.00023947979,0.000021167187,0.00010314141,0.0006797531,0.001882997,0.0008229383],"genre_scores_gemma":[0.6155331,0.00029299041,0.38155496,0.00011167994,0.00003183005,0.00019392975,0.0011348685,0.00018008173,0.0009665545],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994184,0.00016579354,0.000019281446,0.00013880736,0.00020532699,0.00005250341],"domain_scores_gemma":[0.9979038,0.0015116725,0.00018235702,0.00017678694,0.00016399201,0.00006135465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001110862,0.00041436194,0.0008371067,0.0008577139,0.00043679154,0.00064768706,0.0009030659,0.000751102,0.0012858044],"category_scores_gemma":[0.0030104907,0.00085219106,0.0011242202,0.00071576465,0.00033304785,0.00062860665,0.00086877786,0.0008957155,0.0005694537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013644336,0.00037473397,0.022473427,0.00050726166,0.00052421313,0.0006314497,0.00025282436,0.34186065,0.38845903,0.00809878,0.0038359622,0.23161733],"study_design_scores_gemma":[0.000019890223,0.00006680845,0.004562902,0.000006346393,0.00004968704,0.00022736056,0.000017106297,0.95773387,0.031765632,0.005025668,0.0005027384,0.000022064456],"about_ca_topic_score_codex":0.0038181886,"about_ca_topic_score_gemma":0.00809458,"teacher_disagreement_score":0.0038181886,"about_ca_system_score_codex":0.0006926945,"about_ca_system_score_gemma":0.0009520294,"threshold_uncertainty_score":0.007591903},"labels":[],"label_agreement":null},{"id":"W2051558301","doi":"10.1016/j.media.2010.07.008","title":"Biomechanically constrained groupwise ultrasound to CT registration of the lumbar spine","year":2010,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kingston General Hospital; National Research Council Canada; University of British Columbia; Queen's University","funders":"Canadian Institutes of Health Research","keywords":"Imaging phantom; Image registration; Artificial intelligence; Curvature; Computer science; Displacement (psychology); Medicine; Metric (unit); Volume (thermodynamics); Computer vision; Cadaver; Similarity (geometry); Radiology; Mathematics; Image (mathematics); Anatomy","score_opus":0.0035896634749833325,"score_gpt":0.229809595028521,"score_spread":0.22621993155353767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051558301","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2900092,0.00050009874,0.7047276,0.00033693048,0.00010350755,0.00015164922,0.00030739425,0.00066282775,0.003200865],"genre_scores_gemma":[0.8720483,0.0004885889,0.122181654,0.000107163345,0.000064540334,0.0001336543,0.0003608558,0.00029099087,0.004324177],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976236,0.000083612234,0.000016252901,0.000039281396,0.00008075215,0.000017712151],"domain_scores_gemma":[0.99961734,0.00016267654,0.00006595715,0.00007006894,0.000063391184,0.000020445768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004115602,0.00037512465,0.00036100778,0.0009104472,0.00025122103,0.0008441444,0.00051003374,0.00065266906,0.002932745],"category_scores_gemma":[0.0029683614,0.0003589797,0.00043574726,0.0007579239,0.00045432977,0.00044046642,0.0008129222,0.00047786642,0.00077186746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084671704,0.00039013647,0.0056911237,0.0005135929,0.00015144072,0.00040604314,0.00053336885,0.2799544,0.3360625,0.008418637,0.0029407667,0.36409128],"study_design_scores_gemma":[0.000048908456,0.00039560863,0.021300916,0.00007545658,0.00011460461,0.0009373298,0.0002492806,0.8769629,0.086060345,0.0077415463,0.0060488535,0.000064196196],"about_ca_topic_score_codex":0.0036532206,"about_ca_topic_score_gemma":0.0055641807,"teacher_disagreement_score":0.0036532206,"about_ca_system_score_codex":0.00024603057,"about_ca_system_score_gemma":0.0010556827,"threshold_uncertainty_score":0.009811044},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034737714,0.0006658925,0.9627701,0.0002501764,0.000051054998,0.00002667355,0.000052808493,0.00012386922,0.0013218224],"genre_scores_gemma":[0.6270483,0.0028534054,0.36367953,0.00014640417,0.0004505012,0.00009969302,0.00021292278,0.0002991879,0.005210056],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978083,0.00006374264,0.00001646687,0.00003957727,0.00007904143,0.00002040717],"domain_scores_gemma":[0.99849665,0.0007490841,0.00028160328,0.000108836335,0.00027026102,0.00009344845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013184958,0.0005312486,0.00061979116,0.0020470142,0.00027285403,0.0010583098,0.00047508764,0.0010063286,0.0011359656],"category_scores_gemma":[0.0048226314,0.0004471513,0.00059911486,0.0010926776,0.0010399909,0.0018237706,0.0006506032,0.00083291996,0.00016124356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017729882,0.00012730696,0.00222495,0.0003597101,0.000102855716,0.00044684793,0.00041346627,0.25007436,0.09840375,0.46599102,0.0022916652,0.17938673],"study_design_scores_gemma":[0.0000063494217,0.000040802137,0.001228535,0.000012960753,0.000020094845,0.00022201228,0.000028234148,0.9400432,0.0039390596,0.05303956,0.00139495,0.000024284082],"about_ca_topic_score_codex":0.001023943,"about_ca_topic_score_gemma":0.0009248957,"teacher_disagreement_score":0.0020470142,"about_ca_system_score_codex":0.00033775668,"about_ca_system_score_gemma":0.00041260358,"threshold_uncertainty_score":0.0069729686},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033013849,0.0005667138,0.9949256,0.00013196707,0.000020768848,0.00004025747,0.000025730944,0.00052180205,0.00046570244],"genre_scores_gemma":[0.13649762,0.0007356866,0.8601316,0.00015282813,0.00007373123,0.00014781693,0.00013359825,0.00037657525,0.0017505272],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911505,0.00030779483,0.000056510424,0.0001409932,0.0003394725,0.000040203067],"domain_scores_gemma":[0.99835616,0.0010814874,0.00016007297,0.00019556444,0.00016793319,0.00003873685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018311448,0.0008931371,0.0010267313,0.0019011535,0.0004147573,0.0020322197,0.001412793,0.0020212682,0.0015657806],"category_scores_gemma":[0.0068959147,0.0016245309,0.0013964395,0.0011800611,0.0007524707,0.0014697886,0.0014146086,0.0014340892,0.0009833253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007474699,0.00016893313,0.0014131882,0.0006072821,0.0002504167,0.00042032657,0.00039202947,0.18548484,0.07476212,0.018881766,0.0036424177,0.71322906],"study_design_scores_gemma":[0.000039902883,0.00007344409,0.00057325314,0.000069957634,0.0000635582,0.000550053,0.000027646614,0.9536015,0.026186708,0.014554766,0.004222643,0.000036534482],"about_ca_topic_score_codex":0.00097151543,"about_ca_topic_score_gemma":0.0010793374,"teacher_disagreement_score":0.0020322197,"about_ca_system_score_codex":0.00061410427,"about_ca_system_score_gemma":0.00086615677,"threshold_uncertainty_score":0.009684145},"labels":[],"label_agreement":null},{"id":"W2056387355","doi":"10.1016/j.media.2011.05.017","title":"Brachytherapy seed reconstruction with joint-encoded C-arm single-axis rotation and motion compensation","year":2011,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","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":"BC Cancer Agency; University of British Columbia; Queen's University","funders":"National Cancer Institute","keywords":"Computer vision; Artificial intelligence; Fiducial marker; Fluoroscopy; Motion compensation; Computer science; Rotation (mathematics); Brachytherapy; Tracking (education); Prostate brachytherapy; Projection (relational algebra); Match moving; Mathematics; Motion (physics); Medicine; Algorithm; Radiation therapy; Radiology","score_opus":0.015524478349288343,"score_gpt":0.247808702962002,"score_spread":0.23228422461271364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056387355","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.089135274,0.000666679,0.90279347,0.00030283292,0.00008239334,0.00011385244,0.000431864,0.0026060808,0.0038675051],"genre_scores_gemma":[0.45464024,0.00033762533,0.5392463,0.00011137832,0.000022503578,0.00008397934,0.00052586844,0.0009375209,0.004094646],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979156,0.000044507862,0.000019991976,0.000030403175,0.000089416506,0.000024108831],"domain_scores_gemma":[0.9996302,0.0000833288,0.00007781252,0.00009604376,0.0000840257,0.000028570343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057032844,0.00069874525,0.000446132,0.00066737167,0.0002716732,0.0010399142,0.00056453637,0.00089222146,0.0030245562],"category_scores_gemma":[0.002084213,0.0006167373,0.00056586193,0.0009619133,0.0002460906,0.0005546501,0.0006082356,0.0007501288,0.0008138468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002721636,0.00020478545,0.004809004,0.000522347,0.00022803043,0.00038288016,0.0003326847,0.18222062,0.4423664,0.0055226465,0.0062917736,0.35439733],"study_design_scores_gemma":[0.00010710832,0.00017145708,0.0061445194,0.00005100725,0.00014742753,0.0012622689,0.000054700253,0.7664096,0.2182382,0.0015605369,0.0057220445,0.00013116366],"about_ca_topic_score_codex":0.0033186784,"about_ca_topic_score_gemma":0.0048913364,"teacher_disagreement_score":0.0033186784,"about_ca_system_score_codex":0.00040314128,"about_ca_system_score_gemma":0.0013779977,"threshold_uncertainty_score":0.010118127},"labels":[],"label_agreement":null},{"id":"W2057441779","doi":"10.1016/j.media.2014.10.004","title":"Right ventricle segmentation from cardiac MRI: A collation study","year":2014,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":242,"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; CARE Canada","funders":"Medical Research Council; National Science Council; Ministerio de Ciencia e Innovación; British Heart Foundation; Engineering and Physical Sciences Research Council; Comunidad de Madrid","keywords":"Hausdorff distance; Segmentation; Computer science; Artificial intelligence; Tracing; Metric (unit); Pattern recognition (psychology); Computer vision; Ventricle; Medicine","score_opus":0.005184458891641832,"score_gpt":0.33445430141747684,"score_spread":0.329269842525835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057441779","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94210535,0.0021857095,0.05087189,0.00018438524,0.000057950703,0.00016748664,0.0011518985,0.0005128249,0.002762577],"genre_scores_gemma":[0.96551025,0.0009789361,0.028740125,0.00010805777,0.000087382,0.000037540518,0.00211584,0.0005633401,0.0018585401],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9987457,0.0004609348,0.000108554734,0.00028859312,0.00026038766,0.00013576631],"domain_scores_gemma":[0.99450594,0.0035339,0.00033047446,0.0006445058,0.00082004914,0.00016503179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026920254,0.00077024434,0.00084357546,0.0026323148,0.0007901204,0.0018843622,0.00069770054,0.0013010653,0.003169129],"category_scores_gemma":[0.0069993567,0.0006290223,0.0010804438,0.0014043743,0.00066214625,0.0009899663,0.001136831,0.00046266953,0.0012650093],"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":[0.011213348,0.001389311,0.13683166,0.0016506661,0.002422217,0.009691466,0.004869888,0.029470256,0.34971976,0.001965606,0.005218328,0.44555753],"study_design_scores_gemma":[0.00085160526,0.0041544144,0.41245937,0.00028237948,0.0035194175,0.02932054,0.003279593,0.34315532,0.18220513,0.0018597841,0.018606208,0.00030626755],"about_ca_topic_score_codex":0.0039934767,"about_ca_topic_score_gemma":0.0041911723,"teacher_disagreement_score":0.0039934767,"about_ca_system_score_codex":0.00043406367,"about_ca_system_score_gemma":0.00050454703,"threshold_uncertainty_score":0.014236987},"labels":[],"label_agreement":null},{"id":"W2060830357","doi":"10.1016/j.media.2011.10.001","title":"Tumor invasion margin on the Riemannian space of brain fibers","year":2011,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","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":"Athabasca University; University of Alberta","funders":"","keywords":"Geodesic; Glioma; Margin (machine learning); Diffusion MRI; Magnetic resonance imaging; Fiber tract; Lesion; Brain tumor; Infiltration (HVAC); Computer science; Medicine; Mathematics; Pathology; Radiology; Mathematical analysis; Physics; Cancer research","score_opus":0.07676911403623363,"score_gpt":0.3431612832338788,"score_spread":0.2663921691976452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060830357","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8890348,0.0007255138,0.10148879,0.00046303583,0.000017264203,0.000033223587,0.00027825436,0.00015266592,0.00780633],"genre_scores_gemma":[0.9830282,0.00036629438,0.013818092,0.000031178224,0.000042679636,0.000019497944,0.00012030305,0.00006666347,0.002507252],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998223,0.00004895479,0.0000075051043,0.00003574999,0.000064965054,0.000020574511],"domain_scores_gemma":[0.9990433,0.00035075162,0.00019248467,0.00005114276,0.00019390075,0.00016843945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048540582,0.00052348094,0.00026311385,0.0015614891,0.0002575763,0.0009999522,0.00031171553,0.00035482278,0.002615344],"category_scores_gemma":[0.0023916399,0.00019191763,0.00021705174,0.00038958475,0.00069012505,0.0008568248,0.0009690768,0.00046212118,0.00038287762],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018925706,0.000253465,0.051890943,0.00062908424,0.00022955306,0.002849629,0.001879552,0.06755116,0.21548183,0.41484028,0.004223023,0.23827893],"study_design_scores_gemma":[0.000105000065,0.0007889152,0.21383111,0.00017756983,0.00020997658,0.0044307113,0.0006378809,0.52507627,0.027494544,0.21506736,0.011998717,0.00018200923],"about_ca_topic_score_codex":0.0014413306,"about_ca_topic_score_gemma":0.0013269704,"teacher_disagreement_score":0.002615344,"about_ca_system_score_codex":0.00041204345,"about_ca_system_score_gemma":0.00028023394,"threshold_uncertainty_score":0.008749187},"labels":[],"label_agreement":null},{"id":"W2062366581","doi":"10.1016/j.media.2010.08.006","title":"Learning to estimate out-of-plane motion in ultrasound imagery of real tissue","year":2010,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Ultrasound Imaging and Elastography","field":"Medicine","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":"École de Technologie Supérieure; McGill University","funders":"","keywords":"Decorrelation; Speckle pattern; Artificial intelligence; Computer vision; Computer science; Position (finance); Transducer; Process (computing); Heuristic; Acoustics; Physics","score_opus":0.006265516535536988,"score_gpt":0.30774618299346,"score_spread":0.30148066645792304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062366581","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07277035,0.0002653013,0.92575556,0.00016262657,0.000022460035,0.000033250206,0.000057740894,0.00044857568,0.0004841045],"genre_scores_gemma":[0.6240777,0.0006163124,0.37082967,0.00026043958,0.00010247944,0.00010532238,0.0004664584,0.000130782,0.0034109147],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997477,0.0000558317,0.000019769124,0.00008989609,0.000056454122,0.000030327594],"domain_scores_gemma":[0.99834585,0.001142626,0.00017034596,0.00011159731,0.00017604789,0.00005357077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086005655,0.0007511943,0.0006197586,0.0004652084,0.00018786691,0.0005622928,0.0005461466,0.0011429982,0.00084464403],"category_scores_gemma":[0.003872572,0.0004355722,0.0004604463,0.00041084914,0.00048949185,0.0008182127,0.00073739066,0.0011483594,0.0004136827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026297139,0.00019533132,0.004142749,0.00017186518,0.00011615225,0.00010534541,0.00015014342,0.25843337,0.056031354,0.0019984948,0.001606561,0.67678565],"study_design_scores_gemma":[0.000008656366,0.00009067948,0.0012944045,0.000006616526,0.000014516814,0.000085391446,0.000020737163,0.9908313,0.0058886143,0.001366858,0.00038473893,0.0000073959604],"about_ca_topic_score_codex":0.0021478983,"about_ca_topic_score_gemma":0.00242087,"teacher_disagreement_score":0.0021478983,"about_ca_system_score_codex":0.00022111385,"about_ca_system_score_gemma":0.000574916,"threshold_uncertainty_score":0.0045484304},"labels":[],"label_agreement":null},{"id":"W2063727717","doi":"10.1016/j.media.2012.01.002","title":"A CANDLE for a deeper in vivo insight","year":2012,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":72,"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; Deutscher Akademischer Austauschdienst; McGill University","keywords":"Candle; Smoothing; Filter (signal processing); Artificial intelligence; Noise reduction; Computer vision; Computer science; Noise (video); Optics; Materials science; Pattern recognition (psychology); Physics; Chemistry; Image (mathematics)","score_opus":0.006283904015054822,"score_gpt":0.3113302873830297,"score_spread":0.3050463833679749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063727717","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018097298,0.07134716,0.72074395,0.12940706,0.014516156,0.00011853763,0.0011708152,0.0049232277,0.039675746],"genre_scores_gemma":[0.22556053,0.06488851,0.5696627,0.04923508,0.0123958625,0.00025077528,0.0012792674,0.0021972326,0.07453009],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99937797,0.00016458704,0.000043075604,0.00007414806,0.0002798188,0.000060263323],"domain_scores_gemma":[0.9929015,0.0026472004,0.00019074933,0.0025356764,0.0011403902,0.00058437575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00280193,0.0013737744,0.0016087121,0.001236793,0.0013455732,0.003576333,0.0020235914,0.0037601069,0.021556353],"category_scores_gemma":[0.0061854697,0.0004965054,0.00085999264,0.0005296322,0.004247141,0.013575386,0.0031542461,0.00898849,0.005522928],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007420411,0.00030660824,0.0010442565,0.0022961367,0.00024137508,0.0013811806,0.00059285975,0.004104038,0.202502,0.43244615,0.10629739,0.24804594],"study_design_scores_gemma":[0.00009845903,0.000318707,0.0017840764,0.00051690196,0.00010228605,0.0037868493,0.00071104674,0.009773854,0.041088738,0.6019847,0.33958915,0.00024527134],"about_ca_topic_score_codex":0.0004744907,"about_ca_topic_score_gemma":0.00049923256,"teacher_disagreement_score":0.021556353,"about_ca_system_score_codex":0.0005681,"about_ca_system_score_gemma":0.00078163197,"threshold_uncertainty_score":0.07211316},"labels":[],"label_agreement":null},{"id":"W2068854578","doi":"10.1016/j.media.2011.01.006","title":"Automatic inference of articulated spine models in CT images using high-order Markov Random Fields","year":2011,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","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":"Centre Hospitalier Universitaire Sainte-Justine","funders":"Institut national de recherche en informatique et en automatique (INRIA)","keywords":"Markov random field; Context (archaeology); Computer science; Artificial intelligence; Markov chain; Transformation (genetics); Inference; Pattern recognition (psychology); Mathematics; Algorithm; Machine learning; Image (mathematics); Image segmentation","score_opus":0.01518001686626846,"score_gpt":0.2554743617237202,"score_spread":0.2402943448574517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068854578","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010600898,0.00018069558,0.9881512,0.000102908154,0.00001806028,0.000020177944,0.00007665921,0.00069860986,0.00015084598],"genre_scores_gemma":[0.60324776,0.00069665554,0.39139795,0.00023336442,0.00012402628,0.00014257351,0.0011685553,0.0004982525,0.002490828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909055,0.000268846,0.000057679386,0.00026455754,0.00021761848,0.00010084418],"domain_scores_gemma":[0.9948813,0.00382187,0.00046569653,0.00038515608,0.0003054937,0.00014044208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023191078,0.0008973569,0.0016114671,0.0018132679,0.0006292432,0.0018303345,0.0021037874,0.002408685,0.0014870103],"category_scores_gemma":[0.009259173,0.0023453014,0.0025738347,0.0010693693,0.0011803207,0.0018406142,0.0013407781,0.0031360267,0.00087114325],"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":[0.00026400248,0.00007983228,0.002131598,0.00011006951,0.00012819299,0.00018548148,0.000093806506,0.87686145,0.006546877,0.006896078,0.0013185536,0.10538416],"study_design_scores_gemma":[0.0000063978755,0.0000069947328,0.00019442836,0.0000061036203,0.000008806657,0.000027189764,0.0000030681974,0.995312,0.0005986123,0.0037363903,0.000093165676,0.0000068785384],"about_ca_topic_score_codex":0.012077094,"about_ca_topic_score_gemma":0.018984985,"teacher_disagreement_score":0.012077094,"about_ca_system_score_codex":0.0012313707,"about_ca_system_score_gemma":0.0019274931,"threshold_uncertainty_score":0.024013579},"labels":[],"label_agreement":null},{"id":"W2070074822","doi":"10.1016/j.media.2011.08.002","title":"A constrained independent component analysis technique for artery–vein separation of two-photon laser scanning microscopy images of the cerebral microvasculature","year":2011,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Blind Source Separation 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 Toronto; Sunnybrook Health Science Centre","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Independent component analysis; Cerebral blood flow; Biomedical engineering; Artificial intelligence; Hemodynamics; Computer science; Pattern recognition (psychology); Medicine; Cardiology","score_opus":0.012446014937098532,"score_gpt":0.29709385187753873,"score_spread":0.2846478369404402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070074822","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015489762,0.00009587471,0.9979031,0.000035712816,0.00001744028,0.000018403749,0.000031511463,0.00020322534,0.00014571197],"genre_scores_gemma":[0.024106111,0.00032684256,0.9738646,0.000041794407,0.00003984948,0.00013661946,0.00014676906,0.00012290773,0.0012144333],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965787,0.000107479216,0.00001997541,0.00006069409,0.00013033109,0.000023545294],"domain_scores_gemma":[0.99932253,0.00030422508,0.00005236102,0.000085858584,0.00020787939,0.000027141647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006966674,0.00083551806,0.0005750818,0.001001982,0.0005411898,0.0006384985,0.0008437752,0.0008053857,0.0019441035],"category_scores_gemma":[0.001963452,0.00050155923,0.0010400133,0.0011937727,0.00046948684,0.00061718305,0.0007844294,0.0014628363,0.0009227563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002672017,0.00014157992,0.00046804125,0.00025695973,0.00021331497,0.00012528879,0.00012862754,0.03995676,0.21285608,0.017746532,0.0054940465,0.7223456],"study_design_scores_gemma":[0.00004071213,0.00010665902,0.0025502378,0.000029914387,0.00013397611,0.00037955897,0.000028015269,0.89071596,0.084387094,0.010525242,0.010997241,0.000105371975],"about_ca_topic_score_codex":0.0041664066,"about_ca_topic_score_gemma":0.0056922645,"teacher_disagreement_score":0.0041664066,"about_ca_system_score_codex":0.0003812574,"about_ca_system_score_gemma":0.0014743508,"threshold_uncertainty_score":0.008284271},"labels":[],"label_agreement":null},{"id":"W2075139145","doi":"10.1016/j.media.2015.01.002","title":"Observation-driven adaptive differential evolution and its application to accurate and smooth bronchoscope three-dimensional motion tracking","year":2015,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Robotics and Sensor-Based Localization","field":"Engineering","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":"Robarts Clinical Trials; Western University","funders":"Japan Society for the Promotion of Science","keywords":"Computer vision; Tracking (education); Artificial intelligence; Computer science; Smoothness; Differential evolution; Mathematics","score_opus":0.02228749615254581,"score_gpt":0.24809980814439847,"score_spread":0.22581231199185267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075139145","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030182382,0.00014967332,0.9686234,0.000105744395,0.000037838472,0.00001469011,0.000011162067,0.00008269097,0.00079237693],"genre_scores_gemma":[0.80232185,0.00025938315,0.19470072,0.00005740095,0.00003070477,0.00006166757,0.000038425464,0.00004917692,0.0024806403],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998996,0.00002367922,0.000006350824,0.000020424877,0.000042574862,0.000007372638],"domain_scores_gemma":[0.99958426,0.00024268279,0.000042373802,0.00003239083,0.000080188795,0.000018013123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039807643,0.0002456888,0.00032110393,0.0002496399,0.00018041808,0.00032067162,0.0004425396,0.00061827217,0.0004867736],"category_scores_gemma":[0.0018276054,0.00023203525,0.00033317204,0.00029277906,0.00035011183,0.0003142435,0.0006668202,0.00045866476,0.00009715598],"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":[0.0001821484,0.00006544179,0.0026570172,0.00014657505,0.000060263086,0.00022553715,0.0003313111,0.6893994,0.06695136,0.021303946,0.0008127382,0.21786422],"study_design_scores_gemma":[0.0000011887215,0.000009713991,0.00013257947,0.0000010960565,0.0000014108356,0.000012674545,0.000001623394,0.9983071,0.0010198752,0.0003402139,0.00016988332,0.000002628606],"about_ca_topic_score_codex":0.0025809975,"about_ca_topic_score_gemma":0.0017269497,"teacher_disagreement_score":0.0025809975,"about_ca_system_score_codex":0.00028889385,"about_ca_system_score_gemma":0.00036333074,"threshold_uncertainty_score":0.00513196},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0060267923,0.00011747002,0.99313676,0.00012588422,0.000008208314,0.000020312094,0.00004204627,0.00022016242,0.00030236357],"genre_scores_gemma":[0.25701016,0.00044331595,0.7374957,0.0002666714,0.000094413874,0.00017234405,0.00047266294,0.000651393,0.0033933774],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913067,0.00032642993,0.000048916583,0.00017158609,0.00024396418,0.0000784406],"domain_scores_gemma":[0.99847466,0.0009138157,0.00014459739,0.00021053247,0.00019428466,0.00006210498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021923366,0.00065605604,0.001596188,0.0012394163,0.00044789445,0.0016204462,0.0018348651,0.001630686,0.0018863919],"category_scores_gemma":[0.005442857,0.001302555,0.0011717477,0.0011052039,0.0010132538,0.0018954842,0.002360187,0.0016178517,0.0007589379],"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":[0.0007527654,0.00013805754,0.0011126891,0.0002930328,0.00019575057,0.0002707607,0.00016505874,0.6322183,0.03412494,0.022877373,0.00356936,0.3042819],"study_design_scores_gemma":[0.000012661632,0.000021849255,0.0001866761,0.0000074736054,0.00001194125,0.0000875804,0.0000076296565,0.9890498,0.0040104887,0.006118076,0.0004772555,0.000008677252],"about_ca_topic_score_codex":0.0033365532,"about_ca_topic_score_gemma":0.003661911,"teacher_disagreement_score":0.0033365532,"about_ca_system_score_codex":0.00072090677,"about_ca_system_score_gemma":0.0014392266,"threshold_uncertainty_score":0.0115942955},"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":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"protocol","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016602468,0.00046549417,0.88817674,0.00018670328,0.00029443981,0.069613926,0.005224386,0.014404106,0.0050317696],"genre_scores_gemma":[0.025616735,0.000360263,0.85326815,0.00014317459,0.00006229154,0.10730509,0.0062373723,0.0017257483,0.005281172],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98992467,0.003665854,0.0023128972,0.0010022474,0.0026438513,0.00045045812],"domain_scores_gemma":[0.9652756,0.011131899,0.0011513497,0.010882115,0.010856621,0.00070233486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01743286,0.002309317,0.0019267512,0.0042774486,0.0024414547,0.0020198913,0.002717587,0.0023200712,0.0315603],"category_scores_gemma":[0.039124724,0.0017316121,0.0012734897,0.002716689,0.0014626544,0.0012013934,0.0023969715,0.002466135,0.01258166],"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":[0.007009707,0.0037823583,0.0029419274,0.0035751988,0.00034678975,0.0014106822,0.0023838365,0.009733675,0.22099084,0.02292382,0.05350582,0.67139536],"study_design_scores_gemma":[0.004638739,0.009958337,0.020962572,0.0019477793,0.0007151646,0.008542384,0.0012825992,0.07812492,0.49270886,0.034936726,0.3447048,0.0014771689],"about_ca_topic_score_codex":0.0011674205,"about_ca_topic_score_gemma":0.0019300568,"teacher_disagreement_score":0.0315603,"about_ca_system_score_codex":0.0011077282,"about_ca_system_score_gemma":0.0039570048,"threshold_uncertainty_score":0.105579734},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006726515,0.00064965285,0.99095213,0.00010119587,0.00006470362,0.000042412306,0.000075322685,0.0002909863,0.0010970752],"genre_scores_gemma":[0.23465642,0.0014573338,0.75793374,0.00022934894,0.00028429244,0.0002754996,0.0006236944,0.000586986,0.00395264],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99687445,0.00085532153,0.00022473009,0.000388291,0.0015786326,0.00007850642],"domain_scores_gemma":[0.99788505,0.00068422355,0.00038096472,0.00037020026,0.00057261175,0.000106822234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020694102,0.00077914,0.0012884008,0.002840745,0.0004327474,0.0015687064,0.0016018159,0.0012757028,0.0011825428],"category_scores_gemma":[0.0060849115,0.00043567127,0.00089546165,0.0024378062,0.00097053894,0.002848326,0.0023239309,0.0009106147,0.00063264766],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000501432,0.00025007653,0.002606128,0.000636202,0.0004434279,0.0002611877,0.0002851658,0.12809066,0.10334197,0.073178485,0.0077521466,0.682653],"study_design_scores_gemma":[0.000020363974,0.00034853275,0.0028488946,0.00004059589,0.00012109447,0.0008651372,0.00006706005,0.92515266,0.032711513,0.025570646,0.012133873,0.00011952774],"about_ca_topic_score_codex":0.0007151282,"about_ca_topic_score_gemma":0.0011125554,"teacher_disagreement_score":0.002840745,"about_ca_system_score_codex":0.0007161645,"about_ca_system_score_gemma":0.0009594283,"threshold_uncertainty_score":0.010944247},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008852158,0.00016344881,0.9896388,0.00005897202,0.000031503347,0.00004431048,0.000041523035,0.0008701008,0.00029922143],"genre_scores_gemma":[0.09457403,0.0002894939,0.90311706,0.00007405903,0.000078333906,0.00013698454,0.00014355706,0.00024323909,0.001343326],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994337,0.00010199915,0.000039671926,0.00007363368,0.0003187478,0.000032292442],"domain_scores_gemma":[0.9985154,0.0006336697,0.00013555882,0.00012206712,0.0005224083,0.000070834714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082414434,0.00067508296,0.000624171,0.0026734027,0.00040622265,0.00076954736,0.00096391555,0.00080055796,0.0018801786],"category_scores_gemma":[0.0025188685,0.0006442453,0.00072235573,0.0010450283,0.0007188281,0.00074022415,0.00065432454,0.0006367739,0.00079633103],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030274232,0.00010428107,0.0017459714,0.00020007584,0.000103421415,0.00017837113,0.00011723898,0.01601825,0.28303337,0.0034430765,0.0022278624,0.6925253],"study_design_scores_gemma":[0.00006184411,0.00033151815,0.012502772,0.000031787415,0.00017461217,0.002665314,0.00010767111,0.77354497,0.19755651,0.0036481018,0.0092171375,0.00015773994],"about_ca_topic_score_codex":0.001540347,"about_ca_topic_score_gemma":0.0033593297,"teacher_disagreement_score":0.0026734027,"about_ca_system_score_codex":0.0004382329,"about_ca_system_score_gemma":0.0010909915,"threshold_uncertainty_score":0.00628978},"labels":[],"label_agreement":null},{"id":"W2099275521","doi":"10.1016/j.media.2008.02.003","title":"A geometric flow for segmenting vasculature in proton-density weighted MRI","year":2008,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced MRI Techniques and Applications","field":"Medicine","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":"Montreal Neurological Institute and Hospital; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Segmentation; Magnetic resonance imaging; Computer vision; Pattern recognition (psychology); Radiology; Medicine","score_opus":0.013182058574639463,"score_gpt":0.3154482726148905,"score_spread":0.30226621404025106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099275521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030216684,0.00016231477,0.995676,0.00008202118,0.000033209788,0.000080239675,0.00006929565,0.00068820844,0.00018703885],"genre_scores_gemma":[0.023886424,0.00046342236,0.9743533,0.00005545553,0.00008834605,0.00014108655,0.00017635501,0.00020989723,0.00062571955],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997075,0.00006927471,0.000025757256,0.00006515886,0.00010131676,0.000030927044],"domain_scores_gemma":[0.99926156,0.00032059263,0.000068794056,0.000075813165,0.00021837951,0.00005485023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001399443,0.0011270274,0.0009657752,0.003220651,0.0007364359,0.0016466704,0.0012799595,0.0016427306,0.0028634733],"category_scores_gemma":[0.003025314,0.0007260477,0.0011331744,0.0016073778,0.0008453111,0.0015717617,0.001162885,0.0010905452,0.0011648673],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000288279,0.00013797985,0.0015351856,0.000329657,0.00006775785,0.00021218951,0.00014762674,0.060459297,0.04998422,0.021538055,0.0054003345,0.85989946],"study_design_scores_gemma":[0.00006253702,0.00022533015,0.001242792,0.000053215736,0.00009131396,0.00069540413,0.000047235822,0.94618857,0.026781062,0.016036998,0.008515892,0.00005953813],"about_ca_topic_score_codex":0.0048942436,"about_ca_topic_score_gemma":0.0034058592,"teacher_disagreement_score":0.0048942436,"about_ca_system_score_codex":0.0007175304,"about_ca_system_score_gemma":0.001834149,"threshold_uncertainty_score":0.009731531},"labels":[],"label_agreement":null},{"id":"W2103187823","doi":"10.1016/j.media.2015.04.008","title":"Pico Lantern: Surface reconstruction and augmented reality in laparoscopic surgery using a pick-up laser projector","year":2015,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":30,"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":"Engineering and Physical Sciences Research Council; Canadian Institutes of Health Research","keywords":"Computer vision; Artificial intelligence; Augmented reality; Computer science; Projector; Surface reconstruction; Biomedical engineering; Medicine; Computer graphics (images); Surface (topology); Mathematics","score_opus":0.05706451839276741,"score_gpt":0.324873144666533,"score_spread":0.26780862627376556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103187823","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060639955,0.0007888683,0.9248115,0.00022160076,0.00011311741,0.0003406306,0.00034440926,0.0062594367,0.0064805653],"genre_scores_gemma":[0.23232484,0.00067234697,0.75765175,0.00015503005,0.00007088208,0.0002568761,0.0003516403,0.0010283838,0.0074883113],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992567,0.00012444347,0.000033876717,0.00008441461,0.00044501913,0.000055563665],"domain_scores_gemma":[0.9994881,0.00020811954,0.00002944259,0.00012537309,0.00009446919,0.000054406035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008860501,0.0006659666,0.000659985,0.00095656636,0.00031755117,0.0012061979,0.0013413011,0.00073794776,0.0104871215],"category_scores_gemma":[0.0012961482,0.0009074183,0.0007682593,0.00062617747,0.00046002143,0.0010367897,0.0021425346,0.0009857706,0.0012281927],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002023727,0.00024633147,0.0036928516,0.0005500294,0.00017474782,0.0010514468,0.0006075962,0.0107972,0.2569263,0.005363074,0.0071291705,0.7114375],"study_design_scores_gemma":[0.0005452782,0.0026072406,0.024453666,0.00027339274,0.00042409525,0.020608028,0.00046914554,0.4028316,0.47257793,0.0029890991,0.07160496,0.0006155949],"about_ca_topic_score_codex":0.0011435781,"about_ca_topic_score_gemma":0.0014921621,"teacher_disagreement_score":0.0104871215,"about_ca_system_score_codex":0.00026002005,"about_ca_system_score_gemma":0.00076854636,"threshold_uncertainty_score":0.035082877},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13323553,0.09409851,0.7212667,0.017452246,0.0015045896,0.0016417023,0.006858001,0.01253866,0.011404114],"genre_scores_gemma":[0.34446433,0.01575528,0.6153658,0.002634051,0.00092195196,0.00042468833,0.009487233,0.0041905968,0.0067561083],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9843444,0.007272494,0.00092244445,0.0016527851,0.0053587123,0.00044911698],"domain_scores_gemma":[0.9541973,0.032246705,0.0013565763,0.004221036,0.006960162,0.0010181669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024877874,0.003142558,0.003898339,0.0045950455,0.0015154853,0.0065323203,0.00478765,0.00569532,0.005149202],"category_scores_gemma":[0.06660043,0.0014714209,0.0022838754,0.0027223532,0.0016044948,0.0034076239,0.0025631264,0.0033969497,0.003088965],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018157461,0.00080367574,0.010371426,0.0033990543,0.0013459966,0.00020451403,0.0003112938,0.08365183,0.027118186,0.009169211,0.0323853,0.8294238],"study_design_scores_gemma":[0.00035373832,0.0019650853,0.009416789,0.0005988244,0.000512966,0.0013274761,0.0006887177,0.8895675,0.04451862,0.023920551,0.026980935,0.00014888159],"about_ca_topic_score_codex":0.0059172506,"about_ca_topic_score_gemma":0.009161247,"teacher_disagreement_score":0.024877874,"about_ca_system_score_codex":0.0020994083,"about_ca_system_score_gemma":0.003921566,"threshold_uncertainty_score":0.13156837},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11978371,0.00062122906,0.8723594,0.00024738218,0.00014645676,0.00014441721,0.0002933523,0.0014763072,0.004927808],"genre_scores_gemma":[0.68440634,0.0012484533,0.29462862,0.00024005142,0.00012624054,0.00025429646,0.00083093485,0.0013041807,0.01696094],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99945766,0.00012878221,0.000039056016,0.0001344036,0.00018446654,0.000055695124],"domain_scores_gemma":[0.9994407,0.00019205012,0.00006922229,0.0001710165,0.000101925674,0.000025082682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009843512,0.00051087036,0.0005452205,0.0011609354,0.0004445151,0.0013627588,0.0004643945,0.0009347497,0.0036038477],"category_scores_gemma":[0.0028749232,0.00051801675,0.0005547384,0.0013407405,0.00047755425,0.0008620831,0.0010502692,0.00089073664,0.0012108382],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011858633,0.0003184496,0.0044094804,0.00041884326,0.00022608758,0.0005271498,0.0010306526,0.048294824,0.34359995,0.009694323,0.0039473907,0.586347],"study_design_scores_gemma":[0.000058308393,0.0006220843,0.047100697,0.00013814744,0.00036484026,0.0045925234,0.0009215862,0.5325968,0.36783838,0.014279913,0.031342305,0.00014442357],"about_ca_topic_score_codex":0.0014184228,"about_ca_topic_score_gemma":0.0029419747,"teacher_disagreement_score":0.0036038477,"about_ca_system_score_codex":0.00025847668,"about_ca_system_score_gemma":0.0008116582,"threshold_uncertainty_score":0.012056053},"labels":[],"label_agreement":null},{"id":"W2109762309","doi":"10.1016/j.media.2014.11.009","title":"Non-invasive evaluation of breast cancer response to chemotherapy using quantitative ultrasonic backscatter parameters","year":2014,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":134,"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; Sunnybrook Health Science Centre; University of Toronto","funders":"National Institute of Biomedical Imaging and Bioengineering; Ontario Ministry of Research and Innovation; Canadian Institutes of Health Research; Research and Innovation Foundation","keywords":"Medicine; Ultrasound; Chemotherapy; Breast cancer; Backscatter (email); Radiology; Cancer; Nuclear medicine; Internal medicine","score_opus":0.02130449228885332,"score_gpt":0.35133687314363643,"score_spread":0.3300323808547831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109762309","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98343754,0.0034456758,0.010078026,0.000077246994,0.00003118109,0.00007494686,0.0002790566,0.00011338958,0.0024629245],"genre_scores_gemma":[0.9937464,0.00068860553,0.0043707513,0.000048719136,0.000035719466,0.000055327924,0.00017892875,0.000016978534,0.00085847965],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99946815,0.00023762854,0.000044312732,0.00006367137,0.00015499296,0.000031216045],"domain_scores_gemma":[0.9983683,0.00095126464,0.0002761977,0.00009964163,0.00024429028,0.000060190097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012569142,0.0003401014,0.0004775301,0.0009287255,0.00009040401,0.00037891127,0.00020774578,0.00029629815,0.0008594852],"category_scores_gemma":[0.002132628,0.0001493057,0.00018917063,0.00042284373,0.00023166055,0.00035277527,0.00020601468,0.000308827,0.00020832954],"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":[0.011431085,0.0005425133,0.43621063,0.00047945423,0.00030479225,0.0003175001,0.00036162004,0.0013329056,0.38513213,0.00021174758,0.00043255492,0.16324298],"study_design_scores_gemma":[0.00013741806,0.0049654134,0.82488567,0.00004454832,0.0004358956,0.0015494167,0.00043207244,0.01463038,0.15065154,0.0002750641,0.0019433104,0.000049329497],"about_ca_topic_score_codex":0.00025373147,"about_ca_topic_score_gemma":0.00046642034,"teacher_disagreement_score":0.0012569142,"about_ca_system_score_codex":0.0001331653,"about_ca_system_score_gemma":0.00012620483,"threshold_uncertainty_score":0.006647289},"labels":[],"label_agreement":null},{"id":"W2111627180","doi":"10.1016/j.media.2009.01.004","title":"Directional functions for orientation distribution estimation","year":2009,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","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":"University of Waterloo","funders":"National Center for Research Resources; National Institute of General Medical Sciences; National Institute of Mental Health","keywords":"Spherical harmonics; Orientation (vector space); Interpolation (computer graphics); Spherical coordinate system; Computer science; Unit sphere; Tractography; Geodesic; Algorithm; Diffusion MRI; Artificial intelligence; Mathematics; Computer vision; Mathematical analysis; Geometry; Image (mathematics)","score_opus":0.03070482850914545,"score_gpt":0.3971433175471677,"score_spread":0.36643848903802223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111627180","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012011859,0.00017296159,0.99805486,0.000035077406,0.000013137028,0.000007456867,0.000081485494,0.00017940004,0.0002544709],"genre_scores_gemma":[0.072142474,0.0013517879,0.9216782,0.000081459206,0.000079271354,0.0001341448,0.0010188097,0.00042892736,0.0030849553],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995346,0.00019179707,0.0000285483,0.00007743717,0.00013558542,0.000031986852],"domain_scores_gemma":[0.9983785,0.0007590863,0.000119512806,0.00029150234,0.00039656562,0.00005483072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012867164,0.00090329914,0.0009020736,0.0014926383,0.00033728287,0.000975865,0.00087983214,0.0009391838,0.002629297],"category_scores_gemma":[0.0062585487,0.0006244655,0.0008951302,0.0017441298,0.00039967557,0.0010812423,0.0008020866,0.0012610244,0.002041106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002514917,0.000094486284,0.0013740538,0.00022811112,0.000114811904,0.00009391188,0.00008325886,0.1382952,0.024782408,0.06703845,0.010133981,0.7575098],"study_design_scores_gemma":[0.00001780756,0.000029746692,0.0010039897,0.000040037598,0.000047759324,0.00019455471,0.00002541537,0.9546271,0.008013881,0.027324704,0.008642383,0.00003254652],"about_ca_topic_score_codex":0.0040433067,"about_ca_topic_score_gemma":0.0034670278,"teacher_disagreement_score":0.0040433067,"about_ca_system_score_codex":0.00040096047,"about_ca_system_score_gemma":0.0008541372,"threshold_uncertainty_score":0.008795917},"labels":[],"label_agreement":null},{"id":"W2112571055","doi":"10.1016/j.media.2015.04.001","title":"Globally optimal co-segmentation of three-dimensional pulmonary 1H and hyperpolarized 3He MRI with spatial consistence prior","year":2015,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Atomic and Subatomic Physics Research","field":"Physics and Astronomy","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":"Robarts Clinical Trials; Western University","funders":"Canadian Institutes of Health Research","keywords":"Segmentation; Convex optimization; Robustness (evolution); Image segmentation; Computer science; Algorithm; Artificial intelligence; Regular polygon; Mathematical optimization; Pattern recognition (psychology); Mathematics","score_opus":0.014910975680764152,"score_gpt":0.2915944086527316,"score_spread":0.27668343297196746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112571055","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058762338,0.001152948,0.9359143,0.0006037249,0.000060506332,0.000087064655,0.00042792465,0.001463519,0.001527808],"genre_scores_gemma":[0.50659263,0.0012388146,0.48326266,0.00035579407,0.0001633784,0.00024579646,0.0020227917,0.0009197887,0.0051983655],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995951,0.00008058733,0.000029424073,0.00012522361,0.00009917469,0.00007052715],"domain_scores_gemma":[0.9990584,0.00042026397,0.00014385037,0.00011826402,0.00018720269,0.00007194478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012432305,0.001026432,0.0015849499,0.0016771124,0.0005997985,0.002259042,0.0013955322,0.0030532978,0.0011751418],"category_scores_gemma":[0.0031542582,0.0011187434,0.0014014769,0.0014518318,0.001083548,0.001095009,0.0015065947,0.0013105845,0.0008744406],"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":[0.0013906164,0.00029529535,0.0035979059,0.00066360214,0.00035700496,0.00062574836,0.00042815963,0.6313983,0.08660985,0.009559339,0.0054862844,0.25958785],"study_design_scores_gemma":[0.000019709772,0.00004262234,0.0010736056,0.00002667373,0.00004697821,0.00017470877,0.00003993302,0.98033327,0.011728291,0.0050147166,0.0014704884,0.000028941085],"about_ca_topic_score_codex":0.008429585,"about_ca_topic_score_gemma":0.016172094,"teacher_disagreement_score":0.008429585,"about_ca_system_score_codex":0.0008110216,"about_ca_system_score_gemma":0.0027809527,"threshold_uncertainty_score":0.016761065},"labels":[],"label_agreement":null},{"id":"W2118740630","doi":"10.1016/j.media.2008.02.002","title":"Regions, systems, and the brain: Hierarchical measures of functional integration in fMRI","year":2008,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":78,"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; Université de Montréal","funders":"","keywords":"Functional integration; Computer science; Information flow; Mutual information; Information integration; Artificial intelligence; Functional connectivity; Neuroscience; Measure (data warehouse); Resting state fMRI; Cognitive science; Machine learning; Psychology; Data mining; Mathematics","score_opus":0.05515823734104367,"score_gpt":0.274552352493841,"score_spread":0.21939411515279736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118740630","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6431163,0.0051838923,0.33896852,0.0010024613,0.000042518168,0.00017751826,0.0007172105,0.00057876593,0.010212835],"genre_scores_gemma":[0.9340232,0.00054988323,0.06462495,0.000066209665,0.00004385885,0.00009074748,0.00015399788,0.00006570069,0.00038144315],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994728,0.00024119919,0.000025964466,0.00010683763,0.000104704275,0.00004849783],"domain_scores_gemma":[0.9989806,0.000594192,0.0001707937,0.00012641332,0.00007202945,0.000056003744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017406505,0.00044458633,0.00033216635,0.0031480226,0.00039966547,0.001280862,0.00042178854,0.0005074174,0.0011328824],"category_scores_gemma":[0.005668948,0.00032868766,0.00039064037,0.0024790685,0.0010482506,0.0021581212,0.0007716645,0.00054728385,0.00010992666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009895415,0.0002712404,0.1296835,0.00090867456,0.0015856038,0.00031028985,0.003755978,0.029732274,0.2231114,0.07838983,0.0036875238,0.5275741],"study_design_scores_gemma":[0.00004960845,0.00035318575,0.72368604,0.00011092512,0.0005423587,0.00078929635,0.0007982307,0.094234325,0.016422788,0.15965022,0.00323576,0.00012724385],"about_ca_topic_score_codex":0.0031452077,"about_ca_topic_score_gemma":0.009056394,"teacher_disagreement_score":0.0031480226,"about_ca_system_score_codex":0.00052689953,"about_ca_system_score_gemma":0.00051508227,"threshold_uncertainty_score":0.00920552},"labels":[],"label_agreement":null},{"id":"W2118891573","doi":"10.1016/s1361-8415(00)00008-6","title":"T-snakes: Topology adaptive snakes","year":2000,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":413,"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":"Topology (electrical circuits); Computer science; Artificial intelligence; Mathematics; Combinatorics","score_opus":0.0049221644127234645,"score_gpt":0.2713293990795815,"score_spread":0.266407234666858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118891573","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00067171355,0.000092216396,0.99551624,0.000039190658,0.00003202751,0.000033797933,0.00007524389,0.0031010928,0.0004384745],"genre_scores_gemma":[0.040505257,0.0005104403,0.95104027,0.00012759109,0.000049905433,0.0003093486,0.0005597596,0.002748276,0.0041492474],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993438,0.0001523756,0.000054175405,0.00011593583,0.00029438495,0.0000392377],"domain_scores_gemma":[0.99897563,0.0004769553,0.00006145113,0.00016803756,0.0002276353,0.00009020981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014013589,0.0010741036,0.0011637034,0.0013208513,0.00038758587,0.0016217243,0.0023665044,0.0018785999,0.012401923],"category_scores_gemma":[0.0045743394,0.0011667396,0.0009442753,0.0020701243,0.00088866183,0.00196136,0.0020459897,0.002355233,0.0052007586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034814185,0.00012582375,0.00037305066,0.0004611088,0.00013010636,0.00017747818,0.00021598444,0.1582202,0.025608387,0.048022565,0.032803588,0.7335136],"study_design_scores_gemma":[0.0000745112,0.00006615941,0.00018145332,0.000056946777,0.00003254278,0.00027826673,0.000028886101,0.9165018,0.014825107,0.04231697,0.025586616,0.000050665556],"about_ca_topic_score_codex":0.0008776861,"about_ca_topic_score_gemma":0.0010287502,"teacher_disagreement_score":0.012401923,"about_ca_system_score_codex":0.00037422404,"about_ca_system_score_gemma":0.00061691215,"threshold_uncertainty_score":0.04148853},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072477856,0.00024721856,0.9918121,0.0001551457,0.000012481552,0.000013825203,0.000040551684,0.00019601185,0.00027487293],"genre_scores_gemma":[0.21906914,0.0011363087,0.7753791,0.000213525,0.00015624893,0.00015799291,0.00040277588,0.00053514773,0.0029496858],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993405,0.00022866837,0.00004323619,0.00016139881,0.00014681283,0.000079383266],"domain_scores_gemma":[0.9981153,0.0011669515,0.00018378676,0.0002154457,0.00021856712,0.000099950485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033954682,0.0010809102,0.0018315405,0.0020742528,0.0008433002,0.0018374816,0.0013460206,0.0019871907,0.0012684822],"category_scores_gemma":[0.006812469,0.0009785034,0.0010951608,0.0012565491,0.0013653068,0.0024576916,0.002167526,0.0022066506,0.00051576935],"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":[0.0008261233,0.00017979852,0.0016870553,0.00044538986,0.00025245556,0.0001729767,0.00036738833,0.42201158,0.0637304,0.110565335,0.0044832462,0.39527822],"study_design_scores_gemma":[0.0000124871085,0.000038282295,0.0008256042,0.00002239835,0.00002671419,0.00008080877,0.000012362621,0.95893925,0.008180407,0.030807702,0.0010270793,0.000026904052],"about_ca_topic_score_codex":0.003992064,"about_ca_topic_score_gemma":0.0046230955,"teacher_disagreement_score":0.003992064,"about_ca_system_score_codex":0.0014130372,"about_ca_system_score_gemma":0.0020198375,"threshold_uncertainty_score":0.017957151},"labels":[],"label_agreement":null},{"id":"W2125700054","doi":"10.1016/j.media.2012.06.001","title":"Ultrasound–fluoroscopy registration for prostate brachytherapy dosimetry","year":2012,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","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":"Queen's University","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; National Cancer Institute; Cancer Care Ontario","keywords":"Image registration; Fluoroscopy; Prostate brachytherapy; Brachytherapy; Imaging phantom; Ultrasound; Fiducial marker; Medicine; Dosimetry; Prostate; Artificial intelligence; Computer vision; Computer science; Nuclear medicine; Radiology; Radiation therapy; Image (mathematics); Cancer","score_opus":0.006868823869743458,"score_gpt":0.319200043771111,"score_spread":0.31233121990136753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125700054","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048879314,0.0047000903,0.9305028,0.00036571367,0.00021282645,0.00021100152,0.00050399394,0.00623447,0.008389712],"genre_scores_gemma":[0.57271844,0.0017006663,0.41265917,0.00025027513,0.00008977599,0.00017704033,0.0007136788,0.0034079154,0.008283027],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99909616,0.0003030348,0.000080034384,0.000111557805,0.00034979358,0.00005946499],"domain_scores_gemma":[0.9984225,0.00069201214,0.00020798075,0.00032391824,0.00029654673,0.00005703348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014755614,0.0006005006,0.00062370626,0.0019994045,0.0007496429,0.0018529298,0.00096155267,0.0007691273,0.008583188],"category_scores_gemma":[0.0058402014,0.00093446876,0.0009839471,0.0016599497,0.00036260806,0.0007835885,0.0009677625,0.001026324,0.0026462208],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018241946,0.00019584688,0.0062331012,0.0008209979,0.00031656123,0.00025535136,0.000666372,0.035145894,0.28810728,0.00792323,0.0083016995,0.6502094],"study_design_scores_gemma":[0.00014282914,0.00084008614,0.0370317,0.00027765974,0.0009859343,0.0043706866,0.00023008375,0.27932927,0.5970165,0.00431612,0.07516147,0.00029765765],"about_ca_topic_score_codex":0.0031120603,"about_ca_topic_score_gemma":0.0036682175,"teacher_disagreement_score":0.008583188,"about_ca_system_score_codex":0.0007005425,"about_ca_system_score_gemma":0.0015490893,"threshold_uncertainty_score":0.028713644},"labels":[],"label_agreement":null},{"id":"W2126675188","doi":"10.1016/j.media.2008.06.002","title":"Robotic assistance for ultrasound-guided prostate brachytherapy","year":2008,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":126,"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":"National Cancer Institute; Engineering Research Centers","keywords":"Brachytherapy; Prostate brachytherapy; Prostate; Ultrasound; Artificial intelligence; Computer science; Medical physics; Medicine; Computer vision; Radiology; Radiation therapy; Internal medicine","score_opus":0.012192385800863625,"score_gpt":0.30938550927034125,"score_spread":0.2971931234694776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126675188","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3054297,0.0064395526,0.6438556,0.0006432267,0.00054451916,0.00029955682,0.0005807941,0.0075441315,0.034662914],"genre_scores_gemma":[0.8222004,0.0010736345,0.16176344,0.00026290392,0.00017271019,0.00013457779,0.0002137035,0.00042552475,0.013753149],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999813,0.00003812687,0.000011935345,0.000032401273,0.00008381591,0.000020747415],"domain_scores_gemma":[0.999775,0.00009595705,0.000029137367,0.00004515368,0.00003655115,0.000018263929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002661389,0.00046758872,0.00025068078,0.00034747316,0.00034873557,0.000496937,0.0006501358,0.0004058768,0.0054199398],"category_scores_gemma":[0.0011080143,0.0002964256,0.000289416,0.00021939832,0.0001982426,0.00038600565,0.00061212445,0.0002566876,0.0011930054],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011965985,0.000117704396,0.0030148062,0.0005873728,0.00016265606,0.001119771,0.00039991052,0.016886482,0.3644717,0.002651555,0.0111939665,0.59819746],"study_design_scores_gemma":[0.0003946474,0.0021119118,0.07336753,0.00025787443,0.0008118919,0.028280765,0.00033856957,0.3499288,0.3619833,0.0051667537,0.1768258,0.0005322197],"about_ca_topic_score_codex":0.0011855897,"about_ca_topic_score_gemma":0.002185258,"teacher_disagreement_score":0.0054199398,"about_ca_system_score_codex":0.00019246555,"about_ca_system_score_gemma":0.00038139478,"threshold_uncertainty_score":0.018131495},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018092928,0.00044986416,0.97933125,0.00016421494,0.000021862086,0.00003628595,0.00009085266,0.00078671915,0.001026016],"genre_scores_gemma":[0.22112018,0.00050101546,0.7736981,0.0001244005,0.00004977204,0.000118533535,0.00028207357,0.00056562776,0.0035402954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997008,0.000072592215,0.000016836517,0.00005712224,0.00011555694,0.00003718501],"domain_scores_gemma":[0.9996861,0.00014095439,0.000045049102,0.000032309657,0.00007490795,0.000020666923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006795083,0.00067428173,0.000987022,0.0015063,0.00047336807,0.0017078073,0.00078435044,0.0010922748,0.002098709],"category_scores_gemma":[0.0012967126,0.00080934045,0.0009946722,0.0012225762,0.00049860077,0.00065101753,0.001074383,0.0007916605,0.0005576448],"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":[0.00080363295,0.00012422576,0.0019541788,0.00049873226,0.00020518256,0.00018398515,0.00024262961,0.6177154,0.0735143,0.014081827,0.0030497594,0.28762618],"study_design_scores_gemma":[0.000013853927,0.000020940563,0.0004505083,0.000009905082,0.000019277704,0.00007962378,0.000014191976,0.9908019,0.005703576,0.0017751176,0.0010956945,0.000015435715],"about_ca_topic_score_codex":0.007598628,"about_ca_topic_score_gemma":0.0106931375,"teacher_disagreement_score":0.007598628,"about_ca_system_score_codex":0.0007953134,"about_ca_system_score_gemma":0.0012792094,"threshold_uncertainty_score":0.015108824},"labels":[],"label_agreement":null},{"id":"W2127556538","doi":"10.1016/j.media.2012.07.001","title":"Tongue contour tracking in dynamic ultrasound via higher-order MRFs and efficient fusion moves","year":2012,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Traditional Chinese Medicine Studies","field":"Medicine","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 Toronto; Simon Fraser University","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Computer vision; Markov random field; Regularization (linguistics); Hidden Markov model; Energy minimization; Tracking (education); Image segmentation; Pattern recognition (psychology)","score_opus":0.009603979836587473,"score_gpt":0.30489716795757427,"score_spread":0.2952931881209868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127556538","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0441903,0.0005483441,0.95088947,0.00018095873,0.00007522784,0.000039443516,0.00009831305,0.00069926836,0.0032786322],"genre_scores_gemma":[0.3879387,0.0007322945,0.6067668,0.00008965174,0.000092538176,0.00007878006,0.00028339913,0.00022634966,0.003791511],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996383,0.000077131284,0.000025542322,0.000065416345,0.00014471497,0.00004897421],"domain_scores_gemma":[0.99940646,0.00018822204,0.0000746437,0.00012831896,0.0001634631,0.00003885145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066925195,0.00055017485,0.00057040027,0.0012838122,0.00054860575,0.0011406743,0.0006353272,0.000908099,0.0022706771],"category_scores_gemma":[0.002189421,0.00049953384,0.00068272994,0.0013973855,0.00035010217,0.0017236836,0.0012245447,0.00066379225,0.0009944456],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050598657,0.0001376118,0.003001609,0.0002664326,0.00010611407,0.00023905742,0.00036730303,0.07212844,0.23337224,0.010307822,0.0021217412,0.6774457],"study_design_scores_gemma":[0.000015110008,0.00011530447,0.0047398414,0.000026304993,0.000071615694,0.0004628268,0.00007753308,0.9240429,0.06149219,0.003497761,0.005403692,0.00005490316],"about_ca_topic_score_codex":0.0025622256,"about_ca_topic_score_gemma":0.0026396685,"teacher_disagreement_score":0.0025622256,"about_ca_system_score_codex":0.0002732721,"about_ca_system_score_gemma":0.00073202397,"threshold_uncertainty_score":0.007596135},"labels":[],"label_agreement":null},{"id":"W2132017284","doi":"10.1016/j.media.2010.02.003","title":"A fast and robust patient specific Finite Element mesh registration technique: Application to 60 clinical cases","year":2010,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"3D Shape Modeling and Analysis","field":"Engineering","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":"University of British Columbia","funders":"","keywords":"Mesh generation; Finite element method; Computer science; Representation (politics); Computation; Algorithm; Process (computing); Computer vision; Artificial intelligence","score_opus":0.014806047416593082,"score_gpt":0.2783070879995928,"score_spread":0.2635010405829997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132017284","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47026184,0.0015595856,0.5211859,0.0003052136,0.00009499291,0.00065338094,0.00061822997,0.0020636143,0.0032572718],"genre_scores_gemma":[0.71579283,0.0009908451,0.28016168,0.000120478195,0.000052810054,0.00024859296,0.00064172765,0.00065753085,0.0013335192],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991285,0.00020490657,0.00015243243,0.00018621492,0.00027245664,0.000055415836],"domain_scores_gemma":[0.9978194,0.001030146,0.00012922879,0.000680733,0.0002572282,0.00008327428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022023648,0.0007870023,0.0010186024,0.0025189526,0.00053360587,0.00078137283,0.0010010279,0.0013649534,0.0025222157],"category_scores_gemma":[0.0064552915,0.00088981754,0.0007626023,0.0017735942,0.0008220033,0.00053512957,0.001529203,0.0007744672,0.0014207055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010981599,0.00062371383,0.02184268,0.0005874054,0.00019932298,0.0038981172,0.0012084796,0.027391262,0.07723359,0.0009488604,0.0026702103,0.86229825],"study_design_scores_gemma":[0.0007073427,0.0035134132,0.15292007,0.00033969924,0.0013012133,0.09369417,0.001938632,0.5092635,0.1961784,0.009789847,0.029725207,0.0006285053],"about_ca_topic_score_codex":0.0010011776,"about_ca_topic_score_gemma":0.001490468,"teacher_disagreement_score":0.0025222157,"about_ca_system_score_codex":0.00024789968,"about_ca_system_score_gemma":0.0007149132,"threshold_uncertainty_score":0.011647344},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033806472,0.00030367615,0.9929153,0.00010549244,0.00008597267,0.000059071415,0.00013216065,0.002432169,0.0005854513],"genre_scores_gemma":[0.034442116,0.00034040326,0.955988,0.000236674,0.00012657681,0.00014321216,0.00068319414,0.000559711,0.007480135],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987613,0.00011607419,0.00007633851,0.00033976647,0.00061434525,0.00009217969],"domain_scores_gemma":[0.99911505,0.00015719392,0.00008435492,0.00019531633,0.00040114048,0.000046947407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001050568,0.0012353172,0.0017886815,0.0020166142,0.00064183254,0.0012669819,0.0020255405,0.002093646,0.0039183693],"category_scores_gemma":[0.0014217753,0.0010048284,0.0017001517,0.0011626529,0.0004911909,0.0012917966,0.0015608587,0.0012766795,0.004729534],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015593483,0.000090220885,0.0004128902,0.00011012889,0.00012577017,0.00007136447,0.00002768295,0.007183593,0.13540648,0.0016293785,0.007522539,0.8472641],"study_design_scores_gemma":[0.00007203881,0.00024753745,0.004813122,0.000043124368,0.0003045753,0.0022765466,0.00003860986,0.7689775,0.18566209,0.0068831756,0.030512037,0.00016967858],"about_ca_topic_score_codex":0.0024365783,"about_ca_topic_score_gemma":0.005027178,"teacher_disagreement_score":0.0039183693,"about_ca_system_score_codex":0.00043254116,"about_ca_system_score_gemma":0.0013289795,"threshold_uncertainty_score":0.013108313},"labels":[],"label_agreement":null},{"id":"W2134834581","doi":"10.1016/s1361-8415(00)00014-1","title":"An algorithmic overview of surface registration techniques for medical imaging","year":2000,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":411,"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; McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Computer science; Rigid transformation; Robustness (evolution); Image registration; Representation (politics); Artificial intelligence; Modalities; Transformation (genetics); Surface (topology); Similarity (geometry); Exploit; Similarity measure; Matching (statistics); Point set registration; Computer vision; Theoretical computer science; Point (geometry); Mathematics; Image (mathematics); Geometry","score_opus":0.01076959209935504,"score_gpt":0.3015921323986959,"score_spread":0.2908225402993409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134834581","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024042839,0.00077049754,0.9979971,0.00005975246,0.000023891742,0.000022180362,0.000022441474,0.00044323958,0.0004203453],"genre_scores_gemma":[0.0058527524,0.0023837339,0.98994315,0.00007917753,0.000100530015,0.000092413735,0.00023080717,0.00024137896,0.0010760028],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99870574,0.00023680097,0.00011665789,0.00021603888,0.00065727136,0.00006737644],"domain_scores_gemma":[0.9991479,0.00031946422,0.000049981187,0.00020749251,0.00024390813,0.00003119726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010073992,0.0013979658,0.0016817973,0.00289855,0.0008319443,0.003140151,0.002737145,0.0022678636,0.005155723],"category_scores_gemma":[0.0026210297,0.0012909116,0.0022291776,0.004332014,0.0009867351,0.0025087628,0.0020951808,0.003148636,0.006288183],"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":[0.000039619576,0.00006148549,0.00034350317,0.000497298,0.00007583304,0.00013107776,0.00012717572,0.026754389,0.016772062,0.031978544,0.007866747,0.9153523],"study_design_scores_gemma":[0.000039809813,0.00017801939,0.0012985537,0.0002055953,0.00011520517,0.001975873,0.00013765681,0.67801636,0.026772661,0.1684296,0.122682795,0.00014791296],"about_ca_topic_score_codex":0.0017467154,"about_ca_topic_score_gemma":0.0022400825,"teacher_disagreement_score":0.005155723,"about_ca_system_score_codex":0.00041049995,"about_ca_system_score_gemma":0.0010926547,"threshold_uncertainty_score":0.017247558},"labels":[],"label_agreement":null},{"id":"W2135407363","doi":"10.1016/j.media.2011.11.007","title":"Construction of 3D MR image-based computer models of pathologic hearts, augmented with histology and optical fluorescence imaging to characterize action potential propagation","year":2011,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":30,"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 Ottawa; Sunnybrook Health Science Centre; Sunnybrook Hospital; University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Artificial intelligence; Computer vision; Histology; Action (physics); Computer science; Image (mathematics); Fluorescence; Optical imaging; Pattern recognition (psychology); Biomedical engineering; Optics; Physics; Medicine; Pathology","score_opus":0.021161846180739845,"score_gpt":0.2794185111249943,"score_spread":0.2582566649442544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135407363","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09013918,0.00012574294,0.9055536,0.00015761038,0.000031500378,0.00021654837,0.00068856846,0.0014931846,0.0015940886],"genre_scores_gemma":[0.5137774,0.00037950656,0.48231798,0.000051419487,0.000010758731,0.00041159076,0.0010059736,0.0003747598,0.0016706644],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982953,0.000030243482,0.000014637182,0.000033429013,0.0000794209,0.000012747118],"domain_scores_gemma":[0.9995084,0.0002081069,0.00007126567,0.00009563112,0.00008666264,0.000029969893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039491113,0.0007993124,0.0005587243,0.0007153306,0.0003219263,0.0012492066,0.000988497,0.0008964676,0.0014638827],"category_scores_gemma":[0.0012854892,0.0008578745,0.0009337238,0.00044199175,0.000586776,0.00041557956,0.0006147754,0.00074219407,0.00039315244],"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":[0.00006418052,0.00006258149,0.0013159491,0.00010577433,0.000043135555,0.00021539534,0.0001782641,0.9409192,0.031066734,0.0043672295,0.000544166,0.02111732],"study_design_scores_gemma":[0.0000059103527,0.000023952207,0.00038411017,0.0000068661666,0.000012980407,0.00008056103,0.000018330113,0.9901592,0.0075785248,0.0007960698,0.00092269515,0.0000107384385],"about_ca_topic_score_codex":0.0059149736,"about_ca_topic_score_gemma":0.007977599,"teacher_disagreement_score":0.0059149736,"about_ca_system_score_codex":0.0005853223,"about_ca_system_score_gemma":0.0014591904,"threshold_uncertainty_score":0.011761129},"labels":[],"label_agreement":null},{"id":"W2149314739","doi":"10.1016/j.media.2006.06.009","title":"3D curve inference for diffusion MRI regularization and fibre tractography☆","year":2006,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","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":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Tractography; Regularization (linguistics); Diffusion MRI; Inference; Artificial intelligence; Computer science; Mathematics; Pattern recognition (psychology); Magnetic resonance imaging; Medicine; Radiology","score_opus":0.019148141866167187,"score_gpt":0.3437552625796552,"score_spread":0.324607120713488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149314739","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00097579154,0.00011295579,0.99810624,0.00011311392,0.000019836263,0.000014256532,0.000047947004,0.0004728018,0.0001370276],"genre_scores_gemma":[0.045446455,0.00029889977,0.95054823,0.000122717,0.000073560754,0.00013826,0.00038784818,0.00074107834,0.0022429363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897456,0.00040829365,0.000058745827,0.00022298965,0.00027527407,0.000060100534],"domain_scores_gemma":[0.9951107,0.0029720117,0.0003084033,0.0008182204,0.00063862687,0.0001520107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003888266,0.0012171796,0.001836027,0.0017200918,0.001120698,0.0019333563,0.0032732498,0.0043276045,0.0042401673],"category_scores_gemma":[0.014565035,0.0023125273,0.002403372,0.0015357439,0.0018160586,0.0024852704,0.0023887032,0.004117902,0.0019912745],"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":[0.00024676305,0.00009691727,0.00087894493,0.00038982605,0.00027370424,0.00013974814,0.00018445347,0.5747792,0.009683801,0.08451291,0.010455748,0.31835794],"study_design_scores_gemma":[0.000009685023,0.000007868726,0.00006931208,0.0000118582375,0.000009275251,0.000023684073,0.0000044358057,0.9706917,0.0011015395,0.02655309,0.0015037203,0.000013855132],"about_ca_topic_score_codex":0.018171154,"about_ca_topic_score_gemma":0.022956932,"teacher_disagreement_score":0.018171154,"about_ca_system_score_codex":0.0015552931,"about_ca_system_score_gemma":0.00345909,"threshold_uncertainty_score":0.036130786},"labels":[],"label_agreement":null},{"id":"W2150903265","doi":"10.1016/j.media.2013.05.008","title":"Medical image processing on the GPU – Past, present and future","year":2013,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":405,"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":"Vetenskapsrådet","keywords":"Computer science; Image processing; Graphics processing unit; Medical imaging; Artificial intelligence; Computer vision; Histogram; General-purpose computing on graphics processing units; Interpolation (computer graphics); Graphics; Computer graphics; Image registration; Computer graphics (images); Image (mathematics); Parallel computing","score_opus":0.03209868690587763,"score_gpt":0.38649137733420813,"score_spread":0.3543926904283305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150903265","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002568382,0.99658406,0.0012619182,0.00025755394,0.00024411544,0.000005351227,0.000019280638,0.000027577891,0.0013433113],"genre_scores_gemma":[0.0017141058,0.9942151,0.0021536362,0.00028258818,0.00038986385,0.0000093544795,0.00005702211,0.0000131826855,0.0011651234],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996613,0.000048120866,0.000040355077,0.00005849764,0.00015706176,0.0000345951],"domain_scores_gemma":[0.99896955,0.0004523869,0.000101530095,0.000036890524,0.00035871114,0.00008094377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081216614,0.0010630722,0.0016509482,0.0026112315,0.00025660807,0.0015285108,0.0014689554,0.0012782454,0.0036842087],"category_scores_gemma":[0.0014904893,0.0005556075,0.00061472936,0.0039444645,0.0007793995,0.001858364,0.00085900247,0.0019626094,0.0022018242],"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":[0.000059887192,0.000055683457,0.00020370822,0.005426265,0.00006386941,0.000057032117,0.000025748297,0.0005979332,0.0015524775,0.0022178444,0.015494217,0.97424537],"study_design_scores_gemma":[0.00003584354,0.00014996837,0.0013577928,0.0029233508,0.00015599375,0.0016890778,0.00006706745,0.0018680275,0.0025509472,0.0054162927,0.9837288,0.000056775898],"about_ca_topic_score_codex":0.002604342,"about_ca_topic_score_gemma":0.0043490976,"teacher_disagreement_score":0.0036842087,"about_ca_system_score_codex":0.00060581206,"about_ca_system_score_gemma":0.0014150242,"threshold_uncertainty_score":0.012324929},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004458806,0.00018391234,0.99333996,0.00014068153,0.000046152578,0.00004347343,0.00012146906,0.0009767399,0.0006886864],"genre_scores_gemma":[0.27298003,0.00052886957,0.71687925,0.00033260015,0.00028035787,0.00036600657,0.001083193,0.0018849353,0.005664727],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880826,0.00036492327,0.000071445065,0.00015442043,0.000532101,0.00006885104],"domain_scores_gemma":[0.9979328,0.0008243519,0.0002937196,0.00037712476,0.00041128954,0.00016075393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019153603,0.00068606815,0.0007408441,0.0028013373,0.0005266103,0.0027254103,0.0014585478,0.0015081291,0.0043233247],"category_scores_gemma":[0.008218566,0.00042960633,0.00066991145,0.0020500168,0.0010826301,0.002609353,0.0022982988,0.0017268256,0.002022238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007489881,0.00025100884,0.0026246365,0.00053571793,0.00013739886,0.0003483929,0.00028670515,0.07125085,0.046753485,0.15954246,0.015583518,0.7019369],"study_design_scores_gemma":[0.00006855508,0.00022589082,0.0019088071,0.000044123703,0.000046291716,0.0008903989,0.00008583871,0.8765571,0.030000048,0.07299721,0.01708157,0.00009410229],"about_ca_topic_score_codex":0.00081940804,"about_ca_topic_score_gemma":0.0008628112,"teacher_disagreement_score":0.0043233247,"about_ca_system_score_codex":0.0006435224,"about_ca_system_score_gemma":0.0010473715,"threshold_uncertainty_score":0.014463007},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24362874,0.0059302407,0.7290681,0.00057598035,0.0005809672,0.0004949036,0.0008592122,0.0113539705,0.007507885],"genre_scores_gemma":[0.5642208,0.0012501725,0.42518413,0.00025566443,0.00013056031,0.00029069406,0.002338979,0.0025582577,0.0037708096],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941444,0.0022310289,0.0005403117,0.0014145015,0.0013816317,0.00028821794],"domain_scores_gemma":[0.9868967,0.0066677616,0.00053409615,0.0025882619,0.0031007675,0.00021245585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009088506,0.0013917543,0.0011385281,0.002864785,0.0010326948,0.0022589173,0.0020369813,0.0016166022,0.003080863],"category_scores_gemma":[0.027737742,0.0006878063,0.0011391937,0.0021472026,0.0008284912,0.0026144711,0.0016451057,0.0009793353,0.0012339054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018688699,0.0005014008,0.0087345475,0.00074726465,0.0009553732,0.00008554658,0.0002357646,0.099344246,0.060829405,0.004881682,0.0075268033,0.81428915],"study_design_scores_gemma":[0.00022759377,0.0005497351,0.013826917,0.00010280599,0.0006662511,0.00037855594,0.00037623936,0.8487998,0.11767843,0.009173381,0.008114426,0.00010588031],"about_ca_topic_score_codex":0.007911022,"about_ca_topic_score_gemma":0.00889307,"teacher_disagreement_score":0.009088506,"about_ca_system_score_codex":0.0016212512,"about_ca_system_score_gemma":0.001573784,"threshold_uncertainty_score":0.048065186},"labels":[],"label_agreement":null},{"id":"W2158727422","doi":"10.1016/j.media.2007.06.002","title":"Activation detection in diffuse optical imaging by means of the general linear model","year":2007,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","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":"École de Technologie Supérieure; Université de Montréal; Polytechnique Montréal; Institut Universitaire de Gériatrie de Montréal","funders":"Canada Research Chairs; Institut National de la Santé et de la Recherche Médicale","keywords":"Computer science; General linear model; Functional magnetic resonance imaging; Artificial intelligence; Diffuse optical imaging; Communication noise; Computer vision; Noise (video); Functional imaging; Basis (linear algebra); Pattern recognition (psychology); Linear model; Image (mathematics); Iterative reconstruction; Machine learning; Mathematics; Neuroscience","score_opus":0.007082030066336433,"score_gpt":0.31845821717452094,"score_spread":0.3113761871081845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158727422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010218953,0.00016771271,0.9889685,0.00012274983,0.00001016412,0.000008079929,0.000021927674,0.0001421833,0.00033977086],"genre_scores_gemma":[0.60530216,0.0010623782,0.38498908,0.00020864338,0.00009911131,0.00016308764,0.00022603208,0.00019709079,0.0077523966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997403,0.00011921868,0.0000081036,0.000058168578,0.000050012874,0.000024198464],"domain_scores_gemma":[0.9996152,0.000246728,0.000035246143,0.000029043716,0.000054718414,0.00001906898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006485043,0.0006157139,0.00070383394,0.0002929533,0.00021508268,0.0005859947,0.00070516864,0.00082280225,0.0008332137],"category_scores_gemma":[0.0016684034,0.00041572753,0.00062082335,0.0004034383,0.00066755654,0.0009175277,0.0007038145,0.0009903645,0.000394033],"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":[0.0003972692,0.00008017704,0.00096309243,0.0003072342,0.0001493508,0.00016517931,0.00018002711,0.7335656,0.05244248,0.050432224,0.0020487078,0.1592686],"study_design_scores_gemma":[0.0000074313434,0.00001937219,0.000154605,0.0000032978874,0.000009662121,0.000032223226,0.0000045321653,0.9906722,0.0021442282,0.0066554165,0.0002867509,0.000010277136],"about_ca_topic_score_codex":0.0021669215,"about_ca_topic_score_gemma":0.0027171217,"teacher_disagreement_score":0.0021669215,"about_ca_system_score_codex":0.00035938615,"about_ca_system_score_gemma":0.00054643065,"threshold_uncertainty_score":0.004308641},"labels":[],"label_agreement":null},{"id":"W2160389475","doi":"10.1016/j.media.2015.04.010","title":"Automatic segmentation of occluded vasculature via pulsatile motion analysis in endoscopic robot-assisted partial nephrectomy video","year":2015,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Retinal Imaging and Analysis","field":"Medicine","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":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Computer vision; Segmentation; Artificial intelligence; Computer science; Pulsatile flow; Nephrectomy; Magnification; Medicine; Kidney; Internal medicine","score_opus":0.016997941704835536,"score_gpt":0.3174904747514418,"score_spread":0.3004925330466063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160389475","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5368032,0.0017513842,0.4574619,0.00027334367,0.00009479824,0.00007154095,0.00036542088,0.0011909163,0.0019874298],"genre_scores_gemma":[0.8576829,0.0009045796,0.13894333,0.000067283516,0.0000693978,0.000031522097,0.00033767128,0.00013614696,0.0018272348],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999052,0.000015732461,0.0000056312037,0.00002032277,0.000033037766,0.000020123025],"domain_scores_gemma":[0.99981016,0.00007618349,0.000028410002,0.000017564847,0.000050825332,0.000016985774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021592945,0.00028787873,0.00021407567,0.0010621197,0.00014717641,0.00051816885,0.00032015875,0.0004693522,0.00061798736],"category_scores_gemma":[0.0006596036,0.0001984741,0.00023361605,0.00045293156,0.00014359041,0.00026411747,0.00023445883,0.00026424692,0.00015904833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008111886,0.000080913844,0.0075084455,0.00024187531,0.00007859,0.0014523768,0.00024910909,0.02203497,0.51594883,0.0012931463,0.002547335,0.4477532],"study_design_scores_gemma":[0.000023153867,0.00013137727,0.040697195,0.00004999514,0.000088863795,0.0021715667,0.00012043343,0.7963916,0.1551329,0.0009789381,0.0041663353,0.00004755933],"about_ca_topic_score_codex":0.0018141839,"about_ca_topic_score_gemma":0.0020631962,"teacher_disagreement_score":0.0018141839,"about_ca_system_score_codex":0.00019747477,"about_ca_system_score_gemma":0.00032778736,"threshold_uncertainty_score":0.003607273},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009382374,0.00021057577,0.986217,0.00013228654,0.00005005898,0.000056814617,0.00018047764,0.0024780252,0.0012924352],"genre_scores_gemma":[0.12819442,0.00047804698,0.8642971,0.00014686602,0.00013328354,0.00013720551,0.0007450755,0.0012615958,0.004606468],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962115,0.00005640091,0.000017320932,0.00008807299,0.00016875177,0.00004837494],"domain_scores_gemma":[0.9992086,0.0002562009,0.000082449515,0.00010473302,0.00030696267,0.00004107726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069331535,0.0011901293,0.0012978127,0.002457446,0.0007832518,0.0016205227,0.00097216916,0.0017174238,0.005818191],"category_scores_gemma":[0.0016876856,0.0007529702,0.0008880135,0.0013750461,0.00043403573,0.0010902232,0.0008735986,0.00089445163,0.0027685752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070940226,0.00010449374,0.0010240305,0.00028585328,0.00009963534,0.00022352605,0.000116940864,0.029199935,0.24191225,0.0074106986,0.010961451,0.7079517],"study_design_scores_gemma":[0.00004691646,0.00010109591,0.0023284082,0.000040248895,0.00007508055,0.000762398,0.000041567386,0.8046469,0.171012,0.006089049,0.014812664,0.000043725573],"about_ca_topic_score_codex":0.0027084372,"about_ca_topic_score_gemma":0.0046526096,"teacher_disagreement_score":0.005818191,"about_ca_system_score_codex":0.0007547242,"about_ca_system_score_gemma":0.0011677792,"threshold_uncertainty_score":0.019463778},"labels":[],"label_agreement":null},{"id":"W2163009740","doi":"10.1016/j.media.2014.11.003","title":"Subject-specific finite-element modeling of normal aortic valve biomechanics from 3D+t TEE images","year":2014,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","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 Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cardiac cycle; Aortic valve; Finite element method; Biomechanics; Biomedical engineering; Tearing; Computer science; Mathematics; Medicine; Structural engineering; Anatomy; Surgery; Cardiology; Mechanical engineering; Engineering","score_opus":0.011956026934218758,"score_gpt":0.30368267789873205,"score_spread":0.2917266509645133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163009740","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19604036,0.0003075521,0.79518014,0.00023623042,0.00007158425,0.00015813272,0.0010581425,0.0015393306,0.005408472],"genre_scores_gemma":[0.88734275,0.0004654302,0.102821454,0.00013705918,0.000026151716,0.00019655153,0.001326614,0.00035150332,0.007332487],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987316,0.000027750937,0.000011391253,0.000024928477,0.000050850886,0.000011833489],"domain_scores_gemma":[0.999798,0.00009925558,0.000022513897,0.000024011078,0.000043842276,0.000012322382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003423504,0.00042743338,0.00036369183,0.0005471092,0.00020929417,0.00090626045,0.00090286974,0.001523346,0.0027729047],"category_scores_gemma":[0.0010653838,0.0004655877,0.0006922995,0.0003895983,0.00037950973,0.00035138015,0.00033538116,0.000430812,0.0008937862],"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":[0.00009693442,0.00009713856,0.0019899374,0.00012467202,0.00003931953,0.0002794773,0.00020697611,0.93830657,0.030240744,0.0013330574,0.0007780803,0.026507113],"study_design_scores_gemma":[0.000004743527,0.000023824887,0.0011401501,0.000010308294,0.000007993468,0.00016016499,0.000033074866,0.9941591,0.0033707565,0.00037185868,0.0007070538,0.00001107696],"about_ca_topic_score_codex":0.0048130564,"about_ca_topic_score_gemma":0.005515399,"teacher_disagreement_score":0.0048130564,"about_ca_system_score_codex":0.00023996238,"about_ca_system_score_gemma":0.00071444054,"threshold_uncertainty_score":0.009570062},"labels":[],"label_agreement":null},{"id":"W2163397436","doi":"10.1016/j.media.2011.02.002","title":"Recent advances in diffusion MRI modeling: Angular and radial reconstruction","year":2011,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":104,"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; Diffusion MRI; Diffusion; Artificial intelligence; Bridge (graph theory); Sampling (signal processing); SIGNAL (programming language); Diffusion imaging; Emphasis (telecommunications); Magnetic resonance imaging; Computer vision; Machine learning; Physics","score_opus":0.07453154444029955,"score_gpt":0.39947682010072955,"score_spread":0.32494527566042997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163397436","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010166761,0.8968693,0.09736331,0.0010744395,0.00039947082,0.000027821625,0.00013917022,0.00031987714,0.0027899314],"genre_scores_gemma":[0.0059895143,0.9314921,0.059469476,0.00027101615,0.00090262934,0.000038602255,0.00024707618,0.00008058247,0.0015089874],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99959344,0.000092783775,0.000058310983,0.00009638036,0.00013994164,0.000019226227],"domain_scores_gemma":[0.9974371,0.0014931755,0.00018120876,0.00013913965,0.0006848478,0.00006438671],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020955119,0.0017399373,0.0022060124,0.0025368747,0.00027422345,0.0016397396,0.001977761,0.0014778466,0.0023089985],"category_scores_gemma":[0.004174288,0.00082440395,0.0010193363,0.0037331053,0.0009523773,0.0018809204,0.0009485477,0.0018138853,0.0021834206],"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":[0.00005238348,0.000056937646,0.0004200055,0.0050892984,0.00012865393,0.000081018225,0.000043592998,0.007386491,0.0023698118,0.0069108065,0.010372455,0.9670885],"study_design_scores_gemma":[0.00007005157,0.00024207939,0.002571413,0.0040443493,0.0007962011,0.003817616,0.00015696502,0.0999416,0.01587402,0.048510894,0.8236621,0.0003126347],"about_ca_topic_score_codex":0.0040489063,"about_ca_topic_score_gemma":0.0036167176,"teacher_disagreement_score":0.0040489063,"about_ca_system_score_codex":0.0006308155,"about_ca_system_score_gemma":0.0016962958,"threshold_uncertainty_score":0.011082232},"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":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014127967,0.7876939,0.18984272,0.00091107684,0.0008487271,0.00017795073,0.00037449042,0.00074764027,0.005275466],"genre_scores_gemma":[0.108842425,0.6418205,0.23971318,0.0007125606,0.000944591,0.00024879718,0.0014486134,0.00045366515,0.0058156205],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982603,0.00029668296,0.00014449633,0.00041871952,0.00083480845,0.000045021807],"domain_scores_gemma":[0.993359,0.0036067253,0.00048002566,0.00039503045,0.002097778,0.00006138189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054084724,0.0010028286,0.0022656173,0.0027607242,0.000305021,0.0015203955,0.0022174309,0.0014408907,0.001715164],"category_scores_gemma":[0.008092922,0.00064763956,0.0008387703,0.002420831,0.0010186678,0.0011305452,0.0005273716,0.0009961738,0.0017234038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011578045,0.000056618515,0.0010749865,0.0034158623,0.0002369592,0.00008599938,0.00005622384,0.0012081433,0.010490991,0.0009687737,0.0027237057,0.9795659],"study_design_scores_gemma":[0.00031351842,0.002665847,0.05093949,0.007162943,0.0050572585,0.020606855,0.00060222327,0.08326534,0.3189287,0.017928781,0.4919239,0.0006051617],"about_ca_topic_score_codex":0.0027356097,"about_ca_topic_score_gemma":0.002487404,"teacher_disagreement_score":0.0054084724,"about_ca_system_score_codex":0.0005291528,"about_ca_system_score_gemma":0.0014383161,"threshold_uncertainty_score":0.028603017},"labels":[],"label_agreement":null},{"id":"W2164382630","doi":"10.1016/j.media.2015.08.004","title":"Joint segmentation of lumen and outer wall from femoral artery MR images: Towards 3D imaging measurements of peripheral arterial disease","year":2015,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Peripheral Artery Disease Management","field":"Medicine","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":"Robarts Clinical Trials; University of Toronto; Western University; Sunnybrook Health Science Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Segmentation; Lumen (anatomy); Algorithm; Artificial intelligence; Computer science; Computer vision; Medicine; Surgery","score_opus":0.036423634535153224,"score_gpt":0.2933001013930873,"score_spread":0.2568764668579341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164382630","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21370766,0.0029952186,0.77577794,0.00043097182,0.0001002647,0.00018460292,0.00097294914,0.0031812938,0.0026490462],"genre_scores_gemma":[0.5554172,0.0018713954,0.43946916,0.00022804273,0.000180232,0.00014481357,0.00083101285,0.00048348453,0.0013747556],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948525,0.00017063961,0.000057781024,0.00008668568,0.00013688467,0.000062751045],"domain_scores_gemma":[0.99881685,0.00041638064,0.0001756316,0.00013424431,0.00036819742,0.000088810324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016604096,0.0007454203,0.00086901546,0.0039749346,0.00031202982,0.0026967532,0.000619766,0.0014770018,0.000983098],"category_scores_gemma":[0.0032601957,0.00051648734,0.0006916355,0.0014590194,0.00030996176,0.0007957686,0.00086263777,0.0006063392,0.00090223347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00096639915,0.00022068102,0.052496225,0.0007249715,0.00027505297,0.00065292814,0.00073613605,0.013487993,0.35638222,0.0019506055,0.0031284932,0.5689784],"study_design_scores_gemma":[0.00006847671,0.00041527144,0.22959651,0.0003735898,0.00086156104,0.004120097,0.00086995953,0.58021903,0.16036454,0.008247569,0.014646098,0.00021741026],"about_ca_topic_score_codex":0.0028001468,"about_ca_topic_score_gemma":0.0034567825,"teacher_disagreement_score":0.0039749346,"about_ca_system_score_codex":0.0002409064,"about_ca_system_score_gemma":0.0007214878,"threshold_uncertainty_score":0.008781195},"labels":[],"label_agreement":null},{"id":"W2167908150","doi":"10.1016/j.media.2012.07.002","title":"Symmetric positive semi-definite Cartesian Tensor fiber orientation distributions (CT-FOD)","year":2012,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","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":"National Institute on Aging; National Center for Research Resources; McGill University; Johns Hopkins University","keywords":"Tensor (intrinsic definition); Diffusion MRI; Cartesian coordinate system; Mathematics; Tensor field; Cartesian tensor; Mathematical analysis; Orientation (vector space); Convolution (computer science); Artificial intelligence; Geometry; Computer science; Tensor density; Magnetic resonance imaging; Exact solutions in general relativity; Artificial neural network","score_opus":0.029280580250433053,"score_gpt":0.35686157417974923,"score_spread":0.32758099392931617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167908150","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011414636,0.00028992858,0.98355526,0.0002530114,0.000078075565,0.00006432717,0.0007894021,0.0005676526,0.0029876414],"genre_scores_gemma":[0.21869014,0.0011446356,0.7701923,0.00027400634,0.00013318176,0.00019249013,0.001518012,0.00049322133,0.0073619364],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971026,0.000074437245,0.000025000047,0.000061642124,0.000104553714,0.000024104962],"domain_scores_gemma":[0.9985361,0.00028782705,0.00034363833,0.00024762368,0.00047082713,0.0001140155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010441621,0.0008369532,0.0003078675,0.0011109128,0.0003445479,0.0014956725,0.0007359091,0.00094916555,0.0048383446],"category_scores_gemma":[0.0038930194,0.00025471215,0.00036641592,0.0009344933,0.0008515862,0.0014810496,0.000584673,0.00082270656,0.0018036962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005398088,0.00019719718,0.006658602,0.0010862796,0.00010104071,0.0011689354,0.00042701195,0.08748921,0.080192655,0.21183331,0.031339172,0.57896674],"study_design_scores_gemma":[0.00004188348,0.00016739113,0.006797374,0.00015898456,0.00005537759,0.0045276345,0.00027174357,0.7805145,0.03303435,0.123347215,0.05094163,0.0001419456],"about_ca_topic_score_codex":0.0015816912,"about_ca_topic_score_gemma":0.0017455575,"teacher_disagreement_score":0.0048383446,"about_ca_system_score_codex":0.0002907723,"about_ca_system_score_gemma":0.0010617322,"threshold_uncertainty_score":0.01618588},"labels":[],"label_agreement":null},{"id":"W2168389179","doi":"10.1016/j.media.2009.10.003","title":"A filtered approach to neural tractography using the Watson directional function","year":2009,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","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 Waterloo","funders":"National Center for Research Resources; National Institute of Mental Health; Fogarty International Center; National Institutes of Health","keywords":"Tractography; Computer science; Diffusion MRI; Noise (video); Fiber; Artificial intelligence; Kalman filter; SIGNAL (programming language); Noise reduction; Algorithm; Mathematics; Computer vision","score_opus":0.072124928754897,"score_gpt":0.37745874984233835,"score_spread":0.30533382108744134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168389179","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020001626,0.00011030572,0.9972481,0.0000412963,0.000025169546,0.0000094644665,0.000036975496,0.00011525529,0.0004132224],"genre_scores_gemma":[0.044432476,0.00076629024,0.94823015,0.000064612745,0.000112692,0.00009249719,0.00027208016,0.00034450207,0.0056846263],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996774,0.00009845072,0.000023174145,0.00007342238,0.0001003296,0.000027208986],"domain_scores_gemma":[0.9993175,0.00026711333,0.00005113787,0.00010510647,0.0002104381,0.000048802147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011466356,0.0008856319,0.0008107191,0.0019987149,0.0007103281,0.0020117625,0.0011166247,0.0014249682,0.003316944],"category_scores_gemma":[0.0027842103,0.0006332389,0.0014412949,0.0014063301,0.0007289196,0.0021946523,0.000988945,0.0015096716,0.0016935848],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023916895,0.000115374954,0.0011761603,0.00027459874,0.00027165076,0.00037596395,0.00023970808,0.14477989,0.052566443,0.51227814,0.005548603,0.28213423],"study_design_scores_gemma":[0.000022839702,0.000088731926,0.0007653093,0.000037494472,0.000058083486,0.00039720425,0.00004841038,0.8323045,0.0072175236,0.14476037,0.014232077,0.000067332025],"about_ca_topic_score_codex":0.004476061,"about_ca_topic_score_gemma":0.0052947653,"teacher_disagreement_score":0.004476061,"about_ca_system_score_codex":0.0005239442,"about_ca_system_score_gemma":0.0013479866,"threshold_uncertainty_score":0.011096239},"labels":[],"label_agreement":null},{"id":"W2170179519","doi":"10.1016/j.media.2011.03.003","title":"Unsupervised dealiasing and denoising of color-Doppler data","year":2011,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Jewish General Hospital; Université de Montréal","funders":"Canadian Institutes of Health Research; Jewish General Hospital","keywords":"Computer science; Artificial intelligence; Computer vision; Doppler effect; Segmentation; Smoothing; Noise (video); Scanner; Algorithm; Pattern recognition (psychology); Image (mathematics)","score_opus":0.07115705155525058,"score_gpt":0.30896473304708905,"score_spread":0.23780768149183845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170179519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039922602,0.0003530153,0.95842224,0.00020541537,0.00007422346,0.000027105922,0.00012631617,0.00044281036,0.00042613214],"genre_scores_gemma":[0.32832816,0.00071816955,0.6652485,0.00013149841,0.00018481219,0.000090088135,0.0011099465,0.00024201821,0.003946768],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995951,0.0001057417,0.000033054686,0.00009306663,0.00011510018,0.00005801145],"domain_scores_gemma":[0.9987411,0.0005751584,0.00009948188,0.0002580383,0.00029048295,0.00003574905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012404906,0.00056080887,0.0006568262,0.0010829469,0.00030268816,0.0007202966,0.0006283587,0.0008390653,0.00089566305],"category_scores_gemma":[0.0034815758,0.00027450937,0.0008079877,0.0009871151,0.000600248,0.0005933749,0.00069724256,0.0010526638,0.000585665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005958721,0.00019002672,0.0027395783,0.00027283668,0.00015796599,0.00018789918,0.00037325334,0.09381036,0.16679858,0.006843572,0.003477195,0.72455287],"study_design_scores_gemma":[0.000015328225,0.000076630095,0.0049708067,0.000020078865,0.00005696495,0.00023715352,0.00008894563,0.93080103,0.054409128,0.00543629,0.0038621633,0.00002551382],"about_ca_topic_score_codex":0.0023121808,"about_ca_topic_score_gemma":0.003452639,"teacher_disagreement_score":0.0023121808,"about_ca_system_score_codex":0.00025584808,"about_ca_system_score_gemma":0.0008088028,"threshold_uncertainty_score":0.006560445},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049228147,0.0002138364,0.9938061,0.000050759507,0.000018202009,0.000023067543,0.000016608827,0.00033328147,0.0006153609],"genre_scores_gemma":[0.20030148,0.00058115553,0.79468805,0.00012486943,0.00009541021,0.00014035833,0.0001955832,0.0005139471,0.0033590863],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988508,0.0003419801,0.00006678478,0.000255228,0.0004332481,0.00005185355],"domain_scores_gemma":[0.99831784,0.0007482418,0.00020967316,0.00042307744,0.0002554594,0.000045714507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017678239,0.0006907834,0.0010185785,0.001997168,0.00041303114,0.0011829039,0.0012306478,0.0010509593,0.0018304137],"category_scores_gemma":[0.0046691783,0.0007197274,0.0010714026,0.0017182016,0.0009854157,0.0017217859,0.0015160932,0.0011349639,0.0010739624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004608618,0.00016551382,0.0012613928,0.00037619605,0.00034128796,0.00017931999,0.00023455571,0.12732913,0.14226012,0.06623216,0.003348246,0.6578113],"study_design_scores_gemma":[0.000040382278,0.00019206476,0.0018901828,0.000032219363,0.00017571617,0.00058453146,0.000037039303,0.88361055,0.06850065,0.03777184,0.0070957276,0.0000691565],"about_ca_topic_score_codex":0.0008869522,"about_ca_topic_score_gemma":0.0012351648,"teacher_disagreement_score":0.001997168,"about_ca_system_score_codex":0.00059287524,"about_ca_system_score_gemma":0.0006708078,"threshold_uncertainty_score":0.009349287},"labels":[],"label_agreement":null},{"id":"W2175900216","doi":"10.1016/j.media.2015.10.012","title":"Sparse Reconstruction Challenge for diffusion MRI: Validation on a physical phantom to determine which acquisition scheme and analysis method to use?","year":2015,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":90,"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":"National Cancer Institute; National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; National Institutes of Health","keywords":"Computer science; Imaging phantom; Diffusion MRI; Artificial intelligence; Set (abstract data type); Data set; Iterative reconstruction; Neuroimaging; Pattern recognition (psychology); Protocol (science); Data mining; Machine learning; Medical physics; Magnetic resonance imaging; Nuclear medicine; Medicine; Radiology","score_opus":0.10680281640726129,"score_gpt":0.42206878803914777,"score_spread":0.31526597163188647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2175900216","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16597266,0.0014953399,0.8205068,0.0042119436,0.0002940464,0.00054103934,0.0013734757,0.002294167,0.003310493],"genre_scores_gemma":[0.3542077,0.0012302252,0.63908094,0.00070952385,0.000075353986,0.00039259897,0.0020435525,0.00040780994,0.0018523323],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99863786,0.00067295413,0.000079033096,0.00017124845,0.00039092114,0.000047961425],"domain_scores_gemma":[0.99005425,0.0070026345,0.00042481895,0.0008418566,0.0014803278,0.00019609634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059024454,0.000568906,0.0006721616,0.00042036615,0.0005463098,0.0011561216,0.0009795597,0.0019353706,0.0018372473],"category_scores_gemma":[0.036216777,0.00040560568,0.00044151905,0.00048192436,0.00086359965,0.0010423587,0.0009701877,0.0015684845,0.0009403568],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002596846,0.0014786622,0.008598,0.0032920323,0.0005404733,0.0010223014,0.0009827336,0.37376085,0.15942027,0.02104219,0.03033684,0.39692876],"study_design_scores_gemma":[0.0003196926,0.0006754423,0.0022327977,0.00022858102,0.00008055906,0.0011756724,0.00019598783,0.9387535,0.039145555,0.009581454,0.007541049,0.00006972963],"about_ca_topic_score_codex":0.0039770366,"about_ca_topic_score_gemma":0.0037143484,"teacher_disagreement_score":0.0059024454,"about_ca_system_score_codex":0.00040227346,"about_ca_system_score_gemma":0.0014803559,"threshold_uncertainty_score":0.03121543},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001071177,0.000038960545,0.99848264,0.000034841003,0.000006364891,0.000010404554,0.000011007556,0.0001658305,0.00017882293],"genre_scores_gemma":[0.1300289,0.00029087585,0.866396,0.00014769814,0.00007667471,0.00016428187,0.00022902589,0.00041503125,0.0022515503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973744,0.0010203385,0.00013713504,0.0006060528,0.00077358476,0.0000885293],"domain_scores_gemma":[0.9976312,0.0010167285,0.00038273534,0.00065411575,0.00026049255,0.000054784086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025748205,0.00085067144,0.0010841856,0.0012626227,0.00042660153,0.0012587472,0.0019780376,0.0013707743,0.0013488606],"category_scores_gemma":[0.008149636,0.0010121656,0.0018090025,0.001407835,0.0014620459,0.0020483872,0.002359288,0.0021841542,0.000840768],"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":[0.00017307892,0.00006780545,0.0007703603,0.00023101344,0.00021878933,0.00019073357,0.0002482731,0.6346861,0.03335981,0.06131613,0.0016756621,0.26706225],"study_design_scores_gemma":[0.000011166263,0.000039439932,0.0003738502,0.00001313502,0.000022104858,0.00015439621,0.000012381197,0.9639716,0.0068946024,0.025993291,0.0024821172,0.00003187495],"about_ca_topic_score_codex":0.001791692,"about_ca_topic_score_gemma":0.0027644036,"teacher_disagreement_score":0.0025748205,"about_ca_system_score_codex":0.00074638665,"about_ca_system_score_gemma":0.0009985731,"threshold_uncertainty_score":0.013617098},"labels":[],"label_agreement":null},{"id":"W2222869326","doi":"10.1016/j.media.2015.10.011","title":"Strengths and weaknesses of state of the art fiber tractography pipelines – A comprehensive in-vivo and phantom evaluation study using Tractometer","year":2015,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","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":"Université de Sherbrooke","funders":"National Institute of Mental Health; Deutsche Forschungsgemeinschaft","keywords":"Tractography; Metric (unit); Imaging phantom; Computer science; Consistency (knowledge bases); Artificial intelligence; Machine learning; Data mining; Diffusion MRI; Magnetic resonance imaging; Engineering; Medicine; Nuclear medicine; Radiology","score_opus":0.0852731398949972,"score_gpt":0.4298637285706721,"score_spread":0.34459058867567494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2222869326","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17316635,0.003154247,0.8067753,0.00062400865,0.0001692292,0.00046703406,0.0012132857,0.011063931,0.0033666978],"genre_scores_gemma":[0.45313057,0.0022831948,0.53640586,0.00021398481,0.000059827544,0.00028865825,0.0021727697,0.0027878105,0.0026572759],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9953566,0.0013734379,0.0003137336,0.0006499818,0.0020924162,0.00021380984],"domain_scores_gemma":[0.98002267,0.009639743,0.0013576563,0.003569257,0.0050202184,0.0003904466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01036063,0.0015937178,0.001038231,0.0021975562,0.0010481736,0.0032718235,0.002551502,0.002207289,0.0024402207],"category_scores_gemma":[0.03155112,0.0012623336,0.0009407278,0.0018440456,0.0010343136,0.004616018,0.0018787063,0.0013228517,0.0013597762],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003001854,0.00081237446,0.029756706,0.0029976957,0.0016238752,0.00054461655,0.0017859213,0.124960266,0.102533706,0.005555168,0.006929274,0.7194986],"study_design_scores_gemma":[0.00014826744,0.0022182663,0.031115312,0.00072925625,0.00081264356,0.003161344,0.0005397817,0.8096465,0.11869141,0.006162103,0.026374409,0.00040072014],"about_ca_topic_score_codex":0.006868652,"about_ca_topic_score_gemma":0.010504846,"teacher_disagreement_score":0.01036063,"about_ca_system_score_codex":0.0010327022,"about_ca_system_score_gemma":0.0022732865,"threshold_uncertainty_score":0.05479288},"labels":[],"label_agreement":null},{"id":"W2254473384","doi":"10.1016/j.media.2016.01.004","title":"Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images","year":2016,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":116,"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":"Engineering and Physical Sciences Research Council; King's College London; Ministerio de Economía y Competitividad; National Institute for Health and Care Research; Wellcome Trust","keywords":"Thresholding; Benchmarking; Segmentation; Ground truth; Computer science; Artificial intelligence; Magnetic resonance imaging; Ventricle; Benchmark (surveying); Steady-state free precession imaging; Algorithm; Medicine; Pattern recognition (psychology); Radiology; Image (mathematics); Cardiology","score_opus":0.02727050351282946,"score_gpt":0.38037277047506146,"score_spread":0.353102266962232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2254473384","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74194854,0.01734647,0.20591173,0.0009024471,0.0015922821,0.0015006185,0.0052797794,0.015369373,0.010148808],"genre_scores_gemma":[0.7011551,0.0028456748,0.26017085,0.00046413994,0.00020679852,0.0004899977,0.030148955,0.0017902034,0.0027282585],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9918853,0.0022221096,0.0010197331,0.0019669868,0.002252361,0.00065352576],"domain_scores_gemma":[0.9919743,0.0033483072,0.0005762314,0.0009592174,0.0027130716,0.0004288394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011717137,0.0028761742,0.0020079825,0.004055975,0.0009475432,0.0029543794,0.0039463327,0.0031160866,0.0016134945],"category_scores_gemma":[0.019105257,0.0005585401,0.0018951017,0.0020370637,0.0011616312,0.0014972235,0.0018459896,0.001125036,0.001066291],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005261378,0.0018339299,0.021470975,0.0038022932,0.0021808585,0.0006648363,0.0005317823,0.284046,0.03876325,0.0032768329,0.029180355,0.6089875],"study_design_scores_gemma":[0.00031190593,0.0021667469,0.019281235,0.00035235102,0.00043986246,0.0007672713,0.0003102989,0.9246278,0.04218068,0.0015848351,0.0078537855,0.00012326268],"about_ca_topic_score_codex":0.010605086,"about_ca_topic_score_gemma":0.0082701035,"teacher_disagreement_score":0.011717137,"about_ca_system_score_codex":0.001783184,"about_ca_system_score_gemma":0.0019758842,"threshold_uncertainty_score":0.061966896},"labels":[],"label_agreement":null},{"id":"W2255928173","doi":"10.1016/j.media.2016.01.006","title":"Geometrical deployment for braided stent","year":2016,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Intracranial Aneurysms: Treatment and Complications","field":"Medicine","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":"Toronto Western Hospital; University Health Network","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Software deployment; Stent; Computer science; Position (finance); RADIUS; Biomedical engineering; Simulation; Radiology; Engineering; Medicine; Software engineering","score_opus":0.020466744944273637,"score_gpt":0.3041720158400374,"score_spread":0.28370527089576375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2255928173","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4454423,0.0016614601,0.5373671,0.0005822432,0.0003337808,0.00025630015,0.00039055507,0.0037294137,0.010236887],"genre_scores_gemma":[0.79769003,0.0007097314,0.1970609,0.00016516433,0.00012622027,0.00008838181,0.00039223908,0.000505695,0.0032616276],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996573,0.00008746097,0.000023611208,0.000056197045,0.00013059365,0.00004492155],"domain_scores_gemma":[0.999597,0.00012657339,0.000042497937,0.000087213746,0.00011711273,0.000029546614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003856108,0.0006807853,0.0003622501,0.0014364526,0.00032901592,0.0009966034,0.0005928973,0.0005914439,0.0034340005],"category_scores_gemma":[0.001492752,0.0004653481,0.0004890122,0.0005767005,0.00034387075,0.00066983263,0.00065985636,0.0003674965,0.0013482762],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022526653,0.00023797233,0.018945273,0.00050201,0.00018346509,0.0040344545,0.0005272632,0.01869189,0.363719,0.0071233646,0.006099529,0.5776831],"study_design_scores_gemma":[0.00022080993,0.0021204052,0.056347117,0.00018778574,0.00044602875,0.029803487,0.0003865627,0.5640659,0.3037508,0.006229891,0.03610657,0.00033460307],"about_ca_topic_score_codex":0.00044628663,"about_ca_topic_score_gemma":0.0005785065,"teacher_disagreement_score":0.0034340005,"about_ca_system_score_codex":0.00021178539,"about_ca_system_score_gemma":0.0003660561,"threshold_uncertainty_score":0.011487901},"labels":[],"label_agreement":null},{"id":"W2284206694","doi":"10.1016/j.media.2016.01.002","title":"The application of a new sampling theorem for non-bandlimited signals on the sphere: Improving the recovery of crossing fibers for low b-value acquisitions","year":2016,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","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 Sherbrooke","funders":"National Institute of Mental Health; NIH Blueprint for Neuroscience Research; McDonnell Center for Systems Neuroscience; Washington University in St. Louis","keywords":"Deconvolution; Bandlimiting; Sampling (signal processing); Mathematics; Algorithm; Convolution (computer science); Kernel (algebra); Nonuniform sampling; Computer science; Mathematical analysis; Fourier transform; Artificial intelligence; Computer vision; Discrete mathematics; Artificial neural network","score_opus":0.038154558591240596,"score_gpt":0.36350859537872743,"score_spread":0.3253540367874868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2284206694","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026750013,0.00011745684,0.99653363,0.00008407665,0.000044303,0.000011273798,0.00001682019,0.000123473,0.0003938274],"genre_scores_gemma":[0.07865921,0.0006766258,0.9175039,0.00019450655,0.0002469323,0.000059450038,0.0001508461,0.00033533006,0.00217323],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895215,0.00036380126,0.0000703234,0.00016994041,0.00039392983,0.000049760245],"domain_scores_gemma":[0.9959182,0.002263136,0.00026367605,0.0006213771,0.0007117549,0.0002218138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026852207,0.0010528057,0.00069146574,0.0011184366,0.00042786894,0.0014388093,0.0013068719,0.0012096985,0.002901818],"category_scores_gemma":[0.009422999,0.00037177705,0.0007443931,0.00095403247,0.0013475406,0.0026810851,0.0017646378,0.001478216,0.0010611174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072338636,0.00023225867,0.0017051547,0.0005452962,0.00015062802,0.00044930945,0.00046163664,0.117523335,0.19964983,0.18905754,0.007440135,0.4820615],"study_design_scores_gemma":[0.000022981583,0.00012629111,0.00049190054,0.00002025145,0.000020794943,0.0004959061,0.000021916676,0.95121497,0.022619538,0.020112544,0.004812745,0.000040145853],"about_ca_topic_score_codex":0.0012010994,"about_ca_topic_score_gemma":0.0013065689,"teacher_disagreement_score":0.002901818,"about_ca_system_score_codex":0.0004822088,"about_ca_system_score_gemma":0.00089664327,"threshold_uncertainty_score":0.014200985},"labels":[],"label_agreement":null},{"id":"W2297040190","doi":"10.1016/j.media.2016.03.004","title":"X-ray and magnetic resonance imaging fusion for cardiac resynchronization therapy","year":2016,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cardiac pacing and defibrillation studies","field":"Medicine","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":"University of Toronto; Sunnybrook Health Science Centre","funders":"Sunnybrook Research Institute","keywords":"Cardiac resynchronization therapy; Coronary sinus; Fluoroscopy; Medicine; Ventricle; Magnetic resonance imaging; Heart failure; Cardiology; Cardiac magnetic resonance; Coronary Vein; Internal medicine; Radiology; Ejection fraction","score_opus":0.009565483470173436,"score_gpt":0.28736634963190233,"score_spread":0.2778008661617289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2297040190","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5846589,0.20308624,0.11369307,0.017739326,0.0021620777,0.0004351083,0.0019802903,0.0009473822,0.07529758],"genre_scores_gemma":[0.93677026,0.025954679,0.029175628,0.0015064953,0.0014259872,0.00009683349,0.0006077589,0.00006104977,0.004401308],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998155,0.0000686954,0.000025666266,0.000023964418,0.000045914083,0.000020363994],"domain_scores_gemma":[0.9997031,0.00011984702,0.00004951258,0.000036971553,0.00006142936,0.000029160428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076531485,0.0004104389,0.00041399003,0.0011202679,0.000344878,0.0011795992,0.00034868385,0.0008106526,0.0039073187],"category_scores_gemma":[0.0019726078,0.00017633039,0.00039282552,0.0004969699,0.0004970585,0.00072900264,0.0006252734,0.0006899718,0.0008851072],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00320059,0.0005596087,0.09950382,0.0011183032,0.0006640475,0.0122287795,0.00029498508,0.0075640655,0.08066199,0.0102407755,0.028466577,0.75549644],"study_design_scores_gemma":[0.0011405449,0.0020635815,0.5277908,0.0018445667,0.0021841347,0.08122968,0.0012841476,0.12022685,0.083920434,0.04100371,0.13693704,0.0003744974],"about_ca_topic_score_codex":0.0013606197,"about_ca_topic_score_gemma":0.0015914063,"teacher_disagreement_score":0.0039073187,"about_ca_system_score_codex":0.00032149508,"about_ca_system_score_gemma":0.0005926128,"threshold_uncertainty_score":0.013071299},"labels":[],"label_agreement":null},{"id":"W2298552625","doi":"10.1016/j.media.2016.02.010","title":"Non Local Spatial and Angular Matching: Enabling higher spatial resolution diffusion MRI datasets through adaptive denoising","year":2016,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":82,"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":"Noise reduction; Noise (video); Computer science; Artificial intelligence; Rician fading; Spatial analysis; Pattern recognition (psychology); Gaussian noise; Noise measurement; Algorithm; Computer vision; Mathematics; Statistics","score_opus":0.029768578420550303,"score_gpt":0.3345688220668706,"score_spread":0.3048002436463203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2298552625","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01247687,0.0002414003,0.985246,0.00028488322,0.00003871056,0.000030797153,0.00010004806,0.000509732,0.0010715921],"genre_scores_gemma":[0.1261269,0.0007264575,0.86853564,0.00024630586,0.0000819096,0.00006388441,0.00043796748,0.00040860742,0.003372275],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993641,0.00015244294,0.000052315376,0.00011997286,0.0002628194,0.00004835429],"domain_scores_gemma":[0.99829143,0.00063264644,0.00019176469,0.00051402993,0.00029123516,0.00007894919],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017545335,0.0007583563,0.00076579634,0.0009896453,0.00034337526,0.0015967587,0.00111501,0.0012928402,0.0028132815],"category_scores_gemma":[0.0063342107,0.00060917693,0.0006144842,0.0016060905,0.0006395153,0.0018514908,0.0019313726,0.0014231565,0.0012102044],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064219494,0.00027924977,0.0022444634,0.0006271286,0.00023223329,0.0002581144,0.00036170366,0.0766334,0.34697482,0.034440383,0.0065868683,0.5307194],"study_design_scores_gemma":[0.000048128186,0.00012076139,0.002169264,0.00005107569,0.00009838901,0.00085659063,0.00009562902,0.796645,0.15708824,0.029001692,0.013756804,0.00006845916],"about_ca_topic_score_codex":0.0015234983,"about_ca_topic_score_gemma":0.003385432,"teacher_disagreement_score":0.0028132815,"about_ca_system_score_codex":0.00033189665,"about_ca_system_score_gemma":0.0010575675,"threshold_uncertainty_score":0.009411335},"labels":[],"label_agreement":null},{"id":"W2423172756","doi":"10.1016/j.media.2016.06.004","title":"Image-guided interventions and computer-integrated therapy: Quo vadis?","year":2016,"lang":"en","type":"editorial","venue":"Medical Image Analysis","topic":"Surgical Simulation and Training","field":"Medicine","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":"Robarts Clinical Trials; Western University","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Canada Foundation for Innovation; Harvard University; Heart and Stroke Foundation of Canada; Natural Sciences and Engineering Research Council of Canada; Mayo Clinic","keywords":"Status quo; Psychological intervention; Implementation; Computer science; Visualization; Data science; Medicine; Artificial intelligence; Political science","score_opus":0.02901492476713416,"score_gpt":0.3715704268395559,"score_spread":0.3425555020724217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2423172756","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000025446156,0.031527102,0.00015466416,0.044354092,0.9230754,0.0000066138,0.000017037974,0.00002615038,0.0008136479],"genre_scores_gemma":[0.00026653046,0.013083237,0.000105862804,0.01908822,0.965397,0.000008055825,0.000006929022,0.000013176603,0.0020309933],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9911578,0.0019738735,0.0013899534,0.0008514769,0.0042161536,0.00041066282],"domain_scores_gemma":[0.95108825,0.027895126,0.0024899137,0.0010424377,0.013528172,0.0039559947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010184803,0.002971561,0.004755505,0.004246403,0.0029340726,0.010609719,0.004768691,0.032640047,0.008731601],"category_scores_gemma":[0.041365996,0.0014021652,0.0029309148,0.002501761,0.0052961973,0.0067830314,0.002563731,0.028846877,0.0056465836],"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":[0.00005535628,0.000023334655,0.000043042004,0.00078240206,0.0000469473,0.00015484936,0.000027541997,0.000053482006,0.000049883813,0.0010233893,0.9803714,0.017368374],"study_design_scores_gemma":[0.00007457592,0.000034735775,0.00024337803,0.0014063366,0.00008808611,0.0005030916,0.00007738032,0.00022152916,0.00006584893,0.0019513835,0.99530256,0.000031189993],"about_ca_topic_score_codex":0.0025597517,"about_ca_topic_score_gemma":0.008252008,"teacher_disagreement_score":0.032640047,"about_ca_system_score_codex":0.0033567394,"about_ca_system_score_gemma":0.004073042,"threshold_uncertainty_score":0.05386305},"labels":[],"label_agreement":null},{"id":"W2429271550","doi":"10.1016/j.media.2016.06.011","title":"Open-source platforms for navigated image-guided interventions","year":2016,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":176,"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; Key (lock); Field (mathematics); Navigation system; Software; Data science; Software engineering; Human–computer interaction; Artificial intelligence; Computer security","score_opus":0.14718353703732354,"score_gpt":0.48497841305535794,"score_spread":0.3377948760180344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2429271550","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021405144,0.99598265,0.0010028927,0.0002063458,0.00022216178,0.00002377712,0.000108662265,0.000073912284,0.0021656824],"genre_scores_gemma":[0.0018696168,0.99391395,0.0021767295,0.0003191438,0.0002397516,0.000028524213,0.00025771014,0.000029357123,0.0011652316],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993686,0.00012365104,0.00011999148,0.00008102399,0.00026127216,0.000045472712],"domain_scores_gemma":[0.99735177,0.0017912482,0.0003187312,0.00006955335,0.00037886962,0.00008973121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011769526,0.001069243,0.0014008031,0.0044115935,0.00025481297,0.0016856769,0.0016052916,0.0014090744,0.011615786],"category_scores_gemma":[0.0044704503,0.0003488729,0.0010991215,0.0028209023,0.00063656265,0.0020172026,0.0015931862,0.0015471285,0.0041827187],"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":[0.00005006658,0.000038409897,0.000096412485,0.020847581,0.00009190501,0.000075099015,0.000039366838,0.00019263831,0.00058982073,0.0010622789,0.01202983,0.96488667],"study_design_scores_gemma":[0.000058294507,0.00012025237,0.0016559203,0.02820494,0.0005285336,0.0019049752,0.00012852346,0.00038059003,0.001387546,0.0030941828,0.9624698,0.00006634903],"about_ca_topic_score_codex":0.001632158,"about_ca_topic_score_gemma":0.0023972339,"teacher_disagreement_score":0.011615786,"about_ca_system_score_codex":0.0004709404,"about_ca_system_score_gemma":0.0017111811,"threshold_uncertainty_score":0.038858652},"labels":[],"label_agreement":null},{"id":"W2461431685","doi":"10.1016/j.media.2016.06.035","title":"Increasing the impact of medical image computing using community-based open-access hackathons: The NA-MIC and 3D Slicer experience","year":2016,"lang":"en","type":"editorial","venue":"Medical Image Analysis","topic":"Biomedical and Engineering Education","field":"Engineering","cited_by":100,"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; National Cancer Institute; National Institutes of Health; University of Toronto; National Heart, Lung, and Blood Institute; Cancer Care Ontario","keywords":"Variety (cybernetics); Computer science; Software; Portfolio; Data science; Open source software; World Wide Web; Engineering management; Engineering; Artificial intelligence; Business","score_opus":0.02545183757932712,"score_gpt":0.3936397144089996,"score_spread":0.36818787682967247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2461431685","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001272129,0.016976653,0.00058596116,0.07556631,0.9044695,0.000018426052,0.00004767332,0.00013755742,0.002070714],"genre_scores_gemma":[0.0010474522,0.011136804,0.00040039283,0.024414364,0.9568863,0.000015882955,0.000027319355,0.000105674684,0.0059657795],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9922327,0.0010837415,0.0009135205,0.0005633229,0.0048280274,0.00037871615],"domain_scores_gemma":[0.9398135,0.03468185,0.0019595416,0.0012723231,0.01640859,0.0058641536],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.010402505,0.0015079675,0.0017890664,0.0026696154,0.0019943272,0.008887087,0.0035189695,0.017997483,0.01094147],"category_scores_gemma":[0.040807344,0.00085300865,0.002129065,0.0013920909,0.0029757584,0.005201992,0.0024819684,0.019334076,0.005523916],"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":[0.00003419676,0.000015250684,0.000030673047,0.00030789562,0.000025873724,0.0001454095,0.00004953952,0.000026004633,0.000094365816,0.0007744597,0.97938997,0.019106388],"study_design_scores_gemma":[0.00004393441,0.000034807854,0.00019692232,0.0005285735,0.000044531214,0.0002947934,0.00008329466,0.00012177775,0.00016792672,0.0014209285,0.99703985,0.000022548713],"about_ca_topic_score_codex":0.0013110971,"about_ca_topic_score_gemma":0.0047229147,"teacher_disagreement_score":0.996481,"about_ca_system_score_codex":0.0017424512,"about_ca_system_score_gemma":0.0023947193,"threshold_uncertainty_score":0.055014312},"labels":[],"label_agreement":null},{"id":"W2466670129","doi":"10.1016/j.media.2016.06.038","title":"Automatic segmentation approach to extracting neonatal cerebral ventricles from 3D ultrasound images","year":2016,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Fetal and Pediatric Neurological Disorders","field":"Medicine","cited_by":52,"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; Western University","funders":"Canadian Institutes of Health Research; Academic Medical Organization of Southwestern Ontario","keywords":"Initialization; Segmentation; Artificial intelligence; 3D ultrasound; Computer science; Cerebral ventricle; Medicine; Pattern recognition (psychology); Ultrasound; Computer vision; Radiology; Anatomy","score_opus":0.010865785127341222,"score_gpt":0.26689803274165386,"score_spread":0.25603224761431265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2466670129","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026515687,0.00060507905,0.9686715,0.00011793558,0.000062973515,0.00013067383,0.00022973241,0.002227912,0.0014385593],"genre_scores_gemma":[0.14734842,0.00081462576,0.84707046,0.00010691421,0.00006817822,0.00015061871,0.00079306884,0.00029048827,0.0033573336],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994931,0.000054565622,0.00004135975,0.00013581311,0.00021072391,0.00006431897],"domain_scores_gemma":[0.9996699,0.00007333869,0.000033256274,0.00003858792,0.0001625491,0.000022386199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000431771,0.00081866654,0.0008494407,0.0026609001,0.00056924747,0.0012871762,0.0012330366,0.0012484762,0.0017519088],"category_scores_gemma":[0.0006753216,0.0006216179,0.0012628627,0.0011937028,0.0003824093,0.00051533984,0.0006888893,0.00060470216,0.0012268376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018196486,0.00012589598,0.0023767496,0.00024022868,0.00014199672,0.00056830654,0.00029054482,0.02052884,0.30730087,0.0035254182,0.004375476,0.6603437],"study_design_scores_gemma":[0.000037091875,0.00017389876,0.01127565,0.00006722879,0.00019287647,0.0020059722,0.00024271579,0.80254585,0.16575223,0.00429124,0.013332188,0.00008303017],"about_ca_topic_score_codex":0.0070589106,"about_ca_topic_score_gemma":0.010928614,"teacher_disagreement_score":0.0070589106,"about_ca_system_score_codex":0.0005693696,"about_ca_system_score_gemma":0.0015477681,"threshold_uncertainty_score":0.014035642},"labels":[],"label_agreement":null},{"id":"W2479734208","doi":"10.1016/j.media.2016.07.012","title":"Learning in data-limited multimodal scenarios: Scandent decision forests and tree-based features","year":2016,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Explainable Artificial Intelligence (XAI)","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":"University of British Columbia","funders":"National Institute on Aging; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Decision tree; Computer science; Artificial intelligence; Classifier (UML); Feature (linguistics); Machine learning; Decision tree learning; Random forest; Pattern recognition (psychology); Tree (set theory); Incremental decision tree; Data mining; Mathematics","score_opus":0.018317545149545804,"score_gpt":0.30574153267386384,"score_spread":0.287423987524318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2479734208","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22580323,0.0018760086,0.76697344,0.002158406,0.00010708545,0.00009394868,0.00043223074,0.00028546262,0.0022702713],"genre_scores_gemma":[0.92857856,0.00039512615,0.069341674,0.00016875347,0.00012515829,0.00007290018,0.0004077907,0.00003611385,0.0008738303],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980914,0.001200845,0.00008430611,0.00029381842,0.000176511,0.00015297184],"domain_scores_gemma":[0.98596835,0.012406072,0.0005026335,0.00038220076,0.0004494801,0.00029131057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007450322,0.00070695044,0.0013886311,0.0013680917,0.00069830095,0.0014444834,0.0013719288,0.0022341297,0.0015179745],"category_scores_gemma":[0.018407656,0.0005023949,0.0010103711,0.0011531404,0.0010000203,0.003457037,0.0014223868,0.0019342197,0.0002278527],"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":[0.0006630852,0.00030442735,0.0081169065,0.00013938165,0.0001850498,0.00033204208,0.00017122472,0.8652062,0.000549708,0.016933227,0.0026551778,0.10474357],"study_design_scores_gemma":[0.000012771328,0.000025471954,0.00030053811,0.000012846835,0.000011157585,0.000024686004,0.000015046638,0.983602,0.000081056234,0.015787348,0.00012151758,0.000005540345],"about_ca_topic_score_codex":0.0051862774,"about_ca_topic_score_gemma":0.0064002206,"teacher_disagreement_score":0.007450322,"about_ca_system_score_codex":0.00077507034,"about_ca_system_score_gemma":0.0008011192,"threshold_uncertainty_score":0.03940159},"labels":[],"label_agreement":null},{"id":"W2484736472","doi":"10.1016/j.media.2016.07.009","title":"ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI","year":2016,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":531,"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; Université de Sherbrooke","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute on Aging; National Cancer Institute; Northeastern University; National Institute for Health and Care Research","keywords":"Segmentation; Benchmarking; Computer science; Artificial intelligence; Stroke (engine); Multispectral image; Benchmark (surveying); Magnetic resonance imaging; Lesion; Medicine; Pattern recognition (psychology); Machine learning; Radiology; Pathology; Cartography; Geography","score_opus":0.027593968755972878,"score_gpt":0.3462863085249213,"score_spread":0.31869233976894845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2484736472","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47362185,0.014094552,0.10845367,0.0051312917,0.002184947,0.0042886413,0.25275183,0.1060222,0.033451032],"genre_scores_gemma":[0.23297282,0.0016897355,0.104116425,0.001173527,0.00031601364,0.0009419047,0.6447128,0.0043182373,0.0097584855],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99457127,0.0014111191,0.00077114394,0.0011959355,0.0016039935,0.0004464612],"domain_scores_gemma":[0.99180335,0.0020751155,0.0005853296,0.0013547831,0.0032586644,0.00092266675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00828653,0.0040186117,0.002242169,0.007843764,0.0014586964,0.003424515,0.0047728005,0.004180483,0.005479233],"category_scores_gemma":[0.021080296,0.00084246846,0.0023093994,0.0042300546,0.0011387721,0.0022580319,0.0037738106,0.0014005951,0.0059414497],"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":[0.0061225696,0.0032652766,0.03340253,0.0049661817,0.0028613294,0.0015152557,0.0004687217,0.074801534,0.018990038,0.0029311148,0.5388391,0.31183633],"study_design_scores_gemma":[0.003124878,0.0024355163,0.11987195,0.0016617444,0.0014722731,0.00493288,0.0015779466,0.61852986,0.049628958,0.011412156,0.18495749,0.0003943215],"about_ca_topic_score_codex":0.04581846,"about_ca_topic_score_gemma":0.06649808,"teacher_disagreement_score":0.04581846,"about_ca_system_score_codex":0.0027894196,"about_ca_system_score_gemma":0.0036233754,"threshold_uncertainty_score":0.091103494},"labels":[],"label_agreement":null},{"id":"W2508153689","doi":"10.1016/j.media.2016.09.001","title":"Conformal invariants for multiply connected surfaces: Application to landmark curve-based brain morphometry analysis","year":2016,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","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 of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; U.S. Department of Defense; National Institute on Aging; National Institutes of Health; National Science Foundation","keywords":"Brain morphometry; Landmark; Invariant (physics); Conformal map; Neuroimaging; Computation; Mathematics; Artificial intelligence; Shape analysis (program analysis); Pattern recognition (psychology); Surface (topology); Geometry; Computer science; Algorithm; Neuroscience; Magnetic resonance imaging; Psychology; Medicine","score_opus":0.03491847557675011,"score_gpt":0.37055534472712737,"score_spread":0.33563686915037727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2508153689","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017291058,0.00011990034,0.9806433,0.00008744441,0.000028325408,0.000053796186,0.00006975346,0.0009695816,0.00073680707],"genre_scores_gemma":[0.38407785,0.0005104007,0.6123063,0.000047475543,0.00010126289,0.000091929614,0.00026502897,0.0009962288,0.001603505],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953806,0.000084061714,0.000027440436,0.00008374932,0.00022162292,0.00004508141],"domain_scores_gemma":[0.998582,0.00060842297,0.00017537878,0.00024988592,0.00029096202,0.00009337412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083551655,0.00060656806,0.0010417755,0.00366678,0.000585723,0.0020881977,0.001071423,0.00089190825,0.0023413629],"category_scores_gemma":[0.005643562,0.00046280117,0.0011670147,0.0028364516,0.0011533079,0.0010576139,0.0018879304,0.0016407367,0.00069572724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013248688,0.00011841554,0.0026428897,0.00024598828,0.00008976582,0.00025843448,0.0004951861,0.15052488,0.033486374,0.076961935,0.0037705791,0.731273],"study_design_scores_gemma":[0.000011830092,0.000055114637,0.0012809321,0.000012999173,0.000023102239,0.00020858287,0.000077544406,0.9537772,0.006668636,0.034203716,0.003648691,0.00003157382],"about_ca_topic_score_codex":0.0025177666,"about_ca_topic_score_gemma":0.0022327122,"teacher_disagreement_score":0.00366678,"about_ca_system_score_codex":0.000658822,"about_ca_system_score_gemma":0.0009451268,"threshold_uncertainty_score":0.007832646},"labels":[],"label_agreement":null},{"id":"W2513067461","doi":"10.1016/j.media.2016.08.007","title":"Brain shift in neuronavigation of brain tumors: A review","year":2016,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"Glioma Diagnosis and Treatment","field":"Medicine","cited_by":318,"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; McGill Genome Centre","funders":"Canadian Institutes of Health Research; Canadian HIV Trials Network, Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada","keywords":"Neuronavigation; Paradigm shift; Computer science; Field (mathematics); Compensation (psychology); Brain activity and meditation; Medical physics; Artificial intelligence; Neuroscience; Medicine; Electroencephalography; Psychology; Magnetic resonance imaging; Radiology","score_opus":0.024902599760763068,"score_gpt":0.3796284851693184,"score_spread":0.3547258854085553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2513067461","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007227849,0.9995183,0.00009910042,0.000077621036,0.00005500006,0.000002483577,0.000009400368,0.0000032206149,0.00016260722],"genre_scores_gemma":[0.00063286687,0.998765,0.0002403242,0.00012647104,0.000118837,0.0000031530242,0.000016354532,0.0000012100496,0.000095920834],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996798,0.000053886255,0.000082746745,0.00007292249,0.00009092781,0.000019766125],"domain_scores_gemma":[0.9988463,0.00070925255,0.00017565371,0.000020951753,0.0002044714,0.000043353008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089253223,0.0010038916,0.0018583039,0.0034966662,0.0002477182,0.0012575261,0.0011229801,0.0012865658,0.002549588],"category_scores_gemma":[0.0020079566,0.00041830793,0.0009879435,0.0024844937,0.00065792387,0.0016933986,0.0008041545,0.0012506275,0.00088085304],"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":[0.000100966085,0.00005959733,0.00052717666,0.047005422,0.00032205816,0.0003556977,0.00008103822,0.00044732858,0.0012460154,0.0010425483,0.014327913,0.9344841],"study_design_scores_gemma":[0.000092531285,0.000385925,0.004413343,0.03111066,0.0024597794,0.00909511,0.00031421278,0.00058626535,0.0017671135,0.0028590814,0.9467658,0.00015034042],"about_ca_topic_score_codex":0.0021999697,"about_ca_topic_score_gemma":0.0043253987,"teacher_disagreement_score":0.0034966662,"about_ca_system_score_codex":0.00054939644,"about_ca_system_score_gemma":0.0014090193,"threshold_uncertainty_score":0.008529246},"labels":[],"label_agreement":null},{"id":"W2513411538","doi":"10.1016/j.media.2016.08.005","title":"Evaluation and comparison of 3D intervertebral disc localization and segmentation methods for 3D T2 MR data: A grand challenge","year":2016,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":75,"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; Hausdorff distance; Artificial intelligence; Computer science; Data set; Ground truth; Magnetic resonance imaging; Pattern recognition (psychology); Intervertebral disc; Computer vision; Medicine; Anatomy; Radiology","score_opus":0.05513416208565318,"score_gpt":0.41719475857006677,"score_spread":0.3620605964844136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2513411538","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11246769,0.029318182,0.8366954,0.004149149,0.00080263085,0.0006932152,0.003901376,0.009468142,0.0025042112],"genre_scores_gemma":[0.21319708,0.009226838,0.7631143,0.00092819706,0.0003735355,0.0003590045,0.0073266295,0.0025323909,0.0029419716],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99299,0.0020368174,0.00071620557,0.001036149,0.002939913,0.00028090083],"domain_scores_gemma":[0.9725611,0.015093675,0.0010843934,0.002965647,0.007621854,0.0006733332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017563336,0.0023701459,0.003226101,0.005416617,0.00080868445,0.0059186304,0.0045433613,0.0045305947,0.0026454634],"category_scores_gemma":[0.024494836,0.0012205552,0.002129971,0.002600813,0.0010281682,0.0030193788,0.0018349696,0.0016911703,0.0018556532],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009462985,0.00041546204,0.0075015062,0.001875924,0.001075731,0.00015440593,0.0003482466,0.049883768,0.041916166,0.002589002,0.012407499,0.880886],"study_design_scores_gemma":[0.00014039484,0.0010638068,0.01892238,0.0003698899,0.0004244691,0.0010993229,0.00073936436,0.901533,0.04767892,0.008107294,0.019695176,0.00022598359],"about_ca_topic_score_codex":0.007371385,"about_ca_topic_score_gemma":0.01158192,"teacher_disagreement_score":0.017563336,"about_ca_system_score_codex":0.0015017356,"about_ca_system_score_gemma":0.0025127337,"threshold_uncertainty_score":0.09288484},"labels":[],"label_agreement":null},{"id":"W2521652955","doi":"10.1016/j.media.2016.08.012","title":"Atlas-based shape analysis and classification of retinal optical coherence tomography images using the functional shape (fshape) framework","year":2016,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Retinal Imaging and Analysis","field":"Medicine","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":"Simon Fraser University","funders":"Canadian Institutes of Health Research","keywords":"Optical coherence tomography; Glaucoma; Artificial intelligence; Computer science; Nerve fiber layer; Pattern recognition (psychology); Retinal; Linear discriminant analysis; Population; Computer vision; Optics; Ophthalmology; Physics; Medicine","score_opus":0.02320369450665437,"score_gpt":0.30954422201178283,"score_spread":0.28634052750512845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2521652955","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018255075,0.00022410677,0.97852933,0.00017641952,0.000026371816,0.000067406014,0.00032476502,0.0014909907,0.00090553326],"genre_scores_gemma":[0.32113603,0.0006333606,0.67168134,0.00021879027,0.000114203656,0.00020348268,0.0020053664,0.0010285783,0.0029787912],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922025,0.00016770516,0.000046082867,0.00014424133,0.00033348176,0.00008815462],"domain_scores_gemma":[0.99904746,0.00025803846,0.00010664601,0.00018979207,0.00031943942,0.00007864019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011605583,0.0006617593,0.0012460527,0.002900109,0.00061674346,0.0024313212,0.0015128469,0.0012047178,0.0018711941],"category_scores_gemma":[0.0026099563,0.00045862602,0.0022580256,0.0020625796,0.00068494445,0.00092908787,0.0018498616,0.0011881324,0.0010793627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033271845,0.0001449641,0.00497663,0.00024484153,0.00023075742,0.0002760933,0.000284555,0.16704544,0.044756617,0.020240754,0.007769844,0.75369674],"study_design_scores_gemma":[0.000008888864,0.0000485462,0.0018154089,0.000016091048,0.000030211544,0.0002696179,0.00005493629,0.9820353,0.0053370865,0.007750481,0.0026084674,0.000024948868],"about_ca_topic_score_codex":0.0066406066,"about_ca_topic_score_gemma":0.008040327,"teacher_disagreement_score":0.0066406066,"about_ca_system_score_codex":0.0007549504,"about_ca_system_score_gemma":0.0016288256,"threshold_uncertainty_score":0.013203919},"labels":[],"label_agreement":null},{"id":"W2542242623","doi":"10.1016/j.media.2016.10.009","title":"Unsupervised boundary delineation of spinal neural foramina using a multi-feature and adaptive spectral segmentation","year":2016,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","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":"Western University","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Segmentation; Feature (linguistics); Computer science; Boundary (topology); Artificial neural network; Computer vision; Mathematics","score_opus":0.016609550336393875,"score_gpt":0.27929440416018625,"score_spread":0.26268485382379236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2542242623","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.110940605,0.0002821003,0.8866106,0.0001027012,0.000029290335,0.000085131076,0.00009111152,0.00090112974,0.0009573069],"genre_scores_gemma":[0.4933475,0.00017560486,0.5046029,0.000047256126,0.000032750908,0.000093272596,0.00024268635,0.00021522462,0.00124289],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997198,0.00004636456,0.00002213207,0.00007963135,0.000084128806,0.00004792797],"domain_scores_gemma":[0.9994161,0.00018764086,0.00006696222,0.000083735744,0.00020845674,0.00003710164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007278704,0.00052314304,0.00060742546,0.0017544066,0.00054517115,0.0011340448,0.00094750396,0.0013476594,0.00097178534],"category_scores_gemma":[0.001536981,0.00036158695,0.00081079867,0.000705768,0.0005172049,0.0010227701,0.0008291565,0.00061686075,0.00040914307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063345995,0.00021158438,0.0053988304,0.0002759287,0.00009759664,0.0003615609,0.0005215763,0.06703094,0.27408472,0.0045889523,0.0017683377,0.64502656],"study_design_scores_gemma":[0.000018905921,0.00008700556,0.005841055,0.000028877559,0.000058989823,0.00039252196,0.00013432035,0.9264876,0.062124718,0.00324549,0.0015436894,0.000036858397],"about_ca_topic_score_codex":0.0021556797,"about_ca_topic_score_gemma":0.0029289478,"teacher_disagreement_score":0.0021556797,"about_ca_system_score_codex":0.00035418183,"about_ca_system_score_gemma":0.0011215258,"threshold_uncertainty_score":0.0042862296},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010737602,0.00008450575,0.988489,0.00007955725,0.000011501492,0.000011513389,0.000036105026,0.00024478123,0.00030557497],"genre_scores_gemma":[0.31883627,0.00039293806,0.6766831,0.00008398149,0.00007417323,0.000076879005,0.00028059288,0.00021219563,0.003359822],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996791,0.00008664289,0.000015391954,0.000060736722,0.00012811623,0.00003012518],"domain_scores_gemma":[0.999328,0.00039510103,0.00007096622,0.00006989511,0.00011088039,0.000025131569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057307864,0.00060274894,0.00057599926,0.0005363927,0.00018369488,0.00067599426,0.0006389423,0.0008531079,0.0013568476],"category_scores_gemma":[0.0028428342,0.0005968209,0.00060665194,0.0005390656,0.00035856979,0.00086099096,0.0009911975,0.0010230376,0.00056320813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035345912,0.00012016578,0.0022808602,0.00023000364,0.00012660991,0.00018055139,0.0002591559,0.33478794,0.12194615,0.00892743,0.0022132033,0.5285745],"study_design_scores_gemma":[0.000010096691,0.000026187054,0.00068399357,0.000008223831,0.00001421425,0.000097912816,0.000013789069,0.98522425,0.011076116,0.0021062065,0.0007295809,0.000009447174],"about_ca_topic_score_codex":0.0018235901,"about_ca_topic_score_gemma":0.0033733004,"teacher_disagreement_score":0.0018235901,"about_ca_system_score_codex":0.0001976654,"about_ca_system_score_gemma":0.0005916773,"threshold_uncertainty_score":0.0045390725},"labels":[],"label_agreement":null},{"id":"W2571012593","doi":"10.1016/j.media.2017.01.004","title":"Robust estimation of carotid artery wall motion using the elasticity-based state-space approach","year":2017,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cardiovascular Health and Disease Prevention","field":"Medicine","cited_by":87,"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":"Initialization; Common carotid artery; Elasticity (physics); Carotid arteries; Mathematics; Ultrasound; Measure (data warehouse); Artificial intelligence; Computer science; Medicine; Surgery; Radiology; Physics; Data mining","score_opus":0.031407864564820386,"score_gpt":0.30930143244943076,"score_spread":0.2778935678846104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2571012593","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037391357,0.00018318571,0.96163374,0.00012346829,0.000021463946,0.000014285938,0.000038163544,0.00019218671,0.0004021693],"genre_scores_gemma":[0.8527039,0.00045175958,0.14305508,0.00009264569,0.000060556755,0.00007841553,0.00022356109,0.000067007335,0.00326699],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99983716,0.000041788895,0.000011036193,0.00004628588,0.000044741326,0.000018976565],"domain_scores_gemma":[0.99953544,0.00027749545,0.00006408712,0.000039899,0.00006571599,0.000017333692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004626115,0.0004944194,0.0006752309,0.0007241533,0.00025698144,0.00059966615,0.0004215821,0.00073576067,0.0011491467],"category_scores_gemma":[0.0018596222,0.00052647153,0.00073224405,0.00036653696,0.00039747672,0.0007703761,0.00069851556,0.0007983803,0.00029816278],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030735854,0.00015243993,0.0028411178,0.00012005746,0.00020549237,0.0001038053,0.00007321,0.7470196,0.037488393,0.0069749397,0.000619031,0.20409459],"study_design_scores_gemma":[0.0000038180324,0.00001830874,0.00079956587,0.0000036162266,0.0000099223,0.000019516463,0.000002989475,0.996443,0.0014718375,0.0010840669,0.00013340403,0.000009900523],"about_ca_topic_score_codex":0.003387289,"about_ca_topic_score_gemma":0.0027690397,"teacher_disagreement_score":0.003387289,"about_ca_system_score_codex":0.00022254513,"about_ca_system_score_gemma":0.00062167656,"threshold_uncertainty_score":0.006735146},"labels":[],"label_agreement":null},{"id":"W2580480204","doi":"10.1016/j.media.2017.01.009","title":"A deep learning approach for the analysis of masses in mammograms with minimal user intervention","year":2017,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":342,"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":"Artificial intelligence; Segmentation; Computer science; Deep learning; Pattern recognition (psychology); Classifier (UML); Mammography; Bayesian probability; Visualization; Test set; Machine learning; Breast cancer","score_opus":0.015229905149617709,"score_gpt":0.2996650691174222,"score_spread":0.2844351639678045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2580480204","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025841588,0.0005220701,0.96979004,0.00029756987,0.000060369945,0.00005832192,0.00020657363,0.0017964887,0.0014269337],"genre_scores_gemma":[0.4316989,0.00055299344,0.5571944,0.000462491,0.0001310463,0.00012700958,0.0006665939,0.0002265815,0.0089399535],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978095,0.000038535425,0.000012733037,0.00005165607,0.00008071938,0.000035460434],"domain_scores_gemma":[0.9995757,0.000191914,0.00002415197,0.000053713542,0.000120944955,0.000033557022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049541716,0.0005283139,0.00047540572,0.00061521557,0.00026897015,0.00062181166,0.0010538533,0.000893607,0.0029699083],"category_scores_gemma":[0.0013921923,0.00033810947,0.00053032325,0.0005014419,0.00025891967,0.00062534545,0.0012378925,0.000968475,0.00084452896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027698217,0.00020423715,0.001345267,0.00011377487,0.00009693916,0.00017737088,0.00006351463,0.08365071,0.04309546,0.0044886735,0.006289421,0.86019754],"study_design_scores_gemma":[0.000006753405,0.000032737866,0.00046186527,0.00000684008,0.000013579145,0.000069457186,0.000008022176,0.9901314,0.0057850583,0.0023175052,0.0011609615,0.0000058995884],"about_ca_topic_score_codex":0.0051031024,"about_ca_topic_score_gemma":0.009093695,"teacher_disagreement_score":0.0051031024,"about_ca_system_score_codex":0.00038748997,"about_ca_system_score_gemma":0.00072222046,"threshold_uncertainty_score":0.010146797},"labels":[],"label_agreement":null},{"id":"W2589644515","doi":"10.1016/j.media.2017.11.005","title":"Learning normalized inputs for iterative estimation in medical image segmentation","year":2017,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":229,"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 de l’Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Computer science; Pipeline (software); Segmentation; Convolutional neural network; Artificial intelligence; Benchmark (surveying); Deep learning; Residual; Image segmentation; Pattern recognition (psychology); Computer vision; Algorithm","score_opus":0.00796106501721744,"score_gpt":0.3132501830028003,"score_spread":0.30528911798558284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2589644515","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043708854,0.00017301766,0.99450123,0.00006536668,0.000015762897,0.000024467801,0.000028275943,0.0006038956,0.00021708707],"genre_scores_gemma":[0.1948673,0.00036345833,0.8011144,0.00014611158,0.000054277796,0.00020448779,0.00040020698,0.00046423645,0.002385454],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986512,0.00042026417,0.00011190912,0.00033531393,0.000354717,0.00012653772],"domain_scores_gemma":[0.99601763,0.002612331,0.000218475,0.00030241554,0.0007564085,0.000092731076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002560676,0.0013125732,0.0015524767,0.0012239236,0.00060724746,0.0017710313,0.0018661185,0.0027618627,0.003038404],"category_scores_gemma":[0.013840291,0.0013086225,0.0010688966,0.0012007962,0.0012871707,0.0020175388,0.002286552,0.002206892,0.0010493391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040342938,0.00011538972,0.0010536129,0.0002852344,0.000105625135,0.00010452955,0.00020961859,0.46055195,0.02005388,0.012636753,0.002651157,0.5018288],"study_design_scores_gemma":[0.0000063989805,0.000022071335,0.000125116,0.000014604285,0.000008087565,0.000020921168,0.000008805701,0.9908381,0.0047889487,0.0036866742,0.00047345192,0.000006825602],"about_ca_topic_score_codex":0.0077972226,"about_ca_topic_score_gemma":0.009101949,"teacher_disagreement_score":0.0077972226,"about_ca_system_score_codex":0.0014124406,"about_ca_system_score_gemma":0.0021256409,"threshold_uncertainty_score":0.015503645},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46863148,0.001370121,0.52434134,0.00023507212,0.000063540116,0.00019007268,0.0011270599,0.00246579,0.001575531],"genre_scores_gemma":[0.7520885,0.00078064203,0.24203803,0.00007511723,0.000058363265,0.00015105083,0.0020952139,0.000267174,0.002445901],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998317,0.000033194443,0.000018695984,0.000061491824,0.000034842196,0.00002012884],"domain_scores_gemma":[0.99923027,0.00017134023,0.00018238273,0.00013700605,0.00021656585,0.00006247467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009894902,0.0004839639,0.0004206835,0.0011162631,0.00031718507,0.000867972,0.0004506033,0.00087041553,0.0012829376],"category_scores_gemma":[0.0018465741,0.0003687921,0.00043552363,0.00052901055,0.00025068055,0.0005265215,0.0006214793,0.00035333028,0.00068182417],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011338033,0.00040858414,0.09453228,0.00054653484,0.00041712896,0.0011099548,0.0010879732,0.07698049,0.3901796,0.0027377699,0.00378872,0.42707717],"study_design_scores_gemma":[0.000038044545,0.00090167625,0.09886456,0.00012508755,0.00031573037,0.004061054,0.00038610396,0.7012201,0.18098935,0.005102786,0.007870829,0.00012480204],"about_ca_topic_score_codex":0.0016213815,"about_ca_topic_score_gemma":0.0035604327,"teacher_disagreement_score":0.0016213815,"about_ca_system_score_codex":0.00021987432,"about_ca_system_score_gemma":0.0006568531,"threshold_uncertainty_score":0.0052330494},"labels":[],"label_agreement":null},{"id":"W2592617572","doi":"10.1016/j.media.2017.03.001","title":"Population model of bladder motion and deformation based on dominant eigenmodes and mixed-effects models in prostate cancer radiotherapy","year":2017,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":23,"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":"Institut National Du Cancer; Institute of Cancer Research; Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)","keywords":"Principal component analysis; Voxel; Population; Computer science; Mathematics; Prostate cancer; Artificial intelligence; Statistics; Pattern recognition (psychology); Medicine; Cancer; Internal medicine","score_opus":0.008159641242234032,"score_gpt":0.29719497717197674,"score_spread":0.28903533592974273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592617572","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048722725,0.00072054577,0.94816864,0.000399377,0.00008703406,0.000044517725,0.00013807062,0.00017080987,0.0015482197],"genre_scores_gemma":[0.885084,0.0017350786,0.08893436,0.0002733326,0.00019510777,0.00037001123,0.00037518126,0.0003044538,0.022728486],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996562,0.0001729354,0.000014778657,0.000057214886,0.00006242623,0.000036461897],"domain_scores_gemma":[0.9988502,0.00078168674,0.00012969805,0.00006245472,0.00012678534,0.000049086015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012903344,0.0006370601,0.0009734436,0.00058225676,0.00043589997,0.0008482323,0.0015262011,0.0017332482,0.0014145384],"category_scores_gemma":[0.0029533303,0.0012437155,0.0014687777,0.00063371763,0.0008364174,0.0014756733,0.0008735599,0.0014893925,0.00036712727],"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":[0.000025099684,0.000027063654,0.00041671164,0.000028492977,0.00005462641,0.000037167236,0.000044292443,0.98658854,0.00092663453,0.007859424,0.00023455762,0.0037573322],"study_design_scores_gemma":[0.0000019594972,0.0000046509554,0.00013359987,0.0000020230832,0.0000064883166,0.0000070890264,0.0000032526607,0.99830365,0.00007213601,0.0013732611,0.00008657031,0.0000052372993],"about_ca_topic_score_codex":0.00968635,"about_ca_topic_score_gemma":0.009633811,"teacher_disagreement_score":0.00968635,"about_ca_system_score_codex":0.00069925317,"about_ca_system_score_gemma":0.001012085,"threshold_uncertainty_score":0.01925993},"labels":[],"label_agreement":null},{"id":"W2612377680","doi":"10.1016/j.media.2017.04.008","title":"A structured latent model for ovarian carcinoma subtyping from histopathology slides","year":2017,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":69,"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":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Subtyping; Computer science; Discriminative model; Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Feature vector; Histopathology; Machine learning; Pathology; Medicine","score_opus":0.025349649899223437,"score_gpt":0.29035625491355294,"score_spread":0.2650066050143295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612377680","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05770875,0.00044857536,0.93621194,0.0008998216,0.000067984765,0.00010827763,0.0024964956,0.00143459,0.00062357925],"genre_scores_gemma":[0.8441178,0.00060313975,0.14088872,0.00037082567,0.00022189287,0.00050718157,0.0062222313,0.000215282,0.0068529225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896586,0.0003093384,0.000065637214,0.0003344739,0.00017637695,0.000148366],"domain_scores_gemma":[0.9965611,0.0019204149,0.00048716183,0.00050177996,0.00041325256,0.000116245625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019299127,0.0006101743,0.0008447338,0.001699863,0.00041542685,0.0014339882,0.0021576437,0.0016140437,0.0019640033],"category_scores_gemma":[0.006038964,0.00068786,0.0016997942,0.0012860899,0.00096030446,0.0015001462,0.0013321813,0.0020326609,0.0014420383],"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":[0.0013178929,0.0007990856,0.02281669,0.00040577256,0.00044804424,0.0004927422,0.00067094347,0.5319103,0.019132363,0.045001864,0.01159671,0.36540762],"study_design_scores_gemma":[0.00001999211,0.000033917102,0.0014576955,0.000015042429,0.000032919135,0.00003843828,0.000015292522,0.9866692,0.0006837993,0.010619974,0.00039958494,0.000014119793],"about_ca_topic_score_codex":0.010137352,"about_ca_topic_score_gemma":0.019128827,"teacher_disagreement_score":0.010137352,"about_ca_system_score_codex":0.0012081307,"about_ca_system_score_gemma":0.0014840219,"threshold_uncertainty_score":0.020156682},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06761968,0.00044439777,0.9306858,0.00008207806,0.00002630673,0.00004187067,0.00011218399,0.00050534745,0.00048233653],"genre_scores_gemma":[0.70893,0.001269009,0.28554985,0.000044683085,0.000044779666,0.0001145909,0.0004832617,0.00014842692,0.0034153461],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998901,0.000015154036,0.000008266543,0.000029658395,0.000039298186,0.000017584656],"domain_scores_gemma":[0.99984396,0.000075812226,0.000026100948,0.000011446348,0.000034348526,0.000008294068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002759513,0.00051886676,0.0004299251,0.0007687401,0.00019651232,0.00069294934,0.00047095932,0.0007972148,0.0006054421],"category_scores_gemma":[0.0007416575,0.00037948095,0.00057629007,0.0005352247,0.00023408403,0.000383197,0.00023066963,0.00037809953,0.00038255734],"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":[0.00032635796,0.000102669575,0.0027759995,0.00025239834,0.00007994344,0.00036826066,0.00016833193,0.4237513,0.21005405,0.0023768134,0.0010290097,0.35871488],"study_design_scores_gemma":[0.00000310328,0.000030341478,0.001387153,0.000009445296,0.000016685959,0.000098146105,0.000010471943,0.9792483,0.018342037,0.00033623347,0.0005089476,0.00000914384],"about_ca_topic_score_codex":0.005917377,"about_ca_topic_score_gemma":0.005544643,"teacher_disagreement_score":0.005917377,"about_ca_system_score_codex":0.0003509983,"about_ca_system_score_gemma":0.00054774596,"threshold_uncertainty_score":0.011765838},"labels":[],"label_agreement":null},{"id":"W2738318712","doi":"10.1016/j.media.2017.07.004","title":"Designing image segmentation studies: Statistical power, sample size and reference standard quality","year":2017,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":35,"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; Medical Research Council; Radboud Universiteit; Cancer Research UK","keywords":"Resampling; Sample size determination; Segmentation; Computer science; Reference data; Statistics; Range (aeronautics); Standard deviation; Matching (statistics); Sample (material); Statistical power; Data set; Artificial intelligence; Mathematics; Data mining","score_opus":0.03164478710718117,"score_gpt":0.3839955934075969,"score_spread":0.35235080630041576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2738318712","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028948933,0.003434127,0.952836,0.0035288432,0.00046822883,0.006488367,0.0004794988,0.00051744765,0.0032986347],"genre_scores_gemma":[0.36553538,0.0013255103,0.6056139,0.0021579189,0.0003509969,0.023375986,0.00042550682,0.00048849045,0.0007263745],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.63279474,0.30714178,0.016231097,0.012496723,0.029969642,0.0013659751],"domain_scores_gemma":[0.28114447,0.6353645,0.026691547,0.032953095,0.02276774,0.0010786874],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.3920904,0.0015691409,0.0036364205,0.0030261397,0.0016757614,0.0054187365,0.0036599908,0.0063382876,0.0029309094],"category_scores_gemma":[0.7120556,0.0016458946,0.003315089,0.0032298737,0.007566846,0.0061594658,0.0040489524,0.003923688,0.0011377864],"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":[0.009410967,0.0012407074,0.08359899,0.009624899,0.007802896,0.0010256723,0.006717747,0.07591683,0.015773652,0.12122368,0.014926408,0.65273756],"study_design_scores_gemma":[0.008113659,0.017502861,0.11242355,0.008218555,0.006014443,0.0023537292,0.0021085308,0.21004911,0.05560054,0.50477785,0.0717734,0.0010637965],"about_ca_topic_score_codex":0.001653743,"about_ca_topic_score_gemma":0.0014176024,"teacher_disagreement_score":0.60790956,"about_ca_system_score_codex":0.0020731774,"about_ca_system_score_gemma":0.004868013,"threshold_uncertainty_score":0.74966073},"labels":[],"label_agreement":null},{"id":"W2741247953","doi":"10.1016/j.media.2017.07.006","title":"Ensemble of expert deep neural networks for spatio-temporal denoising of contrast-enhanced MRI sequences","year":2017,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Image and Signal Denoising Methods","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":"Dalhousie University","funders":"FP7 Ideas: European Research Council; Israel Science Foundation; United States-Israel Binational Science Foundation","keywords":"Computer science; Artificial intelligence; Voxel; Pattern recognition (psychology); Dynamic contrast-enhanced MRI; Artificial neural network; Diffusion MRI; Classifier (UML); Noise reduction; Noise (video); Deep learning; Contrast (vision); Magnetic resonance imaging; Image (mathematics)","score_opus":0.023359598990500108,"score_gpt":0.32877905013837194,"score_spread":0.30541945114787183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2741247953","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048578467,0.0007113724,0.9489645,0.00012818443,0.0000680221,0.00002043401,0.00008071486,0.00045229832,0.0009960906],"genre_scores_gemma":[0.70252395,0.00079195923,0.28890243,0.00022591488,0.00009954015,0.00006241491,0.00051747554,0.000124107,0.0067521995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974304,0.000054198135,0.000017675478,0.00006917689,0.000071080176,0.000044852026],"domain_scores_gemma":[0.999388,0.00021410619,0.000054426735,0.00007251318,0.00023416875,0.00003674656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001311836,0.00071137643,0.00080065365,0.00049267284,0.00022398627,0.0004889847,0.0010056722,0.0013723916,0.0010851807],"category_scores_gemma":[0.002111387,0.00042764572,0.00070376147,0.00043876815,0.0002805799,0.0007896797,0.00081559597,0.0011655244,0.0003493037],"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":[0.000274137,0.00015278308,0.0012521176,0.00008948549,0.00020579495,0.00007954102,0.00006594069,0.6745489,0.017826457,0.0026156507,0.0022588954,0.30063027],"study_design_scores_gemma":[0.0000016744767,0.000016530672,0.00013818224,0.0000033552915,0.000012357037,0.000012443436,0.0000026091095,0.9975816,0.0016573427,0.0004183365,0.00015291807,0.000002767077],"about_ca_topic_score_codex":0.0054885484,"about_ca_topic_score_gemma":0.010317801,"teacher_disagreement_score":0.0054885484,"about_ca_system_score_codex":0.00046971065,"about_ca_system_score_gemma":0.0007146727,"threshold_uncertainty_score":0.010913193},"labels":[],"label_agreement":null},{"id":"W2757633676","doi":"10.1016/j.media.2017.09.005","title":"Full left ventricle quantification via deep multitask relationships learning","year":2017,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":161,"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":"Softmax function; Multi-task learning; Artificial intelligence; Deep learning; Computer science; Pattern recognition (psychology); Classifier (UML); Convolutional neural network; Ventricle; Machine learning; Task (project management); Cardiology; Medicine","score_opus":0.023500276494085866,"score_gpt":0.32301923516133463,"score_spread":0.29951895866724876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2757633676","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017069016,0.00048892706,0.9782218,0.00028103185,0.00004591207,0.00002940018,0.0005164463,0.0021797484,0.001167716],"genre_scores_gemma":[0.49971235,0.00075985346,0.4880526,0.000494269,0.0002227862,0.00012057586,0.0024980812,0.00090269,0.00723687],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996567,0.0000710407,0.000019007695,0.00011447376,0.00008959274,0.00004910129],"domain_scores_gemma":[0.99925977,0.00031358784,0.00008548016,0.0001617559,0.00012060247,0.000058783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010177715,0.0012216404,0.00082667073,0.0011976578,0.000351634,0.0012724798,0.0011224017,0.0016515771,0.0027450467],"category_scores_gemma":[0.0022086757,0.00072836026,0.000988329,0.00066535664,0.0003057142,0.0013458553,0.0019076357,0.0015484779,0.0018930662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003525726,0.0002442544,0.003711827,0.00026526776,0.000268404,0.00034556483,0.00009314017,0.16258737,0.06704993,0.0048603937,0.012758293,0.74746305],"study_design_scores_gemma":[0.00001310195,0.00005392083,0.0013878703,0.00002947742,0.000055849192,0.00024022465,0.000015649279,0.9756422,0.013223942,0.006980956,0.002335368,0.00002131383],"about_ca_topic_score_codex":0.0019459188,"about_ca_topic_score_gemma":0.004042464,"teacher_disagreement_score":0.0027450467,"about_ca_system_score_codex":0.00025379536,"about_ca_system_score_gemma":0.00079830113,"threshold_uncertainty_score":0.009183049},"labels":[],"label_agreement":null},{"id":"W2770706469","doi":"10.1016/j.media.2017.11.007","title":"The semiotics of medical image Segmentation","year":2017,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","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":"Western University; Robarts Clinical Trials","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Semiotics; Computer science; Sign (mathematics); Segmentation; Interpretation (philosophy); Metaphor; Symbol (formal); Semiosis; Perspective (graphical); Artificial intelligence; Image (mathematics); Image segmentation; Interface (matter); Natural language processing; Human–computer interaction; Linguistics; Mathematics","score_opus":0.012701397737422833,"score_gpt":0.3466525148679864,"score_spread":0.3339511171305636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770706469","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018372945,0.0079102395,0.9249979,0.0064927978,0.0005292356,0.00015034828,0.0004210494,0.00038809018,0.040737472],"genre_scores_gemma":[0.43020037,0.005134216,0.5513012,0.00106584,0.0007614826,0.0005594248,0.0007572157,0.00024089521,0.009979385],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9964791,0.0017799116,0.0004095745,0.0005057867,0.0007094781,0.000116071235],"domain_scores_gemma":[0.9933882,0.004490259,0.0004939203,0.0006585098,0.0007355223,0.00023356032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003853895,0.00052336295,0.0006317776,0.004617837,0.0018616661,0.006695305,0.0011605512,0.001994446,0.0025497142],"category_scores_gemma":[0.008837,0.00087490655,0.0016073466,0.0023655747,0.014116453,0.0060658823,0.002548356,0.0022473608,0.00069356913],"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":[0.00001378926,0.000007493838,0.00017277565,0.000088856825,0.000008993232,0.00009955242,0.00071372016,0.00106763,0.00053775276,0.98835003,0.00059349847,0.008345909],"study_design_scores_gemma":[0.000009880639,0.000013247681,0.00025699957,0.00007581498,0.000011251162,0.00024694816,0.00032309233,0.0074199736,0.00052773766,0.97229904,0.018801734,0.000014263633],"about_ca_topic_score_codex":0.0029375972,"about_ca_topic_score_gemma":0.0018238488,"teacher_disagreement_score":0.006695305,"about_ca_system_score_codex":0.0019135316,"about_ca_system_score_gemma":0.0014867745,"threshold_uncertainty_score":0.02038163},"labels":[],"label_agreement":null},{"id":"W2773397026","doi":"10.1016/j.media.2017.12.001","title":"Automatic spinal cord localization, robust to MRI contrasts using global curve optimization","year":2017,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":46,"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; Polytechnique Montréal","funders":"National Institute of Neurological Disorders and Stroke; National Eye Institute; Fonds de recherche du Québec – Nature et technologies; Fondation Aix-Marseille Universite; Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Centre National de la Recherche Scientifique; National Multiple Sclerosis Society; Canada Foundation for Innovation; Canadian HIV Trials Network, Canadian Institutes of Health Research; Agence Nationale de la Recherche; Natural Sciences and Engineering Research Council of Canada","keywords":"Spinal cord; Feature (linguistics); Artificial intelligence; Computer science; Pattern recognition (psychology); Diffusion MRI; Computer vision; Magnetic resonance imaging; Medicine; Radiology","score_opus":0.01740338147861816,"score_gpt":0.3089809144577952,"score_spread":0.29157753297917705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2773397026","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021628514,0.00046078692,0.97438383,0.00017227892,0.000029705912,0.00004905142,0.000107733234,0.0024079857,0.00076015416],"genre_scores_gemma":[0.28420985,0.0007229516,0.70727384,0.0001480755,0.00010126002,0.00012116476,0.000616906,0.0019914387,0.004814502],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946684,0.00010957863,0.000037251866,0.00015068096,0.00018478023,0.00005085946],"domain_scores_gemma":[0.99909127,0.00030202986,0.00015374903,0.00017808209,0.00022813807,0.00004661704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091303984,0.0012343981,0.0013521746,0.0017395316,0.00051008456,0.0016848954,0.0010587969,0.0013498636,0.0017539582],"category_scores_gemma":[0.0026636233,0.0007312786,0.0009791963,0.0014172777,0.000736056,0.0011796099,0.0013769679,0.0012623786,0.0014226388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004954928,0.00013946883,0.0014783853,0.00032558801,0.0001728382,0.0001323413,0.00013567002,0.12842585,0.14117885,0.003947061,0.0047098976,0.71885866],"study_design_scores_gemma":[0.00004077461,0.00012232739,0.0023958432,0.000023875178,0.00007742614,0.00036875473,0.00003625521,0.95157903,0.037352934,0.005009046,0.0029469277,0.00004683404],"about_ca_topic_score_codex":0.0033718501,"about_ca_topic_score_gemma":0.004969608,"teacher_disagreement_score":0.0033718501,"about_ca_system_score_codex":0.0004658059,"about_ca_system_score_gemma":0.0018202723,"threshold_uncertainty_score":0.0067044497},"labels":[],"label_agreement":null},{"id":"W2773727367","doi":"10.1016/j.media.2017.12.007","title":"The first MICCAI challenge on PET tumor segmentation","year":2017,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":140,"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":"National Cancer Institute; Agence Nationale de la Recherche","keywords":"Computer science; Segmentation; Artificial intelligence; Convolutional neural network; Pipeline (software); Deep learning; Imaging phantom; Benchmark (surveying); Machine learning; Pattern recognition (psychology); Medicine; Nuclear medicine","score_opus":0.02087583818442917,"score_gpt":0.35591176630178334,"score_spread":0.33503592811735416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2773727367","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056127712,0.1177565,0.51487666,0.20167024,0.043493036,0.0010029754,0.016278626,0.02032717,0.028467111],"genre_scores_gemma":[0.13593242,0.027829152,0.69052935,0.021098197,0.032609,0.0007572503,0.031813163,0.006866918,0.052564472],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9947455,0.0011055508,0.00036252989,0.0014321335,0.0019682269,0.00038604945],"domain_scores_gemma":[0.9804552,0.009635067,0.00033494245,0.0029748965,0.0050182496,0.0015816576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010945817,0.0028955815,0.005175417,0.0029679688,0.0019653365,0.0058906106,0.004768555,0.008473597,0.0058802017],"category_scores_gemma":[0.031366445,0.0017149756,0.0027231094,0.0032074067,0.002734071,0.0040874,0.0036846043,0.009469038,0.005838953],"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":[0.0006283054,0.00023774075,0.00071022246,0.0007451041,0.00039801936,0.00037711542,0.0001552098,0.015744384,0.004471831,0.008446884,0.49900663,0.46907854],"study_design_scores_gemma":[0.0005239677,0.00034272854,0.0048656114,0.00044786505,0.0003248691,0.0025503172,0.00042482064,0.39913556,0.025191927,0.08654614,0.47937855,0.00026763146],"about_ca_topic_score_codex":0.025710246,"about_ca_topic_score_gemma":0.034254167,"teacher_disagreement_score":0.025710246,"about_ca_system_score_codex":0.0031937046,"about_ca_system_score_gemma":0.005490467,"threshold_uncertainty_score":0.057887673},"labels":[],"label_agreement":null},{"id":"W2775432161","doi":"10.1016/j.media.2017.12.003","title":"Multi-hypothesis tracking of the tongue surface in ultrasound video recordings of normal and impaired speech","year":2017,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Speech and Audio Processing","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":"Université du Québec à Montréal; Centre for Research on Brain Language and Music; École de Technologie Supérieure","funders":"","keywords":"Tongue; Computer science; Artificial intelligence; Robustness (evolution); Computer vision; Pattern recognition (psychology); Set (abstract data type); Speech recognition","score_opus":0.01999198382214388,"score_gpt":0.276528776163784,"score_spread":0.2565367923416401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2775432161","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96549493,0.0005083544,0.032302722,0.00007685888,0.000064932356,0.000033725326,0.00048317248,0.000212768,0.000822564],"genre_scores_gemma":[0.98844135,0.00024856543,0.009847576,0.000029699562,0.000027897337,0.000018000816,0.00049014617,0.000042311218,0.0008544588],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998203,0.00003156219,0.000017243401,0.00004338576,0.00005664358,0.00003084864],"domain_scores_gemma":[0.9994691,0.0003145252,0.000041075782,0.00003522406,0.00010109726,0.00003887905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042090932,0.0002375827,0.00028057588,0.0007215879,0.00016177038,0.00037747665,0.00020295405,0.0007142955,0.0009060303],"category_scores_gemma":[0.0017412165,0.00014317204,0.00024332124,0.00036194135,0.00018780728,0.0002733271,0.0002978071,0.00024804697,0.00031834477],"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":[0.0035428798,0.00014849663,0.022169773,0.00032863705,0.000099576006,0.0011883205,0.0008775623,0.0087707415,0.79219824,0.00033785962,0.0009483671,0.16938953],"study_design_scores_gemma":[0.0000643572,0.0011703083,0.6093381,0.000078780235,0.00021504119,0.0050910152,0.00079626206,0.17951535,0.20134544,0.00041795138,0.0018888565,0.00007859337],"about_ca_topic_score_codex":0.0020114607,"about_ca_topic_score_gemma":0.0025136853,"teacher_disagreement_score":0.0020114607,"about_ca_system_score_codex":0.00015958093,"about_ca_system_score_gemma":0.00023082914,"threshold_uncertainty_score":0.0039995313},"labels":[],"label_agreement":null},{"id":"W2775469102","doi":"10.1016/j.media.2018.12.007","title":"Learning to detect chest radiographs containing pulmonary lesions using visual attention networks","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":131,"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":"Guy's and St Thomas' NHS Foundation Trust; National Medical Research Council; Medical Research Council; Centro de Investigación Biomédica en Red de Salud Mental; University College London; Engineering and Physical Sciences Research Council; National Institute for Health and Care Research; Cancer Research UK; King's College London; King's University College; King's College Hospital NHS Foundation Trust","keywords":"Softmax function; Artificial intelligence; Radiography; Computer science; Convolutional neural network; Deep learning; Bounding overwatch; Pattern recognition (psychology); Chest radiograph; Nodule (geology); Radiology; Medicine","score_opus":0.017178629149785983,"score_gpt":0.33547331330847213,"score_spread":0.31829468415868617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2775469102","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3296314,0.0021373837,0.65590525,0.0011496057,0.00016604191,0.00019280954,0.00037241934,0.0048818,0.005563304],"genre_scores_gemma":[0.9206598,0.0003707213,0.073630825,0.0003912014,0.0001242045,0.000064850785,0.00048386457,0.00006610527,0.0042085038],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957234,0.0000680733,0.000018235462,0.00018143741,0.00007362991,0.00008623328],"domain_scores_gemma":[0.9989479,0.0005300372,0.00017210103,0.000068438916,0.00022617509,0.000055409462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086394727,0.0012503839,0.00059156766,0.001226629,0.00030080587,0.0007557595,0.0011802146,0.0012141047,0.0011632782],"category_scores_gemma":[0.0029783668,0.00039929512,0.00071506173,0.0005288936,0.00049058726,0.00092555326,0.0009054733,0.00085098075,0.0004745131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005233507,0.0003184475,0.0091459155,0.00013886642,0.00018058269,0.00029066918,0.00016366725,0.3451741,0.03295305,0.0017573717,0.004063795,0.6052901],"study_design_scores_gemma":[0.00001082751,0.000056720408,0.0015880137,0.000009992841,0.000027614546,0.000048213886,0.00001344585,0.9920474,0.0044635404,0.0013937845,0.0003331468,0.0000072977805],"about_ca_topic_score_codex":0.015425132,"about_ca_topic_score_gemma":0.015173451,"teacher_disagreement_score":0.015425132,"about_ca_system_score_codex":0.0013805875,"about_ca_system_score_gemma":0.0006668831,"threshold_uncertainty_score":0.030670702},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02483296,0.00032308407,0.9724439,0.00020644475,0.000044589786,0.00004105097,0.0000601481,0.0007736449,0.0012742112],"genre_scores_gemma":[0.3630639,0.0011857522,0.6223312,0.00020275502,0.00014478188,0.00014037332,0.00041463107,0.00090696727,0.011609585],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994844,0.00011343432,0.000029357694,0.000094590105,0.0002340632,0.00004420047],"domain_scores_gemma":[0.99896204,0.00050281733,0.00013335855,0.00023719337,0.0001186774,0.00004581709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010772188,0.0007910256,0.00077381614,0.0013655026,0.0004555236,0.0011985822,0.00096615066,0.0014369601,0.0028083017],"category_scores_gemma":[0.0037487457,0.00091454794,0.0007744779,0.0015241251,0.0007704352,0.0017602812,0.0020863058,0.0014558702,0.001291973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061301846,0.00014656117,0.00095174427,0.00034770207,0.00015296448,0.0005480327,0.00037686163,0.20165119,0.24408294,0.017256476,0.0037186344,0.5301539],"study_design_scores_gemma":[0.000026132262,0.00009481874,0.0013943748,0.000026300424,0.000036127687,0.0006883924,0.000077670804,0.9262114,0.051450502,0.015928231,0.0040318053,0.00003415187],"about_ca_topic_score_codex":0.0010637617,"about_ca_topic_score_gemma":0.0018584251,"teacher_disagreement_score":0.0028083017,"about_ca_system_score_codex":0.00033292183,"about_ca_system_score_gemma":0.00065680145,"threshold_uncertainty_score":0.009394705},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011687323,0.000052917716,0.9983028,0.000033647197,0.00000833246,0.00001953647,0.000020949841,0.0002292113,0.00016380336],"genre_scores_gemma":[0.15462579,0.0005195394,0.8394512,0.00016822331,0.000091098256,0.00050169,0.0004545204,0.00050997746,0.0036780043],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829954,0.00034656792,0.000091149835,0.00055194716,0.0006290568,0.0000817422],"domain_scores_gemma":[0.99865615,0.00060104125,0.0002675515,0.00021404613,0.00019919885,0.00006210987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026446432,0.0009574057,0.0011643465,0.0014219439,0.0004693544,0.001443079,0.0021946388,0.0019400978,0.002066826],"category_scores_gemma":[0.0048403353,0.001566357,0.0025636968,0.0010931795,0.0015690081,0.0013043053,0.0017582221,0.0020400565,0.0010844908],"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":[0.00008895389,0.00003826151,0.0004868903,0.00014432061,0.00007373356,0.0001075669,0.000104251645,0.8890337,0.019608637,0.01730474,0.0006833402,0.07232562],"study_design_scores_gemma":[0.000011739914,0.00003618532,0.00021137076,0.0000118783455,0.000017518214,0.00008752703,0.0000065001186,0.987663,0.0033779498,0.0072262883,0.0013256923,0.00002433314],"about_ca_topic_score_codex":0.004481025,"about_ca_topic_score_gemma":0.0067295106,"teacher_disagreement_score":0.004481025,"about_ca_system_score_codex":0.0011285213,"about_ca_system_score_gemma":0.0022468155,"threshold_uncertainty_score":0.013986349},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009825796,0.00020639216,0.9859261,0.00012084194,0.000035953457,0.00005249122,0.00011458262,0.0029764578,0.000741336],"genre_scores_gemma":[0.15252425,0.0004748928,0.8401381,0.00025966225,0.00006435611,0.00019658572,0.0007497607,0.0016274713,0.0039650425],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995409,0.00007633829,0.000027716354,0.00009963002,0.00020293496,0.00005239504],"domain_scores_gemma":[0.99926466,0.00029482294,0.00006444301,0.00015931677,0.00017549851,0.000041234205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000626941,0.0010705716,0.0012675504,0.0013268024,0.0004217819,0.0012949655,0.001408965,0.0013448257,0.002711235],"category_scores_gemma":[0.001885653,0.0009125285,0.0012052819,0.001505165,0.0005484575,0.0007820853,0.0017057599,0.0013783074,0.001857318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028018898,0.000099444434,0.0008996364,0.00025463384,0.00016111943,0.00023473386,0.00020823358,0.093589194,0.21320039,0.0051546614,0.010083943,0.6758338],"study_design_scores_gemma":[0.000019735358,0.000044944914,0.0013306462,0.000017666367,0.000032629123,0.0003805703,0.0000345471,0.9520822,0.03447057,0.007121091,0.0044316417,0.000033614997],"about_ca_topic_score_codex":0.004358145,"about_ca_topic_score_gemma":0.008279237,"teacher_disagreement_score":0.004358145,"about_ca_system_score_codex":0.0004411146,"about_ca_system_score_gemma":0.001783813,"threshold_uncertainty_score":0.009069979},"labels":[],"label_agreement":null},{"id":"W2791687065","doi":"10.1016/j.media.2018.03.004","title":"Riemannian metric optimization on surfaces (RMOS) for intrinsic brain mapping in the Laplace–Beltrami embedding space","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","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; National Institute of Mental Health; Canadian Institutes of Health Research; Foundation of the American Society of Neuroradiology; National Institutes of Health; BioClinica; National Eye Institute; National Institute on Aging; Alzheimer's Association; AbbVie; Biogen; American Society of Neuroradiology; Northern California Institute for Research and Education; Alzheimer's Drug Discovery Foundation; U.S. Department of Defense","keywords":"Embedding; Mathematics; Surface (topology); Riemannian geometry; Metric (unit); Polygon mesh; Artificial intelligence; Mathematical analysis; Topology (electrical circuits); Computer science; Algorithm; Geometry; Combinatorics","score_opus":0.04481816951865586,"score_gpt":0.39064356498760916,"score_spread":0.3458253954689533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791687065","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01773744,0.0007321853,0.97764105,0.00061056303,0.00006761221,0.000054425116,0.00013416601,0.00019807057,0.002824451],"genre_scores_gemma":[0.46936902,0.0022876502,0.50677115,0.00043072374,0.000494454,0.00045805288,0.0009827279,0.0010271926,0.018178983],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999201,0.0003510997,0.000045015095,0.00016443804,0.00019107046,0.0000473694],"domain_scores_gemma":[0.9982482,0.00090384815,0.00021078101,0.00017837458,0.00029464724,0.00016418163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014144138,0.0012884107,0.0012849548,0.0013064855,0.0004212443,0.0015865171,0.00127885,0.0015801099,0.0025810343],"category_scores_gemma":[0.0062685683,0.0004925196,0.0011539469,0.0007636325,0.0016074077,0.0023605027,0.0031307628,0.0024946565,0.0008152836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010243661,0.00008771215,0.00082811643,0.00044067885,0.00010472582,0.00013592381,0.0003325029,0.18415026,0.007928184,0.69030154,0.008267406,0.10732047],"study_design_scores_gemma":[0.0000066430125,0.000058564667,0.0003120643,0.00001788136,0.000011423962,0.00006661521,0.00003466012,0.7749272,0.00070108275,0.22090265,0.0029376892,0.000023419087],"about_ca_topic_score_codex":0.0025266008,"about_ca_topic_score_gemma":0.002016027,"teacher_disagreement_score":0.0025810343,"about_ca_system_score_codex":0.00097357534,"about_ca_system_score_gemma":0.0010494696,"threshold_uncertainty_score":0.008634388},"labels":[],"label_agreement":null},{"id":"W2793804994","doi":"10.1016/j.media.2018.02.002","title":"Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer’s disease","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":203,"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":"Pacific Alzheimer Research Foundation; National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; Eisai; U.S. Department of Defense; Eli Lilly and Company; Lundbeckfonden; Michael Smith Health Research BC; AbbVie; Fondation Brain Canada; DoD Alzheimer's Disease Neuroimaging Initiative; Natural Sciences and Engineering Research Council of Canada; Pfizer; BioClinica; Biogen; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; F. Hoffmann-La Roche; Roche; Merck; Alzheimer's Drug Discovery Foundation; Takeda Pharmaceutical Company; Fujirebio Europe; Alzheimer's Association; GE Healthcare; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics","keywords":"Dementia; Prodromal Stage; Positron emission tomography; Neuroimaging; Disease; Cognition; Psychology; Medicine; Cognitive impairment; Alzheimer's disease; Neuroscience; Pathology","score_opus":0.020387958468980453,"score_gpt":0.34946875540623984,"score_spread":0.3290807969372594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793804994","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6165166,0.0031892832,0.37625894,0.0005380728,0.00007860619,0.000053233987,0.0009328664,0.000655777,0.0017766142],"genre_scores_gemma":[0.9526038,0.00085977255,0.044606287,0.00006276936,0.000038961378,0.000021784226,0.00043837551,0.000036327463,0.0013319562],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999515,0.000011134468,0.0000032464297,0.0000123980335,0.000011191529,0.000010515755],"domain_scores_gemma":[0.99992275,0.000031542946,0.00001246138,0.000006589785,0.000019234687,0.000007379814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023837165,0.00034345384,0.00035293755,0.0006708036,0.00009000438,0.0003325082,0.00022242568,0.00033145427,0.00074938504],"category_scores_gemma":[0.00052681065,0.00013509062,0.00042958074,0.00034727147,0.00009079461,0.00025549167,0.00025615562,0.00029217286,0.00015233588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009235136,0.00036042888,0.022056643,0.00034720104,0.0004309645,0.0008457791,0.00013955415,0.2006254,0.17672355,0.004144871,0.0049674623,0.58843464],"study_design_scores_gemma":[0.0000075166436,0.000044437336,0.009519888,0.000013215365,0.00005834035,0.0001518484,0.000018718212,0.979909,0.00814636,0.0016413609,0.00048028037,0.000009143921],"about_ca_topic_score_codex":0.0034146623,"about_ca_topic_score_gemma":0.005710852,"teacher_disagreement_score":0.0034146623,"about_ca_system_score_codex":0.0002210812,"about_ca_system_score_gemma":0.00022540387,"threshold_uncertainty_score":0.006789565},"labels":[],"label_agreement":null},{"id":"W2799738340","doi":"10.1016/j.media.2019.02.009","title":"Constrained-CNN losses for weakly supervised segmentation","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":282,"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; Kahnawake Education Center","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Leverage (statistics); Computer science; Segmentation; Iterated function; Mathematical optimization; Differentiable function; Artificial intelligence; Dual (grammatical number); Pixel; Machine learning; Mathematics","score_opus":0.011301270334979404,"score_gpt":0.29366071320176274,"score_spread":0.28235944286678333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799738340","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026024178,0.0012572615,0.96405,0.00064520055,0.0001363556,0.00013418657,0.0009780059,0.0024712223,0.004303598],"genre_scores_gemma":[0.56533104,0.0013531786,0.38396266,0.00093075633,0.00032670857,0.00044266458,0.0063952403,0.0015526133,0.039705098],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994029,0.00013120247,0.00003305747,0.0001872794,0.00015147368,0.00009425543],"domain_scores_gemma":[0.99889475,0.00047367217,0.0000837022,0.00022180051,0.00024834037,0.000077703226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016388218,0.0016554262,0.0015669686,0.000909861,0.0005054626,0.0014272539,0.0024585181,0.0030531366,0.0073113246],"category_scores_gemma":[0.004553595,0.00094488903,0.0009362351,0.0009504641,0.0009293265,0.0019997633,0.0022236619,0.0021694223,0.002170645],"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":[0.00070238,0.00023896832,0.0011916423,0.00037217946,0.0001807636,0.0002141122,0.00008170997,0.47230983,0.015682941,0.022890363,0.022514451,0.46362072],"study_design_scores_gemma":[0.000008412842,0.000021671767,0.00019162924,0.000016908585,0.0000100524285,0.000035775163,0.000005969469,0.9905709,0.0020891326,0.0060431347,0.0010008413,0.000005471987],"about_ca_topic_score_codex":0.011036296,"about_ca_topic_score_gemma":0.016490294,"teacher_disagreement_score":0.011036296,"about_ca_system_score_codex":0.0015028092,"about_ca_system_score_gemma":0.0019708553,"threshold_uncertainty_score":0.024458766},"labels":[],"label_agreement":null},{"id":"W2803806607","doi":"10.1016/j.media.2018.05.007","title":"Quantifying the uncertainty in model parameters using Gaussian process-based Markov chain Monte Carlo in cardiac electrophysiology","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cardiac electrophysiology and arrhythmias","field":"Medicine","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":"Dalhousie University","funders":"Pioneer Hi-Bred; National Heart, Lung, and Blood Institute; Foundation for the National Institutes of Health; National Institutes of Health; National Science Foundation","keywords":"Markov chain Monte Carlo; Surrogate model; Posterior probability; Computer science; Sampling (signal processing); Importance sampling; Bayesian inference; Gaussian process; Inference; Mathematical optimization; Algorithm; Approximate inference; Monte Carlo method; Bayesian probability; Gaussian; Mathematics; Artificial intelligence; Machine learning; Statistics","score_opus":0.02091680177372739,"score_gpt":0.3267277054927146,"score_spread":0.3058109037189872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803806607","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026632436,0.00031631053,0.971941,0.00029413955,0.000021156528,0.000028081948,0.000042557433,0.00014488686,0.00057939434],"genre_scores_gemma":[0.84971076,0.00059808814,0.14766775,0.00023843862,0.00008673055,0.00012705161,0.00020498472,0.00016041542,0.0012058794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804866,0.0010363813,0.00009786847,0.00026014468,0.00039550415,0.00016137783],"domain_scores_gemma":[0.96861666,0.02842797,0.0010070413,0.0007533578,0.0008365463,0.0003583842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008322809,0.0010563316,0.0017586908,0.0015239968,0.0008546628,0.0029399097,0.0019353806,0.0030226875,0.0010290606],"category_scores_gemma":[0.031675737,0.0016824027,0.0013652822,0.0009544939,0.0032866723,0.0032776806,0.0024428498,0.0029691772,0.00018305214],"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":[0.000035998455,0.000016361708,0.00054998556,0.000021654749,0.00002647256,0.000027266304,0.00003600711,0.98224777,0.00025417085,0.013633094,0.00008436044,0.003066924],"study_design_scores_gemma":[0.0000032622472,0.000005163518,0.000098392244,0.0000065200816,0.000004776462,0.000008959224,0.0000037482264,0.9909306,0.00013028442,0.008752306,0.00004926643,0.000006724229],"about_ca_topic_score_codex":0.01764282,"about_ca_topic_score_gemma":0.012809618,"teacher_disagreement_score":0.01764282,"about_ca_system_score_codex":0.002466611,"about_ca_system_score_gemma":0.0026356305,"threshold_uncertainty_score":0.044015706},"labels":[],"label_agreement":null},{"id":"W2803866553","doi":"10.1016/j.media.2018.05.006","title":"Superpixel and multi-atlas based fusion entropic model for the segmentation of X-ray images","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","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":"Centre Hospitalier de l’Université de Montréal; Université de Montréal","funders":"","keywords":"Artificial intelligence; Segmentation; Computer science; Atlas (anatomy); Pattern recognition (psychology); Computer vision; Pruning; Image segmentation; Medicine","score_opus":0.011840635446570899,"score_gpt":0.2703603444348402,"score_spread":0.2585197089882693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803866553","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021234674,0.00046734128,0.9767294,0.00011722137,0.000028923925,0.00002256896,0.000089065376,0.0004316845,0.0008791565],"genre_scores_gemma":[0.7675451,0.00094615825,0.22343865,0.00015637233,0.000096539516,0.00011117619,0.0004591929,0.00028832717,0.006958447],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970394,0.00006267548,0.000016264992,0.000078996396,0.00010272549,0.00003536706],"domain_scores_gemma":[0.9997569,0.00008692749,0.000034184795,0.00003292637,0.00006992231,0.00001913379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067587214,0.00047492806,0.00076417875,0.000892881,0.00027307184,0.0007543586,0.0010884145,0.0009380727,0.0013360267],"category_scores_gemma":[0.001082009,0.00032669544,0.0009153944,0.0008126473,0.00043044324,0.0009197522,0.0007516641,0.00070737366,0.00031484413],"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":[0.00026315075,0.00005755291,0.0007720804,0.00014318874,0.00013774844,0.00019956015,0.00010253624,0.8391744,0.022608288,0.016749132,0.0017040139,0.11808831],"study_design_scores_gemma":[0.0000012056718,0.000008804005,0.00016867537,0.0000018431015,0.000008214001,0.000021135707,0.0000023376795,0.9974482,0.0009944661,0.0011878433,0.00015318091,0.000004114652],"about_ca_topic_score_codex":0.00807106,"about_ca_topic_score_gemma":0.007760565,"teacher_disagreement_score":0.00807106,"about_ca_system_score_codex":0.000906488,"about_ca_system_score_gemma":0.0009428737,"threshold_uncertainty_score":0.016048193},"labels":[],"label_agreement":null},{"id":"W2803954248","doi":"10.1016/j.media.2018.05.005","title":"Automated comprehensive Adolescent Idiopathic Scoliosis assessment using MVC-Net","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Scoliosis diagnosis and treatment","field":"Medicine","cited_by":156,"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; Western University","funders":"Government of Ontario","keywords":"Idiopathic scoliosis; Scoliosis; Computer science; Net (polyhedron); Artificial intelligence; Medicine; Physical medicine and rehabilitation; Machine learning; Medical physics; Mathematics; Surgery","score_opus":0.03776483629591423,"score_gpt":0.3889479305121381,"score_spread":0.3511830942162239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803954248","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20220938,0.0020309559,0.7508718,0.0004065729,0.00021238012,0.0005948236,0.010439933,0.024344187,0.008890023],"genre_scores_gemma":[0.517933,0.0006935479,0.46222487,0.00023849215,0.00013604421,0.0002594183,0.010046568,0.0005037268,0.007964415],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977523,0.00001886894,0.000017088856,0.0000648515,0.0000965218,0.000027535903],"domain_scores_gemma":[0.9997415,0.00005363379,0.000021012609,0.000025602272,0.00013329623,0.000025070256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002921748,0.00057650567,0.00054251787,0.0024537782,0.00031167187,0.0007724723,0.00055417215,0.00061166263,0.004865824],"category_scores_gemma":[0.00064896204,0.0002528206,0.0004865236,0.0008326791,0.00012215735,0.00034312104,0.0006428457,0.00025987133,0.001862853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044216044,0.00016047165,0.018607436,0.00023311554,0.00014187324,0.000380352,0.000047534864,0.019236054,0.048959136,0.0006927554,0.014394324,0.89670473],"study_design_scores_gemma":[0.000041692743,0.000115356284,0.043631934,0.0000763952,0.000090904105,0.001025793,0.000076627664,0.9121467,0.030445507,0.0013685377,0.010941198,0.00003945094],"about_ca_topic_score_codex":0.009550347,"about_ca_topic_score_gemma":0.021802248,"teacher_disagreement_score":0.009550347,"about_ca_system_score_codex":0.0004001546,"about_ca_system_score_gemma":0.0008169397,"threshold_uncertainty_score":0.018989503},"labels":[],"label_agreement":null},{"id":"W2806788401","doi":"10.1016/j.media.2018.05.010","title":"A deep learning approach for real time prostate segmentation in freehand ultrasound guided biopsy","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":94,"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; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Computer science; Artificial intelligence; Segmentation; Sørensen–Dice coefficient; Prostate biopsy; Deep learning; Convolutional neural network; Hausdorff distance; Ultrasound; Pattern recognition (psychology); Computer vision; Image segmentation; Prostate; Radiology; Medicine; Cancer","score_opus":0.012907864425345618,"score_gpt":0.30040093726861444,"score_spread":0.2874930728432688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806788401","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02061187,0.00042068394,0.9763657,0.00019435391,0.000050474366,0.00004285747,0.00008960487,0.0011348387,0.0010894939],"genre_scores_gemma":[0.46081468,0.00055991206,0.52814895,0.0003804877,0.00008801637,0.00010462025,0.00036767113,0.00031622648,0.0092194155],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974674,0.000041913187,0.000014274831,0.000054324908,0.000097433156,0.000045220873],"domain_scores_gemma":[0.9996797,0.0001176149,0.000026817066,0.000037790003,0.00011037566,0.000027787431],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055014313,0.000492505,0.0005398258,0.0005925357,0.00033442426,0.0008129989,0.0011513421,0.0013598469,0.00270823],"category_scores_gemma":[0.0010383012,0.0005811119,0.00061426027,0.00047768993,0.00027599788,0.00067793846,0.0011724824,0.00091126654,0.0006384769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035707862,0.00016294644,0.0012503812,0.00012897293,0.00009060103,0.00017367706,0.00008153194,0.25153735,0.048475787,0.003876516,0.0039892504,0.6898759],"study_design_scores_gemma":[0.0000041065896,0.00002401058,0.00024310772,0.0000053409453,0.000009116534,0.000056337463,0.0000053640997,0.993614,0.0047326917,0.0007535605,0.00054664974,0.000005823075],"about_ca_topic_score_codex":0.0076255025,"about_ca_topic_score_gemma":0.013795158,"teacher_disagreement_score":0.0076255025,"about_ca_system_score_codex":0.0006229191,"about_ca_system_score_gemma":0.0011543509,"threshold_uncertainty_score":0.01516223},"labels":[],"label_agreement":null},{"id":"W2810924486","doi":"10.1016/j.media.2018.07.001","title":"Synthesizing retinal and neuronal images with generative adversarial nets","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Retinal Imaging and Analysis","field":"Medicine","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":"University of Alberta","funders":"Chinese Government Scholarship; China Scholarship Council; Government of Jiangxi Province","keywords":"Computer science; Artificial intelligence; Annotation; Set (abstract data type); Generative grammar; Image (mathematics); Pattern recognition (psychology); Computer vision","score_opus":0.007527008097328543,"score_gpt":0.2789910835645772,"score_spread":0.27146407546724866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810924486","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023805803,0.0002484092,0.9729831,0.00018029648,0.000059572903,0.00004938998,0.000083168496,0.000729189,0.0018610625],"genre_scores_gemma":[0.6369857,0.00036479233,0.3561619,0.00043966842,0.000083683706,0.00013589868,0.00049662025,0.00030971572,0.005022005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975926,0.000054599775,0.000009501121,0.00007148936,0.00007792995,0.000027175476],"domain_scores_gemma":[0.99950635,0.00028786395,0.000057215042,0.000059537022,0.0000576081,0.000031511383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061607134,0.0010189211,0.0005087267,0.00043479045,0.00014948392,0.0006020571,0.0008211574,0.00091634126,0.0011972573],"category_scores_gemma":[0.0019328577,0.00048042086,0.00076030643,0.00028280524,0.0005876841,0.00047051284,0.0008777907,0.0010926245,0.00037409813],"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":[0.00006832609,0.0000216391,0.00038677466,0.000048113863,0.00003754165,0.0001629586,0.000041727566,0.9366776,0.010923175,0.0038792666,0.0010573326,0.046695475],"study_design_scores_gemma":[0.0000027976407,0.00001188353,0.00004735494,0.0000037727602,0.000003836407,0.000034405126,0.0000028883917,0.9967092,0.0016692403,0.0012481328,0.0002628885,0.0000035544767],"about_ca_topic_score_codex":0.001739501,"about_ca_topic_score_gemma":0.002222931,"teacher_disagreement_score":0.001739501,"about_ca_system_score_codex":0.00054904335,"about_ca_system_score_gemma":0.00038515637,"threshold_uncertainty_score":0.0040051937},"labels":[],"label_agreement":null},{"id":"W2888400665","doi":"10.1016/j.media.2018.08.004","title":"Algorithms for left atrial wall segmentation and thickness – Evaluation on an open-source CT and MRI image database","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":70,"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":"Centre For Medical Engineering, King’s College London; Engineering and Physical Sciences Research Council; National Institute for Health and Care Research; Canadian Institutes of Health Research; King's College London; British Heart Foundation; Wellcome Trust","keywords":"Segmentation; Algorithm; Artificial intelligence; Cardiac imaging; Computer science; Magnetic resonance imaging; Modality (human–computer interaction); Limiting; Atrial fibrillation; Left atrium; Database; Medical imaging; Medicine; Radiology","score_opus":0.030757148990069972,"score_gpt":0.3879660480808898,"score_spread":0.3572088990908198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888400665","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6324374,0.019334521,0.25375733,0.0021380903,0.0022966824,0.004785572,0.023212735,0.051248264,0.010789396],"genre_scores_gemma":[0.37343013,0.0031555386,0.5300498,0.0007561702,0.00026880487,0.0016425233,0.08391175,0.0020445948,0.0047407076],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9922868,0.0012124028,0.0015051538,0.0018474914,0.002596748,0.0005515256],"domain_scores_gemma":[0.989031,0.004481695,0.0007259172,0.0012157777,0.0040011634,0.0005444522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009790702,0.0030213918,0.0025077572,0.006187551,0.0011425257,0.0035210885,0.004321634,0.003752701,0.002039282],"category_scores_gemma":[0.02092976,0.0007102283,0.0022868728,0.0034758467,0.00084219157,0.0024404677,0.0020160885,0.0018593211,0.0022303977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0048065432,0.002100946,0.016288863,0.0027444612,0.0017715696,0.001011473,0.0007284937,0.0806644,0.025064552,0.0016579577,0.061790556,0.8013703],"study_design_scores_gemma":[0.000989505,0.0022024491,0.02986726,0.0005337776,0.00063411356,0.0032088733,0.0009443224,0.88797235,0.04679818,0.0028228513,0.023791503,0.00023483559],"about_ca_topic_score_codex":0.01278976,"about_ca_topic_score_gemma":0.015817745,"teacher_disagreement_score":0.01278976,"about_ca_system_score_codex":0.0021410324,"about_ca_system_score_gemma":0.0025328875,"threshold_uncertainty_score":0.051778793},"labels":[],"label_agreement":null},{"id":"W2888443510","doi":"10.1016/j.media.2018.08.005","title":"Spine-GAN: Semantic segmentation of multiple spinal structures","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":215,"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":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Segmentation; Computer science; Artificial intelligence; Convolutional neural network; Pattern recognition (psychology); Concatenation (mathematics); Computer vision; Mathematics","score_opus":0.0074848948427753085,"score_gpt":0.27293141559568,"score_spread":0.2654465207529047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888443510","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0099249,0.0008397795,0.9487745,0.00036957307,0.00018223652,0.00024940513,0.0040900228,0.031720076,0.003849536],"genre_scores_gemma":[0.11140757,0.0006781293,0.86566204,0.00062130386,0.00013913428,0.00034467212,0.010333254,0.0058646193,0.004949212],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954456,0.00007174553,0.000026245118,0.00016073787,0.00014757804,0.000049023074],"domain_scores_gemma":[0.9996834,0.00009175752,0.000025026782,0.00009519895,0.00007925911,0.000025423466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076700875,0.0019125439,0.0012767451,0.0017179806,0.00048900244,0.0019023907,0.0022210735,0.002431416,0.009103467],"category_scores_gemma":[0.0016714592,0.0014133522,0.002125577,0.0016374369,0.0004822182,0.0010219851,0.0017044029,0.0019943083,0.0037749938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007956666,0.00033077784,0.0020745774,0.00096923707,0.0007563746,0.00035294794,0.00022572464,0.15521345,0.038198344,0.014111004,0.10660063,0.6803712],"study_design_scores_gemma":[0.00009294375,0.000082372266,0.0009373617,0.00006443418,0.00008770664,0.000522457,0.000046615758,0.9499863,0.015903577,0.014012624,0.018216211,0.000047292175],"about_ca_topic_score_codex":0.0085950615,"about_ca_topic_score_gemma":0.02695572,"teacher_disagreement_score":0.009103467,"about_ca_system_score_codex":0.0008684869,"about_ca_system_score_gemma":0.002229816,"threshold_uncertainty_score":0.030454159},"labels":[],"label_agreement":null},{"id":"W2889609823","doi":"10.1016/j.media.2018.09.001","title":"Direct delineation of myocardial infarction without contrast agents using a joint motion feature learning architecture","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":127,"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":"Artificial intelligence; Computer science; Computer vision; Feature (linguistics); Pixel; Pattern recognition (psychology); Region of interest; Discriminative model; Feature extraction; Optical flow; Kinematics; Image (mathematics); Physics","score_opus":0.02144859605931108,"score_gpt":0.333218146903022,"score_spread":0.3117695508437109,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889609823","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06718489,0.0005152752,0.93027675,0.00020742326,0.000033682103,0.00005762491,0.000076921955,0.0006824953,0.00096481707],"genre_scores_gemma":[0.6329247,0.0005664462,0.36168936,0.0001780598,0.000064204854,0.00009693156,0.0002716569,0.0001240534,0.004084664],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998888,0.000020564794,0.000008127808,0.000032929333,0.000030211258,0.00001945883],"domain_scores_gemma":[0.9998344,0.00006224878,0.000021124615,0.000024301626,0.000043427193,0.000014529996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004624823,0.0006187326,0.00061146094,0.00048222393,0.00021357839,0.0005191339,0.00056897843,0.0008548366,0.0007310849],"category_scores_gemma":[0.0008296105,0.00034750844,0.00057411846,0.00035347667,0.00020226651,0.0005467531,0.0008244747,0.00065312925,0.0003011436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045334862,0.0001687339,0.0029718417,0.00011296821,0.000149312,0.00013787589,0.00006805721,0.14474322,0.10992879,0.002181274,0.0017085131,0.73737603],"study_design_scores_gemma":[0.000013187457,0.00010820064,0.0016318522,0.000008518938,0.000056446286,0.0001644189,0.0000081121,0.97843575,0.017684128,0.0011075778,0.00077153376,0.000010276064],"about_ca_topic_score_codex":0.0028636982,"about_ca_topic_score_gemma":0.005032853,"teacher_disagreement_score":0.0028636982,"about_ca_system_score_codex":0.00025501347,"about_ca_system_score_gemma":0.0007321617,"threshold_uncertainty_score":0.0056940913},"labels":[],"label_agreement":null},{"id":"W2890139949","doi":"10.1016/j.media.2019.101552","title":"Generative adversarial network in medical imaging: A review","year":2019,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":1852,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Philips (Canada); University of Saskatchewan","funders":"","keywords":"Adversarial system; Computer science; Discriminator; Artificial intelligence; Generative grammar; Consistency (knowledge bases); Machine learning; Image translation; Segmentation; Domain (mathematical analysis); Image (mathematics); Translation (biology); Adaptation (eye); Medical imaging; Deep learning; Mathematics","score_opus":0.02797709482192573,"score_gpt":0.36800618248329203,"score_spread":0.3400290876613663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890139949","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00027149313,0.98830533,0.008875309,0.0004962556,0.00033831137,0.000011438258,0.000056336637,0.00005556529,0.0015899127],"genre_scores_gemma":[0.00391127,0.98871094,0.0050661857,0.0003811556,0.0006535824,0.000017036573,0.000105472376,0.000022218142,0.0011321931],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997092,0.0000690326,0.000037068265,0.000076490134,0.00009093291,0.000017237653],"domain_scores_gemma":[0.99856997,0.001085824,0.000068812566,0.00004435574,0.0001938209,0.000037210877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012458296,0.001046482,0.0012002491,0.0015929191,0.00020401027,0.0011619821,0.0012396312,0.0014131523,0.0041513955],"category_scores_gemma":[0.0027356253,0.0004846102,0.0007382231,0.0021844609,0.0006221089,0.0015447984,0.0009255573,0.0015005921,0.0018500182],"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":[0.000046905912,0.00008641572,0.0003394873,0.00807168,0.00015006686,0.00008676262,0.000033876724,0.005137568,0.00077102956,0.0058069387,0.021788822,0.9576805],"study_design_scores_gemma":[0.00004701312,0.00027650545,0.0021237626,0.009134445,0.00078042946,0.0020195711,0.00011333993,0.026408404,0.0035204017,0.025218433,0.9302024,0.00015529006],"about_ca_topic_score_codex":0.0023456037,"about_ca_topic_score_gemma":0.0023419478,"teacher_disagreement_score":0.0041513955,"about_ca_system_score_codex":0.0004453154,"about_ca_system_score_gemma":0.001089399,"threshold_uncertainty_score":0.013887823},"labels":[],"label_agreement":null},{"id":"W2892938835","doi":"10.1016/j.media.2018.09.005","title":"Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple experts","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":177,"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. Paul's Hospital; Richmond Hospital; Vancouver General Hospital; BC Cancer Agency; University of British Columbia","funders":"Canadian Institutes of Health Research; Prostate Cancer Canada","keywords":"Grading (engineering); Histopathology; Artificial intelligence; Prostate cancer; Computer science; Computer vision; Prostate; Pattern recognition (psychology); Medicine; Medical physics; Cancer; Pathology; Internal medicine; Biology","score_opus":0.010282521718145456,"score_gpt":0.2790481769492846,"score_spread":0.2687656552311391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2892938835","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22150113,0.0014677809,0.7711194,0.00040656005,0.0001248267,0.00019577374,0.00019456391,0.0015096149,0.0034803173],"genre_scores_gemma":[0.7954193,0.0004995065,0.20034216,0.00027281677,0.000107285305,0.00007027587,0.00032741975,0.000093831935,0.0028673853],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843615,0.00035057525,0.00012780083,0.00046917095,0.00041822117,0.00019800676],"domain_scores_gemma":[0.99711215,0.0011674158,0.00021386199,0.00030402266,0.0010398164,0.0001626869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027840252,0.0007703622,0.0011249749,0.0025502162,0.00049625535,0.00131723,0.001235023,0.0018206112,0.0009351274],"category_scores_gemma":[0.006281604,0.0004718119,0.0010866858,0.0009020838,0.0005217895,0.0011816634,0.0012329879,0.0010526056,0.00050363597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006201188,0.00031912458,0.016401604,0.0002057407,0.0003520287,0.00025563952,0.0002487569,0.053117577,0.03045182,0.001083907,0.0030662618,0.8938773],"study_design_scores_gemma":[0.0000351222,0.0002614339,0.010594825,0.00004941531,0.00024898874,0.00053830986,0.0001905456,0.96268857,0.019518057,0.0042688064,0.0015674266,0.000038477705],"about_ca_topic_score_codex":0.003593553,"about_ca_topic_score_gemma":0.0065708454,"teacher_disagreement_score":0.003593553,"about_ca_system_score_codex":0.0005659663,"about_ca_system_score_gemma":0.00080257363,"threshold_uncertainty_score":0.014723539},"labels":[],"label_agreement":null},{"id":"W2894870703","doi":"10.1016/j.media.2018.09.006","title":"A hybrid camera- and ultrasound-based approach for needle localization and tracking using a 3D motorized curvilinear ultrasound probe","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Soft Robotics and Applications","field":"Engineering","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":"Concordia University","funders":"","keywords":"Ultrasound; 3D ultrasound; Computer vision; Artificial intelligence; Computer science; Tracking (education); Kalman filter; Biomedical engineering; Medicine; Radiology","score_opus":0.01376895410654164,"score_gpt":0.2618212940485883,"score_spread":0.24805233994204665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2894870703","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012732471,0.00039107338,0.98463595,0.000104805935,0.000055458968,0.00006491073,0.000044044118,0.0009096384,0.0010616566],"genre_scores_gemma":[0.16787256,0.00045511572,0.82778555,0.00021791317,0.00004293069,0.00012741372,0.00009888962,0.00009238005,0.0033073197],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99926585,0.00010569176,0.00004774243,0.000184281,0.00035209776,0.000044259425],"domain_scores_gemma":[0.99945396,0.00014174343,0.000064731124,0.00009643246,0.00019029151,0.000052905623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005067364,0.000624362,0.00080330664,0.0008541397,0.0003073575,0.0009189515,0.0013810798,0.0016768601,0.002475876],"category_scores_gemma":[0.00095298956,0.0008065512,0.0006505133,0.00073875795,0.00031572214,0.0010963817,0.0013378112,0.0006697828,0.001100666],"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":[0.00034838845,0.000085070176,0.0017716052,0.00029640493,0.000092889895,0.00059076067,0.00019680869,0.0104283905,0.66654915,0.0018974757,0.0017654888,0.31597754],"study_design_scores_gemma":[0.00012549009,0.0012097977,0.010242643,0.000108946915,0.0002624474,0.008007301,0.00015716614,0.641903,0.31190488,0.0014718105,0.024221465,0.00038506257],"about_ca_topic_score_codex":0.001871391,"about_ca_topic_score_gemma":0.002765345,"teacher_disagreement_score":0.002475876,"about_ca_system_score_codex":0.0004341988,"about_ca_system_score_gemma":0.0011042514,"threshold_uncertainty_score":0.008282661},"labels":[],"label_agreement":null},{"id":"W2898898617","doi":"10.1016/j.media.2018.10.011","title":"A graph-based lesion characterization and deep embedding approach for improved computer-aided diagnosis of nonmass breast MRI lesions","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"MRI in cancer diagnosis","field":"Medicine","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 Toronto; Sunnybrook Health Science Centre","funders":"National Institute for Materials Science; Canadian Cancer Society; Ontario Institute for Cancer Research","keywords":"Artificial intelligence; Embedding; Computer-aided diagnosis; Graph; Breast MRI; Pattern recognition (psychology); Computer-aided; Computer science; Lesion; Computer vision; Medicine; Theoretical computer science; Breast cancer; Pathology; Mammography; Internal medicine","score_opus":0.01694372814630471,"score_gpt":0.3100155083522342,"score_spread":0.2930717802059295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898898617","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09492202,0.0010047342,0.8997731,0.0003768986,0.000078960205,0.00009900655,0.00051717216,0.0018075076,0.0014206042],"genre_scores_gemma":[0.6424257,0.0007419219,0.34962863,0.0003272353,0.00010132589,0.000104701365,0.0016949995,0.00022417874,0.004751301],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981505,0.000030142093,0.00001201214,0.00005825837,0.00004906483,0.000035399975],"domain_scores_gemma":[0.99970895,0.00009660279,0.000035345318,0.00003436538,0.00009927788,0.000025340942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002603986,0.0007878686,0.0007332003,0.0014786883,0.00025820994,0.00053419336,0.0009690555,0.000940063,0.0012267552],"category_scores_gemma":[0.0006983535,0.00028649275,0.0007783841,0.0007692867,0.00024266257,0.0007096596,0.00084530463,0.0006963232,0.00058980554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041841003,0.0003579023,0.003275027,0.00020096097,0.00017585943,0.0003707178,0.000128053,0.1346334,0.082229115,0.0031207032,0.0059971237,0.7690927],"study_design_scores_gemma":[0.000006200844,0.000045372035,0.0008177757,0.00000747387,0.00003418817,0.00010834506,0.000021418378,0.99030894,0.0063160723,0.0016464641,0.00067850866,0.000009217237],"about_ca_topic_score_codex":0.0062800925,"about_ca_topic_score_gemma":0.010551838,"teacher_disagreement_score":0.0062800925,"about_ca_system_score_codex":0.00032790832,"about_ca_system_score_gemma":0.00054839824,"threshold_uncertainty_score":0.012487054},"labels":[],"label_agreement":null},{"id":"W2901235535","doi":"10.1016/j.media.2018.11.008","title":"CATARACTS: Challenge on automatic tool annotation for cataRACT surgery","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":128,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"D-Wave Systems (Canada)","funders":"European Regional Development Fund; Centro de Matemática Universidade do Porto; Fundação para a Ciência e a Tecnologia","keywords":"Workflow; Annotation; Context (archaeology); Computer science; Cataracts; Cataract surgery; Artificial intelligence; Medicine; Surgery","score_opus":0.04315227666897925,"score_gpt":0.35849689856686356,"score_spread":0.3153446218978843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901235535","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051928565,0.02057249,0.67397696,0.01237448,0.0043834164,0.0019275676,0.05005183,0.16677214,0.018012516],"genre_scores_gemma":[0.13865955,0.006169249,0.70082116,0.004833422,0.00067114574,0.00084879936,0.121086486,0.011450402,0.015459797],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98281777,0.0024457616,0.0017311205,0.0031849032,0.008539559,0.0012809187],"domain_scores_gemma":[0.9722048,0.009342867,0.0010678082,0.0064000348,0.009376916,0.0016075405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01172029,0.0036605387,0.0038074194,0.0077667437,0.0030787573,0.006786528,0.006311457,0.008055633,0.0071922084],"category_scores_gemma":[0.02981836,0.0015533816,0.0040494944,0.0040095486,0.001790869,0.0044462327,0.008914302,0.0046238196,0.011924187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006989404,0.00043836876,0.0043606297,0.0031171895,0.0003792913,0.0005695565,0.0004501394,0.0049062422,0.027597798,0.004478964,0.23986708,0.7131359],"study_design_scores_gemma":[0.00040415418,0.00087828527,0.019033274,0.0025606805,0.0007166801,0.0062291245,0.0020375235,0.2692325,0.12774415,0.04111311,0.52957237,0.0004780802],"about_ca_topic_score_codex":0.018453596,"about_ca_topic_score_gemma":0.029074742,"teacher_disagreement_score":0.018453596,"about_ca_system_score_codex":0.0017453249,"about_ca_system_score_gemma":0.0072817625,"threshold_uncertainty_score":0.061983526},"labels":[],"label_agreement":null},{"id":"W2901481277","doi":"10.1016/j.media.2020.101751","title":"Graph refinement based airway extraction using mean-field networks and graph neural networks","year":2020,"lang":"en","type":"preprint","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","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":"Canadian Institute for Advanced Research","funders":"Danmarks Frie Forskningsfond; Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Computer science; Graph; Artificial neural network; Inference; Algorithm; Artificial intelligence; Pattern recognition (psychology); Theoretical computer science","score_opus":0.023797524028233572,"score_gpt":0.30691302252036046,"score_spread":0.28311549849212686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901481277","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009372101,0.0002572863,0.9884399,0.00015995302,0.000036305075,0.000055165525,0.00010087735,0.0009880175,0.0005904036],"genre_scores_gemma":[0.22556631,0.00046428436,0.7686874,0.0001994464,0.00007933361,0.00013224961,0.00054941286,0.00038952928,0.0039320434],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965405,0.000060936633,0.000019507972,0.000117013675,0.000109484354,0.000038971823],"domain_scores_gemma":[0.9993543,0.0002966531,0.0000772252,0.00007679754,0.00016760404,0.000027444157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059957814,0.0010652381,0.0011000816,0.0020160626,0.0005887864,0.0010018193,0.0013093224,0.0018529667,0.0020336302],"category_scores_gemma":[0.002224318,0.00075472886,0.001369964,0.0015926076,0.0005315251,0.0014072821,0.00090682233,0.0014322774,0.0009870849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002324883,0.000112283524,0.001707893,0.00022320173,0.00012359247,0.00018856244,0.00010543485,0.38759208,0.038866095,0.00749815,0.005900522,0.5574497],"study_design_scores_gemma":[0.0000052210175,0.000014816779,0.00028292375,0.000008107133,0.000014850079,0.000043938446,0.000007564661,0.9926313,0.0035953736,0.002851668,0.000536953,0.000007328223],"about_ca_topic_score_codex":0.010582255,"about_ca_topic_score_gemma":0.017525027,"teacher_disagreement_score":0.010582255,"about_ca_system_score_codex":0.00072390627,"about_ca_system_score_gemma":0.0009939153,"threshold_uncertainty_score":0.021041334},"labels":[],"label_agreement":null},{"id":"W2909627766","doi":"10.1016/j.media.2019.01.004","title":"Training recurrent neural networks robust to incomplete data: Application to Alzheimer’s disease progression modeling","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":139,"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; U.S. Department of Defense","keywords":"Computer science; Artificial intelligence; Artificial neural network; Machine learning; Training (meteorology); Disease; Training set; Recurrent neural network; Medicine; Pathology","score_opus":0.08360766209578195,"score_gpt":0.3858014648162579,"score_spread":0.3021938027204759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909627766","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35004812,0.0015894797,0.64419943,0.00080673036,0.00014235516,0.00006891978,0.00047541768,0.0016599399,0.0010095248],"genre_scores_gemma":[0.9429883,0.00025327384,0.054125927,0.000074918964,0.00006801993,0.00004862385,0.0006849683,0.00007840546,0.0016775453],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964535,0.000110319146,0.00002882706,0.000101832564,0.00006498949,0.000048648337],"domain_scores_gemma":[0.9974291,0.00159714,0.00023480019,0.00023832337,0.00043984165,0.00006082455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030682117,0.0007695731,0.0010956738,0.0005080865,0.00035659238,0.00066930806,0.0010151544,0.0011796228,0.0006447097],"category_scores_gemma":[0.008045124,0.0004889395,0.00077787065,0.00052092725,0.00031526072,0.0007228577,0.0006577712,0.0015003898,0.00022020478],"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":[0.00025985762,0.00015646279,0.0038104055,0.00006580545,0.00017561579,0.00015065029,0.00007966424,0.8519515,0.0036153705,0.0013388875,0.001472028,0.13692379],"study_design_scores_gemma":[0.0000027004153,0.000013105071,0.00027916656,0.0000017272672,0.000007879628,0.0000062653867,0.0000023611015,0.99885106,0.0003669624,0.0004106675,0.00005560285,0.0000024516662],"about_ca_topic_score_codex":0.014126332,"about_ca_topic_score_gemma":0.013364281,"teacher_disagreement_score":0.014126332,"about_ca_system_score_codex":0.00057005597,"about_ca_system_score_gemma":0.0009317692,"threshold_uncertainty_score":0.028088212},"labels":[],"label_agreement":null},{"id":"W2910002198","doi":"10.1016/j.media.2019.01.005","title":"Recurrent inference machines for reconstructing heterogeneous MRI data","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":85,"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 Institute for Advanced Research","keywords":"Artificial intelligence; Computer science; Inference; Overfitting; Deep learning; Iterative reconstruction; Process (computing); Imaging phantom; SIGNAL (programming language); Inverse problem; Compressed sensing; Pattern recognition (psychology); Benchmark (surveying); Machine learning; Real-time MRI; Magnetic resonance imaging; Artificial neural network; Mathematics; Radiology; Medicine","score_opus":0.03739816782364099,"score_gpt":0.4081645798559363,"score_spread":0.3707664120322953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910002198","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008301833,0.00039906162,0.9898914,0.00016453068,0.000037479305,0.00002211625,0.00013149169,0.00080918917,0.00024293644],"genre_scores_gemma":[0.3917063,0.0008925736,0.5976992,0.00035414123,0.00031034564,0.00028349823,0.0021763092,0.00056942005,0.006008189],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991258,0.0003385486,0.000068908805,0.00023785475,0.00015109051,0.00007774685],"domain_scores_gemma":[0.99324965,0.005257578,0.00042855478,0.0005526782,0.00038375563,0.00012789763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028593333,0.0012302441,0.001751958,0.001209069,0.0004871826,0.0012175611,0.002059511,0.001986992,0.0018791176],"category_scores_gemma":[0.011184969,0.0012513859,0.0017147934,0.0013185511,0.00083752524,0.0014976748,0.0014672385,0.0028292695,0.0010390284],"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":[0.00024017476,0.000112282345,0.0014716713,0.00014256005,0.0002580644,0.00022221287,0.00012163203,0.7244864,0.0039672516,0.015654761,0.004052111,0.24927084],"study_design_scores_gemma":[0.000003916653,0.000007706529,0.000055935747,0.0000039063275,0.000009279939,0.000009254874,0.0000026935304,0.9949209,0.00033408005,0.004497226,0.00015138053,0.000003713964],"about_ca_topic_score_codex":0.0073929857,"about_ca_topic_score_gemma":0.011710447,"teacher_disagreement_score":0.0073929857,"about_ca_system_score_codex":0.0008888948,"about_ca_system_score_gemma":0.000909241,"threshold_uncertainty_score":0.015121818},"labels":[],"label_agreement":null},{"id":"W2910094941","doi":"10.1016/j.media.2022.102680","title":"The Liver Tumor Segmentation Benchmark (LiTS)","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1164,"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; Polytechnique Montréal","funders":"National Cancer Institute; National Institutes of Health; Fonds de Recherche du Québec - Santé; Fondation de l'Association des radiologistes du Québec; International Graduate School of Science and Engineering; Universität Zürich; Deutsche Forschungsgemeinschaft","keywords":"Benchmark (surveying); Segmentation; Computer science; Artificial intelligence; Medical physics; Medicine; Pattern recognition (psychology); Cartography","score_opus":0.007724142912540659,"score_gpt":0.2656009642944292,"score_spread":0.25787682138188855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910094941","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42324162,0.041945077,0.17879502,0.0045684488,0.004326615,0.0043564653,0.19601631,0.11313708,0.033613406],"genre_scores_gemma":[0.24428019,0.002701101,0.15211746,0.0011793226,0.00050127594,0.0010524613,0.58559906,0.005246593,0.0073225843],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99538225,0.0011745319,0.00056749757,0.001019261,0.0014560603,0.00040028678],"domain_scores_gemma":[0.9954099,0.0012574448,0.0004532657,0.0008631189,0.0014438767,0.00057232234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005894725,0.0039706947,0.0016845713,0.0066247755,0.0012944728,0.0032363525,0.0037341407,0.0029909571,0.0033612235],"category_scores_gemma":[0.010877986,0.00071278017,0.0021289394,0.0039703595,0.00093481696,0.0014914314,0.0028527742,0.0013563747,0.0035986241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0040956517,0.0014638398,0.028798977,0.005908074,0.002914254,0.0012590729,0.00042253957,0.093586154,0.026756786,0.003844791,0.4522717,0.3786782],"study_design_scores_gemma":[0.0023991447,0.004364596,0.07247692,0.0013949635,0.0015450915,0.010834231,0.0009637294,0.521034,0.10249995,0.013728915,0.26826888,0.0004896255],"about_ca_topic_score_codex":0.011136428,"about_ca_topic_score_gemma":0.016433904,"teacher_disagreement_score":0.011136428,"about_ca_system_score_codex":0.002057533,"about_ca_system_score_gemma":0.0022984552,"threshold_uncertainty_score":0.03117466},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013600995,0.002764599,0.7749289,0.001428425,0.0010589284,0.000189583,0.0009566013,0.010608843,0.19446315],"genre_scores_gemma":[0.20776768,0.0025770736,0.5133508,0.0013151342,0.00042446255,0.0003406943,0.004313749,0.003757576,0.26615298],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99938357,0.00010263705,0.000030888496,0.00022320396,0.0001887653,0.000071008144],"domain_scores_gemma":[0.99932265,0.00009968286,0.000033607448,0.0002790904,0.00018214129,0.000082825725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007584036,0.0009553255,0.00088198297,0.0028255752,0.0013467368,0.0026173287,0.0013095682,0.0024015105,0.06153904],"category_scores_gemma":[0.0020610215,0.0008893713,0.0011918339,0.0014676605,0.0008570395,0.0028230236,0.0031610392,0.0017043062,0.024735138],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030006684,0.000107397966,0.0005317617,0.00019011878,0.000093744595,0.00024644472,0.00016374205,0.014528773,0.012750365,0.16869886,0.06901668,0.733372],"study_design_scores_gemma":[0.000106992054,0.00016917974,0.0012322094,0.00026387314,0.00013084123,0.0018671933,0.00016066972,0.27813715,0.03734424,0.15248111,0.5280066,0.00010001206],"about_ca_topic_score_codex":0.0009603176,"about_ca_topic_score_gemma":0.0014853659,"teacher_disagreement_score":0.06153904,"about_ca_system_score_codex":0.0005134162,"about_ca_system_score_gemma":0.00062114827,"threshold_uncertainty_score":0.2058686},"labels":[],"label_agreement":null},{"id":"W2913510405","doi":"10.1016/j.media.2019.01.013","title":"Weakly supervised mitosis detection in breast histopathology images using concentric loss","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":165,"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 Health Network","funders":"National Science Foundation of Sri Lanka; National Natural Science Foundation of China","keywords":"Histopathology; Concentric; Artificial intelligence; Pattern recognition (psychology); Mathematics; Computer vision; Computer science; Medicine; Pathology; Geometry","score_opus":0.006263077141884169,"score_gpt":0.2467378444246055,"score_spread":0.24047476728272132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913510405","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23669559,0.00069692836,0.7569464,0.00044176626,0.000060183367,0.00010654038,0.00027759964,0.0017469305,0.0030280894],"genre_scores_gemma":[0.8022878,0.00044555723,0.18986057,0.0001662277,0.00009946425,0.000077177036,0.00060153444,0.00018193242,0.0062797344],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994197,0.00013235411,0.000033322198,0.00011597295,0.00022300032,0.000075586264],"domain_scores_gemma":[0.99875486,0.0003755835,0.00017506856,0.00025055872,0.00035903155,0.00008488592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014299258,0.00064567383,0.00070351607,0.0011855367,0.00036751336,0.0010854463,0.0009406767,0.00088283175,0.0014432109],"category_scores_gemma":[0.0031750165,0.00030250123,0.000500619,0.0006499224,0.00064566865,0.0010254649,0.0013288194,0.00059187756,0.00095007074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018362425,0.0003793826,0.0130300345,0.00035988036,0.00018814807,0.0003474359,0.0002313601,0.07675237,0.21412563,0.00646166,0.005169752,0.6811181],"study_design_scores_gemma":[0.000024485604,0.00020531502,0.0066137654,0.000024997464,0.0000603469,0.0004523304,0.000058181573,0.92596596,0.061460007,0.0035246026,0.00159467,0.000015375559],"about_ca_topic_score_codex":0.001128094,"about_ca_topic_score_gemma":0.0021887866,"teacher_disagreement_score":0.0014432109,"about_ca_system_score_codex":0.00043180006,"about_ca_system_score_gemma":0.00074032886,"threshold_uncertainty_score":0.0075622797},"labels":[],"label_agreement":null},{"id":"W2917393555","doi":"10.1016/j.media.2019.02.011","title":"Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":160,"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":"Fondation pour la Recherche sur Alzheimer; Genome British Columbia; Simon Fraser University; Michael Smith Health Research BC; Canadian Institutes of Health Research; Alzheimer Society; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada","keywords":"Optical coherence tomography; Artificial intelligence; Computer science; Convolutional neural network; Segmentation; Retinal; Pattern recognition (psychology); Computer vision; Cut; Retina; Pixel; Image segmentation; Ophthalmology; Medicine; Optics; Physics","score_opus":0.007756989084423179,"score_gpt":0.2748887174620818,"score_spread":0.26713172837765864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917393555","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15528254,0.0011996187,0.8377569,0.00057634513,0.000072526076,0.00009617981,0.00046280018,0.0024771825,0.002075925],"genre_scores_gemma":[0.7410781,0.00063848734,0.25187185,0.00021572842,0.00007104883,0.00007684599,0.00063014816,0.00014780152,0.005270011],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983585,0.000021733049,0.000008886054,0.00004711058,0.00004728117,0.00003896783],"domain_scores_gemma":[0.9997465,0.00008463031,0.00004229052,0.000027677623,0.00007228893,0.00002648776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039799578,0.00062723015,0.00058183126,0.00084649393,0.00028304584,0.0007587727,0.00081356737,0.000796547,0.0011717823],"category_scores_gemma":[0.0008127189,0.00038670987,0.0005937107,0.0005343008,0.000270416,0.0005498635,0.00075400213,0.0006130736,0.00042535347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007414439,0.00029806505,0.006957945,0.00019306438,0.0002142317,0.0002816769,0.00011898583,0.19598882,0.08842638,0.0038273262,0.0055311974,0.6974209],"study_design_scores_gemma":[0.00000439715,0.000017933555,0.0008247681,0.000005791902,0.000012315459,0.000042460197,0.0000062208064,0.9905538,0.007634073,0.000572336,0.0003202661,0.0000056528143],"about_ca_topic_score_codex":0.013608899,"about_ca_topic_score_gemma":0.021105053,"teacher_disagreement_score":0.013608899,"about_ca_system_score_codex":0.0007359694,"about_ca_system_score_gemma":0.0012225866,"threshold_uncertainty_score":0.027059376},"labels":[],"label_agreement":null},{"id":"W2927384312","doi":"10.1016/j.media.2019.03.012","title":"Graph Convolutions on Spectral Embeddings for Cortical Surface Parcellation","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":64,"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; Nvidia","keywords":"Surface (topology); Computer science; Graph; Artificial intelligence; Euclidean geometry; Surface reconstruction; Pattern recognition (psychology); Algorithm; Theoretical computer science; Mathematics; Geometry","score_opus":0.021703436753890352,"score_gpt":0.29752471837269956,"score_spread":0.2758212816188092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2927384312","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027348043,0.00020046646,0.9700076,0.00027482375,0.000055313754,0.000030033098,0.00022743398,0.0006952209,0.00116116],"genre_scores_gemma":[0.6108803,0.0007235923,0.37741992,0.00022120395,0.00017397777,0.00014219234,0.0012592654,0.0008907777,0.008288727],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960464,0.00013508779,0.000021601887,0.00010229776,0.00008478518,0.00005156138],"domain_scores_gemma":[0.9982431,0.000886403,0.00014801539,0.00033656883,0.000263948,0.000122001824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008457364,0.00073599815,0.0006643818,0.0013140555,0.00043072604,0.0011504655,0.0009774966,0.0011537975,0.003915798],"category_scores_gemma":[0.0052729137,0.0004215534,0.00086078624,0.0012472449,0.0010208845,0.0023628755,0.0016852274,0.0014490921,0.0014149023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035878885,0.00016306879,0.0019197725,0.0002523663,0.0001255918,0.0002600901,0.0004316789,0.29389018,0.025075061,0.2464854,0.010857561,0.42018038],"study_design_scores_gemma":[0.0000053744325,0.00002037862,0.00045095553,0.00001128445,0.000009976058,0.000051009483,0.00004848434,0.90153235,0.001595051,0.09482043,0.0014427858,0.000011827581],"about_ca_topic_score_codex":0.0041556614,"about_ca_topic_score_gemma":0.005395585,"teacher_disagreement_score":0.0041556614,"about_ca_system_score_codex":0.0006870231,"about_ca_system_score_gemma":0.00052479864,"threshold_uncertainty_score":0.013099611},"labels":[],"label_agreement":null},{"id":"W2941681265","doi":"10.1016/j.media.2019.04.012","title":"Direct automated quantitative measurement of spine by cascade amplifier regression network with manifold regularization","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":49,"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; China Scholarship Council; National Natural Science Foundation of China","keywords":"Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Computer science; Discriminative model; Embedding; Regularization (linguistics); Cascade; Mathematics; Overfitting; Regression; Nonlinear dimensionality reduction; Curse of dimensionality; Dimensionality reduction; Artificial neural network; Statistics; Engineering","score_opus":0.007527160090647314,"score_gpt":0.24274834865850764,"score_spread":0.23522118856786034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2941681265","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029331619,0.00027183717,0.9674646,0.00012119883,0.00004327495,0.000050915227,0.00009211207,0.0011323357,0.00149203],"genre_scores_gemma":[0.54208344,0.00045472957,0.45256227,0.00013803325,0.00007075758,0.00016040259,0.0002716011,0.0002455123,0.004013308],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995567,0.00007661432,0.000014263382,0.00013870138,0.0001724115,0.00004128451],"domain_scores_gemma":[0.99968266,0.00007682541,0.000042003736,0.000046954417,0.00013816645,0.000013523232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005325878,0.0010051903,0.0006737685,0.00073002005,0.0003392355,0.0006305712,0.00093346107,0.00091308955,0.00180759],"category_scores_gemma":[0.0012141758,0.00044079195,0.0005572522,0.00067271944,0.0003377805,0.0009190496,0.00082373177,0.00068953924,0.00087945495],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003631358,0.00016027759,0.0042946865,0.00030060907,0.00016805952,0.00019068863,0.00020171182,0.08339628,0.2946496,0.0050530015,0.0041575185,0.6070644],"study_design_scores_gemma":[0.000014132694,0.00014219388,0.0037172057,0.000021428892,0.00006773705,0.0003207562,0.000029963086,0.94058675,0.050471794,0.0027998528,0.0017889483,0.000039270475],"about_ca_topic_score_codex":0.002457294,"about_ca_topic_score_gemma":0.00514458,"teacher_disagreement_score":0.002457294,"about_ca_system_score_codex":0.00041410036,"about_ca_system_score_gemma":0.00078596734,"threshold_uncertainty_score":0.0060470104},"labels":[],"label_agreement":null},{"id":"W2950994280","doi":"10.1016/j.media.2019.101557","title":"Exploring uncertainty measures in deep networks for Multiple sclerosis lesion detection and segmentation","year":2019,"lang":"en","type":"preprint","venue":"Medical Image Analysis","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":39,"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":"Dropout (neural networks); Segmentation; Voxel; Artificial intelligence; Computer science; Context (archaeology); Sigmoid function; Image segmentation; Pattern recognition (psychology); Deep learning; Machine learning; Lesion; Monte Carlo method; Artificial neural network; Medicine; Mathematics; Statistics; Pathology","score_opus":0.059854850753197124,"score_gpt":0.29529285201127436,"score_spread":0.23543800125807723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950994280","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18461096,0.0021611918,0.8093241,0.0013326715,0.000037910548,0.0000517948,0.00030784283,0.0010590624,0.0011144013],"genre_scores_gemma":[0.9252921,0.0005271282,0.07241955,0.00026546398,0.000061809726,0.000075653195,0.0004651659,0.000116539406,0.0007766978],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988165,0.0004073847,0.0000713355,0.00030075898,0.00028741953,0.00011662365],"domain_scores_gemma":[0.9920351,0.0061121783,0.00086313364,0.00036199854,0.00045898094,0.00016862791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045068115,0.0013268611,0.00087330694,0.0013105503,0.0005175565,0.0014170186,0.0014741858,0.0017208888,0.00059673615],"category_scores_gemma":[0.017984029,0.0007429931,0.00087056647,0.0006437086,0.001543162,0.0024937214,0.001993592,0.0023838764,0.000120526594],"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":[0.0002531119,0.000047032412,0.0049473825,0.000091081456,0.000105356994,0.00009347608,0.00012369746,0.93288374,0.0027432807,0.010201443,0.0006019698,0.047908504],"study_design_scores_gemma":[0.0000042350407,0.00001980449,0.00050108536,0.000010940458,0.000008934972,0.000016017422,0.0000068362187,0.9894573,0.0011133548,0.008729198,0.00012532195,0.000007021211],"about_ca_topic_score_codex":0.007978841,"about_ca_topic_score_gemma":0.007345225,"teacher_disagreement_score":0.007978841,"about_ca_system_score_codex":0.0027292,"about_ca_system_score_gemma":0.001116688,"threshold_uncertainty_score":0.023834586},"labels":[],"label_agreement":null},{"id":"W2951135390","doi":"10.1016/j.media.2019.06.002","title":"A methodology for generating four-dimensional arterial spin labeling MR angiography virtual phantoms","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced MRI Techniques and Applications","field":"Medicine","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":"University of Calgary","funders":"Fundação Amazônia Paraense de Amparo à Pesquisa; Canada Research Chairs; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Alberta Innovates - Health Solutions; Fundação de Amparo à Pesquisa do Estado de São Paulo; Natural Sciences and Engineering Research Council of Canada; Hotchkiss Brain Institute, University of Calgary","keywords":"Computer science; Arterial spin labeling; Artificial intelligence; Magnetic resonance angiography; Ground truth; Scanner; Computer vision; Noise (video); Blood flow; Image processing; Medical imaging; Modality (human–computer interaction); Volume (thermodynamics); Magnetic resonance imaging; Image (mathematics); Radiology; Physics; Medicine","score_opus":0.04414022967995426,"score_gpt":0.38645552914150777,"score_spread":0.3423152994615535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951135390","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013324519,0.000019665458,0.9975914,0.000021231801,0.000008075177,0.000042659918,0.00002869032,0.00057490275,0.00038104117],"genre_scores_gemma":[0.03820312,0.00007704593,0.960344,0.000030031359,0.000008574483,0.00013161429,0.00012894375,0.00032875923,0.0007479151],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999509,0.00012794475,0.000035302073,0.000070904716,0.00021721046,0.00003967597],"domain_scores_gemma":[0.9987073,0.00050844403,0.00013731398,0.000279893,0.00028787888,0.00007918678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010527129,0.00081206905,0.000489969,0.0011377676,0.0005174717,0.001805957,0.0018996231,0.0009140113,0.0044141016],"category_scores_gemma":[0.0029107886,0.0010600822,0.00072877825,0.0009002219,0.00058960455,0.00065212284,0.0014326895,0.0010579983,0.0013627692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031113796,0.00020929512,0.0013033235,0.0005776876,0.00014897948,0.0007128131,0.0006162162,0.23498651,0.2421743,0.061367817,0.00580886,0.45178318],"study_design_scores_gemma":[0.000039264145,0.00018311376,0.00054773135,0.00004487122,0.0000572948,0.001366918,0.00008662882,0.8413107,0.12137934,0.012472433,0.022412334,0.00009936723],"about_ca_topic_score_codex":0.00095627294,"about_ca_topic_score_gemma":0.0012327648,"teacher_disagreement_score":0.0044141016,"about_ca_system_score_codex":0.00041726368,"about_ca_system_score_gemma":0.0010379025,"threshold_uncertainty_score":0.014766693},"labels":[],"label_agreement":null},{"id":"W2953031404","doi":"10.1016/j.media.2019.06.010","title":"XQ-SR: Joint x-q space super-resolution with application to infant diffusion MRI","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","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":"Huawei Technologies (Canada)","funders":"NIH Blueprint for Neuroscience Research; National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; National Institute on Aging; National Institutes of Health","keywords":"Diffusion MRI; Resolution (logic); Computer science; Image resolution; Focus (optics); Artificial intelligence; SIGNAL (programming language); Pattern recognition (psychology); Domain (mathematical analysis); Joint (building); Mathematics; Magnetic resonance imaging; Physics; Optics; Mathematical analysis; Medicine","score_opus":0.014259364138998113,"score_gpt":0.31385705844009204,"score_spread":0.29959769430109395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953031404","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0077559347,0.000579227,0.9829972,0.00022698642,0.00006720651,0.00008828166,0.00035722426,0.006424107,0.0015038777],"genre_scores_gemma":[0.04341768,0.00056642113,0.95182794,0.00016126642,0.00007020332,0.00012886459,0.00037139188,0.0010916939,0.0023645542],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971896,0.00007811618,0.000019226862,0.000037759924,0.00012030802,0.000025633406],"domain_scores_gemma":[0.9991254,0.0003442936,0.00008804141,0.00013952424,0.00021987915,0.00008284998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015303319,0.0008338375,0.0005350347,0.0008519397,0.0003190022,0.001134263,0.0009345359,0.0009841102,0.006272785],"category_scores_gemma":[0.0028956737,0.0004448648,0.00040485975,0.0010275054,0.00038623053,0.0011793729,0.0014066992,0.00092866353,0.002039733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008011658,0.00018155022,0.0019206976,0.0007216911,0.00018111218,0.0009049954,0.00032652591,0.026342321,0.12590815,0.02871868,0.034441672,0.77955145],"study_design_scores_gemma":[0.00011160211,0.00026595884,0.0035182904,0.000076849305,0.00008214464,0.002416792,0.00010259925,0.8268556,0.106504805,0.015530414,0.04442381,0.00011116332],"about_ca_topic_score_codex":0.0011563782,"about_ca_topic_score_gemma":0.0022012126,"teacher_disagreement_score":0.006272785,"about_ca_system_score_codex":0.00016987743,"about_ca_system_score_gemma":0.0006663101,"threshold_uncertainty_score":0.02098459},"labels":[],"label_agreement":null},{"id":"W2955932350","doi":"10.1016/j.media.2020.101758","title":"Automated characterization of noise distributions in diffusion MRI data","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","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":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Noise (video); Scanner; Computer science; Imaging phantom; Artificial intelligence; Noise reduction; Algorithm; Sensitivity (control systems); Pattern recognition (psychology); Physics; Optics","score_opus":0.05534162756861639,"score_gpt":0.3762756233350233,"score_spread":0.3209339957664069,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955932350","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020564826,0.00021327334,0.9778765,0.00006911456,0.000013452322,0.00004188697,0.00009342866,0.00085495034,0.00027264058],"genre_scores_gemma":[0.1547961,0.00038719963,0.8420998,0.00008809479,0.000034670815,0.00014191112,0.00085877813,0.00064736995,0.0009460958],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986953,0.00036784273,0.0001204052,0.0003232774,0.00039735655,0.000095787815],"domain_scores_gemma":[0.9961302,0.0018930214,0.00045401652,0.00069780514,0.00074232067,0.00008267522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024778873,0.0011270005,0.0012071524,0.0018011231,0.0005638043,0.0016827438,0.0013062349,0.0012134735,0.0008208421],"category_scores_gemma":[0.009013872,0.00049578375,0.0009151109,0.0011301392,0.00090859353,0.0014205289,0.0012989554,0.0012832541,0.0006000503],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056345284,0.00016151887,0.0040375716,0.0006131995,0.00018539718,0.00043288348,0.0005671795,0.24200082,0.16172439,0.014020312,0.0023478332,0.5733454],"study_design_scores_gemma":[0.00003415045,0.0001034496,0.0041345307,0.00003968888,0.000042880612,0.00058461877,0.000075084616,0.89450186,0.08454388,0.011627714,0.0042247106,0.00008746827],"about_ca_topic_score_codex":0.0020298122,"about_ca_topic_score_gemma":0.0034009072,"teacher_disagreement_score":0.0024778873,"about_ca_system_score_codex":0.000594765,"about_ca_system_score_gemma":0.0014427877,"threshold_uncertainty_score":0.013104439},"labels":[],"label_agreement":null},{"id":"W2959828872","doi":"10.1016/j.media.2019.07.005","title":"Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":124,"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":"Canadian Institutes of Health Research; Prostate Cancer Canada","keywords":"Artificial intelligence; Segmentation; Computer science; Convolutional neural network; Hausdorff distance; Deep learning; Pattern recognition (psychology); Image segmentation; Computer vision","score_opus":0.006231307933401923,"score_gpt":0.2571856918820442,"score_spread":0.2509543839486423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2959828872","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06617511,0.0014759479,0.9257796,0.0005207102,0.00007428793,0.00007768265,0.00055864506,0.003704649,0.0016334075],"genre_scores_gemma":[0.62913543,0.0011027557,0.35971802,0.0004661902,0.0001289048,0.00010962294,0.0017340222,0.0009068413,0.0066982126],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995751,0.00007580217,0.000026683547,0.00010361689,0.00015404193,0.000064702814],"domain_scores_gemma":[0.9994498,0.00019733037,0.00008434674,0.00007902166,0.00015115422,0.000038328402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006886386,0.00080044125,0.0008085419,0.0013257148,0.00031047224,0.0012775413,0.00093598576,0.0012734078,0.001079573],"category_scores_gemma":[0.0020876853,0.00070761336,0.00078165537,0.00074440235,0.00039428723,0.0006870224,0.0010543048,0.001170603,0.0009431109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005576046,0.00017304387,0.004704155,0.0003337968,0.00015586066,0.00022004625,0.00018173608,0.29109266,0.09065736,0.003765982,0.009652468,0.5985053],"study_design_scores_gemma":[0.00000714125,0.000025030868,0.001571216,0.000019841536,0.000017052631,0.00014799535,0.000016358834,0.98103344,0.014290581,0.0017259567,0.0011311758,0.000014162024],"about_ca_topic_score_codex":0.0084585305,"about_ca_topic_score_gemma":0.013385303,"teacher_disagreement_score":0.0084585305,"about_ca_system_score_codex":0.00087792653,"about_ca_system_score_gemma":0.0016243885,"threshold_uncertainty_score":0.016818583},"labels":[],"label_agreement":null},{"id":"W2963190374","doi":"10.1016/j.media.2019.101534","title":"Learning the implicit strain reconstruction in ultrasound elastography using privileged information","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Ultrasound Imaging and Elastography","field":"Medicine","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":"Western University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Process (computing); Artificial neural network; Artificial intelligence; Deep learning; Causality (physics); Elastography; Machine learning; Data mining; Ultrasound","score_opus":0.004420114574558858,"score_gpt":0.2471541416463416,"score_spread":0.24273402707178277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963190374","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.070517905,0.0005332537,0.926893,0.00050692057,0.000041098778,0.000025500154,0.000083542516,0.0005840941,0.0008146803],"genre_scores_gemma":[0.7929096,0.00060867664,0.2020513,0.0002106887,0.00011544602,0.00008016365,0.00034348833,0.00011716721,0.0035635084],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946815,0.00019270577,0.000031929598,0.00012204511,0.00012806965,0.00005714376],"domain_scores_gemma":[0.9953318,0.0031268438,0.0003195823,0.00065941643,0.0003780662,0.00018432277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015946771,0.00065760076,0.0010757195,0.0005691243,0.00036945805,0.0010437886,0.0012574106,0.0017873148,0.001992647],"category_scores_gemma":[0.00945071,0.00064045034,0.0005497975,0.00048595626,0.0012184874,0.0029795195,0.0020752165,0.0020871102,0.0006407187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009334897,0.0003381469,0.005010788,0.00026547164,0.00009982052,0.00025191848,0.00024270885,0.40057254,0.014535923,0.028739514,0.0036892712,0.5453204],"study_design_scores_gemma":[0.000013298274,0.00006390237,0.00025762996,0.000014442617,0.0000067502106,0.000054926135,0.000010943766,0.9876924,0.0023365612,0.009197564,0.0003430004,0.000008649656],"about_ca_topic_score_codex":0.0015784235,"about_ca_topic_score_gemma":0.0023001223,"teacher_disagreement_score":0.001992647,"about_ca_system_score_codex":0.0003392659,"about_ca_system_score_gemma":0.0011303293,"threshold_uncertainty_score":0.00843358},"labels":[],"label_agreement":null},{"id":"W2963666472","doi":"10.1016/j.media.2019.101533","title":"Automatic spondylolisthesis grading from MRIs across modalities using faster adversarial recognition network","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":36,"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":"Discriminator; Computer science; Artificial intelligence; Pattern recognition (psychology); Spondylolisthesis; Grading (engineering); Bounding overwatch; Machine learning; Lumbar; Radiology; Detector; Medicine","score_opus":0.013419027629881397,"score_gpt":0.25469032976568107,"score_spread":0.24127130213579967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963666472","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.078820586,0.0018021393,0.91033125,0.00065604865,0.0003858067,0.00014606812,0.00085766026,0.0045758286,0.002424588],"genre_scores_gemma":[0.76756865,0.0010077895,0.21674557,0.00062550674,0.00028963285,0.00014553101,0.0022981053,0.00041738202,0.010901845],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994924,0.00008193608,0.000024820412,0.0002127227,0.000110550805,0.000077550736],"domain_scores_gemma":[0.9993331,0.00024243516,0.00007119238,0.00013228622,0.0001848253,0.000036190642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094007957,0.0010843776,0.0008664242,0.0009809252,0.00028905284,0.0007562335,0.0011253672,0.0010509733,0.0019070919],"category_scores_gemma":[0.0017549585,0.00044541198,0.0009319303,0.0004507324,0.0002823957,0.000662712,0.0011185106,0.0012715886,0.0014382169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005434215,0.00024837203,0.0066845496,0.0001508061,0.0002486965,0.0003035172,0.000069527865,0.1238042,0.051119138,0.00178561,0.010257901,0.80478424],"study_design_scores_gemma":[0.000007410116,0.00005758055,0.002091616,0.00001659114,0.000055585573,0.00022008772,0.000014870272,0.9839461,0.01058009,0.0015951253,0.0014020868,0.0000129039645],"about_ca_topic_score_codex":0.0050469534,"about_ca_topic_score_gemma":0.009261977,"teacher_disagreement_score":0.0050469534,"about_ca_system_score_codex":0.0004622974,"about_ca_system_score_gemma":0.00054740754,"threshold_uncertainty_score":0.010035157},"labels":[],"label_agreement":null},{"id":"W2966434031","doi":"10.1016/j.media.2020.101851","title":"Boundary loss for highly unbalanced segmentation","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":459,"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","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Boundary (topology); Computer science; Artificial intelligence; Computer vision; Mathematical optimization; Mathematics; Mathematical analysis","score_opus":0.030341759310164765,"score_gpt":0.3062704956848611,"score_spread":0.27592873637469634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966434031","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07027932,0.0011944005,0.9205022,0.0010999287,0.00023207409,0.00011699571,0.00064420473,0.0028376398,0.0030932769],"genre_scores_gemma":[0.6177833,0.0006077821,0.36769655,0.0008418096,0.00034341897,0.0001862232,0.0035308253,0.001205517,0.0078045567],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880314,0.00025696176,0.00006403932,0.00027327024,0.00045006396,0.00015245737],"domain_scores_gemma":[0.9968918,0.0016828435,0.00023934196,0.0005416959,0.0004883293,0.00015607121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036836697,0.0010045475,0.0011963169,0.00142056,0.0007782599,0.0013650007,0.0013023447,0.0026195128,0.0038471061],"category_scores_gemma":[0.011349809,0.0006257521,0.0006194072,0.0010520703,0.00089467445,0.0016362733,0.0020828012,0.0014677409,0.0014080174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027105869,0.0003297172,0.0042382604,0.00038620466,0.00017227804,0.0006285267,0.00020269079,0.24644004,0.07819716,0.020227201,0.025631342,0.6208361],"study_design_scores_gemma":[0.000032866752,0.000064775624,0.0013461761,0.000020869875,0.000022236001,0.0002713026,0.000021834594,0.9712659,0.011724006,0.012608955,0.0026113067,0.000009796599],"about_ca_topic_score_codex":0.0030279125,"about_ca_topic_score_gemma":0.0033318663,"teacher_disagreement_score":0.0038471061,"about_ca_system_score_codex":0.00095117395,"about_ca_system_score_gemma":0.000995349,"threshold_uncertainty_score":0.01948136},"labels":[],"label_agreement":null},{"id":"W2968439290","doi":"10.1016/j.media.2019.101542","title":"Accurate automated Cobb angles estimation using multi-view extrapolation net","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Scoliosis diagnosis and treatment","field":"Medicine","cited_by":95,"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":"Extrapolation; CobB; Net (polyhedron); Artificial intelligence; Estimation; Computer science; Mathematics; Computer vision; Algorithm; Mathematical optimization; Pattern recognition (psychology); Statistics; Geometry; Engineering; Biology","score_opus":0.038072274048796734,"score_gpt":0.3853353582943171,"score_spread":0.3472630842455203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968439290","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022858702,0.00058577134,0.97222084,0.00006363313,0.000059932885,0.000048163813,0.00023974985,0.002076297,0.0018468122],"genre_scores_gemma":[0.2969022,0.00073553657,0.6954547,0.00012508278,0.00007827913,0.00007454389,0.0009401629,0.00034351015,0.005346016],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995074,0.00007021783,0.00002426617,0.00009696577,0.00026385416,0.000037275375],"domain_scores_gemma":[0.99953866,0.00010099078,0.000041111914,0.000071061506,0.00021626073,0.00003194418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042374438,0.0007419399,0.0008172067,0.0017178515,0.0002791617,0.0008754986,0.00064316805,0.00067825,0.0036363928],"category_scores_gemma":[0.00078485365,0.00053342606,0.00053863437,0.00083921675,0.00012898806,0.0006661085,0.00094416464,0.00060650933,0.0022804074],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004568623,0.00009691132,0.003453819,0.00016335354,0.00008910537,0.0001967061,0.0000604301,0.022940956,0.10348842,0.0007729182,0.0045476924,0.86373293],"study_design_scores_gemma":[0.000027912785,0.00011327546,0.008597022,0.00006035962,0.00005664894,0.0011046351,0.000044314656,0.9476479,0.035894405,0.0013820176,0.0050301915,0.00004118245],"about_ca_topic_score_codex":0.002454794,"about_ca_topic_score_gemma":0.0061692866,"teacher_disagreement_score":0.0036363928,"about_ca_system_score_codex":0.00021916124,"about_ca_system_score_gemma":0.00064338045,"threshold_uncertainty_score":0.01216495},"labels":[],"label_agreement":null},{"id":"W2972635947","doi":"10.1016/j.media.2019.101554","title":"PV-LVNet: Direct left ventricle multitype indices estimation from 2D echocardiograms of paired apical views with deep neural networks","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","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":"Western University","funders":"Science and Technology Planning Project of Guangdong Province; China Scholarship Council; National Natural Science Foundation of China","keywords":"Ventricle; Resampling; Artificial intelligence; Segmentation; Image quality; Consistency (knowledge bases); Computer science; Pattern recognition (psychology); Image segmentation; Mathematics; Artificial neural network; Computer vision; Image (mathematics); Medicine; Cardiology","score_opus":0.007737155493168633,"score_gpt":0.24722477783468197,"score_spread":0.23948762234151333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972635947","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10746769,0.0019183757,0.86158115,0.00049362757,0.00047064258,0.00022651,0.007608924,0.017105998,0.0031271486],"genre_scores_gemma":[0.5669391,0.0009989971,0.40980685,0.00061746786,0.00033836704,0.0004315647,0.01086028,0.0008294917,0.009177841],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982977,0.000028264652,0.000008442818,0.000062121755,0.000041172312,0.000030249144],"domain_scores_gemma":[0.99981004,0.00007319022,0.0000196786,0.000027086659,0.000047041663,0.000022912944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004833247,0.001166589,0.00072756736,0.000886711,0.00017577808,0.0007396209,0.00087972067,0.0010501101,0.0030826486],"category_scores_gemma":[0.0011511254,0.00047975642,0.0005634326,0.0004726989,0.00013606748,0.0004749846,0.001145604,0.00082184543,0.0017169955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000836967,0.00038958006,0.010424392,0.00024961613,0.00039262007,0.00035330802,0.00005244138,0.06237826,0.027760785,0.0011941729,0.03301783,0.86295],"study_design_scores_gemma":[0.000057456145,0.00012254588,0.004652023,0.00003789461,0.00005433249,0.00031134908,0.000023848219,0.9797391,0.010671694,0.0015744021,0.002723456,0.000031876003],"about_ca_topic_score_codex":0.0047407635,"about_ca_topic_score_gemma":0.011453125,"teacher_disagreement_score":0.0047407635,"about_ca_system_score_codex":0.00025754631,"about_ca_system_score_gemma":0.0006528019,"threshold_uncertainty_score":0.010312557},"labels":[],"label_agreement":null},{"id":"W2974263843","doi":"10.1016/j.media.2019.101560","title":"Special issue on MICCAI 2018","year":2019,"lang":"en","type":"editorial","venue":"Medical Image Analysis","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","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":"Queen's University","funders":"Engineering and Physical Sciences Research Council","keywords":"Systems biology; Computer science; Gene regulatory network; Synthetic biology; Modelling biological systems; Computational biology; Key (lock); Data science; Artificial intelligence; Biology; Gene","score_opus":0.0033260884908028147,"score_gpt":0.2558565078153947,"score_spread":0.2525304193245919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2974263843","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004406591,0.0052434974,0.00025497787,0.027335001,0.9632329,0.00002259943,0.00012136647,0.00012205792,0.0036235275],"genre_scores_gemma":[0.00042765713,0.0024405762,0.00019674507,0.007819026,0.96944594,0.000034870467,0.00010299069,0.00009624612,0.019435829],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99593145,0.0006202935,0.0004426076,0.00062751287,0.002002777,0.0003754258],"domain_scores_gemma":[0.98145527,0.0045942576,0.0015084266,0.00074747845,0.008064672,0.003629788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004934816,0.0037633807,0.0047086608,0.006836337,0.002984703,0.009783275,0.002933303,0.010881424,0.056537323],"category_scores_gemma":[0.019660678,0.0013795766,0.0027990178,0.0021416417,0.0022787526,0.0029806488,0.0023330674,0.013152737,0.032385614],"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":[0.00003418413,0.0000052039563,0.000022342922,0.00008055945,0.000011780282,0.00006781457,0.0000022196284,0.000020949275,0.000037139056,0.00014677299,0.9953081,0.0042630215],"study_design_scores_gemma":[0.00007022582,0.000021446198,0.0004565911,0.00025078707,0.000045400862,0.00035800465,0.000013507913,0.0002754675,0.000110461755,0.001366933,0.9970106,0.0000205668],"about_ca_topic_score_codex":0.0020137345,"about_ca_topic_score_gemma":0.00768235,"teacher_disagreement_score":0.056537323,"about_ca_system_score_codex":0.00428743,"about_ca_system_score_gemma":0.0021979671,"threshold_uncertainty_score":0.1891362},"labels":[],"label_agreement":null},{"id":"W2975180264","doi":"10.1016/j.media.2021.101996","title":"Multi-scale fully convolutional neural networks for histopathology image segmentation: From nuclear aberrations to the global tissue architecture","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","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":"Artificial Intelligence in Medicine (Canada)","funders":"Olympus","keywords":"Convolutional neural network; Artificial intelligence; Segmentation; Computer science; Pattern recognition (psychology); Scale (ratio); Histopathology; Image segmentation; Image (mathematics); Computer vision; Architecture; Pathology; Cartography; Medicine; Geography","score_opus":0.00989152442796864,"score_gpt":0.28675368304538734,"score_spread":0.2768621586174187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2975180264","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3141561,0.003585385,0.66349316,0.00091452384,0.00017883824,0.000116580544,0.0011337564,0.010881073,0.005540595],"genre_scores_gemma":[0.8143251,0.0007071645,0.17852919,0.00027632824,0.00004576923,0.000049596863,0.001574236,0.00025148768,0.004241185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997609,0.000035472913,0.000008659575,0.00009073699,0.00005764064,0.000046477275],"domain_scores_gemma":[0.9997197,0.00008934624,0.000039975526,0.000062152896,0.000058263755,0.0000305523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006176617,0.0010917194,0.00042978986,0.000548725,0.00024356192,0.0006615792,0.0010050951,0.0008550273,0.0012483852],"category_scores_gemma":[0.00138592,0.00044863924,0.00068897795,0.0005969347,0.00040649774,0.0012474938,0.00094939215,0.0011331317,0.0004962459],"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":[0.00039248393,0.0001333167,0.0026660315,0.00014216806,0.00015304847,0.00024369803,0.00010742928,0.6242485,0.047167663,0.0034708313,0.005494387,0.31578043],"study_design_scores_gemma":[0.000005911566,0.0000315788,0.00081883365,0.000009520465,0.000016790322,0.00003687506,0.000010421035,0.989913,0.0068316357,0.0015000938,0.0008148887,0.000010444356],"about_ca_topic_score_codex":0.013685461,"about_ca_topic_score_gemma":0.025377236,"teacher_disagreement_score":0.013685461,"about_ca_system_score_codex":0.00093069737,"about_ca_system_score_gemma":0.00076085405,"threshold_uncertainty_score":0.027211607},"labels":[],"label_agreement":null},{"id":"W2977457995","doi":"10.1016/j.media.2019.101568","title":"Segmentation and quantification of infarction without contrast agents via spatiotemporal generative adversarial learning","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":71,"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":"Western University","keywords":"Artificial intelligence; Computer science; Segmentation; Pattern recognition (psychology); Discriminator; Encoder; Generator (circuit theory); Centroid; Contrast (vision); Feature (linguistics); Power (physics)","score_opus":0.008969040013805935,"score_gpt":0.3076035449077018,"score_spread":0.29863450489389587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2977457995","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032942083,0.00039470545,0.9648049,0.00030554945,0.000027919587,0.000038203023,0.0000902202,0.0004006169,0.0009958663],"genre_scores_gemma":[0.73678035,0.0008830395,0.25717252,0.00027248196,0.000088918096,0.0000942411,0.00032614375,0.00028562514,0.0040967814],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998714,0.000029245051,0.000006633367,0.000033432738,0.000038963197,0.000020247777],"domain_scores_gemma":[0.99967825,0.00015431103,0.00006498357,0.00004561291,0.000032467146,0.000024445606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067175715,0.00061065605,0.00055657566,0.0006616126,0.0002100761,0.0011145084,0.00073955354,0.0010405331,0.00073838636],"category_scores_gemma":[0.0016270903,0.00049332227,0.00060959975,0.00043073384,0.00061379356,0.0006444191,0.001019786,0.0009350249,0.00029607958],"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":[0.0004253411,0.00009989046,0.003692523,0.00018027415,0.00013855012,0.0005585064,0.0002121288,0.7299423,0.07106937,0.0234024,0.0026319628,0.1676468],"study_design_scores_gemma":[0.00000609911,0.000026348496,0.000571172,0.000010205392,0.000019712445,0.00018304837,0.000010172249,0.9842829,0.009485373,0.004681214,0.00071403617,0.00000960803],"about_ca_topic_score_codex":0.0024609717,"about_ca_topic_score_gemma":0.0026881376,"teacher_disagreement_score":0.0024609717,"about_ca_system_score_codex":0.0005726025,"about_ca_system_score_gemma":0.00075253774,"threshold_uncertainty_score":0.0048932433},"labels":[],"label_agreement":null},{"id":"W2979808779","doi":"10.1016/j.media.2019.101587","title":"‘Squeeze &amp; excite’ guided few-shot segmentation of volumetric images","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":166,"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":"Bayerisches Staatsministerium für Bildung und Kultus, Wissenschaft und Kunst; Nvidia","keywords":"Artificial intelligence; Segmentation; Computer vision; Shot (pellet); Computer science; Image segmentation; Pattern recognition (psychology); Chemistry","score_opus":0.02131291264568962,"score_gpt":0.32293430772163023,"score_spread":0.3016213950759406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979808779","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056220543,0.00037916686,0.93543816,0.0006029645,0.00008244495,0.00008002141,0.00018580462,0.002591591,0.0044194087],"genre_scores_gemma":[0.30714682,0.00031323108,0.6833651,0.00045102736,0.000051457897,0.00008093488,0.0004635769,0.00076436385,0.007363486],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985003,0.000020907924,0.0000059635386,0.000033358232,0.000057208243,0.00003254493],"domain_scores_gemma":[0.99966824,0.00015253441,0.000035212935,0.0000586014,0.00004876246,0.000036764686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040357618,0.00060933834,0.0006063857,0.00063860905,0.00043323834,0.0011887646,0.0010837937,0.0016824653,0.0054870443],"category_scores_gemma":[0.0014113971,0.0005770397,0.0004375953,0.00047486782,0.0007045191,0.0009961189,0.0012762668,0.0010084425,0.0010040241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077288534,0.00012786663,0.00071882986,0.00053916,0.00012872886,0.00052497163,0.0006761936,0.110847175,0.41639403,0.014783563,0.0079580685,0.4465286],"study_design_scores_gemma":[0.000029285475,0.00011689426,0.0016874925,0.000048491976,0.000034609944,0.00062381156,0.0001196054,0.8559757,0.12500101,0.008777762,0.0075228107,0.0000625543],"about_ca_topic_score_codex":0.0022181852,"about_ca_topic_score_gemma":0.006196169,"teacher_disagreement_score":0.0054870443,"about_ca_system_score_codex":0.00044926297,"about_ca_system_score_gemma":0.0008880854,"threshold_uncertainty_score":0.018356025},"labels":[],"label_agreement":null},{"id":"W2979992932","doi":"10.1016/j.media.2020.101796","title":"BIAS: Transparent reporting of biomedical image analysis challenges","year":2020,"lang":"en","type":"preprint","venue":"Medical Image Analysis","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","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":"McGill University; University of Toronto; Sunnybrook Health Science Centre","funders":"National Institute of Biomedical Imaging and Bioengineering; Natural Sciences and Engineering Research Council of Canada; Ministerstvo Školství, Mládeže a Tělovýchovy; Canadian Cancer Society; NIH Clinical Center; European Research Council; National Institutes of Health; National Science Foundation","keywords":"Interpretability; Checklist; Benchmarking; Computer science; Data science; Transparency (behavior); Set (abstract data type); Quality (philosophy); Artificial intelligence; Psychology; Business; Computer security","score_opus":0.4378958910839068,"score_gpt":0.501754488514744,"score_spread":0.06385859743083722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979992932","genre_codex":"methods","genre_gemma":"methods","domain_codex":"methods","domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010110305,0.0140274465,0.6491997,0.18112433,0.046441518,0.053376682,0.0067593823,0.009722433,0.029238135],"genre_scores_gemma":[0.08961707,0.0072812047,0.7063974,0.05235231,0.013212319,0.10681474,0.0063516945,0.004837548,0.013135634],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.15039015,0.52051604,0.20528433,0.015151148,0.10372695,0.0049314606],"domain_scores_gemma":[0.034067474,0.43774787,0.12155015,0.1220103,0.2782308,0.0063933833],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.66183114,0.0038231085,0.004439496,0.020429298,0.011421994,0.02767489,0.012253159,0.021464266,0.0096967565],"category_scores_gemma":[0.89408696,0.004688193,0.007815882,0.009495193,0.015424097,0.019266699,0.037400104,0.016995966,0.010870032],"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":[0.0020185285,0.00031773828,0.009351914,0.043283287,0.0017919544,0.0014140441,0.0255455,0.0027026203,0.007998498,0.078115016,0.48704177,0.34041905],"study_design_scores_gemma":[0.0009259788,0.0006445691,0.0060986606,0.036789123,0.0008614913,0.001651338,0.0047755265,0.0061826967,0.013398328,0.09400788,0.8335652,0.0010992303],"about_ca_topic_score_codex":0.0033747451,"about_ca_topic_score_gemma":0.0037708534,"teacher_disagreement_score":0.33816886,"about_ca_system_score_codex":0.013307105,"about_ca_system_score_gemma":0.08470623,"threshold_uncertainty_score":0.41702247},"labels":[],"label_agreement":null},{"id":"W2981744453","doi":"10.1016/j.media.2019.101591","title":"Commensal correlation network between segmentation and direct area estimation for bi-ventricle quantification","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced MRI Techniques and Applications","field":"Medicine","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":"Western University","funders":"National Natural Science Foundation of China","keywords":"Benchmark (surveying); Segmentation; Computer science; Artificial intelligence; Inference; Differentiable function; Cardiac magnetic resonance imaging; Ventricle; Pattern recognition (psychology); Convergence (economics); Estimation; Algorithm; Magnetic resonance imaging; Mathematics; Medicine; Radiology; Cardiology","score_opus":0.020180454323840285,"score_gpt":0.3508524836921112,"score_spread":0.3306720293682709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981744453","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06948438,0.0004835632,0.9264395,0.00016096346,0.00004714623,0.000058803216,0.00022016042,0.0005225213,0.0025829962],"genre_scores_gemma":[0.7694069,0.0004928133,0.22406304,0.00011231578,0.000099626865,0.00015545556,0.0006867281,0.00017798116,0.004805155],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989323,0.000283621,0.00004651122,0.00039884413,0.00024667094,0.000092007154],"domain_scores_gemma":[0.9982956,0.00066525675,0.00024120798,0.00022692849,0.00049943244,0.000071578506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014805575,0.0006828822,0.0007109288,0.0016236935,0.0005702651,0.0011457317,0.0012968712,0.001098043,0.002085475],"category_scores_gemma":[0.004758762,0.0005449356,0.00065325096,0.0015414879,0.00066958275,0.0016092538,0.0015200241,0.00075430615,0.0008174739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006989373,0.0002791296,0.022457274,0.0003620096,0.00036209176,0.0007420867,0.0003758285,0.30742475,0.0768031,0.036645833,0.0072345394,0.54661435],"study_design_scores_gemma":[0.000005175059,0.000059955044,0.003980496,0.000015014599,0.000033420423,0.000168741,0.000022682074,0.983509,0.005283934,0.006012132,0.00089154625,0.000017755345],"about_ca_topic_score_codex":0.0039026092,"about_ca_topic_score_gemma":0.006017261,"teacher_disagreement_score":0.0039026092,"about_ca_system_score_codex":0.0006841801,"about_ca_system_score_gemma":0.0010075882,"threshold_uncertainty_score":0.007830083},"labels":[],"label_agreement":null},{"id":"W2982287615","doi":"10.1016/j.media.2019.101593","title":"Multi-indices quantification of optic nerve head in fundus image via multitask collaborative learning","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Retinal Imaging and Analysis","field":"Medicine","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":"Western University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Multi-task learning; Artificial intelligence; Feature (linguistics); Fundus (uterus); Task (project management); Pattern recognition (psychology); Representation (politics); Feature learning; Ensemble learning; Segmentation; Machine learning","score_opus":0.014979672065277522,"score_gpt":0.33932552860065307,"score_spread":0.32434585653537557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982287615","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09981309,0.00035497776,0.89767003,0.000104977866,0.000045220444,0.00006261142,0.00012015243,0.0007427891,0.0010861252],"genre_scores_gemma":[0.71696067,0.00020625524,0.280234,0.00009242163,0.000079758785,0.00009387205,0.0002942236,0.00011428187,0.0019245484],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994142,0.000117662305,0.000032845222,0.00019306583,0.00017070072,0.00007149509],"domain_scores_gemma":[0.99889225,0.0003935373,0.00014030299,0.00014158989,0.00035041728,0.00008192093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013886509,0.0007357961,0.000876856,0.0017944085,0.0005048826,0.0009428737,0.00088130124,0.0011366783,0.0008553251],"category_scores_gemma":[0.0022119726,0.0003662591,0.0010452822,0.00094319135,0.0003489399,0.00093670835,0.0011766736,0.00063434336,0.0003916654],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063247874,0.0006185219,0.012733767,0.00029470498,0.00044166076,0.00030460616,0.00037451906,0.276293,0.096046686,0.0018076109,0.0028796752,0.60757273],"study_design_scores_gemma":[0.0000070374444,0.00005604908,0.002774263,0.0000064489,0.000049159113,0.000071194525,0.000024425972,0.98550165,0.010133673,0.0010735941,0.00028558806,0.000016827766],"about_ca_topic_score_codex":0.0048183217,"about_ca_topic_score_gemma":0.006616195,"teacher_disagreement_score":0.0048183217,"about_ca_system_score_codex":0.0004008044,"about_ca_system_score_gemma":0.00066143606,"threshold_uncertainty_score":0.009580553},"labels":[],"label_agreement":null},{"id":"W2982313557","doi":"10.1016/j.media.2019.101588","title":"A partial augmented reality system with live ultrasound and registered preoperative MRI for guiding robot-assisted radical prostatectomy","year":2019,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":44,"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 Hospital and Health Sciences Centre; University of British Columbia","funders":"Canadian Institutes of Health Research; University of British Columbia","keywords":"Magnetic resonance imaging; Artificial intelligence; Sagittal plane; Computer science; Computer vision; Ultrasound; Augmented reality; Medicine; Prostatectomy; Image-guided surgery; Image registration; Robot; Prostate; Radiology; Image (mathematics)","score_opus":0.03819850888818283,"score_gpt":0.3235354810076974,"score_spread":0.2853369721195146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982313557","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08534164,0.0011032117,0.8928444,0.0003430713,0.0003533484,0.00036051107,0.0013824917,0.014151632,0.0041197385],"genre_scores_gemma":[0.5237008,0.0011460747,0.46642286,0.0005597862,0.00014552736,0.0005977813,0.001565468,0.00077522674,0.0050864643],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935263,0.00016958266,0.000058517733,0.000101184625,0.0002675819,0.000050512164],"domain_scores_gemma":[0.999337,0.00021101879,0.00005480114,0.00014590855,0.00017263649,0.00007860051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006927143,0.0009825821,0.000840458,0.0007937533,0.000271044,0.0011749269,0.0012464402,0.0012029593,0.006617913],"category_scores_gemma":[0.0015309025,0.00092401396,0.0008118253,0.00047684257,0.00026493586,0.0007638701,0.0016253205,0.0007917345,0.0016974784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033109307,0.0005914049,0.0051864847,0.00091508136,0.00037483583,0.0018556332,0.00087837415,0.04849188,0.21089806,0.0023922373,0.020984529,0.7041206],"study_design_scores_gemma":[0.00060711574,0.0027129299,0.020744247,0.0002569404,0.0008013656,0.010161682,0.0003980334,0.78418636,0.11834511,0.0036514176,0.057300754,0.0008341376],"about_ca_topic_score_codex":0.0018177433,"about_ca_topic_score_gemma":0.0020791732,"teacher_disagreement_score":0.006617913,"about_ca_system_score_codex":0.00024802357,"about_ca_system_score_gemma":0.0010730906,"threshold_uncertainty_score":0.022139072},"labels":[],"label_agreement":null},{"id":"W3000109616","doi":"10.1016/j.media.2020.101638","title":"Deep Atlas Network for Efficient 3D Left Ventricle Segmentation on Echocardiography","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","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":"Western University","funders":"National Key Research and Development Program of China Stem Cell and Translational Research; National Key Research and Development Program of China","keywords":"Segmentation; Artificial intelligence; Deep learning; Computer science; Hausdorff distance; Inference; Atlas (anatomy); Pattern recognition (psychology); Medicine; Anatomy","score_opus":0.011147254427377887,"score_gpt":0.27307106594755104,"score_spread":0.26192381152017313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000109616","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022198753,0.0006523271,0.9677586,0.00026146526,0.00011586463,0.00007119991,0.0012425173,0.00638099,0.0013182963],"genre_scores_gemma":[0.3844961,0.0011639604,0.6002139,0.00045355473,0.00017356718,0.0002696525,0.003910078,0.0010291173,0.00829007],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997609,0.00003582287,0.000014149903,0.00007263758,0.00007180721,0.00004456974],"domain_scores_gemma":[0.9996408,0.000122945,0.000037626745,0.00006498275,0.00009558353,0.000038044545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004674753,0.0009840919,0.0011343614,0.0012986393,0.00049195864,0.0013048999,0.0011794413,0.0012703339,0.003444827],"category_scores_gemma":[0.0011684847,0.0007745539,0.0012229407,0.001308068,0.00032856356,0.0006519768,0.0015105422,0.0014378751,0.0021324412],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041394265,0.00012556935,0.0024814256,0.00017614654,0.00018894955,0.00031977415,0.00012339173,0.2046529,0.0245199,0.00543218,0.018974144,0.7425917],"study_design_scores_gemma":[0.0000090041085,0.000024840734,0.00053141953,0.000014919494,0.000021122165,0.000089583235,0.00001463338,0.98902357,0.005288199,0.003236097,0.0017329229,0.000013780444],"about_ca_topic_score_codex":0.013836828,"about_ca_topic_score_gemma":0.026731232,"teacher_disagreement_score":0.013836828,"about_ca_system_score_codex":0.00088796526,"about_ca_system_score_gemma":0.0018990836,"threshold_uncertainty_score":0.02751261},"labels":[],"label_agreement":null},{"id":"W3000524228","doi":"10.1016/j.media.2020.101636","title":"Improving cardiac MRI convolutional neural network segmentation on small training datasets and dataset shift: A continuous kernel cut approach","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":64,"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","funders":"Engineering and Physical Sciences Research Council; Canadian Institutes of Health Research; British Heart Foundation; Medical Research Council; National Institute for Health and Care Research","keywords":"Segmentation; Convolutional neural network; Computer science; Artificial intelligence; Pattern recognition (psychology); Deep learning; Kernel (algebra); Market segmentation; Image segmentation; Scale-space segmentation; Machine learning; Mathematics","score_opus":0.01914023953641023,"score_gpt":0.28293529042554033,"score_spread":0.2637950508891301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000524228","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11643441,0.0010968248,0.87296313,0.00045751964,0.00012250598,0.00010527835,0.00044230773,0.0070293015,0.0013487559],"genre_scores_gemma":[0.48310128,0.0005174848,0.506929,0.0003629523,0.00012218063,0.0001181517,0.0027379538,0.0013358975,0.0047750236],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992508,0.00010357104,0.000047859598,0.00028236883,0.00021557728,0.000099675126],"domain_scores_gemma":[0.99769634,0.0009092612,0.00018641693,0.00052185176,0.00058984215,0.00009633451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015076079,0.0015137749,0.001800584,0.0013917139,0.00056096766,0.0014016639,0.0021105914,0.002030931,0.0020191541],"category_scores_gemma":[0.0042456957,0.00083407754,0.0011061418,0.0014896692,0.0006275977,0.0015373665,0.0018317334,0.0019896226,0.001159481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00080099667,0.00039219146,0.0029470858,0.00026025865,0.00028778324,0.00017434821,0.00016889934,0.21074437,0.06512471,0.0035294995,0.008661367,0.7069085],"study_design_scores_gemma":[0.000013308891,0.00004171372,0.0005822278,0.0000058820024,0.000024898214,0.000037602887,0.00001682836,0.99093586,0.0064952793,0.0012562299,0.0005827077,0.000007475621],"about_ca_topic_score_codex":0.01749129,"about_ca_topic_score_gemma":0.027042624,"teacher_disagreement_score":0.01749129,"about_ca_system_score_codex":0.001209835,"about_ca_system_score_gemma":0.0023280482,"threshold_uncertainty_score":0.034778953},"labels":[],"label_agreement":null},{"id":"W3006047402","doi":"10.1016/j.media.2020.101879","title":"Detect and correct bias in multi-site neuroimaging datasets","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":120,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Center for Research Resources; National Institute of General Medical Sciences; National Institute on Drug Abuse; National Institute of Mental Health; Leibniz-Rechenzentrum; Leibniz-Gemeinschaft; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; Servier; U.S. Department of Energy; Eisai; National Institute on Aging; Commonwealth Scientific and Industrial Research Organisation; Northern California Institute for Research and Education; University of California, San Diego; Pfizer; Biogen; BioClinica; Medpace; Bayerisches Staatsministerium für Bildung und Kultus, Wissenschaft und Kunst; Synarc; University of Southern California; 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":"Neuroimaging; Artificial intelligence; Computer science; Pattern recognition (psychology); Machine learning; Psychology; Neuroscience","score_opus":0.09558029519063245,"score_gpt":0.32719995885862985,"score_spread":0.2316196636679974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006047402","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4842356,0.004336988,0.49724013,0.0026812886,0.0008148264,0.00033970922,0.0043025976,0.003936949,0.0021118275],"genre_scores_gemma":[0.86939454,0.0005214093,0.122400984,0.0007267066,0.0004017952,0.00018947062,0.0043939534,0.0009692124,0.0010018876],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9919763,0.0035413743,0.00062901014,0.0024473974,0.00094734767,0.0004586582],"domain_scores_gemma":[0.9663816,0.021388534,0.002922047,0.0060601938,0.0027955135,0.00045209212],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.019140474,0.0014006617,0.001686415,0.0023021377,0.0009013299,0.0024168326,0.0015544232,0.0029085225,0.0011123281],"category_scores_gemma":[0.077473685,0.00068895234,0.0013559272,0.0018395326,0.0009593863,0.002009708,0.0016022903,0.0016545729,0.0007378271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0055771605,0.00050447806,0.32069635,0.0019442529,0.0058782566,0.0025076875,0.0012446945,0.026124269,0.185845,0.01384596,0.026719494,0.4091124],"study_design_scores_gemma":[0.0006502438,0.0006593759,0.2762683,0.0003139486,0.0030336357,0.0061185816,0.0006377849,0.45395225,0.12906523,0.107100286,0.021948554,0.0002518498],"about_ca_topic_score_codex":0.0021524911,"about_ca_topic_score_gemma":0.0057926537,"teacher_disagreement_score":0.9808595,"about_ca_system_score_codex":0.00048356436,"about_ca_system_score_gemma":0.0016383047,"threshold_uncertainty_score":0.101225674},"labels":[],"label_agreement":null},{"id":"W3006062690","doi":"10.1016/j.media.2020.101640","title":"SDAE-GAN: Enable high-dimensional pathological images in liver cancer survival prediction with a policy gradient based data augmentation method","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","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":"Western University","funders":"National Natural Science Foundation of China","keywords":"Feature (linguistics); Computer science; Immunohistochemistry; Pattern recognition (psychology); Artificial intelligence; Stage (stratigraphy); Pathology; Medicine; Biology","score_opus":0.04969742740291736,"score_gpt":0.335134612735509,"score_spread":0.2854371853325916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006062690","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013147701,0.00031546204,0.980587,0.00035294847,0.000137363,0.00007821589,0.000432027,0.0036193936,0.0013298026],"genre_scores_gemma":[0.41962442,0.00043032778,0.56938463,0.0010623842,0.00017735572,0.00035543172,0.0018941738,0.0006906677,0.0063806456],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970895,0.00008975495,0.0000133681115,0.000081306134,0.00007160367,0.000034935663],"domain_scores_gemma":[0.99946505,0.00025522715,0.000031282165,0.000088399516,0.00012659881,0.000033398443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010954259,0.0006804201,0.0007260719,0.0004049724,0.00020409802,0.0006405163,0.0014104566,0.0010648709,0.002575107],"category_scores_gemma":[0.0024006662,0.00044939813,0.00070676184,0.00039195828,0.00035546065,0.0006768258,0.0010676103,0.0016952228,0.0010809797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045183027,0.0003207711,0.0024665557,0.00017599382,0.0001379889,0.00013335216,0.00009470489,0.4329417,0.017221507,0.0089512635,0.020985402,0.51611894],"study_design_scores_gemma":[0.0000045547113,0.00001299545,0.00009551839,0.0000041936446,0.000003852784,0.000018198183,0.0000022275888,0.996271,0.0016842433,0.0012667836,0.0006325895,0.000003788635],"about_ca_topic_score_codex":0.0031797513,"about_ca_topic_score_gemma":0.005510816,"teacher_disagreement_score":0.0031797513,"about_ca_system_score_codex":0.00038090063,"about_ca_system_score_gemma":0.00079257495,"threshold_uncertainty_score":0.00861454},"labels":[],"label_agreement":null},{"id":"W3007894695","doi":"10.1016/j.media.2020.101670","title":"Embedding high-dimensional Bayesian optimization via generative modeling: Parameter personalization of cardiac electrophysiological models","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cardiac electrophysiology and arrhythmias","field":"Medicine","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":"Dalhousie University","funders":"National Heart, Lung, and Blood Institute; National Institutes of Health; National Science Foundation","keywords":"Embedding; Generative model; Computer science; Bayesian optimization; Dimension (graph theory); Parameter space; Dimensionality reduction; Bayesian probability; Artificial intelligence; Space (punctuation); Encoder; Code (set theory); Generative grammar; Range (aeronautics); Algorithm; Pattern recognition (psychology); Machine learning; Mathematics","score_opus":0.014823666582116634,"score_gpt":0.26777361907298153,"score_spread":0.2529499524908649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007894695","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0060699633,0.000119926524,0.9930983,0.00016896422,0.000011296592,0.00001344248,0.000050358107,0.00019132812,0.00027647216],"genre_scores_gemma":[0.6058652,0.00083853933,0.38646716,0.00046955564,0.00019345903,0.0002756226,0.00094111694,0.0006397944,0.0043094973],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989674,0.0005466616,0.000043638523,0.00022008037,0.00015025362,0.000071943155],"domain_scores_gemma":[0.99510205,0.0038228002,0.00028608128,0.00040149046,0.00025840852,0.00012911957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026148467,0.0010523149,0.0016300668,0.0009787817,0.00044937435,0.0015218484,0.001776263,0.002176957,0.0018947419],"category_scores_gemma":[0.011167516,0.0016624628,0.0019032547,0.0009290551,0.0014163884,0.0018514866,0.0022218819,0.0028638288,0.00069376145],"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":[0.00005804809,0.000035838086,0.00045043867,0.000051887193,0.000056644923,0.000043636304,0.00009548103,0.95105183,0.0011610825,0.015492429,0.00092106365,0.030581627],"study_design_scores_gemma":[0.0000029203145,0.0000043002997,0.000044685883,0.0000046138734,0.000004252981,0.000008649182,0.0000029787418,0.9924688,0.0001240955,0.0071902866,0.00014005022,0.0000043451187],"about_ca_topic_score_codex":0.0055375905,"about_ca_topic_score_gemma":0.007439413,"teacher_disagreement_score":0.0055375905,"about_ca_system_score_codex":0.0009344118,"about_ca_system_score_gemma":0.0011284573,"threshold_uncertainty_score":0.013828754},"labels":[],"label_agreement":null},{"id":"W3008167392","doi":"10.1016/j.media.2019.101612","title":"Trophectoderm segmentation in human embryo images via inceptioned U-Net","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Reproductive Biology and Fertility","field":"Medicine","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":"Canadian Hemophilia Society; Pacific Centre for Reproductive Medicine; Simon Fraser University","funders":"","keywords":"Jaccard index; Artificial intelligence; Segmentation; Computer science; Sørensen–Dice coefficient; Pattern recognition (psychology); Convolutional neural network; Dice; Deep learning; Image segmentation; Minimum bounding box; Machine learning; Image (mathematics); Mathematics; Statistics","score_opus":0.017150756764093975,"score_gpt":0.3192022697082575,"score_spread":0.3020515129441635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008167392","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5892816,0.001620631,0.358512,0.0010087843,0.00032997885,0.0005180665,0.0063702003,0.017158559,0.025200123],"genre_scores_gemma":[0.4815157,0.0009414454,0.499453,0.00025295053,0.000067111694,0.00019476595,0.0036141954,0.0017516086,0.012209097],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999088,0.000012695861,0.0000072232947,0.000022839195,0.000027916449,0.000020494917],"domain_scores_gemma":[0.99983525,0.00005594522,0.000016720443,0.000022342423,0.00004782228,0.00002197536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035064027,0.0004166877,0.00017659606,0.0014279997,0.00029471397,0.00083675387,0.00034572717,0.0006750427,0.009377704],"category_scores_gemma":[0.0006958672,0.0002768198,0.00035778934,0.0006580994,0.00017340785,0.00034012733,0.00041313528,0.0003437182,0.0013948977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001905725,0.00023573228,0.008837112,0.000787772,0.00009982371,0.001271727,0.0004924059,0.013320902,0.3479289,0.003979861,0.0165631,0.604577],"study_design_scores_gemma":[0.00009455519,0.0003229138,0.042832486,0.0001882992,0.00015842852,0.0028391432,0.00045275688,0.50591487,0.41537774,0.0024052584,0.029315075,0.00009849311],"about_ca_topic_score_codex":0.0032907284,"about_ca_topic_score_gemma":0.0070028272,"teacher_disagreement_score":0.009377704,"about_ca_system_score_codex":0.00030433637,"about_ca_system_score_gemma":0.0006300005,"threshold_uncertainty_score":0.031371593},"labels":[],"label_agreement":null},{"id":"W3008753367","doi":"10.1016/j.media.2020.101668","title":"Contrast agent-free synthesis and segmentation of ischemic heart disease images using progressive sequential causal GANs","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":85,"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; Computer science; Artificial intelligence; Regularization (linguistics); Pixel; Pattern recognition (psychology); Leverage (statistics); Computer vision","score_opus":0.014827723108367795,"score_gpt":0.3162005930765553,"score_spread":0.3013728699681875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008753367","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013398379,0.00019980667,0.9840596,0.00013316279,0.000031850686,0.00002617189,0.00009074757,0.0006967808,0.0013635537],"genre_scores_gemma":[0.48706374,0.00039215293,0.50708383,0.00030605088,0.00007417533,0.00010658271,0.000534956,0.00038363392,0.0040548267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99990237,0.000025539228,0.0000039914503,0.0000271887,0.000027053326,0.0000139129015],"domain_scores_gemma":[0.999765,0.0001345623,0.000023733022,0.000028341366,0.000032387852,0.000015979205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003485805,0.00055690657,0.00046370664,0.00034336545,0.00013070011,0.00049187185,0.000571524,0.0006149485,0.001489711],"category_scores_gemma":[0.00081126235,0.0004113476,0.00057237496,0.0002723563,0.00032525716,0.00032711617,0.00043955038,0.00069949013,0.00036489379],"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":[0.00038538146,0.0000777494,0.00079510815,0.00018724536,0.00010717257,0.00024050489,0.00012468784,0.7214301,0.066248246,0.013654664,0.0036206534,0.19312847],"study_design_scores_gemma":[0.0000050613385,0.000013985883,0.000084919695,0.0000034559366,0.0000062496088,0.000033138116,0.0000030810022,0.99436694,0.0033675567,0.0016206234,0.00049160427,0.0000034445486],"about_ca_topic_score_codex":0.0024962977,"about_ca_topic_score_gemma":0.005484354,"teacher_disagreement_score":0.0024962977,"about_ca_system_score_codex":0.00033623018,"about_ca_system_score_gemma":0.0005169804,"threshold_uncertainty_score":0.0049835443},"labels":[],"label_agreement":null},{"id":"W3009496543","doi":"10.1016/j.media.2020.101685","title":"An integrated deep learning framework for joint segmentation of blood pool and myocardium","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","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":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Segmentation; Pyramid (geometry); Pooling; Computer science; Artificial intelligence; Deep learning; Pattern recognition (psychology); Feature (linguistics); Task (project management); Image segmentation; Residual; Feature learning; Feature extraction; Computer vision; Engineering; Mathematics","score_opus":0.015040855844964224,"score_gpt":0.2940954518938342,"score_spread":0.27905459604886995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009496543","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072733276,0.000423917,0.99009806,0.00012850399,0.00003800578,0.000032118365,0.00016235582,0.0011824801,0.00066120335],"genre_scores_gemma":[0.26726368,0.0008042326,0.72101647,0.00045477768,0.00014222949,0.0001947056,0.0011139552,0.0004782785,0.008531653],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997857,0.000027397311,0.000009876209,0.00005784367,0.00006955826,0.00004955399],"domain_scores_gemma":[0.9998171,0.00004243208,0.0000172252,0.00002101955,0.00007476045,0.00002748288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006812441,0.0008284311,0.0009954115,0.00079769053,0.0003662645,0.0010025454,0.0018744788,0.0015982677,0.0021888232],"category_scores_gemma":[0.00079777796,0.0006252185,0.0009761708,0.00073447695,0.00029235566,0.00076242356,0.0014448618,0.0012554337,0.00090661197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003162666,0.00020370023,0.0013845355,0.00013516188,0.0002012735,0.00017477307,0.000068282265,0.34025577,0.042811595,0.009914568,0.008128804,0.59640527],"study_design_scores_gemma":[0.000006142488,0.000020148109,0.00015521713,0.000006317377,0.000015363199,0.00003163696,0.0000032054566,0.9935149,0.003569732,0.0018555626,0.00081552967,0.0000062936124],"about_ca_topic_score_codex":0.0137907015,"about_ca_topic_score_gemma":0.023904879,"teacher_disagreement_score":0.0137907015,"about_ca_system_score_codex":0.0007912985,"about_ca_system_score_gemma":0.0020573158,"threshold_uncertainty_score":0.027420878},"labels":[],"label_agreement":null},{"id":"W3016428943","doi":"10.1016/j.media.2021.101988","title":"Magic DIAMOND: Multi-fascicle diffusion compartment imaging with tensor distribution modeling and tensor-valued diffusion encoding","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","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":"Philips (Canada); CARE Canada; Université de Sherbrooke","funders":"Université de Sherbrooke","keywords":"Diffusion MRI; Voxel; Tractography; Tensor (intrinsic definition); Computer science; Fascicle; Algorithm; Artificial intelligence; Mathematics; Statistical physics; Physics; Geometry; Geology; Magnetic resonance imaging","score_opus":0.03966792182640934,"score_gpt":0.34059918555620194,"score_spread":0.3009312637297926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016428943","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003473894,0.00027555268,0.99204844,0.00024723902,0.000047781374,0.000039158494,0.00031288416,0.0028868902,0.00066808285],"genre_scores_gemma":[0.04273577,0.0005361879,0.95227224,0.00014540646,0.00004024183,0.00014907558,0.00056716317,0.0014523136,0.0021016505],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977535,0.00007136354,0.000016871705,0.000035141762,0.000084754545,0.000016446047],"domain_scores_gemma":[0.9991159,0.00043052647,0.00008875833,0.0001526266,0.00012523176,0.00008710271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012444764,0.0010979647,0.0007558791,0.0006277469,0.00036839803,0.0019800938,0.0016996654,0.0018262845,0.005177443],"category_scores_gemma":[0.0036171938,0.000766849,0.0005646777,0.000879947,0.0003872278,0.0018743086,0.0015616042,0.0017455053,0.0019703873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017456037,0.00024101291,0.0012322139,0.0013604685,0.0004704829,0.0014265531,0.00036421692,0.14188074,0.13306202,0.06794391,0.049397614,0.6008752],"study_design_scores_gemma":[0.000139949,0.00014056911,0.0003214399,0.000068930494,0.000065163316,0.0013269017,0.000041077612,0.89269763,0.051686622,0.02821965,0.025201624,0.000090460475],"about_ca_topic_score_codex":0.0012985243,"about_ca_topic_score_gemma":0.001981437,"teacher_disagreement_score":0.005177443,"about_ca_system_score_codex":0.00030242885,"about_ca_system_score_gemma":0.0011646065,"threshold_uncertainty_score":0.017320275},"labels":[],"label_agreement":null},{"id":"W3018177167","doi":"10.1016/j.media.2020.101667","title":"Tripartite-GAN: Synthesizing liver contrast-enhanced MRI to improve tumor detection","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":111,"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; Western University","funders":"Primary Research and Development Plan of Zhejiang Province; Taishan Scholar Project of Shandong Province; China Scholarship Council; National Natural Science Foundation of China","keywords":"Computer science; Discriminator; Convolutional neural network; Context (archaeology); Generator (circuit theory); Detector; Materials science; Artificial intelligence; Power (physics)","score_opus":0.007824679294856297,"score_gpt":0.272096335132687,"score_spread":0.2642716558378307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3018177167","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010899529,0.00025225748,0.9849855,0.00015437434,0.00007471736,0.000034413428,0.00024671492,0.001371183,0.0019813771],"genre_scores_gemma":[0.27145496,0.00037035186,0.7166643,0.0006393415,0.00010298511,0.000107289154,0.0014655105,0.000821305,0.008373912],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998066,0.00004840976,0.0000063999105,0.000057674446,0.000058482376,0.000022448678],"domain_scores_gemma":[0.9996817,0.00014364324,0.00002691394,0.000050743496,0.00007538094,0.000021606273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040254107,0.000885221,0.00048271051,0.00037179954,0.00012531852,0.00041981976,0.0010036764,0.0007883463,0.002488192],"category_scores_gemma":[0.0009846142,0.00040130885,0.00068953366,0.00035340525,0.0002885627,0.000467957,0.000573195,0.001061361,0.00111518],"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":[0.00031099544,0.00016109784,0.00074965693,0.0003300355,0.00015241596,0.0002306974,0.00007938562,0.44046012,0.14404722,0.008567158,0.017715402,0.38719583],"study_design_scores_gemma":[0.0000039240704,0.000024700123,0.0000956667,0.000004185289,0.000009846567,0.00005280311,0.0000037535733,0.9885923,0.008774978,0.0012673918,0.0011649118,0.000005565937],"about_ca_topic_score_codex":0.0016609179,"about_ca_topic_score_gemma":0.0056370925,"teacher_disagreement_score":0.002488192,"about_ca_system_score_codex":0.0002828182,"about_ca_system_score_gemma":0.00040089706,"threshold_uncertainty_score":0.008323848},"labels":[],"label_agreement":null},{"id":"W3023773746","doi":"10.1016/j.media.2020.101714","title":"The reliability of a deep learning model in clinical out-of-distribution MRI data: A multicohort study","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":171,"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 Institutes of Health; IXICO; H. Lundbeck A/S; Servier; Karolinska Institutet; Alzheimerfonden; Swedish Brain Power; Hjärnfonden; Vetenskapsrådet; Eisai; Stiftelsen Olle Engkvist Byggmästare; Genentech; Stiftelsen för Strategisk Forskning; Center for Innovative Medicine; Northern California Institute for Research and Education; Stockholms Läns Landsting; DoD Alzheimer's Disease Neuroimaging Initiative; Pfizer; Biogen; BioClinica; Nvidia; University of Southern California; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Alzheimer's Association; Åke Wiberg Stiftelse","keywords":"Artificial intelligence; Computer science; Reliability (semiconductor); Deep learning; Convolutional neural network; Protocol (science); Machine learning; Neuroradiologist; Neuroimaging; Medical physics; Pattern recognition (psychology); Magnetic resonance imaging; Medicine; Pathology; Radiology","score_opus":0.07275462749978208,"score_gpt":0.42609457491245006,"score_spread":0.353339947412668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023773746","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98552215,0.0011922207,0.010785896,0.0003695794,0.00014259008,0.00011325647,0.0012520183,0.0001290246,0.00049320766],"genre_scores_gemma":[0.9956328,0.00012647724,0.001781978,0.00014918305,0.000060755734,0.00005356201,0.0019208506,0.00008960715,0.00018469653],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.97220427,0.01655379,0.0019708362,0.0066340305,0.0017786521,0.00085850974],"domain_scores_gemma":[0.80792207,0.11994623,0.013854268,0.040416174,0.014876318,0.0029849089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07475618,0.0014681051,0.0016890366,0.0017604881,0.0008375671,0.0023128486,0.002731606,0.0024426582,0.0012589865],"category_scores_gemma":[0.14137855,0.00086525065,0.002313621,0.0010643495,0.0026174097,0.0028069,0.0026886298,0.0021162995,0.0010065334],"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":[0.008699098,0.0008811336,0.9157873,0.0003113235,0.0038363207,0.0005456995,0.0016560527,0.02946939,0.0022693374,0.00066614663,0.002750056,0.033128213],"study_design_scores_gemma":[0.0005786474,0.0057037957,0.5387443,0.0004113867,0.002985762,0.0033339455,0.0030460071,0.42274815,0.008125905,0.00861022,0.0052806903,0.00043122453],"about_ca_topic_score_codex":0.0034510118,"about_ca_topic_score_gemma":0.0016545927,"teacher_disagreement_score":0.07475618,"about_ca_system_score_codex":0.0008617961,"about_ca_system_score_gemma":0.0007737483,"threshold_uncertainty_score":0.3953532},"labels":[],"label_agreement":null},{"id":"W3024398883","doi":"10.1016/j.media.2020.101728","title":"Prediction of inter-fractional radiotherapy dose plans with domain translation in spatiotemporal embeddings","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","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":"Centre de Santé et de Services Sociaux Cavendish; Centre Hospitalier de l’Université de Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Artificial intelligence; Radiation therapy; Translation (biology); Artificial neural network; Machine learning; Pattern recognition (psychology); Medicine; Radiology","score_opus":0.012261696318670202,"score_gpt":0.27999613511641136,"score_spread":0.26773443879774117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3024398883","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11349685,0.0006348088,0.8819023,0.0006040736,0.000099883335,0.000063195585,0.00046524437,0.0014169241,0.0013167757],"genre_scores_gemma":[0.88334125,0.0002751981,0.11380716,0.00010377932,0.000050869014,0.00006689132,0.00063245965,0.0002771072,0.0014452777],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981016,0.00005828042,0.0000100532625,0.00005338191,0.000042933923,0.000025233005],"domain_scores_gemma":[0.99879825,0.00082864927,0.00011790421,0.00008014065,0.000116193456,0.000058918802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000558776,0.00070528226,0.0006349691,0.0005996036,0.00018721672,0.00082573143,0.0006771271,0.0010440651,0.0013865167],"category_scores_gemma":[0.0031782251,0.00052650314,0.0007791533,0.00051387004,0.00042283002,0.0007840501,0.00074075937,0.0011829749,0.00036077294],"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":[0.0001694165,0.00005143705,0.000983078,0.00005305142,0.000027524002,0.00004137749,0.000025662384,0.958496,0.0021248253,0.0016859015,0.0011491725,0.035192683],"study_design_scores_gemma":[0.0000018011543,0.000004906946,0.000051892497,0.000001631129,0.0000014928165,0.000004483776,0.0000016949836,0.9992131,0.00024577553,0.00042066106,0.00005135919,0.0000012528645],"about_ca_topic_score_codex":0.0069808443,"about_ca_topic_score_gemma":0.005842126,"teacher_disagreement_score":0.0069808443,"about_ca_system_score_codex":0.00081918197,"about_ca_system_score_gemma":0.0010916033,"threshold_uncertainty_score":0.013880432},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021451237,0.00016892671,0.9764839,0.00010614237,0.000067495734,0.000019627325,0.00008405301,0.001001008,0.0006176286],"genre_scores_gemma":[0.46310487,0.0002623492,0.53063196,0.0001156434,0.00009662035,0.000108705426,0.0004958801,0.00040360432,0.0047804057],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960333,0.00008133624,0.000016101281,0.00013064352,0.00011949384,0.000049088972],"domain_scores_gemma":[0.9991725,0.00036216018,0.00007700349,0.00010263182,0.00025379585,0.00003186215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007842529,0.0007875316,0.00073781237,0.0008350672,0.00046887455,0.0007331696,0.0011953954,0.0012530402,0.001598521],"category_scores_gemma":[0.0028563903,0.00059711025,0.0004349428,0.0006872346,0.00037840172,0.0010621571,0.001014136,0.0012851794,0.000672638],"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":[0.00034252167,0.000093435396,0.0018854544,0.000067150446,0.00008220864,0.00019909155,0.00009321796,0.5051926,0.026988482,0.004748778,0.0037675193,0.45653957],"study_design_scores_gemma":[0.0000022266913,0.000009610655,0.00019961114,0.0000031912432,0.000006317686,0.000026454936,0.0000036047795,0.99555814,0.003163356,0.0006960426,0.0003277441,0.0000037035456],"about_ca_topic_score_codex":0.008703145,"about_ca_topic_score_gemma":0.010331112,"teacher_disagreement_score":0.008703145,"about_ca_system_score_codex":0.00070071314,"about_ca_system_score_gemma":0.0009491113,"threshold_uncertainty_score":0.017304957},"labels":[],"label_agreement":null},{"id":"W3025299467","doi":"10.1016/j.media.2020.101722","title":"Dense biased networks with deep priori anatomy and hard region adaptation: Semi-supervised learning for fine renal artery segmentation","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Renal and Vascular Pathologies","field":"Medicine","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":"Western University","funders":"Jiangsu Provincial Key Research and Development Program; National Natural Science Foundation of China","keywords":"Segmentation; Artificial intelligence; Computer science; Pattern recognition (psychology); Feature (linguistics); Renal artery; Computer vision; Kidney; Medicine","score_opus":0.02783024500628021,"score_gpt":0.2722791180322217,"score_spread":0.2444488730259415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3025299467","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040977344,0.00075099553,0.9541513,0.00032022008,0.000056334065,0.00010589184,0.00024568898,0.0023230403,0.0010692187],"genre_scores_gemma":[0.6486866,0.0005074116,0.34167773,0.00074589235,0.00015458262,0.0003060891,0.0016905431,0.00057375175,0.0056573935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916565,0.00025082869,0.00003829767,0.00027652984,0.00014968314,0.0001189602],"domain_scores_gemma":[0.9973968,0.0013968665,0.00026757142,0.00037743407,0.00043165742,0.00012975794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020303205,0.0015094803,0.0017263589,0.00089857826,0.00062969077,0.0012767481,0.003056615,0.0033126415,0.0015313727],"category_scores_gemma":[0.0051901564,0.0013383203,0.0012947213,0.0008609884,0.0010944501,0.0014034022,0.0024265088,0.0024660972,0.0010087582],"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":[0.0005826689,0.00031120158,0.003169673,0.00027418954,0.00024550877,0.00020125553,0.00022506194,0.5496515,0.015130269,0.005757342,0.007462968,0.41698825],"study_design_scores_gemma":[0.000008548469,0.000028354967,0.00016856115,0.000011319386,0.000011159752,0.00003236158,0.000006084366,0.99599934,0.0014613519,0.001999792,0.00026740044,0.0000058356623],"about_ca_topic_score_codex":0.008802755,"about_ca_topic_score_gemma":0.016909974,"teacher_disagreement_score":0.008802755,"about_ca_system_score_codex":0.001120309,"about_ca_system_score_gemma":0.0018487419,"threshold_uncertainty_score":0.017503023},"labels":[],"label_agreement":null},{"id":"W3033511611","doi":"10.1016/j.media.2020.101721","title":"MB-FSGAN: Joint segmentation and quantification of kidney tumor on CT by the multi-branch feature sharing generative adversarial network","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","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":"Western University","funders":"Primary Research and Development Plan of Zhejiang Province; Taishan Scholar Project of Shandong Province; National Natural Science Foundation of China","keywords":"Segmentation; Computer science; Artificial intelligence; Feature (linguistics); Pattern recognition (psychology)","score_opus":0.026175718185868488,"score_gpt":0.28758356056552725,"score_spread":0.26140784237965875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033511611","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026765496,0.0004504016,0.9677472,0.00030889327,0.0000669986,0.000059876667,0.0003616872,0.003121344,0.0011181463],"genre_scores_gemma":[0.48295832,0.00048155585,0.507248,0.0004824791,0.00009884875,0.00014568288,0.0015447616,0.0006729957,0.00636737],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997495,0.000062239684,0.000008052374,0.00007214802,0.000071675575,0.000036432662],"domain_scores_gemma":[0.9997712,0.00009888982,0.000023858143,0.00004747809,0.000042055435,0.000016527643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083442806,0.0008541136,0.0006858269,0.0005763079,0.00020449364,0.00066190213,0.0011820833,0.0011418457,0.0014990937],"category_scores_gemma":[0.0013542565,0.00053888786,0.00073894596,0.00039905804,0.00041671016,0.00071900245,0.0013191754,0.0011585089,0.0006314474],"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":[0.0005289677,0.000102228114,0.0023187466,0.00017062075,0.000245382,0.00018050194,0.00009107996,0.50762045,0.03968184,0.0073676403,0.01009553,0.43159708],"study_design_scores_gemma":[0.000005359849,0.000018608776,0.0002994693,0.000006055512,0.000013053525,0.00006069819,0.0000032261678,0.992289,0.0054032346,0.0013053096,0.0005884639,0.0000075680045],"about_ca_topic_score_codex":0.005948951,"about_ca_topic_score_gemma":0.008256434,"teacher_disagreement_score":0.005948951,"about_ca_system_score_codex":0.000556729,"about_ca_system_score_gemma":0.00084624067,"threshold_uncertainty_score":0.011828661},"labels":[],"label_agreement":null},{"id":"W3035739940","doi":"10.1016/j.media.2020.101754","title":"Prediction of in-plane organ deformation during free-breathing radiotherapy via discriminative spatial transformer networks","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mitel (Canada); Centre Hospitalier de l’Université de Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Artificial intelligence; Computer science; Ground truth; Segmentation; Discriminative model; Encoder; Computer vision; Pattern recognition (psychology)","score_opus":0.006308130476169781,"score_gpt":0.24193867177710657,"score_spread":0.23563054130093677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035739940","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19761358,0.0007739991,0.79763657,0.00036421922,0.000101771555,0.000058121608,0.0004728772,0.0014226371,0.0015562986],"genre_scores_gemma":[0.956904,0.0002989921,0.039821245,0.000106527616,0.000041639025,0.000030185069,0.00054358825,0.000105473155,0.0021484075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998838,0.000022345637,0.00000478363,0.00003654533,0.000030029223,0.000022496468],"domain_scores_gemma":[0.9997193,0.00015226344,0.000037897575,0.000024693416,0.00004487338,0.00002091205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032218176,0.0005752128,0.00043380988,0.00040408917,0.00013266783,0.00032018655,0.00064038445,0.00054213486,0.00089316856],"category_scores_gemma":[0.0010133793,0.00035903804,0.00049472676,0.00038201318,0.00022596744,0.0004864756,0.0005095495,0.00072753924,0.00034970554],"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":[0.0004991247,0.00014921668,0.0053757136,0.000090265894,0.00009082303,0.00020297842,0.00005043212,0.73951083,0.028734744,0.0012834554,0.0027327242,0.22127971],"study_design_scores_gemma":[0.0000021736173,0.000014951775,0.00050299824,0.0000017252021,0.0000056664926,0.000026421936,0.0000028625498,0.9974228,0.0016002203,0.00032496607,0.00009313328,0.0000020337816],"about_ca_topic_score_codex":0.0050880928,"about_ca_topic_score_gemma":0.008124782,"teacher_disagreement_score":0.0050880928,"about_ca_system_score_codex":0.00033718772,"about_ca_system_score_gemma":0.00048750456,"threshold_uncertainty_score":0.010116935},"labels":[],"label_agreement":null},{"id":"W3037040939","doi":"10.1016/j.media.2020.101769","title":"The state-of-the-art in ultrasound-guided spine interventions","year":2020,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","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":"McGill University Health Centre; McGill University; Montreal Neurological Institute and Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Usability; Workflow; Computer science; Initialization; Robustness (evolution); Visualization; Artificial intelligence; Reliability (semiconductor); Medical physics; Image registration; Radiology; Computer vision; Medicine; Human–computer interaction; Image (mathematics)","score_opus":0.025079506062803607,"score_gpt":0.33708206618333864,"score_spread":0.312002560120535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037040939","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000039227045,0.9994041,0.00013421579,0.00010770842,0.00008454679,0.000003047316,0.0000112140915,0.0000040906093,0.00021182516],"genre_scores_gemma":[0.00045556366,0.99880624,0.0002993315,0.00014463313,0.00016467496,0.0000047138133,0.000017883847,0.0000018439097,0.00010507043],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99897265,0.0002177969,0.00020423844,0.00019222085,0.00035364166,0.000059425154],"domain_scores_gemma":[0.9934789,0.0050387685,0.00050492096,0.00009820073,0.0007330341,0.00014630445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027663289,0.001270979,0.0034956099,0.0042965985,0.00041273143,0.0027501606,0.0019256914,0.00253243,0.0061046574],"category_scores_gemma":[0.0059584617,0.0005411797,0.0015519253,0.0038802885,0.0011853176,0.0025430915,0.0012285238,0.0025109965,0.0019484996],"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":[0.00010268391,0.00006289633,0.00019426401,0.061415892,0.00020823766,0.000079150566,0.000060237315,0.00035777304,0.0006533432,0.0027754959,0.010545667,0.92354435],"study_design_scores_gemma":[0.00007039667,0.00041844606,0.0027201306,0.06711285,0.0017035573,0.00236111,0.00030524813,0.0007835317,0.0011772983,0.006635994,0.9165982,0.00011313704],"about_ca_topic_score_codex":0.0018427934,"about_ca_topic_score_gemma":0.0030625009,"teacher_disagreement_score":0.0061046574,"about_ca_system_score_codex":0.0007873549,"about_ca_system_score_gemma":0.002391004,"threshold_uncertainty_score":0.020422101},"labels":[],"label_agreement":null},{"id":"W3037731665","doi":"10.1016/j.media.2020.101757","title":"Yottixel – An Image Search Engine for Large Archives of Histopathology Whole Slide Images","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":142,"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; Hamilton Health Sciences; Toronto General Hospital; Jacobs (Canada); Northern Digital (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Government of Ontario","keywords":"Computer science; Search engine indexing; Digital pathology; Pixel; Information retrieval; Mosaic; Atlas (anatomy); Search engine; Image retrieval; Artificial intelligence; Computer vision; Image (mathematics); Biology","score_opus":0.015046636709015432,"score_gpt":0.2963813551718508,"score_spread":0.2813347184628354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037731665","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10186214,0.0063651293,0.52618074,0.00081614987,0.00031027105,0.0009286613,0.023768391,0.32397735,0.015791256],"genre_scores_gemma":[0.32092535,0.0027157206,0.5898572,0.0007629399,0.00015642667,0.0006203992,0.060991816,0.006717423,0.017252715],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996997,0.000028993203,0.000028657882,0.00007827479,0.00012849404,0.00003600068],"domain_scores_gemma":[0.99941576,0.00023544497,0.00006728829,0.000102398764,0.00012169423,0.00005742139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054950797,0.0007701262,0.0006436111,0.0033036266,0.00036232074,0.001095716,0.0012885905,0.0006286441,0.0082916925],"category_scores_gemma":[0.001980267,0.0003743384,0.00070614245,0.0017905096,0.00021335715,0.0017293511,0.0013219411,0.00036209682,0.0032464664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024507863,0.00022154767,0.006387289,0.0017698961,0.0004566076,0.0012160363,0.00048947526,0.007067981,0.10262991,0.005260378,0.15676479,0.71528524],"study_design_scores_gemma":[0.0005894168,0.0009622245,0.02022946,0.00036871698,0.00030736983,0.0041303444,0.00074360945,0.47678506,0.22860949,0.013551246,0.2533845,0.00033862732],"about_ca_topic_score_codex":0.0045097563,"about_ca_topic_score_gemma":0.005977559,"teacher_disagreement_score":0.0082916925,"about_ca_system_score_codex":0.00058051595,"about_ca_system_score_gemma":0.00071379874,"threshold_uncertainty_score":0.027738452},"labels":[],"label_agreement":null},{"id":"W3041342951","doi":"10.1016/j.media.2020.101783","title":"Holistic multitask regression network for multiapplication shape regression segmentation","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","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":"London Health Sciences Centre; Western University","funders":"","keywords":"Artificial intelligence; Computer science; Regression; Segmentation; Multi-task learning; Pattern recognition (psychology); Regression analysis; Machine learning; Task (project management); Mathematics; Statistics","score_opus":0.025699172228552274,"score_gpt":0.3124252000212351,"score_spread":0.2867260277926828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041342951","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019420316,0.0003043282,0.97647995,0.00014942426,0.00005566409,0.000035691053,0.00018763998,0.0021674908,0.0011994737],"genre_scores_gemma":[0.47317606,0.00055327197,0.5097431,0.00032104968,0.00016983219,0.00018529693,0.0018762433,0.0007829615,0.013192223],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994697,0.00009327815,0.000019073936,0.00020272919,0.00012338528,0.00009178665],"domain_scores_gemma":[0.99940515,0.00015042888,0.00004872792,0.00012625003,0.00021586612,0.000053555443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085463014,0.0011795744,0.0011060322,0.0009716623,0.0004960799,0.0008203416,0.0016166976,0.001625053,0.0037186157],"category_scores_gemma":[0.001736798,0.00057974044,0.0010383881,0.0011627133,0.00036469175,0.0012272005,0.0016568336,0.0013486833,0.0018233313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039786904,0.00024142428,0.0018615692,0.000111725734,0.00015681083,0.00021563772,0.000085461295,0.37597626,0.046343938,0.0038525718,0.008223391,0.5625333],"study_design_scores_gemma":[0.0000027832848,0.000026537515,0.00022360761,0.000003060828,0.0000119305105,0.00002634745,0.000007749705,0.99546415,0.002455225,0.0011474034,0.0006262564,0.000004861821],"about_ca_topic_score_codex":0.006406552,"about_ca_topic_score_gemma":0.010323548,"teacher_disagreement_score":0.006406552,"about_ca_system_score_codex":0.0006355573,"about_ca_system_score_gemma":0.00096451316,"threshold_uncertainty_score":0.012738526},"labels":[],"label_agreement":null},{"id":"W3041500444","doi":"10.1016/j.media.2020.101770","title":"Supervised learning with cyclegan for low-dose FDG PET image denoising","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":194,"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); University Health Network","funders":"National University Cancer Institute, Singapore","keywords":"Artificial intelligence; Noise reduction; Image denoising; Computer science; Pattern recognition (psychology); Computer vision; Supervised learning; Artificial neural network","score_opus":0.015696871443824582,"score_gpt":0.3122459519873317,"score_spread":0.2965490805435071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041500444","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031042391,0.0007712401,0.9629711,0.0002709699,0.00010595025,0.000093291004,0.00023216885,0.002562263,0.0019506039],"genre_scores_gemma":[0.3760856,0.00071333285,0.6092945,0.0005805143,0.00014780833,0.00028138256,0.002210952,0.0006216195,0.010064231],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996673,0.00008951382,0.000015625646,0.000101188016,0.00007542775,0.00005092072],"domain_scores_gemma":[0.9994748,0.00024201743,0.000035313325,0.00009165836,0.0001361778,0.000020057492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009769598,0.0008967218,0.00079139083,0.0005471446,0.00030628688,0.00058503565,0.0009863963,0.0010728631,0.0020731783],"category_scores_gemma":[0.0019040412,0.00035220935,0.00086900964,0.0003925049,0.00045531808,0.0005545597,0.00093631,0.0014446761,0.0008983963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055628346,0.00028920852,0.0012932419,0.00026427596,0.00021605345,0.00013602924,0.00010439516,0.1887022,0.047269586,0.0062942645,0.009850952,0.74502355],"study_design_scores_gemma":[0.00001411031,0.00006180906,0.0003981659,0.000015135467,0.000025566436,0.00006465536,0.0000114000795,0.9832665,0.011454324,0.0027617586,0.0019167525,0.00000999071],"about_ca_topic_score_codex":0.0038465927,"about_ca_topic_score_gemma":0.008059163,"teacher_disagreement_score":0.0038465927,"about_ca_system_score_codex":0.00047362156,"about_ca_system_score_gemma":0.0010491788,"threshold_uncertainty_score":0.0076484084},"labels":[],"label_agreement":null},{"id":"W3042519501","doi":"10.1016/j.media.2020.101791","title":"Attention convolutional neural network for accurate segmentation and quantification of lesions in ischemic stroke disease","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":126,"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":"Hunan Provincial Science and Technology Department; National Natural Science Foundation of China","keywords":"Hyperintensity; Stroke (engine); Segmentation; Convolutional neural network; Lesion; Magnetic resonance imaging; Artificial intelligence; Ischemic stroke; Medicine; Computer science; Pattern recognition (psychology); Cardiology; Radiology; Ischemia; Pathology","score_opus":0.029334498782924903,"score_gpt":0.31634122606994886,"score_spread":0.287006727287024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042519501","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23977752,0.005746276,0.7434969,0.0011459019,0.00026437643,0.00014623525,0.0013225392,0.0033818763,0.0047183805],"genre_scores_gemma":[0.89072883,0.0014372905,0.10095529,0.00029775113,0.00013497414,0.000070359565,0.0009846374,0.00014135728,0.0052494714],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99982786,0.000032541477,0.000010098228,0.00005213645,0.000034196775,0.000043081232],"domain_scores_gemma":[0.99969685,0.00013804749,0.000030497837,0.000026832466,0.000086079264,0.000021709006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073322933,0.0006693926,0.00058285915,0.0011605446,0.0003044449,0.00072120427,0.0006490382,0.0008685237,0.0014141917],"category_scores_gemma":[0.0013772984,0.00032786425,0.0005666418,0.00056081096,0.00021425862,0.00045476048,0.0005980526,0.0006979124,0.0004168655],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00090767455,0.0002991395,0.012503501,0.00022933481,0.00028139167,0.00035155064,0.00011514393,0.14519832,0.05099385,0.003608716,0.010095416,0.77541596],"study_design_scores_gemma":[0.000008246391,0.000038305472,0.004381648,0.000016609434,0.0000627288,0.00008918784,0.000010924068,0.98375815,0.009055753,0.0015821863,0.0009863399,0.000009877472],"about_ca_topic_score_codex":0.022623485,"about_ca_topic_score_gemma":0.024583234,"teacher_disagreement_score":0.022623485,"about_ca_system_score_codex":0.00082683616,"about_ca_system_score_gemma":0.0009899263,"threshold_uncertainty_score":0.044983566},"labels":[],"label_agreement":null},{"id":"W3042619146","doi":"10.1016/j.media.2020.101792","title":"Handling confounding variables in statistical shape analysis - application to cardiac remodelling","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","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":"McGill University","funders":"Agència de Gestió d'Ajuts Universitaris i de Recerca; Horizon 2020; Instituto de Salud Carlos III; Ministerio de Economía y Competitividad; “la Caixa” Foundation","keywords":"Confounding; Robustness (evolution); Statistics; Medicine; Computer science; Artificial intelligence; Internal medicine; Mathematics; Biology","score_opus":0.014677644115568107,"score_gpt":0.2970592467573982,"score_spread":0.2823816026418301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042619146","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003429148,0.00008490405,0.99483055,0.00010631818,0.000034135122,0.00004311242,0.000055515575,0.0013244287,0.000091955975],"genre_scores_gemma":[0.06790121,0.000190612,0.9293579,0.00012820367,0.00009113519,0.00016378354,0.00021564077,0.0010737049,0.00087787316],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99380034,0.0035002758,0.0004719083,0.0006443981,0.0013888469,0.00019428569],"domain_scores_gemma":[0.96934444,0.022333054,0.0011918176,0.0038545788,0.0027567437,0.00051932957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020136021,0.0013531632,0.0017445997,0.0019944543,0.001468889,0.0026678387,0.002533602,0.0020338616,0.0034316257],"category_scores_gemma":[0.06117803,0.0011229594,0.002695643,0.0021164585,0.0017250535,0.0012715468,0.0035565558,0.0031724023,0.0014827402],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059047656,0.00020871685,0.018724333,0.0005377827,0.0007732827,0.0008513978,0.00083616184,0.0716145,0.02610608,0.016523631,0.0063677663,0.85686576],"study_design_scores_gemma":[0.000079654164,0.00021071691,0.005738145,0.00006384986,0.00017842375,0.0009864863,0.00014335153,0.9348279,0.01617796,0.0330843,0.008438721,0.000070492526],"about_ca_topic_score_codex":0.0038872024,"about_ca_topic_score_gemma":0.0053419103,"teacher_disagreement_score":0.020136021,"about_ca_system_score_codex":0.0004392609,"about_ca_system_score_gemma":0.0027214421,"threshold_uncertainty_score":0.10649067},"labels":[],"label_agreement":null},{"id":"W3089090082","doi":"10.1016/j.media.2020.101813","title":"Deep neural network models for computational histopathology: A survey","year":2020,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":727,"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","funders":"National Cancer Institute; Canadian Cancer Society","keywords":"Artificial intelligence; Artificial neural network; Computer science; Deep learning; Pattern recognition (psychology); Machine learning","score_opus":0.0621987312965477,"score_gpt":0.3509627319969386,"score_spread":0.28876400070039093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089090082","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001217091,0.9550576,0.03691551,0.0014379867,0.00041602523,0.000026715716,0.00019969408,0.00015918344,0.0045701456],"genre_scores_gemma":[0.0104215685,0.96878415,0.016407419,0.00048683234,0.00059132115,0.00003778373,0.0003434282,0.000048947382,0.0028785854],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99980277,0.000038845657,0.000021846268,0.00004718627,0.00007576552,0.000013648924],"domain_scores_gemma":[0.9989926,0.0006386689,0.000049498867,0.00004745261,0.00023691576,0.00003487626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010187945,0.0010837085,0.001131558,0.0012846373,0.00016091319,0.0010420827,0.0016838057,0.0011663401,0.0035759485],"category_scores_gemma":[0.0023159445,0.0004722961,0.0006546551,0.0020557255,0.00053109543,0.0015341669,0.0008476001,0.0014780894,0.0018396194],"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":[0.000052215764,0.00009561654,0.00046326895,0.0038729392,0.00010401889,0.00004268039,0.000024140078,0.007618434,0.00076796324,0.009045621,0.018105906,0.9598073],"study_design_scores_gemma":[0.00005987618,0.0003662991,0.0027120307,0.0069707027,0.00052284356,0.0009740659,0.000107499036,0.08297429,0.005931822,0.060983997,0.83826447,0.00013207232],"about_ca_topic_score_codex":0.004007179,"about_ca_topic_score_gemma":0.005026005,"teacher_disagreement_score":0.004007179,"about_ca_system_score_codex":0.00072051253,"about_ca_system_score_gemma":0.0013505259,"threshold_uncertainty_score":0.011962771},"labels":[],"label_agreement":null},{"id":"W3089741206","doi":"10.1016/j.media.2021.102191","title":"Realistic image normalization for multi-Domain segmentation","year":2021,"lang":"en","type":"preprint","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","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":"École de Technologie Supérieure","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Fonds Québécois de la Recherche sur la Nature et les Technologies; Nvidia","keywords":"Normalization (sociology); Computer science; Artificial intelligence; Segmentation; Dice; Pattern recognition (psychology); Adversarial system; Image segmentation; Spatial normalization; Machine learning; Mathematics","score_opus":0.028599127600292113,"score_gpt":0.35044219239771185,"score_spread":0.32184306479741975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089741206","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004485251,0.00023919171,0.99217564,0.00019074482,0.00006014807,0.000033848784,0.000094778465,0.00080621423,0.0019141077],"genre_scores_gemma":[0.19083425,0.00079158705,0.79522663,0.00035312033,0.00014076832,0.00015484486,0.0009264626,0.0017936606,0.009778711],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915385,0.00019704217,0.000043837266,0.00022612208,0.00029873883,0.000080414895],"domain_scores_gemma":[0.99889946,0.0003106576,0.00009819376,0.00042157384,0.00021089117,0.00005915483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001389049,0.0010167883,0.000978922,0.0010478776,0.0006066811,0.002302154,0.0012898995,0.0020317507,0.006790479],"category_scores_gemma":[0.004859082,0.0009701225,0.001092484,0.0013441113,0.0009738587,0.0019168877,0.0021359285,0.0021752021,0.0025719407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006011817,0.00017377976,0.0010817262,0.0006768914,0.00021773326,0.0004494331,0.00024431804,0.24737829,0.22664584,0.098312065,0.0135519,0.41066688],"study_design_scores_gemma":[0.000020106954,0.000046969137,0.00078770693,0.000036202015,0.000039125756,0.0006734434,0.000039885806,0.88547945,0.06250774,0.037231512,0.013106132,0.00003181733],"about_ca_topic_score_codex":0.0018933141,"about_ca_topic_score_gemma":0.0027124006,"teacher_disagreement_score":0.006790479,"about_ca_system_score_codex":0.0010399459,"about_ca_system_score_gemma":0.0010760751,"threshold_uncertainty_score":0.022716463},"labels":[],"label_agreement":null},{"id":"W3091088513","doi":"10.1016/j.media.2020.101916","title":"Fully automated left atrium segmentation from anatomical cine long-axis MRI sequences using deep convolutional neural network with unscented Kalman filter","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":31,"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; Canadian VIGOUR Centre","funders":"Servier; Mitacs","keywords":"Artificial intelligence; Convolutional neural network; Segmentation; Computer science; Kalman filter; Sørensen–Dice coefficient; Pattern recognition (psychology); Computer vision; Artificial neural network; Image segmentation; Deep learning; Ground truth","score_opus":0.01682712962209134,"score_gpt":0.3152889253414733,"score_spread":0.298461795719382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091088513","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026043521,0.0006714826,0.9692806,0.00015088911,0.000052229647,0.000045846955,0.0003583938,0.002585215,0.00081172795],"genre_scores_gemma":[0.40109006,0.0010250778,0.5895249,0.00026408702,0.000107873246,0.0001161999,0.0020636062,0.00063812383,0.005170028],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998159,0.000022071885,0.000012295181,0.00006431864,0.000047604262,0.00003797153],"domain_scores_gemma":[0.9997211,0.00010264705,0.000037208007,0.000035611825,0.00007899232,0.000024396648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045021923,0.00081737066,0.0007924088,0.0009241222,0.00030332516,0.0008522803,0.0006059914,0.0009868484,0.0014634675],"category_scores_gemma":[0.0008761899,0.00065755594,0.00085645996,0.00048320543,0.0002470022,0.0004676493,0.0006704323,0.0008933009,0.0010108275],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063028256,0.00015024243,0.004527122,0.00031588328,0.00022604167,0.00046286354,0.00022443836,0.15968607,0.12268022,0.0027946262,0.008315279,0.6999869],"study_design_scores_gemma":[0.0000121243975,0.00003671114,0.0017412317,0.00002594946,0.000033388726,0.00019896282,0.00001955246,0.978501,0.016034555,0.0014754905,0.0019022936,0.000018706492],"about_ca_topic_score_codex":0.011790748,"about_ca_topic_score_gemma":0.030286802,"teacher_disagreement_score":0.011790748,"about_ca_system_score_codex":0.00042265572,"about_ca_system_score_gemma":0.0014821761,"threshold_uncertainty_score":0.023444235},"labels":[],"label_agreement":null},{"id":"W3091878842","doi":"10.1016/j.media.2020.101838","title":"ELNet:Automatic classification and segmentation for esophageal lesions using convolutional neural network","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Esophageal Cancer Research and Treatment","field":"Medicine","cited_by":78,"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":"Jiangsu Provincial Key Research and Development Program; China Scholarship Council; National Natural Science Foundation of China","keywords":"Convolutional neural network; Artificial intelligence; Segmentation; Computer science; Lesion; Pattern recognition (psychology); Esophageal cancer; Deep learning; Image segmentation; Medicine; Pathology; Cancer","score_opus":0.06490156277561017,"score_gpt":0.37936036881238244,"score_spread":0.3144588060367723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091878842","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12469989,0.0031754766,0.7490204,0.00094505283,0.00062460423,0.0006171661,0.013619342,0.09921308,0.008085092],"genre_scores_gemma":[0.3083095,0.0012519718,0.6291503,0.0009535069,0.00021055278,0.0006451553,0.025619965,0.0026159484,0.031243097],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999778,0.000019466082,0.000012298971,0.00007554993,0.000059505175,0.00005523351],"domain_scores_gemma":[0.9998198,0.000044283745,0.000018615736,0.000026909445,0.00006982003,0.000020537898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057832483,0.0014109295,0.0007208652,0.0016178186,0.0004570991,0.0009488766,0.0013387959,0.0012495065,0.0071855164],"category_scores_gemma":[0.00071571517,0.0006423603,0.00078803685,0.0007794626,0.00020748904,0.0007736396,0.0010575509,0.00084737845,0.0035663028],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009212474,0.00041274034,0.0063977493,0.00039394235,0.00035092313,0.0004410155,0.00008232613,0.035791855,0.05343848,0.0024099052,0.07936431,0.8199956],"study_design_scores_gemma":[0.000104805426,0.00017584025,0.0054294723,0.00006533211,0.00010453252,0.0004916938,0.000041038482,0.89862436,0.07468562,0.0025335066,0.017690344,0.00005341749],"about_ca_topic_score_codex":0.014645746,"about_ca_topic_score_gemma":0.025471503,"teacher_disagreement_score":0.014645746,"about_ca_system_score_codex":0.0009599975,"about_ca_system_score_gemma":0.0013269794,"threshold_uncertainty_score":0.029121041},"labels":[],"label_agreement":null},{"id":"W3092065674","doi":"10.1016/j.media.2020.101826","title":"Automatic vertebrae recognition from arbitrary spine MRI images by a category-Consistent self-calibration detection framework","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","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":"Western University","funders":"","keywords":"Discriminative model; Artificial intelligence; Computer science; Bounding overwatch; Pattern recognition (psychology); Calibration; Computer vision; Mathematics","score_opus":0.006815777504003528,"score_gpt":0.2115675228620812,"score_spread":0.20475174535807766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092065674","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013708676,0.00024671733,0.98385066,0.00005876223,0.000027792465,0.000046756453,0.000073057796,0.0013560023,0.0006315858],"genre_scores_gemma":[0.29261816,0.00045807747,0.7006041,0.00025248216,0.000096800424,0.0001571106,0.00082493946,0.0004222814,0.004566101],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983885,0.00014985555,0.000071513394,0.0006393559,0.0005597421,0.00019103182],"domain_scores_gemma":[0.99905175,0.00012720334,0.00011056214,0.00024011504,0.00042382602,0.000046599544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090807985,0.000702562,0.0016506566,0.002108684,0.00061238324,0.0012304187,0.002474508,0.0020648944,0.0019615935],"category_scores_gemma":[0.001497235,0.00062114233,0.0017845655,0.0016667497,0.0007944351,0.0010604444,0.0019124873,0.0009971472,0.002040337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002464254,0.00018635663,0.0027247593,0.00019071646,0.00017029307,0.00013964789,0.0001022649,0.026462398,0.13095847,0.0044501517,0.0029908877,0.83137774],"study_design_scores_gemma":[0.000020495432,0.00021470664,0.005778164,0.000035371788,0.00012271019,0.00073251,0.000072113726,0.9368826,0.045281254,0.006046286,0.0047474476,0.00006634323],"about_ca_topic_score_codex":0.0043698796,"about_ca_topic_score_gemma":0.009249227,"teacher_disagreement_score":0.0043698796,"about_ca_system_score_codex":0.0004508338,"about_ca_system_score_gemma":0.0015608729,"threshold_uncertainty_score":0.008688927},"labels":[],"label_agreement":null},{"id":"W3092480895","doi":"10.1016/j.media.2020.101852","title":"Discriminative and generative models for anatomical shape analysis on point clouds with deep neural networks","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":24,"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; Bayerisches Staatsministerium für Wissenschaft, Forschung und Kunst","keywords":"Discriminative model; Artificial intelligence; Generative grammar; Point cloud; Computer science; Artificial neural network; Generative model; Point (geometry); Pattern recognition (psychology); Deep neural networks; Deep learning; Mathematics; Geometry","score_opus":0.014336654568467324,"score_gpt":0.24223078918025806,"score_spread":0.22789413461179073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092480895","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012522111,0.00034145868,0.9848296,0.0002442945,0.000031735537,0.000028709299,0.00023097993,0.0011949075,0.00057630934],"genre_scores_gemma":[0.70401794,0.0009736125,0.28526437,0.00055132416,0.0001728319,0.0002118328,0.0018369615,0.00056503125,0.006406128],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955744,0.000081572296,0.000023124077,0.00013620021,0.00013220833,0.000069383255],"domain_scores_gemma":[0.9990551,0.00048201403,0.00010536757,0.00016013082,0.00013251505,0.000064800406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085199537,0.0010203171,0.0012677592,0.0012120515,0.0004473242,0.0011385314,0.0024684584,0.0019332616,0.0023161815],"category_scores_gemma":[0.0026270996,0.0015167515,0.002133117,0.0017852911,0.0011949894,0.0012816327,0.0020684602,0.0027893295,0.0011880599],"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":[0.00008632225,0.00004465047,0.0006599601,0.000050707677,0.00005901787,0.000064119035,0.000047316124,0.8850395,0.0031689128,0.0071485904,0.0016604406,0.10197042],"study_design_scores_gemma":[0.0000014899045,0.0000033220451,0.00005594624,0.0000025051108,0.000002492091,0.000008878739,0.0000016854212,0.99722606,0.00019180922,0.0024174815,0.000085993386,0.0000023248679],"about_ca_topic_score_codex":0.01603737,"about_ca_topic_score_gemma":0.026694568,"teacher_disagreement_score":0.01603737,"about_ca_system_score_codex":0.0015900249,"about_ca_system_score_gemma":0.0011481467,"threshold_uncertainty_score":0.031888068},"labels":[],"label_agreement":null},{"id":"W3092490063","doi":"10.1016/j.media.2020.101861","title":"Sequential conditional reinforcement learning for simultaneous vertebral body detection and segmentation with modeling the spine anatomy","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","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":"Western University","funders":"","keywords":"Segmentation; Artificial intelligence; Computer science; Minimum bounding box; Pattern recognition (psychology); Reinforcement learning; Context (archaeology); Deep learning; Feature (linguistics); Image segmentation; Artificial neural network; Computer vision; Image (mathematics)","score_opus":0.007901384006566854,"score_gpt":0.24945136659967931,"score_spread":0.24154998259311247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092490063","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020741029,0.00014183926,0.9776458,0.00013074986,0.000021852784,0.00003617058,0.00004263472,0.0006719007,0.00056805543],"genre_scores_gemma":[0.8224972,0.0001393422,0.17358999,0.00019910134,0.00006450167,0.00016135517,0.00022606064,0.00016219521,0.0029601804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994407,0.000147102,0.00002391006,0.00017451959,0.00012514101,0.000088542394],"domain_scores_gemma":[0.997919,0.0014375494,0.00016218334,0.00012718812,0.00024341354,0.00011076208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013664237,0.00086464896,0.0015754573,0.00053239724,0.00038369192,0.00074868265,0.001612745,0.0015184799,0.0023168141],"category_scores_gemma":[0.0039983084,0.0008569938,0.00075877924,0.00049620547,0.0010056425,0.0008427703,0.0013617028,0.0015441182,0.00046244974],"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":[0.0002976107,0.000089993766,0.0008781455,0.00006972554,0.0000597864,0.00009922812,0.000063478015,0.89663625,0.004456704,0.0035662525,0.0012568727,0.09252592],"study_design_scores_gemma":[0.0000046919213,0.000009733808,0.000050743838,0.0000013557886,0.000002835535,0.0000062117324,0.0000012246089,0.998887,0.0002740558,0.0007158308,0.000044499837,0.0000018224705],"about_ca_topic_score_codex":0.014758184,"about_ca_topic_score_gemma":0.01405226,"teacher_disagreement_score":0.014758184,"about_ca_system_score_codex":0.00097651035,"about_ca_system_score_gemma":0.0018619692,"threshold_uncertainty_score":0.029344559},"labels":[],"label_agreement":null},{"id":"W3093149564","doi":"10.1016/j.media.2020.101847","title":"A deep community based approach for large scale content based X-ray image retrieval","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Image Retrieval and Classification 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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Image retrieval; Deep learning; Artificial intelligence; Scale (ratio); Automatic image annotation; Feature (linguistics); Image (mathematics); Content-based image retrieval; Pattern recognition (psychology); Data mining; Information retrieval; Computer vision","score_opus":0.04811755281095157,"score_gpt":0.2887763633008569,"score_spread":0.2406588104899053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093149564","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019897738,0.0010315322,0.97453576,0.00039474072,0.00012363467,0.00015396741,0.0004388027,0.0018298082,0.001594071],"genre_scores_gemma":[0.4029971,0.0010344139,0.57537013,0.00063886005,0.0005091566,0.0003427065,0.0029166457,0.00041622287,0.01577475],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888176,0.0002077021,0.00005934975,0.00020967638,0.00047607505,0.00016547916],"domain_scores_gemma":[0.9980946,0.00053395133,0.00014344734,0.00030891536,0.0007777635,0.00014120224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011639151,0.000872617,0.0016958299,0.0038134,0.0010338308,0.0011628789,0.0024608453,0.0024352241,0.003839827],"category_scores_gemma":[0.0029022344,0.00049947645,0.0014482788,0.0037198826,0.0007434162,0.0023999573,0.002495131,0.0014301519,0.0018275002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063365366,0.0009451042,0.0022201259,0.00033871332,0.00023995088,0.00031802242,0.0002136731,0.08178915,0.0591172,0.01580962,0.021314548,0.8170602],"study_design_scores_gemma":[0.000021078395,0.00006889853,0.0004283198,0.000011268033,0.000028949897,0.00010353938,0.00003240188,0.9859299,0.0035900606,0.00783673,0.0019330814,0.000015722742],"about_ca_topic_score_codex":0.011221422,"about_ca_topic_score_gemma":0.01890813,"teacher_disagreement_score":0.011221422,"about_ca_system_score_codex":0.0008699599,"about_ca_system_score_gemma":0.001123817,"threshold_uncertainty_score":0.022312164},"labels":[],"label_agreement":null},{"id":"W3094447715","doi":"10.1016/j.media.2020.101872","title":"Unifying neural learning and symbolic reasoning for spinal medical report generation","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","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":"Western University","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Domain (mathematical analysis); Machine learning; Deep learning; Artificial neural network; Segmentation; Graph; Theoretical computer science","score_opus":0.01925856031216853,"score_gpt":0.29782291182126175,"score_spread":0.2785643515090932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094447715","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025198035,0.00046585017,0.96207464,0.0006327262,0.00009982815,0.0001688007,0.0006291257,0.0072540273,0.0034768777],"genre_scores_gemma":[0.44482642,0.000406909,0.5479836,0.00029670386,0.00010862538,0.00013405478,0.0019367737,0.0004035632,0.0039033268],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833363,0.00030482942,0.00021781457,0.0003859561,0.000573418,0.0001844539],"domain_scores_gemma":[0.9951847,0.0026891658,0.00030775488,0.0010041456,0.0006840178,0.00013016567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019867045,0.00085166097,0.0009987097,0.0019369528,0.00069721235,0.0026123319,0.0030417412,0.0014816583,0.0073544825],"category_scores_gemma":[0.008027521,0.0005515897,0.0018755819,0.0012746643,0.0011890692,0.0041389023,0.0028498296,0.0018351331,0.0016898755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040899788,0.0003957883,0.0026500386,0.0003894957,0.00016619179,0.0003613899,0.0002799411,0.2209182,0.009253876,0.04903443,0.007381795,0.7087599],"study_design_scores_gemma":[0.000018767298,0.00003755316,0.00020339746,0.00003434775,0.000039369635,0.00004996315,0.000035762805,0.9376065,0.0049817893,0.05512014,0.0018576897,0.000014644365],"about_ca_topic_score_codex":0.011921024,"about_ca_topic_score_gemma":0.022527289,"teacher_disagreement_score":0.011921024,"about_ca_system_score_codex":0.0015065409,"about_ca_system_score_gemma":0.0029616966,"threshold_uncertainty_score":0.024603128},"labels":[],"label_agreement":null},{"id":"W3095505136","doi":"10.1016/j.media.2020.101887","title":"Bone and joint enhancement filtering: Application to proximal femur segmentation from uncalibrated computed tomography datasets","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Orthopaedic implants and arthroplasty","field":"Medicine","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":"Hotchkiss Brain Institute; Alberta Bone and Joint Health Institute; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Artificial intelligence; Computer vision; Hausdorff distance; Computer science; Femur; Image segmentation; Pattern recognition (psychology); Mathematics; Medicine","score_opus":0.015063896182320723,"score_gpt":0.26937526624213015,"score_spread":0.2543113700598094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3095505136","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051623333,0.00046198018,0.94077444,0.00014919654,0.000029483681,0.00027783136,0.00044297642,0.0057762233,0.0004645688],"genre_scores_gemma":[0.09887664,0.00025953742,0.89874595,0.000045290148,0.000017883709,0.0002142815,0.00065950153,0.00057077274,0.00061000814],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982134,0.00051842676,0.00019094096,0.00034979044,0.0006159765,0.00011148911],"domain_scores_gemma":[0.99623317,0.0022386347,0.00040482223,0.00039214117,0.0006596845,0.00007150731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060351808,0.00091718324,0.00089772255,0.0031822552,0.000652889,0.0013004316,0.00083225867,0.001487205,0.0018898083],"category_scores_gemma":[0.011547977,0.0006616451,0.0010465597,0.0021255082,0.0006543395,0.0004726025,0.0011548157,0.0006338394,0.0009499477],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00094186276,0.0001631851,0.010339625,0.00070680515,0.00043980926,0.00057480665,0.0007647854,0.049917903,0.19343907,0.0019984676,0.004368595,0.73634505],"study_design_scores_gemma":[0.000112320464,0.00040542305,0.038279507,0.00008898,0.00023542313,0.002247126,0.00019275756,0.76850253,0.17173539,0.0056045256,0.012437355,0.00015869118],"about_ca_topic_score_codex":0.0055968207,"about_ca_topic_score_gemma":0.010522377,"teacher_disagreement_score":0.0060351808,"about_ca_system_score_codex":0.0005748004,"about_ca_system_score_gemma":0.0018112081,"threshold_uncertainty_score":0.031917453},"labels":[],"label_agreement":null},{"id":"W3095560178","doi":"10.1016/j.media.2021.102306","title":"Surgical data science – from concepts toward clinical translation","year":2021,"lang":"en","type":"preprint","venue":"Medical Image Analysis","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":39,"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 Health Network; Queen's University","funders":"H2020 European Research Council; National Institute of Biomedical Imaging and Bioengineering; National Institute of Diabetes and Digestive and Kidney Diseases; NIHR Imperial Biomedical Research Centre; Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; Science of Learning Institute, Johns Hopkins University; Horizon 2020 Framework Programme; University of Toronto; Ministry of Education, Culture, Sports, Science and Technology; Royal Society; Bundesministerium für Bildung und Forschung; National Institute for Health and Care Research; National Institutes of Health; Agence Nationale de la Recherche; Bundesministerium für Wirtschaft und Energie; Royal Academy of Engineering; Wellcome Trust; Polytechnique Montréal; Deutsche Forschungsgemeinschaft; Deutsches Krebsforschungszentrum; Memorial Sloan-Kettering Cancer Center; Johns Hopkins University; National Cancer Institute; HORIZON EUROPE Framework Programme; U.S. Department of Defense; Wellcome / EPSRC Centre for Interventional and Surgical Sciences; Nationales Centrum für Tumorerkrankungen Heidelberg; Nvidia; Université de Strasbourg; National Institute of Dental and Craniofacial Research; Bundesministerium für Forschung und Technologie","keywords":"Data science; Translational research; Data sharing; Field (mathematics); Translational science; Computer science; Analytics; Medicine; Pathology","score_opus":0.5100521637426162,"score_gpt":0.6062329828184161,"score_spread":0.0961808190757999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3095560178","genre_codex":"commentary","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059124203,0.14366493,0.30493584,0.46657142,0.012436262,0.0006492161,0.0012563323,0.00061610533,0.063957505],"genre_scores_gemma":[0.16246751,0.29058754,0.3973612,0.10712779,0.020625828,0.0021367406,0.0026517936,0.001601308,0.015440371],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9267212,0.048077736,0.00587411,0.003179182,0.014975466,0.0011723194],"domain_scores_gemma":[0.85129905,0.1130764,0.003025732,0.012276335,0.017393937,0.0029285457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08857414,0.0011117895,0.0013594818,0.009939265,0.0022105838,0.018396776,0.0031289882,0.0056446726,0.0075356453],"category_scores_gemma":[0.117648564,0.0010069021,0.0012251326,0.0076569603,0.029398495,0.030194055,0.014694007,0.012711716,0.00293729],"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":[0.00005841633,0.000048990827,0.00048798323,0.0021534055,0.000038745697,0.00018880938,0.005222423,0.0008987843,0.00059977814,0.8062417,0.03183311,0.1522278],"study_design_scores_gemma":[0.000018155632,0.00006098901,0.00040189995,0.0040596183,0.000013362672,0.00033777716,0.0046017077,0.00085287256,0.0007404517,0.4971251,0.49174425,0.000043824166],"about_ca_topic_score_codex":0.0015595997,"about_ca_topic_score_gemma":0.00080312905,"teacher_disagreement_score":0.08857414,"about_ca_system_score_codex":0.007995767,"about_ca_system_score_gemma":0.017541949,"threshold_uncertainty_score":0.46843046},"labels":[],"label_agreement":null},{"id":"W3097943992","doi":"10.1016/j.media.2021.102146","title":"Self-paced and self-consistent co-training for semi-supervised image segmentation","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":65,"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 Natural Science Foundation of China-Zhejiang Joint Fund for the Integration of Industrialization and Informatization; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Segmentation; Robustness (evolution); Cross entropy; Image segmentation; Machine learning; Pattern recognition (psychology); Entropy (arrow of time); Training set; Deep neural networks; Artificial neural network; Deep learning","score_opus":0.02099580457286689,"score_gpt":0.2998354642452361,"score_spread":0.27883965967236923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097943992","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01843966,0.0004795835,0.97865045,0.00008893715,0.000046595713,0.000050250306,0.00006483329,0.0016970604,0.00048266086],"genre_scores_gemma":[0.5046578,0.00038470677,0.48824826,0.0003684708,0.00012441678,0.0002278156,0.00081327703,0.00072058954,0.004454725],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902594,0.000264759,0.000055529545,0.00037868292,0.0001659375,0.000109272274],"domain_scores_gemma":[0.9970849,0.0016067328,0.00022591179,0.0004621604,0.00048096987,0.00013929361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020825595,0.000881591,0.001797628,0.00091042684,0.00053677656,0.0008724765,0.0024791474,0.0021424224,0.0014556798],"category_scores_gemma":[0.0050129257,0.0007501263,0.0009404339,0.0010599232,0.0009225488,0.0014283764,0.0019832756,0.0017168534,0.00088242133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078267447,0.00046337227,0.0014066312,0.0003571704,0.0002460135,0.0001916697,0.00029268005,0.31729004,0.05636334,0.0033495633,0.0065361112,0.6127208],"study_design_scores_gemma":[0.0000060297916,0.000029783423,0.00026681487,0.000005939474,0.000010735681,0.000048493886,0.00001015277,0.9931304,0.005061024,0.0010894138,0.00033448948,0.0000067617652],"about_ca_topic_score_codex":0.003413564,"about_ca_topic_score_gemma":0.005407036,"teacher_disagreement_score":0.003413564,"about_ca_system_score_codex":0.0004728328,"about_ca_system_score_gemma":0.0011647291,"threshold_uncertainty_score":0.011013746},"labels":[],"label_agreement":null},{"id":"W3098535516","doi":"10.1016/j.media.2021.102038","title":"SoftSeg: Advantages of soft versus binary training for image segmentation","year":2021,"lang":"en","type":"preprint","venue":"Medical Image Analysis","topic":"Domain Adaptation and Few-Shot Learning","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":"Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Fonds de recherche du Québec – Nature et technologies; Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Canada First Research Excellence Fund; Canada Research Chairs; Canada Foundation for Innovation; Nvidia","keywords":"Artificial intelligence; Segmentation; Computer science; Pattern recognition (psychology); Voxel; Preprocessor; Binary classification; Image segmentation; Binary number; Ground truth; Pixel; Mathematics; Support vector machine","score_opus":0.04295141484576424,"score_gpt":0.35170314741180464,"score_spread":0.3087517325660404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3098535516","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02617947,0.000744801,0.96797156,0.00032533883,0.000074622614,0.00005892594,0.0001435445,0.0029435912,0.0015582306],"genre_scores_gemma":[0.34064066,0.00078286295,0.64999783,0.00042009228,0.00019109232,0.00010267685,0.0009008525,0.0013036348,0.005660386],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931765,0.00020552667,0.000035395828,0.00020119039,0.00017579728,0.00006441129],"domain_scores_gemma":[0.99779785,0.0012615033,0.00008821941,0.0004175062,0.00031906785,0.00011595094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020283659,0.0010102586,0.0012966724,0.0011926617,0.0004989454,0.0015941301,0.001538525,0.0024316253,0.003863213],"category_scores_gemma":[0.0063373474,0.0005129796,0.00067515176,0.0008191484,0.0009492456,0.0021187982,0.0021531181,0.0018465135,0.0013760594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010504717,0.00034537233,0.0011352848,0.00030951475,0.00015439157,0.00010254248,0.00013208104,0.14307395,0.05165881,0.011075491,0.0048621744,0.7860999],"study_design_scores_gemma":[0.000020310143,0.00010639846,0.0006860216,0.000023365465,0.000029482399,0.00015577266,0.000025413961,0.96408534,0.023349516,0.010224174,0.001276199,0.000018015045],"about_ca_topic_score_codex":0.0022224095,"about_ca_topic_score_gemma":0.003095512,"teacher_disagreement_score":0.003863213,"about_ca_system_score_codex":0.0004361795,"about_ca_system_score_gemma":0.0007760197,"threshold_uncertainty_score":0.0129237175},"labels":[],"label_agreement":null},{"id":"W3107625569","doi":"10.1016/j.media.2020.101912","title":"Learning to segment images with classification labels","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","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":"Sunnybrook Health Science Centre; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Cancer Society","keywords":"Computer science; Segmentation; Artificial intelligence; Ground truth; Annotation; Class (philosophy); Task (project management); Pattern recognition (psychology); Image segmentation; Labeled data; Machine learning","score_opus":0.015341976670133681,"score_gpt":0.2692718852778553,"score_spread":0.2539299086077216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107625569","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08180677,0.0013190269,0.90021193,0.0023148176,0.00036906355,0.00045028105,0.0013711668,0.007020484,0.0051364163],"genre_scores_gemma":[0.5595965,0.0010469089,0.42200983,0.0013495503,0.0005919905,0.0005718737,0.0044632214,0.0003353335,0.010034877],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991486,0.00013520128,0.000056020424,0.00038572893,0.00016686469,0.00010764485],"domain_scores_gemma":[0.9976956,0.0011257789,0.00020684897,0.00036632753,0.00051441934,0.00009104154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001024126,0.0014943826,0.0011166453,0.0021334337,0.000687192,0.0017088613,0.0016210435,0.002340658,0.0037273688],"category_scores_gemma":[0.0041823513,0.0004898468,0.0011576705,0.0016473792,0.0009711416,0.0017995106,0.001136104,0.0023269588,0.0025631618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004941062,0.00051656674,0.006659168,0.0002941186,0.00014967938,0.000108511646,0.00014031406,0.025948415,0.028492875,0.004867456,0.015963241,0.9163656],"study_design_scores_gemma":[0.00006284886,0.00031172924,0.0025123185,0.00008787832,0.00013079429,0.00018683939,0.00016180307,0.94508886,0.022307685,0.023305628,0.005820166,0.000023387409],"about_ca_topic_score_codex":0.0062505584,"about_ca_topic_score_gemma":0.008368008,"teacher_disagreement_score":0.0062505584,"about_ca_system_score_codex":0.0010373772,"about_ca_system_score_gemma":0.001581808,"threshold_uncertainty_score":0.012469292},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052631106,0.0007569265,0.99221426,0.00036708894,0.000031779127,0.000022439343,0.00006114894,0.00077749573,0.0005057722],"genre_scores_gemma":[0.31163245,0.0015501389,0.6771259,0.00028326522,0.00023872463,0.00014249768,0.00041029294,0.0007591918,0.00785752],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992581,0.00026519306,0.000045640547,0.00016605524,0.0002153861,0.000049662263],"domain_scores_gemma":[0.99826306,0.0009113086,0.0002622481,0.00025017417,0.00023404621,0.00007914883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002462351,0.0008422743,0.0014924003,0.0013727817,0.00040127538,0.0015008529,0.0015208978,0.002038751,0.0021039085],"category_scores_gemma":[0.0065430733,0.00088940945,0.0009587301,0.0015291615,0.00116166,0.0024019834,0.0019953658,0.0022961623,0.0008629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036915785,0.00015192984,0.001344081,0.00027326893,0.00016589282,0.000083520266,0.00009333937,0.3732265,0.010695359,0.04509308,0.004843588,0.5636603],"study_design_scores_gemma":[0.000008019934,0.00002921548,0.00029524183,0.000014372514,0.000015566708,0.000052483952,0.000005546681,0.9772575,0.0029254863,0.018682579,0.0007000628,0.000014033337],"about_ca_topic_score_codex":0.0041103764,"about_ca_topic_score_gemma":0.005606946,"teacher_disagreement_score":0.0041103764,"about_ca_system_score_codex":0.0010839909,"about_ca_system_score_gemma":0.0012945117,"threshold_uncertainty_score":0.013022363},"labels":[],"label_agreement":null},{"id":"W3125868735","doi":"10.1016/j.media.2021.101976","title":"Synthesis of gadolinium-enhanced liver tumors on nonenhanced liver MR images using pixel-level graph reinforcement learning","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","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":"Western University","funders":"","keywords":"Pixel; Reinforcement learning; Computer science; Artificial intelligence; Graph; Computer vision; Pattern recognition (psychology); Theoretical computer science","score_opus":0.01843634630885849,"score_gpt":0.30063667401865585,"score_spread":0.28220032770979736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125868735","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05447519,0.00019008186,0.93844646,0.0002305397,0.00009901169,0.000090119276,0.0001649656,0.0014509928,0.0048524872],"genre_scores_gemma":[0.6689776,0.00021380022,0.3252448,0.00009251569,0.000028815859,0.00008684492,0.00034108077,0.00027841545,0.0047360854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999405,0.000009697713,0.0000021586764,0.00001836104,0.000020884645,0.000008371311],"domain_scores_gemma":[0.999788,0.00008928547,0.000026716352,0.000019900863,0.000060029986,0.00001606773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015836704,0.0004259065,0.00032099616,0.0002621101,0.00014894595,0.00032080803,0.00041625657,0.0004578559,0.002539044],"category_scores_gemma":[0.00057746645,0.0002627932,0.00044211562,0.00015069294,0.00024831318,0.00022053884,0.00028964208,0.00037984576,0.00037955635],"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":[0.00012752097,0.000041068106,0.00025385956,0.00018121793,0.000030690702,0.00023844397,0.000052357365,0.8557796,0.06933672,0.006432032,0.0013412231,0.06618524],"study_design_scores_gemma":[0.000006445462,0.000027108163,0.000094360235,0.000003942539,0.0000062700938,0.000019536601,0.0000035188175,0.99053407,0.0074803517,0.0013307708,0.0004899705,0.0000036864794],"about_ca_topic_score_codex":0.00300598,"about_ca_topic_score_gemma":0.0036969394,"teacher_disagreement_score":0.00300598,"about_ca_system_score_codex":0.0003620079,"about_ca_system_score_gemma":0.00045237664,"threshold_uncertainty_score":0.00849396},"labels":[],"label_agreement":null},{"id":"W3126367900","doi":"10.1016/j.media.2021.101982","title":"OF-UMRN: Uncertainty-guided multitask regression network aided by optical flow for fully automated comprehensive analysis of carotid artery","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cardiovascular Health and Disease Prevention","field":"Medicine","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":"Primary Research and Development Plan of Zhejiang Province; Taishan Scholar Project of Shandong Province; National Natural Science Foundation of China","keywords":"Optical flow; Artificial intelligence; Computer science; Regression; Regression analysis; Carotid arteries; Machine learning; Pattern recognition (psychology); Mathematics; Medicine; Statistics; Cardiology; Image (mathematics)","score_opus":0.016314441597565253,"score_gpt":0.3334473112606048,"score_spread":0.31713286966303955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126367900","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020499943,0.0008948466,0.9701468,0.00022912672,0.00014558098,0.000082487895,0.000665682,0.0064045647,0.0009309531],"genre_scores_gemma":[0.34022176,0.00064404774,0.6481892,0.0005949419,0.00027564773,0.0003174838,0.003459792,0.0008570019,0.0054401834],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999371,0.0001690276,0.000025272653,0.00020495278,0.00013136481,0.00009844129],"domain_scores_gemma":[0.9993024,0.00034266504,0.000049549973,0.000076511744,0.0001886249,0.000040274237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017574565,0.0018157985,0.0016034546,0.001361378,0.00068021164,0.0007911914,0.0019489643,0.0018256805,0.0026226507],"category_scores_gemma":[0.002938026,0.0007016647,0.0015448661,0.0009302207,0.00036826072,0.0010936632,0.001732981,0.0017001162,0.0012898018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005977357,0.00043973603,0.002869183,0.00023781316,0.00042124596,0.00022141862,0.00013082537,0.23024909,0.025480427,0.0026222118,0.02013957,0.71659064],"study_design_scores_gemma":[0.00000789302,0.000022936889,0.00026718617,0.0000059626877,0.000018987406,0.000025845831,0.0000050819253,0.9964463,0.0017065939,0.00089549436,0.0005878287,0.000009996303],"about_ca_topic_score_codex":0.013140464,"about_ca_topic_score_gemma":0.018479083,"teacher_disagreement_score":0.013140464,"about_ca_system_score_codex":0.0005987938,"about_ca_system_score_gemma":0.0013474674,"threshold_uncertainty_score":0.026127994},"labels":[],"label_agreement":null},{"id":"W3128158496","doi":"10.1016/j.media.2021.102256","title":"Self-supervised driven consistency training for annotation efficient histopathology image analysis","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":107,"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","funders":"Canadian Institutes of Health Research; Canadian Cancer Society; Compute Canada","keywords":"Consistency (knowledge bases); Benchmark (surveying); Bootstrapping (finance); Annotation; Feature (linguistics); Pattern recognition (psychology); Feature learning; Representation (politics); Transfer of learning","score_opus":0.017349676241491793,"score_gpt":0.28616298130608026,"score_spread":0.26881330506458845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128158496","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026542412,0.00038956423,0.96532226,0.00014483187,0.00009185763,0.000120519544,0.00027728843,0.0059444667,0.001166828],"genre_scores_gemma":[0.40589964,0.00023543266,0.58243906,0.00043124784,0.00015629342,0.00030999206,0.0025625005,0.0015035853,0.0064622196],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974147,0.00044808528,0.00017073298,0.0009319824,0.0007681438,0.00026632444],"domain_scores_gemma":[0.99621034,0.0010914862,0.00031948605,0.00089397054,0.0013458601,0.00013884612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003078579,0.000898812,0.0015241005,0.001561979,0.00078131654,0.0015260493,0.003523608,0.0017534982,0.003091261],"category_scores_gemma":[0.0056413277,0.0007159717,0.001293357,0.0015146572,0.0007905648,0.0013369101,0.0022130199,0.0019451819,0.0020483355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070918404,0.00038983292,0.005215713,0.00030252125,0.0002856958,0.0001648689,0.0001932512,0.05718125,0.09177743,0.0032543584,0.011270781,0.8292551],"study_design_scores_gemma":[0.000025450528,0.00013196266,0.0025154504,0.00002588541,0.00007067387,0.00025879574,0.00004820762,0.94747967,0.042857975,0.002665034,0.003899098,0.000021733145],"about_ca_topic_score_codex":0.0034292063,"about_ca_topic_score_gemma":0.0060165315,"teacher_disagreement_score":0.003523608,"about_ca_system_score_codex":0.00076061155,"about_ca_system_score_gemma":0.0018833946,"threshold_uncertainty_score":0.016281307},"labels":[],"label_agreement":null},{"id":"W3129517280","doi":"10.1016/j.media.2021.102005","title":"Weakly-Supervised teacher-Student network for liver tumor segmentation from non-enhanced images","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","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":"Western University","funders":"","keywords":"Segmentation; Artificial intelligence; Computer science; Contrast (vision); Liver tumor; Minimum bounding box; Pattern recognition (psychology); Image (mathematics); Computer vision; Medicine","score_opus":0.007663096682908042,"score_gpt":0.30743983234332845,"score_spread":0.2997767356604204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129517280","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16036032,0.0013183862,0.830276,0.00080917636,0.00011999356,0.0001550732,0.0006454073,0.003887122,0.0024284679],"genre_scores_gemma":[0.8511929,0.00035030144,0.13119857,0.00039237586,0.00018359254,0.00021102934,0.002564617,0.00029530708,0.013611423],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944514,0.00016901194,0.000024257668,0.00018727373,0.000077026045,0.00009725404],"domain_scores_gemma":[0.99858475,0.0006857786,0.00009905829,0.00017177124,0.00035318843,0.00010554721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012569729,0.001097586,0.0014438803,0.0007262678,0.0006385293,0.0007410227,0.0022163792,0.0025322444,0.002229066],"category_scores_gemma":[0.0033130648,0.00055007444,0.000918748,0.0005870098,0.0005700103,0.0010355874,0.0014577432,0.0015817643,0.0012632723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002018362,0.00051663147,0.0055775605,0.00030755586,0.00023426888,0.00037511718,0.0002481306,0.3982166,0.018810073,0.0029871818,0.010794986,0.5599135],"study_design_scores_gemma":[0.0000107168735,0.00004357413,0.00026985354,0.0000050166122,0.000013707613,0.00003224844,0.000012549229,0.99639904,0.0020163055,0.00093538716,0.00025711153,0.0000044633784],"about_ca_topic_score_codex":0.0068323496,"about_ca_topic_score_gemma":0.0129978005,"teacher_disagreement_score":0.0068323496,"about_ca_system_score_codex":0.00078141113,"about_ca_system_score_gemma":0.0013900866,"threshold_uncertainty_score":0.01358521},"labels":[],"label_agreement":null},{"id":"W3130628855","doi":"10.1016/j.media.2021.101984","title":"EIS-Net: Segmenting early infarct and scoring ASPECTS simultaneously on non-contrast CT of patients with acute ischemic stroke","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":69,"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":"Canadian Institutes of Health Research","keywords":"Medicine; Segmentation; Artificial intelligence; Convolutional neural network; Intraclass correlation; Radiology; Computer science; Pattern recognition (psychology)","score_opus":0.003524322564388348,"score_gpt":0.22794550264631425,"score_spread":0.2244211800819259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3130628855","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9847904,0.0007443664,0.004593956,0.00021471019,0.000057979687,0.00033544132,0.0050969743,0.00052698544,0.0036391902],"genre_scores_gemma":[0.9771359,0.00051916845,0.013570573,0.000086428525,0.00017423645,0.00029683489,0.0063088867,0.00007239191,0.0018356021],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998259,0.000045962795,0.000035954716,0.00003448093,0.000033629633,0.000024060115],"domain_scores_gemma":[0.99957865,0.00014779773,0.00007263133,0.000025508107,0.000082048165,0.00009323681],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068590464,0.00077729044,0.00051159697,0.0020643019,0.00015446969,0.00077543757,0.00029104884,0.00034464023,0.0027132197],"category_scores_gemma":[0.0016048248,0.00020065393,0.00028246624,0.0007005889,0.000105928695,0.00067453727,0.00061725156,0.00017652349,0.00066472316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.012101341,0.0005868867,0.7951528,0.00024458976,0.00054541713,0.00072871096,0.000097792006,0.0016946788,0.00907027,0.00020587647,0.0059672664,0.17360432],"study_design_scores_gemma":[0.00044235308,0.0010929555,0.96873784,0.000045514367,0.0005223897,0.0019631395,0.0001492558,0.018890949,0.004630468,0.00042249658,0.0030537792,0.0000488692],"about_ca_topic_score_codex":0.00078087294,"about_ca_topic_score_gemma":0.0021113276,"teacher_disagreement_score":0.0027132197,"about_ca_system_score_codex":0.00015136114,"about_ca_system_score_gemma":0.00035620152,"threshold_uncertainty_score":0.009076655},"labels":[],"label_agreement":null},{"id":"W3131127151","doi":"10.1016/j.media.2021.102009","title":"Predicting future cognitive decline with hyperbolic stochastic coding","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Computability, Logic, AI Algorithms","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":"Division of Information and Intelligent Systems; National Institute of Biomedical Imaging and Bioengineering; National Heart, Lung, and Blood Institute; National Institute on Aging; Canadian Institutes of Health Research","keywords":"Cognition; Coding (social sciences); Computer science; Cognitive decline; Artificial intelligence; Mathematics; Psychology; Statistics; Medicine; Dementia","score_opus":0.008760066034389334,"score_gpt":0.26211837901828505,"score_spread":0.2533583129838957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131127151","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82078356,0.0004736483,0.1747896,0.0010317797,0.000044241475,0.00006424904,0.00070565485,0.0002020087,0.0019053153],"genre_scores_gemma":[0.9902518,0.000091110705,0.0090288855,0.000044261164,0.000018009328,0.000014143628,0.00014963694,0.000006981121,0.0003951404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997602,0.0000803041,0.000016155032,0.00005460842,0.00005484872,0.00003387973],"domain_scores_gemma":[0.99564654,0.003026766,0.00042195665,0.0002409948,0.00046871867,0.00019501432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014765391,0.00036002207,0.0003892478,0.0010764818,0.00016264767,0.001030034,0.0006043202,0.0007271113,0.0009841238],"category_scores_gemma":[0.01064831,0.00016610815,0.00036963457,0.0005202683,0.00058029353,0.0010237235,0.0006480038,0.00090516487,0.00014557564],"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":[0.0015291977,0.0004841055,0.30287856,0.0001428127,0.00028655672,0.00043174342,0.00036750073,0.44125906,0.005940808,0.025767045,0.003322603,0.21759003],"study_design_scores_gemma":[0.000013963141,0.00006250909,0.015986737,0.000012117095,0.00001551122,0.000076068216,0.000038840615,0.9636324,0.00062654086,0.01940992,0.00011163946,0.000013897751],"about_ca_topic_score_codex":0.010977361,"about_ca_topic_score_gemma":0.008861478,"teacher_disagreement_score":0.010977361,"about_ca_system_score_codex":0.00079846015,"about_ca_system_score_gemma":0.00058865285,"threshold_uncertainty_score":0.021826923},"labels":[],"label_agreement":null},{"id":"W3131131418","doi":"10.1016/j.media.2021.102001","title":"Estimating dual-energy CT imaging from single-energy CT data with material decomposition convolutional neural network","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced X-ray and CT Imaging","field":"Engineering","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":"Western University","funders":"National Cancer Institute; National Institutes of Health; National Natural Science Foundation of China; Clinical Special Fund of Jiangsu Province","keywords":"Digital Enhanced Cordless Telecommunications; Artificial intelligence; Convolutional neural network; Energy (signal processing); Computer science; Deep learning; Contrast (vision); Medicine; Pattern recognition (psychology); Computer vision; Mathematics; Statistics; Wireless","score_opus":0.0077171823628405965,"score_gpt":0.24115619580662262,"score_spread":0.233439013443782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131131418","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056914385,0.0007199719,0.9401747,0.00022758245,0.0000485534,0.00003928552,0.0002602531,0.00094597595,0.000669343],"genre_scores_gemma":[0.6154531,0.0012434545,0.37695128,0.00023752698,0.00009492877,0.000108969325,0.0014553079,0.00023150328,0.0042238883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986494,0.000019426268,0.0000081046555,0.000041988562,0.000041246032,0.000024313145],"domain_scores_gemma":[0.99956995,0.00021938178,0.00005426301,0.0000452366,0.00008592236,0.000025248073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059244677,0.00088614173,0.0007479891,0.0009158616,0.00015953103,0.00072008313,0.0008608359,0.0012045681,0.0008653227],"category_scores_gemma":[0.001851804,0.00080063427,0.00082925137,0.0007339218,0.00038298717,0.00083672395,0.0008870642,0.0010948704,0.00063662365],"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":[0.00047743885,0.00018928507,0.005215034,0.00024349925,0.00021923441,0.00045463175,0.000062976746,0.5791729,0.075437896,0.0039475933,0.0030649607,0.33151457],"study_design_scores_gemma":[0.0000032678327,0.000007824573,0.0004109356,0.000004709207,0.000012719263,0.000037114405,0.0000032881587,0.99604094,0.00253016,0.00073668547,0.00020705037,0.000005276628],"about_ca_topic_score_codex":0.0076311263,"about_ca_topic_score_gemma":0.010598778,"teacher_disagreement_score":0.0076311263,"about_ca_system_score_codex":0.0005184161,"about_ca_system_score_gemma":0.00092765933,"threshold_uncertainty_score":0.015173435},"labels":[],"label_agreement":null},{"id":"W3135100175","doi":"10.1016/j.media.2021.102026","title":"A structural enriched functional network: An application to predict brain cognitive performance","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","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":"McGill University; Montreal Neurological Institute and Hospital","funders":"NIH Blueprint for Neuroscience Research; National Institute of Biomedical Imaging and Bioengineering; National Institute of Mental Health; U.S. National Library of Medicine; National Institute on Aging; National Research Foundation of Korea; McDonnell Center for Systems Neuroscience; Directorate for Computer and Information Science and Engineering; National Institutes of Health; National Science Foundation","keywords":"Connectome; Computer science; Artificial intelligence; Functional magnetic resonance imaging; Cognition; Diffusion MRI; Network analysis; Default mode network; Cognitive network; Network model; Consistency (knowledge bases); Resting state fMRI; Machine learning; Network architecture; Functional connectivity; Neuroscience; Cognitive radio; Magnetic resonance imaging; Psychology; Computer network","score_opus":0.022270609101040756,"score_gpt":0.2857288497640012,"score_spread":0.2634582406629604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135100175","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7264665,0.0012230172,0.2542445,0.0008014038,0.00007990893,0.00029645296,0.011098613,0.0023079347,0.0034817713],"genre_scores_gemma":[0.9061555,0.00064739166,0.08686052,0.000059388476,0.00009909419,0.0002543291,0.0034199755,0.00016427941,0.002339532],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989676,0.000023370985,0.0000049105047,0.000042837433,0.000019727924,0.000012317173],"domain_scores_gemma":[0.9996847,0.00015023984,0.000056035842,0.00002294256,0.0000536354,0.000032558295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052812765,0.0006435589,0.00039606367,0.002309917,0.00040597143,0.00061048236,0.0003739077,0.00051487365,0.0022948496],"category_scores_gemma":[0.0017383768,0.00018901,0.0005885333,0.0016434721,0.00022547373,0.000423828,0.00043953865,0.0003497224,0.00041039934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024143907,0.00088108936,0.19193216,0.00075222895,0.0020735874,0.0014353152,0.0006453279,0.11805758,0.11452047,0.008711179,0.01646949,0.5421072],"study_design_scores_gemma":[0.00010150834,0.00047479934,0.31871915,0.00006817899,0.0007461162,0.0014959406,0.00020188942,0.64226544,0.0144015625,0.016345505,0.0050847842,0.00009508165],"about_ca_topic_score_codex":0.0074379104,"about_ca_topic_score_gemma":0.016422339,"teacher_disagreement_score":0.0074379104,"about_ca_system_score_codex":0.00039076977,"about_ca_system_score_gemma":0.00046027784,"threshold_uncertainty_score":0.014789224},"labels":[],"label_agreement":null},{"id":"W3135491122","doi":"10.1016/j.media.2021.102003","title":"Identifying associations among genomic, proteomic and imaging biomarkers via adaptive sparse multi-view canonical correlation analysis","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":54,"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; China Postdoctoral Science Foundation; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China; U.S. Department of Defense","keywords":"Robustness (evolution); Neuroimaging; Imaging genetics; Computer science; Canonical correlation; Artificial intelligence; Feature selection; Genomics; Machine learning; Computational biology; Correlation; Genome-wide association study; Pattern recognition (psychology); Data mining; Biology; Mathematics; Genome; Genetics; Gene; Neuroscience; Single-nucleotide polymorphism","score_opus":0.010449435964477269,"score_gpt":0.2623913397837242,"score_spread":0.2519419038192469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135491122","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.114223644,0.0008676847,0.8820262,0.00043763526,0.00005330294,0.00007493879,0.000552599,0.00072978076,0.0010341784],"genre_scores_gemma":[0.84803903,0.00075598725,0.14769116,0.000217153,0.0001016095,0.00016638904,0.0014275516,0.000110069836,0.001491019],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991449,0.00025870136,0.000037202666,0.00025155462,0.00020741593,0.00010033001],"domain_scores_gemma":[0.9984395,0.000638536,0.00024036986,0.00019678923,0.0004041532,0.00008052748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011109277,0.00074721116,0.0010610162,0.0019339314,0.00038655856,0.0011698356,0.00082308805,0.00060587993,0.0008339515],"category_scores_gemma":[0.004072477,0.00034922012,0.0012456243,0.0020322565,0.00062998314,0.0008396368,0.000996639,0.00086893886,0.00041250486],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010824797,0.00095538754,0.062106866,0.0005257155,0.0014920025,0.0010101697,0.00041685643,0.2962667,0.08354408,0.02994039,0.011981128,0.51067823],"study_design_scores_gemma":[0.000017741366,0.00007968443,0.0070086075,0.000013792344,0.00009589483,0.00022681331,0.00005980121,0.9782809,0.0038745499,0.009233213,0.0010816025,0.00002740932],"about_ca_topic_score_codex":0.0054411096,"about_ca_topic_score_gemma":0.009326303,"teacher_disagreement_score":0.0054411096,"about_ca_system_score_codex":0.00040258095,"about_ca_system_score_gemma":0.001414729,"threshold_uncertainty_score":0.010818839},"labels":[],"label_agreement":null},{"id":"W3136424010","doi":"10.1016/j.media.2021.102035","title":"Loss odyssey in medical image segmentation","year":2021,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":618,"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; University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Segmentation; Computer science; Dice; Artificial intelligence; Function (biology); Hausdorff distance; Loss function; Boundary (topology); Benchmark (surveying); Image segmentation; Mathematics; Geography; Statistics; Cartography","score_opus":0.02602045313479882,"score_gpt":0.3855040646034736,"score_spread":0.35948361146867475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136424010","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00037271244,0.98915124,0.007856286,0.000527213,0.0004061311,0.000008348506,0.000034911598,0.000045971377,0.0015970933],"genre_scores_gemma":[0.0054700677,0.98430115,0.0054437923,0.0005508203,0.0011493977,0.00001620984,0.000099108285,0.000029256373,0.0029402003],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996811,0.000051138915,0.000033417866,0.000061441184,0.00015080904,0.000022183363],"domain_scores_gemma":[0.9993032,0.00037272647,0.00006860901,0.000029953855,0.00019805551,0.000027334852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011180217,0.00090258423,0.0014057223,0.0021993816,0.00026989542,0.0012157063,0.0012003582,0.0012593006,0.00418424],"category_scores_gemma":[0.0022114,0.0004781133,0.00060659985,0.0027067338,0.0010530816,0.002058569,0.0010357097,0.0018247701,0.0016075392],"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":[0.00007522477,0.00006054797,0.00018280066,0.0060266666,0.00011695594,0.00007442783,0.000026709396,0.0021113674,0.0010492821,0.008187703,0.024317363,0.95777106],"study_design_scores_gemma":[0.00007684224,0.00034438205,0.0028415788,0.007170967,0.00065939425,0.0025757418,0.000108549444,0.018692488,0.007419587,0.03680409,0.9231823,0.00012416199],"about_ca_topic_score_codex":0.0025470427,"about_ca_topic_score_gemma":0.0025494508,"teacher_disagreement_score":0.00418424,"about_ca_system_score_codex":0.00068016595,"about_ca_system_score_gemma":0.0010655471,"threshold_uncertainty_score":0.013997674},"labels":[],"label_agreement":null},{"id":"W3138739590","doi":"10.1016/j.media.2021.102040","title":"APRIL: Anatomical prior-guided reinforcement learning for accurate carotid lumen diameter and intima-media thickness measurement","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cardiovascular Health and Disease Prevention","field":"Medicine","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":"Western University","funders":"Fundamental Research Funds for the Central Universities; Xiamen University; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Artificial intelligence; Reinforcement learning; Variance (accounting); Computer science; Carotid arteries; Lumen (anatomy); Pixel; Computer vision; Stability (learning theory); Machine learning; Pattern recognition (psychology); Medicine; Surgery","score_opus":0.02889763293586841,"score_gpt":0.316519889316278,"score_spread":0.2876222563804096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138739590","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062418666,0.00036218012,0.9302417,0.00031994577,0.0001642952,0.00011206214,0.000098810124,0.0045141475,0.0017681912],"genre_scores_gemma":[0.7728986,0.00012909244,0.22211817,0.00034303375,0.0001026467,0.00014944511,0.00026537557,0.0002380084,0.0037556419],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950755,0.00014795503,0.000021588543,0.00014384234,0.00010420694,0.0000748463],"domain_scores_gemma":[0.99800664,0.0010931399,0.00012746328,0.00016760055,0.00046982866,0.00013540577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019451216,0.00094662554,0.00096381083,0.000565509,0.00035337423,0.0006493837,0.0014459533,0.0014307697,0.0028522073],"category_scores_gemma":[0.0059253927,0.00056002045,0.0005361455,0.00025364803,0.00062836614,0.0007612144,0.0013099631,0.0015827442,0.0007306262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001754474,0.00058556435,0.0049266475,0.00015998457,0.00013546816,0.00021121511,0.00011632757,0.39508012,0.015265272,0.002689515,0.0067707244,0.57230467],"study_design_scores_gemma":[0.000027199969,0.00007320831,0.00040635327,0.000006087985,0.000008967895,0.0000306962,0.000004032287,0.9967781,0.0017253798,0.0006726923,0.00026111034,0.0000061383503],"about_ca_topic_score_codex":0.0062377816,"about_ca_topic_score_gemma":0.0057298527,"teacher_disagreement_score":0.0062377816,"about_ca_system_score_codex":0.000572228,"about_ca_system_score_gemma":0.0011954756,"threshold_uncertainty_score":0.012402952},"labels":[],"label_agreement":null},{"id":"W3138973186","doi":"10.1016/j.media.2021.102032","title":"Fine-Tuning and training of densenet for histopathology image representation using TCGA diagnostic slides","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":211,"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; McMaster University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Ontario; Ontario Research Foundation","keywords":"Representation (politics); Artificial intelligence; Computer science; Training (meteorology); Image (mathematics); Computer vision; Pattern recognition (psychology); Geography","score_opus":0.038616818534811165,"score_gpt":0.3334691023019692,"score_spread":0.294852283767158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138973186","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6401109,0.0011837156,0.33526546,0.00059693825,0.00032046405,0.00043619264,0.0015243135,0.014035144,0.0065268483],"genre_scores_gemma":[0.8493689,0.00028004893,0.14210057,0.0003223745,0.000034011362,0.00031363126,0.0035773707,0.00029026583,0.003712865],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975187,0.00003766252,0.000013768372,0.000097666685,0.00004496009,0.000054067223],"domain_scores_gemma":[0.9994748,0.00017034923,0.000050495462,0.00009659914,0.00016838902,0.000039337046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009112873,0.001158925,0.00047812882,0.00080239127,0.00026717797,0.0005322828,0.0014133266,0.00084631605,0.0018908737],"category_scores_gemma":[0.0026350254,0.00045120696,0.00046902144,0.0005748237,0.00035696692,0.0010762304,0.0007888734,0.00085558434,0.000713809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068116345,0.0005910614,0.013146491,0.0004136859,0.00024170364,0.00034845844,0.00022575996,0.39626014,0.053479597,0.0022048869,0.012790069,0.519617],"study_design_scores_gemma":[0.000028611106,0.00015127614,0.0018800828,0.000018965422,0.000030420575,0.0000683276,0.0000449671,0.97659403,0.018755235,0.0008147127,0.0016007525,0.000012538128],"about_ca_topic_score_codex":0.010794014,"about_ca_topic_score_gemma":0.014876272,"teacher_disagreement_score":0.010794014,"about_ca_system_score_codex":0.0009452304,"about_ca_system_score_gemma":0.0009593844,"threshold_uncertainty_score":0.02146238},"labels":[],"label_agreement":null},{"id":"W3139837796","doi":"10.1016/j.media.2022.102374","title":"Weakly supervised segmentation with cross-modality equivariant constraints","year":2022,"lang":"en","type":"preprint","venue":"Medical Image Analysis","topic":"Multimodal Machine Learning Applications","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":"École de Technologie Supérieure","funders":"Compute Canada","keywords":"Computer science; Segmentation; Modality (human–computer interaction); Modalities; Artificial intelligence; Equivariant map; Machine learning; Constraint (computer-aided design); Exploit; Pattern recognition (psychology); Divergence (linguistics); Pixel; Mathematics","score_opus":0.016168797614745857,"score_gpt":0.34553033631595975,"score_spread":0.3293615387012139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3139837796","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011706343,0.00022212318,0.9844296,0.00023464262,0.000045639677,0.000045277724,0.00027325345,0.001148399,0.0018947445],"genre_scores_gemma":[0.43835688,0.00062555843,0.54395336,0.00055834465,0.0004260888,0.00030270623,0.0029023737,0.0020761604,0.010798502],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818414,0.00055219216,0.000106762884,0.00060828903,0.0003656404,0.00018295231],"domain_scores_gemma":[0.9973418,0.0010143752,0.0002933,0.00077848014,0.0003992019,0.00017281396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018320654,0.0015080774,0.0022686245,0.0016706763,0.00071597507,0.0029801643,0.0022127526,0.002611321,0.005224087],"category_scores_gemma":[0.006611521,0.0012370211,0.0018890891,0.0020332334,0.0013525332,0.0025469395,0.0040094955,0.0029346852,0.002415689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014088732,0.00034057623,0.0019914575,0.0006884213,0.0005054009,0.0007145763,0.0003021548,0.33573985,0.09138796,0.067342095,0.013322754,0.48625585],"study_design_scores_gemma":[0.000022822971,0.00006983536,0.00050597277,0.000027360858,0.000043466967,0.00019599452,0.000035968496,0.946338,0.013647418,0.03632512,0.0027615956,0.000026331583],"about_ca_topic_score_codex":0.0024347329,"about_ca_topic_score_gemma":0.004126684,"teacher_disagreement_score":0.005224087,"about_ca_system_score_codex":0.00075493497,"about_ca_system_score_gemma":0.0014148281,"threshold_uncertainty_score":0.01747626},"labels":[],"label_agreement":null},{"id":"W3142204669","doi":"10.1016/j.media.2021.102245","title":"A multiparametric volumetric quantitative ultrasound imaging technique for soft tissue characterization","year":2021,"lang":"en","type":"preprint","venue":"Medical Image Analysis","topic":"Ultrasound Imaging and Elastography","field":"Medicine","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":"Canadian Institutes of Health Research","keywords":"Regularization (linguistics); Imaging phantom; Computer science; Parametric statistics; Mathematics; Algorithm; Artificial intelligence; Pattern recognition (psychology); Physics; Statistics; Optics","score_opus":0.01379314014657327,"score_gpt":0.3223820486060981,"score_spread":0.3085889084595248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3142204669","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015560591,0.000994049,0.98204815,0.00011791545,0.000026852664,0.00003590581,0.0000837921,0.0005396311,0.0005930047],"genre_scores_gemma":[0.43780324,0.00095806445,0.5592279,0.00023297848,0.00008872583,0.00015778537,0.00026047512,0.00021442023,0.001056506],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989993,0.00037451735,0.00004804435,0.00016914467,0.00035538417,0.00005368523],"domain_scores_gemma":[0.99874014,0.00057524676,0.0002189857,0.00023031989,0.00019610365,0.00003926991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002169987,0.0006863088,0.0005568144,0.0015677424,0.0001944101,0.00077851576,0.0008025798,0.00078931707,0.0009088321],"category_scores_gemma":[0.0028697855,0.00044147085,0.00063783256,0.00090940035,0.00064438256,0.00079531944,0.00089542835,0.0007457354,0.00029995415],"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":[0.00016334686,0.0000495618,0.0018311532,0.00047093857,0.00009746002,0.00011483334,0.000120092074,0.015702711,0.81333274,0.005593163,0.00085344585,0.16167058],"study_design_scores_gemma":[0.000024985382,0.00038053768,0.006062454,0.00005863287,0.000133505,0.0018235863,0.000056862806,0.37457168,0.60158104,0.004505242,0.01065351,0.00014792674],"about_ca_topic_score_codex":0.00031947638,"about_ca_topic_score_gemma":0.00039205057,"teacher_disagreement_score":0.002169987,"about_ca_system_score_codex":0.00035454906,"about_ca_system_score_gemma":0.00035377097,"threshold_uncertainty_score":0.0114760995},"labels":[],"label_agreement":null},{"id":"W3149170955","doi":"10.1016/j.media.2021.102055","title":"Meta grayscale adaptive network for 3D integrated renal structures segmentation","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","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":"Western University","funders":"","keywords":"Grayscale; Artificial intelligence; Segmentation; Computer science; Computer vision; Pattern recognition (psychology); Deep learning; Image segmentation; Image (mathematics)","score_opus":0.029290088657429034,"score_gpt":0.303212484430983,"score_spread":0.27392239577355393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3149170955","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029605169,0.00044043618,0.9671665,0.00012298443,0.000042919626,0.000039519207,0.000120376004,0.0009124226,0.0015497936],"genre_scores_gemma":[0.4945441,0.00059025415,0.49656397,0.00022420887,0.00006301639,0.00012820745,0.0003860765,0.00023598438,0.007264094],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988115,0.000016468082,0.0000070575757,0.000039976036,0.000039258277,0.00001611241],"domain_scores_gemma":[0.9998952,0.000031787884,0.000013548016,0.000018179977,0.000033620872,0.000007620926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003220405,0.0004638123,0.00043004597,0.00069285,0.00022271473,0.0005668028,0.0007699178,0.0007930902,0.0023552116],"category_scores_gemma":[0.00048481004,0.0003581506,0.0006326266,0.0005831025,0.00018272325,0.00050456554,0.0005804512,0.0004793934,0.0004593403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028564877,0.000099518584,0.002169131,0.00010864663,0.00016931315,0.00018544833,0.000087039065,0.24604055,0.061548278,0.004523344,0.0031538226,0.68162936],"study_design_scores_gemma":[0.0000032893631,0.00002184579,0.0005781761,0.0000057314355,0.0000209828,0.000056817997,0.0000061246624,0.99184835,0.00605627,0.00079899706,0.00059801614,0.000005436048],"about_ca_topic_score_codex":0.0034852885,"about_ca_topic_score_gemma":0.005872714,"teacher_disagreement_score":0.0034852885,"about_ca_system_score_codex":0.00044053464,"about_ca_system_score_gemma":0.0005096909,"threshold_uncertainty_score":0.007878959},"labels":[],"label_agreement":null},{"id":"W3152831887","doi":"10.1016/j.media.2021.102059","title":"MGN-Net: A multi-view graph normalizer for integrating heterogeneous biological network populations","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"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":"H2020 Marie Skłodowska-Curie Actions; National Institute on Aging; Canadian Institutes of Health Research","keywords":"Biological network; Computer science; Population; Graph; Normalization (sociology); Artificial intelligence; Theoretical computer science; Machine learning; Computational biology; Biology","score_opus":0.024109946087972005,"score_gpt":0.30125367800228153,"score_spread":0.2771437319143095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3152831887","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011408498,0.00021857304,0.8323629,0.00015264285,0.00016144961,0.00018118059,0.009207856,0.14409888,0.0022080673],"genre_scores_gemma":[0.069694415,0.00032681052,0.8797129,0.00019842072,0.00006138966,0.00068079296,0.022687728,0.021857353,0.004780056],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994266,0.000100064426,0.000035183883,0.00019192416,0.00020144887,0.000044833516],"domain_scores_gemma":[0.99890006,0.0003520961,0.00012102745,0.0003152305,0.00022800526,0.00008354418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014703072,0.0018369429,0.0009957858,0.0037214365,0.0008192975,0.0018971105,0.0018879422,0.0008297086,0.01261859],"category_scores_gemma":[0.004804218,0.0010189784,0.001570944,0.0019444701,0.0004122014,0.0020281707,0.002108286,0.001932283,0.004221591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00079874793,0.00031397265,0.0081173545,0.0012136356,0.00096649065,0.00059662334,0.00065910816,0.05078512,0.094432645,0.023432348,0.14170142,0.67698264],"study_design_scores_gemma":[0.00016566136,0.00013105304,0.0071615004,0.00013390102,0.0003027059,0.0007807536,0.00032746486,0.73002493,0.10640976,0.046872858,0.107518904,0.00017038846],"about_ca_topic_score_codex":0.0057209954,"about_ca_topic_score_gemma":0.01572314,"teacher_disagreement_score":0.01261859,"about_ca_system_score_codex":0.0008545748,"about_ca_system_score_gemma":0.00110702,"threshold_uncertainty_score":0.04221344},"labels":[],"label_agreement":null},{"id":"W3157261413","doi":"10.1016/j.media.2021.102093","title":"Track-to-Learn: A general framework for tractography with deep reinforcement learning","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":36,"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 de Sherbrooke","funders":"","keywords":"Tractography; Reinforcement learning; Artificial intelligence; Computer science; Leverage (statistics); Deep learning; Machine learning; Artificial neural network; Prior probability; Diffusion MRI; Bayesian probability; Magnetic resonance imaging","score_opus":0.02872814233882151,"score_gpt":0.36618501131255343,"score_spread":0.33745686897373195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157261413","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001051401,0.00009472924,0.9973699,0.000075550095,0.000026329906,0.000024061103,0.000069816466,0.0010189415,0.0002692428],"genre_scores_gemma":[0.16867432,0.0004169835,0.82168823,0.0002358453,0.00013675075,0.0004316814,0.00067147875,0.0009811494,0.006763577],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996364,0.00010203531,0.000021358432,0.00010232433,0.000085448715,0.00005249298],"domain_scores_gemma":[0.9990376,0.00045279888,0.00007476346,0.0001506999,0.00018368714,0.000100477795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016606341,0.0013741835,0.0014636096,0.0006660152,0.00051251776,0.0013166198,0.0035409064,0.0027597672,0.00506652],"category_scores_gemma":[0.004783224,0.00095208065,0.0010175442,0.0008953486,0.00091929524,0.0017241053,0.00263017,0.0034161117,0.0017042138],"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":[0.00014822255,0.00007655396,0.00055673753,0.00009571309,0.000099256955,0.00008707806,0.00004527293,0.8090463,0.00167176,0.024349239,0.00566022,0.15816359],"study_design_scores_gemma":[0.0000059131985,0.000008901915,0.000019414756,0.0000030860049,0.000003391114,0.000006850056,0.0000010929589,0.9930375,0.0002803346,0.0062236665,0.00040673505,0.0000030490598],"about_ca_topic_score_codex":0.012970424,"about_ca_topic_score_gemma":0.01586726,"teacher_disagreement_score":0.012970424,"about_ca_system_score_codex":0.0011179487,"about_ca_system_score_gemma":0.002037788,"threshold_uncertainty_score":0.025789857},"labels":[],"label_agreement":null},{"id":"W3164110046","doi":"10.1016/j.media.2021.102106","title":"Conditional generation of medical images via disentangled adversarial inference","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Generative Adversarial Networks and Image Synthesis","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":"Montreal Heart Institute; Université de Montréal","funders":"","keywords":"Inference; Computer science; Latent variable; Artificial intelligence; Regularization (linguistics); Machine learning; Adversarial system; Image (mathematics); Unsupervised learning; Variable (mathematics); Pattern recognition (psychology); Mathematics","score_opus":0.018622804010336474,"score_gpt":0.29449437174993365,"score_spread":0.2758715677395972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164110046","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044439873,0.00012842179,0.9939296,0.00022728715,0.000031056148,0.00002974679,0.00009603206,0.0003771937,0.0007366443],"genre_scores_gemma":[0.5930852,0.00062721927,0.3939231,0.00063954986,0.00018072082,0.00028009145,0.0009954886,0.00041183227,0.0098567465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956185,0.0001501088,0.000016657836,0.00010605591,0.0001256471,0.000039748367],"domain_scores_gemma":[0.9985359,0.0010041647,0.00012235224,0.0001679562,0.00011798502,0.00005166471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012140624,0.0008153174,0.0006701394,0.0006981279,0.00022755766,0.0007954365,0.0013399312,0.001311034,0.002739424],"category_scores_gemma":[0.004106127,0.0008713523,0.0011245544,0.0005132459,0.00095020205,0.00089278864,0.0019410767,0.0020217183,0.00081519195],"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":[0.00015151707,0.000052129617,0.0004674812,0.00010354613,0.000075799835,0.00016237405,0.00006172929,0.89014226,0.008873404,0.029039208,0.002709447,0.068161085],"study_design_scores_gemma":[0.0000056834187,0.0000096710955,0.00006296857,0.0000051239167,0.000005573359,0.00003106866,0.0000018753619,0.9918411,0.001141517,0.0065691113,0.00032220187,0.00000407372],"about_ca_topic_score_codex":0.0019283602,"about_ca_topic_score_gemma":0.0027202931,"teacher_disagreement_score":0.002739424,"about_ca_system_score_codex":0.0006959046,"about_ca_system_score_gemma":0.00071307464,"threshold_uncertainty_score":0.009164333},"labels":[],"label_agreement":null},{"id":"W3168283161","doi":"10.1016/j.media.2022.102624","title":"Towards annotation-efficient segmentation via image-to-image translation","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Generative Adversarial Networks and Image Synthesis","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":"Centre Hospitalier de l’Université de Montréal; Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Artificial intelligence; Translation (biology); Automatic image annotation; Image (mathematics); Computer vision; Computer science; Annotation; Image translation; Image segmentation; Segmentation; Pattern recognition (psychology); Image processing; Biology","score_opus":0.01095319566051169,"score_gpt":0.26759391648783776,"score_spread":0.2566407208273261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168283161","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002762817,0.00017845794,0.9946418,0.00015208374,0.00004302375,0.000031598927,0.000065982356,0.0014343233,0.00068996125],"genre_scores_gemma":[0.18965285,0.0006264534,0.79824924,0.0006398797,0.00020521476,0.00021618599,0.0010301861,0.0017940516,0.007585934],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984366,0.00049021514,0.00007322146,0.0004995116,0.00034729185,0.0001531579],"domain_scores_gemma":[0.99747556,0.0013035365,0.00023220811,0.00060948665,0.0002817919,0.00009731885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018418403,0.0017930842,0.001993017,0.0014486988,0.0006194846,0.0021708584,0.002574769,0.0033024256,0.0036763288],"category_scores_gemma":[0.0055251475,0.001332098,0.0018439412,0.0015923172,0.001811002,0.0016327951,0.003947643,0.00312071,0.003927434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006072649,0.00015403114,0.0006418992,0.00041287355,0.00018942196,0.00036615026,0.00036742017,0.3940189,0.08606747,0.035384513,0.009401971,0.4723881],"study_design_scores_gemma":[0.000012987352,0.000032217446,0.00012732798,0.00001561605,0.000016973549,0.000105462495,0.000019604107,0.96833885,0.012971428,0.01651341,0.001833918,0.000012300787],"about_ca_topic_score_codex":0.0031081515,"about_ca_topic_score_gemma":0.0037399852,"teacher_disagreement_score":0.0036763288,"about_ca_system_score_codex":0.0010057004,"about_ca_system_score_gemma":0.0015441277,"threshold_uncertainty_score":0.012298524},"labels":[],"label_agreement":null},{"id":"W3169635423","doi":"10.1016/j.media.2021.102107","title":"Ultra-short echo-time magnetic resonance imaging lung segmentation with under-Annotations and domain shift","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Atomic and Subatomic Physics Research","field":"Physics and Astronomy","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":"Western University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Segmentation; Artificial intelligence; Computer science; Atlas (anatomy); Magnetic resonance imaging; Cluster analysis; Pattern recognition (psychology); Computer vision; Medicine; Radiology; Anatomy","score_opus":0.005021025350039202,"score_gpt":0.27568338162182165,"score_spread":0.2706623562717825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169635423","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.083533555,0.0009702499,0.9109993,0.00031470275,0.00009236312,0.00007684372,0.00039632973,0.002389152,0.001227599],"genre_scores_gemma":[0.37234065,0.0008446683,0.6181875,0.00029065312,0.0001342394,0.00011240407,0.001966049,0.0009775456,0.005146261],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996013,0.00007100093,0.00003421995,0.0001102598,0.0000992405,0.000083991705],"domain_scores_gemma":[0.9991891,0.00020202235,0.00007666134,0.00020426438,0.00026332124,0.00006461463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001032149,0.0008116358,0.00083293143,0.0016495137,0.00062194443,0.001434107,0.0008846603,0.0017441266,0.0014930996],"category_scores_gemma":[0.0019551811,0.0004800233,0.00092870026,0.0010461909,0.0005381228,0.0009097262,0.0012418004,0.0009972288,0.0013143896],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011689415,0.00029182527,0.0050717727,0.0005153364,0.0003309344,0.000690987,0.0004691558,0.034299713,0.38490447,0.0040715043,0.0056872303,0.56249815],"study_design_scores_gemma":[0.000046807625,0.0002043393,0.010459484,0.0000690397,0.0003843439,0.0018164949,0.0002900303,0.7249473,0.23978233,0.007926073,0.013978218,0.00009554622],"about_ca_topic_score_codex":0.003563049,"about_ca_topic_score_gemma":0.005731374,"teacher_disagreement_score":0.003563049,"about_ca_system_score_codex":0.00027058416,"about_ca_system_score_gemma":0.0014764076,"threshold_uncertainty_score":0.007084608},"labels":[],"label_agreement":null},{"id":"W3169902296","doi":"10.1016/j.media.2021.102115","title":"Evaluation and comparison of accurate automated spinal curvature estimation algorithms with spinal anterior-posterior X-Ray images: The AASCE2019 challenge","year":2021,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"Scoliosis diagnosis and treatment","field":"Medicine","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":"Western University","funders":"","keywords":"Scoliosis; Cobb angle; Spinal Curvatures; Curvature; Estimation; Artificial intelligence; Computer science; Task (project management); Computer vision; Algorithm; Mathematics; Medicine; Surgery; Engineering; Geometry","score_opus":0.07490463179044513,"score_gpt":0.4557718512083843,"score_spread":0.3808672194179392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169902296","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00112587,0.9936173,0.0036325026,0.00038577546,0.00019392968,0.00003946663,0.0002682032,0.00007050452,0.0006664811],"genre_scores_gemma":[0.011091488,0.97108567,0.014470181,0.00046423427,0.0003995817,0.00007119548,0.0013830493,0.00007946778,0.00095514755],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984471,0.00027314704,0.00020660914,0.00029818297,0.00072374725,0.000051209136],"domain_scores_gemma":[0.9931005,0.0038643882,0.0004488028,0.0002342394,0.0022584673,0.0000936022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046379217,0.0014509659,0.0032437595,0.0037516158,0.0002368689,0.0020912564,0.0020993093,0.0016292786,0.0029729598],"category_scores_gemma":[0.008801212,0.00068592007,0.0018881066,0.00237599,0.00061548507,0.0013703052,0.0008893128,0.001504521,0.0017941899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001419703,0.00005687172,0.0006217328,0.014137719,0.0004435487,0.000032273056,0.0000312982,0.00094443955,0.0015341599,0.00096430036,0.008585957,0.9725056],"study_design_scores_gemma":[0.00052626024,0.0016836113,0.039990667,0.042056922,0.0075824996,0.0048127947,0.0004201779,0.025279418,0.016000897,0.010089373,0.8510232,0.0005342177],"about_ca_topic_score_codex":0.0057600336,"about_ca_topic_score_gemma":0.0056821527,"teacher_disagreement_score":0.0057600336,"about_ca_system_score_codex":0.00074427115,"about_ca_system_score_gemma":0.0029040372,"threshold_uncertainty_score":0.024527967},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4631833,0.005220946,0.52047175,0.003817582,0.00010355594,0.00023495397,0.0011546513,0.0009252877,0.0048880223],"genre_scores_gemma":[0.7901629,0.0025880893,0.2040812,0.00021405173,0.000084505315,0.00010961016,0.0012910289,0.00018270267,0.0012858687],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926215,0.0003195687,0.000028102086,0.00010256646,0.00025107406,0.000036528538],"domain_scores_gemma":[0.99767107,0.0009998926,0.0002110696,0.0002746887,0.0007649449,0.00007823403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028525519,0.0004346009,0.0006693671,0.0015288005,0.00047460923,0.0011281032,0.0008378269,0.00079847383,0.0010166304],"category_scores_gemma":[0.010445239,0.0002528722,0.0007433638,0.0012639926,0.0006821556,0.0012971498,0.0009594717,0.0010467477,0.0003179371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009531334,0.00066958,0.042335223,0.0004110972,0.00070258207,0.00017103055,0.0006184892,0.102392584,0.011251121,0.017796949,0.012233241,0.81046486],"study_design_scores_gemma":[0.00006375315,0.0003347318,0.04469066,0.0001332139,0.00019494479,0.00041800732,0.00041409038,0.8841414,0.0057890876,0.057623886,0.0061171576,0.000079017685],"about_ca_topic_score_codex":0.010484519,"about_ca_topic_score_gemma":0.010905192,"teacher_disagreement_score":0.010484519,"about_ca_system_score_codex":0.00057150086,"about_ca_system_score_gemma":0.00093616813,"threshold_uncertainty_score":0.020846963},"labels":[],"label_agreement":null},{"id":"W3172893236","doi":"10.1016/j.media.2021.102126","title":"Filtering in tractography using autoencoders (FINTA)","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":51,"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":"National Institute of Mental Health","keywords":"Tractography; Streamlines, streaklines, and pathlines; Artificial intelligence; Autoencoder; Pattern recognition (psychology); Computer science; Deep learning; Filter (signal processing); Diffusion MRI; Human Connectome Project; Voxel; Computer vision; Physics; Magnetic resonance imaging; Functional connectivity","score_opus":0.08399795419258586,"score_gpt":0.4187703104105577,"score_spread":0.3347723562179718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172893236","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032095269,0.00024388143,0.99552804,0.00006355981,0.0000555026,0.000011497044,0.00003970373,0.0004958493,0.00035236095],"genre_scores_gemma":[0.15181218,0.0009568939,0.8391259,0.00019183083,0.00017241598,0.00017555857,0.0004297928,0.00035543155,0.006779967],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961936,0.000094300216,0.00003405856,0.00011532427,0.00009003864,0.00004682485],"domain_scores_gemma":[0.99840254,0.0008898158,0.00012586635,0.00020041736,0.00032821004,0.000053063904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001222546,0.0008476053,0.0010168828,0.00074552296,0.00055478944,0.0010932086,0.0009761843,0.0017263457,0.0022008063],"category_scores_gemma":[0.002833765,0.0008445124,0.0015071916,0.0007174812,0.0006700235,0.001146426,0.0011699409,0.002042273,0.0012072588],"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":[0.00012058657,0.00007528634,0.0009613743,0.00019652862,0.00024029972,0.00014128687,0.00013897284,0.48609728,0.014833478,0.023619728,0.0036565112,0.4699187],"study_design_scores_gemma":[0.0000043755686,0.000019768358,0.00022415562,0.000018028653,0.000018600414,0.00004630806,0.000008316978,0.98992187,0.0031096644,0.005108628,0.0015105368,0.000009763227],"about_ca_topic_score_codex":0.013357692,"about_ca_topic_score_gemma":0.015907355,"teacher_disagreement_score":0.013357692,"about_ca_system_score_codex":0.00058773736,"about_ca_system_score_gemma":0.0011109001,"threshold_uncertainty_score":0.02655989},"labels":[],"label_agreement":null},{"id":"W3176861478","doi":"10.1016/j.media.2021.102154","title":"United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","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":"Western University","funders":"Primary Research and Development Plan of Zhejiang Province; Taishan Scholar Project of Shandong Province; China Scholarship Council; National Natural Science Foundation of China","keywords":"Computer science; Modality (human–computer interaction); Artificial intelligence; Feature (linguistics); Feature selection; Segmentation; Feature extraction; Pattern recognition (psychology); Discriminator; Magnetic resonance imaging; Computer vision; Radiology; Medicine","score_opus":0.009171958537115481,"score_gpt":0.2997309343071358,"score_spread":0.2905589757700203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176861478","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042034175,0.0008336752,0.95431745,0.00042318282,0.000066919834,0.000060050028,0.00013290512,0.001110743,0.0010209801],"genre_scores_gemma":[0.8315845,0.00052403443,0.1592311,0.0004985345,0.00012514659,0.00014698683,0.0006109255,0.0002582052,0.00702055],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993604,0.00025199473,0.0000333923,0.00015019179,0.00011318296,0.00009086816],"domain_scores_gemma":[0.99800426,0.0014384174,0.00012979572,0.00013883268,0.0002110336,0.000077585406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023679188,0.0009629714,0.0011319552,0.0008999586,0.0004435824,0.0008716048,0.0015712873,0.0016345022,0.0015177961],"category_scores_gemma":[0.004361742,0.0007460087,0.0010183622,0.000542401,0.0008485755,0.0007387192,0.0018645037,0.0014148544,0.0005164391],"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":[0.00043163984,0.000097436656,0.0014551085,0.00011409744,0.00014019695,0.0001529675,0.0000724487,0.84367853,0.005761763,0.0048453594,0.0026970184,0.14055341],"study_design_scores_gemma":[0.0000019239578,0.000008547577,0.00008942544,0.0000025058416,0.0000048188135,0.000012168256,0.0000020890182,0.9985392,0.0006989399,0.00056008954,0.00007827949,0.000002050724],"about_ca_topic_score_codex":0.0063498844,"about_ca_topic_score_gemma":0.0066818,"teacher_disagreement_score":0.0063498844,"about_ca_system_score_codex":0.000993973,"about_ca_system_score_gemma":0.00091584364,"threshold_uncertainty_score":0.012625873},"labels":[],"label_agreement":null},{"id":"W3195607837","doi":"10.1016/j.media.2021.102218","title":"Automatic annotation of cervical vertebrae in videofluoroscopy images via deep learning","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Dysphagia Assessment and Management","field":"Health Professions","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":"Toronto General Hospital; North York General Hospital","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institutes of Health","keywords":"Artificial intelligence; Computer science; Swallowing; Pixel; Gold standard (test); Convolutional neural network; Kinematics; Deep learning; Landmark; Computer vision; Pattern recognition (psychology); Medicine; Radiology","score_opus":0.013095892006795232,"score_gpt":0.393504932193157,"score_spread":0.3804090401863618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195607837","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22411257,0.006474679,0.74631286,0.00089867885,0.00033876524,0.00042929637,0.0054476084,0.008287064,0.007698517],"genre_scores_gemma":[0.6901123,0.0023451778,0.29311702,0.00044274438,0.00017743408,0.00023382291,0.005127329,0.00044178197,0.008002342],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996879,0.00003222466,0.000021183449,0.000104622595,0.00008035267,0.00007369745],"domain_scores_gemma":[0.9996443,0.00010258172,0.00003900297,0.000042729196,0.00014648426,0.000024926145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038388252,0.00075778563,0.0005936117,0.0021566194,0.00039175074,0.0010651159,0.00076100306,0.0014899266,0.0018783861],"category_scores_gemma":[0.0014757715,0.000327017,0.000796821,0.00089425646,0.00025731465,0.0004883461,0.0007359234,0.0007551314,0.0015431893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005214188,0.0001839769,0.014795415,0.00054445333,0.00015657354,0.0007498041,0.00017970052,0.018322604,0.1166819,0.0010674053,0.012690402,0.8341063],"study_design_scores_gemma":[0.000045538873,0.00023303575,0.04125045,0.00035036565,0.000291189,0.0019495438,0.00032117113,0.83873606,0.09600099,0.0038082332,0.016918648,0.000094736715],"about_ca_topic_score_codex":0.019708881,"about_ca_topic_score_gemma":0.029854152,"teacher_disagreement_score":0.019708881,"about_ca_system_score_codex":0.00052320387,"about_ca_system_score_gemma":0.0011361078,"threshold_uncertainty_score":0.039188325},"labels":[],"label_agreement":null},{"id":"W3199008037","doi":"10.1016/j.media.2021.102233","title":"BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":708,"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 Institute of Neurological Disorders and Stroke; National Institutes of Health","keywords":"Computer science; Functional magnetic resonance imaging; Artificial intelligence; Pooling; Connectome; Neuroimaging; Pattern recognition (psychology); Graph; Human Connectome Project; Machine learning; Psychology; Neuroscience; Functional connectivity; Theoretical computer science","score_opus":0.02012230046150534,"score_gpt":0.2959105910532257,"score_spread":0.2757882905917204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199008037","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0086288005,0.0003348395,0.9515782,0.00031770824,0.00018174744,0.00016521358,0.00709932,0.030108038,0.001586163],"genre_scores_gemma":[0.17304577,0.00064010674,0.7976639,0.00034155828,0.00012760374,0.0012394629,0.013457817,0.006234427,0.007249262],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986815,0.00003339395,0.000008099212,0.00004287058,0.000033751097,0.000013737627],"domain_scores_gemma":[0.9995957,0.00021218951,0.000038868184,0.0000703726,0.00005873943,0.000024120964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005288762,0.0011716394,0.00050848647,0.0011175197,0.00038494106,0.000875024,0.0011945297,0.0009718835,0.014251824],"category_scores_gemma":[0.003840017,0.00046609994,0.00088827714,0.0008813752,0.00028549207,0.0008129261,0.00091934775,0.0011553207,0.0027456852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006742117,0.0002303474,0.0028092759,0.000900171,0.00039491747,0.00076619204,0.00031815268,0.23074186,0.0301303,0.041260418,0.14762537,0.5441488],"study_design_scores_gemma":[0.000056591645,0.00004389091,0.0012092687,0.000046821988,0.00005556098,0.00021403893,0.0000308442,0.9288287,0.006828704,0.046672888,0.015973425,0.00003918729],"about_ca_topic_score_codex":0.008566115,"about_ca_topic_score_gemma":0.016502867,"teacher_disagreement_score":0.014251824,"about_ca_system_score_codex":0.0005300493,"about_ca_system_score_gemma":0.00074075616,"threshold_uncertainty_score":0.0476771},"labels":[],"label_agreement":null},{"id":"W3199360076","doi":"10.1016/j.media.2021.102231","title":"Real-time multimodal image registration with partial intraoperative point-set data","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":35,"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; University College London; Wellcome Trust","keywords":"Image registration; Artificial intelligence; Rigid transformation; Computer science; Point set registration; Ground truth; Iterative closest point; Computer vision; Point (geometry); Pattern recognition (psychology); Algorithm; Image (mathematics); Mathematics; Point cloud","score_opus":0.011420725786102707,"score_gpt":0.2592982857670171,"score_spread":0.24787755998091437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199360076","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0404019,0.00014335464,0.95633334,0.00013118217,0.000040836363,0.00006379526,0.0001142097,0.0018137338,0.00095767615],"genre_scores_gemma":[0.57256526,0.00020129456,0.4244907,0.000119157696,0.00003631305,0.00015496988,0.00045409263,0.00032820893,0.0016499527],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996371,0.00008864683,0.000020316951,0.00008891379,0.00013088863,0.000034177116],"domain_scores_gemma":[0.9995197,0.00015662044,0.00007507676,0.00014406799,0.00007529516,0.000029258277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000894239,0.0005803342,0.00048615786,0.00048417706,0.00019072532,0.0007400481,0.0010566758,0.00063267513,0.0016188395],"category_scores_gemma":[0.0032220306,0.0004653011,0.0005116833,0.0006462455,0.0004918674,0.0011573023,0.0014152081,0.0009317562,0.00061288197],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006413033,0.00013451821,0.003507612,0.00020037383,0.00014620095,0.00051359873,0.00025735967,0.34067854,0.07525885,0.0044588754,0.0034291237,0.57077366],"study_design_scores_gemma":[0.000013476771,0.00012057669,0.0008571787,0.000008141095,0.000014970897,0.00027131772,0.000026485919,0.9695636,0.02454512,0.003232655,0.0013259441,0.000020580805],"about_ca_topic_score_codex":0.001590632,"about_ca_topic_score_gemma":0.0026440108,"teacher_disagreement_score":0.0016188395,"about_ca_system_score_codex":0.00044539032,"about_ca_system_score_gemma":0.00069108827,"threshold_uncertainty_score":0.005415559},"labels":[],"label_agreement":null},{"id":"W3201974489","doi":"10.1016/j.media.2021.102250","title":"Probabilistic 4D predictive model from in-room surrogates using conditional generative networks for image-guided radiotherapy","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","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":"Centre Hospitalier de l’Université de Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Computer science; Artificial intelligence; Probabilistic logic; Robustness (evolution); Generative model; Population; Scalability; Machine learning; Pattern recognition (psychology); Computer vision; Generative grammar","score_opus":0.01827540480223388,"score_gpt":0.32744500957929706,"score_spread":0.3091696047770632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201974489","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019304268,0.00042202312,0.9766816,0.00030533274,0.000052486605,0.000036184836,0.00027019423,0.0008899662,0.0020379187],"genre_scores_gemma":[0.8441247,0.00074453064,0.14432685,0.00045110827,0.00010796651,0.0002363753,0.0013213714,0.00063422375,0.0080528185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996444,0.00013532618,0.000013221271,0.000062636485,0.00010444767,0.000040024777],"domain_scores_gemma":[0.9990239,0.0006335848,0.00010169231,0.00007841771,0.00010464966,0.00005768071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007907716,0.00075314107,0.0010174535,0.0007018917,0.0003469437,0.0010252778,0.0017773878,0.0019382685,0.002699987],"category_scores_gemma":[0.0025857375,0.0012105296,0.0014239936,0.00086782343,0.00091101776,0.0009869514,0.0014779663,0.0020637254,0.00091555575],"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":[0.000042661122,0.000014900899,0.0002086181,0.000018137427,0.000018139572,0.00003091271,0.00001936864,0.98926723,0.00062073854,0.0028536425,0.00032172486,0.0065839654],"study_design_scores_gemma":[0.0000012822101,0.0000021987478,0.000027551336,0.0000021912288,0.0000018608922,0.000004910758,0.000001131458,0.9988446,0.00009108131,0.0009480198,0.00007299107,0.0000022359966],"about_ca_topic_score_codex":0.0098967645,"about_ca_topic_score_gemma":0.0104205655,"teacher_disagreement_score":0.0098967645,"about_ca_system_score_codex":0.0011082407,"about_ca_system_score_gemma":0.00087977515,"threshold_uncertainty_score":0.019678295},"labels":[],"label_agreement":null},{"id":"W3204174615","doi":"10.1016/j.media.2021.102252","title":"Cross-covariance isolate detect: A new change-point method for estimating dynamic functional connectivity","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","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 Alberta","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Covariance; Computer science; Sliding window protocol; Change detection; Dynamic functional connectivity; A priori and a posteriori; Resting state fMRI; Pattern recognition (psychology); Artificial intelligence; Time point; Functional magnetic resonance imaging; Data point; Window (computing); Mathematics; Statistics","score_opus":0.05422623268108318,"score_gpt":0.36570928125433916,"score_spread":0.311483048573256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204174615","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041270787,0.00016068018,0.9945702,0.000039102073,0.000029249759,0.00004154526,0.0001099941,0.0007241508,0.00019794313],"genre_scores_gemma":[0.088231586,0.00041329674,0.9065015,0.00015111908,0.00013653486,0.00028125453,0.0009731866,0.0009284759,0.0023829287],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870455,0.00032933947,0.000061936786,0.00032736262,0.00050164014,0.000075096556],"domain_scores_gemma":[0.9969716,0.001591559,0.00022989516,0.00037205403,0.0007343355,0.000100492296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030582352,0.0011377643,0.0015137626,0.0028688496,0.00069849053,0.0012848844,0.0019028934,0.0015506978,0.0025044864],"category_scores_gemma":[0.009286649,0.00077314436,0.0014914967,0.0025155235,0.00067250145,0.0017617221,0.0015439895,0.0018647461,0.0014053448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000551525,0.00033251478,0.0043676742,0.00024798256,0.00079595216,0.00020754921,0.00022224117,0.050113965,0.04908693,0.010010204,0.008222426,0.8758411],"study_design_scores_gemma":[0.000044911827,0.00015592079,0.0054966835,0.000024354275,0.00014441716,0.00028312366,0.000040217332,0.96755755,0.012432328,0.007554477,0.006185001,0.00008096041],"about_ca_topic_score_codex":0.00655617,"about_ca_topic_score_gemma":0.011088844,"teacher_disagreement_score":0.00655617,"about_ca_system_score_codex":0.00051480153,"about_ca_system_score_gemma":0.0013095058,"threshold_uncertainty_score":0.01617372},"labels":[],"label_agreement":null},{"id":"W3206709865","doi":"10.1016/j.media.2021.102260","title":"Population-based 3D respiratory motion modelling from convolutional autoencoders for 2D ultrasound-guided radiotherapy","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","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":"Centre Hospitalier de l’Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Image-guided radiation therapy; Artificial intelligence; Computer science; Computer vision; Population; Modality (human–computer interaction); Medical imaging; 3D ultrasound; Motion (physics); Ultrasound; Radiology; Medicine","score_opus":0.017838422751916605,"score_gpt":0.3104975805297779,"score_spread":0.2926591577778613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206709865","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030709095,0.0006492262,0.96671027,0.00016366098,0.000042748685,0.000025490448,0.00013688726,0.0008153436,0.0007472682],"genre_scores_gemma":[0.77532065,0.0009273157,0.21670584,0.00026183794,0.00007813276,0.00014246136,0.0006423261,0.00027084167,0.005650621],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999858,0.00003867307,0.000008080629,0.000037913494,0.000036742327,0.00002051175],"domain_scores_gemma":[0.9996203,0.00024019921,0.000042254385,0.000022366867,0.000059692775,0.000015094482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047389767,0.00054062135,0.0005928526,0.0003301354,0.00017432678,0.00044200232,0.0007935151,0.001107904,0.00096566946],"category_scores_gemma":[0.0013451101,0.0007964528,0.0008429764,0.00044995494,0.00032402104,0.0004451009,0.0006274547,0.0010689176,0.00047513048],"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":[0.00005281472,0.000023809956,0.00041706796,0.000029392677,0.000050931456,0.000032901484,0.00003368268,0.93787163,0.0038247313,0.0008624299,0.0005258058,0.056274813],"study_design_scores_gemma":[0.0000010407655,0.0000035050275,0.00009310243,0.0000020094865,0.0000026098598,0.0000058160063,9.714581e-7,0.9992742,0.00032417817,0.00021438273,0.00007643578,0.0000018188839],"about_ca_topic_score_codex":0.0149681205,"about_ca_topic_score_gemma":0.016501619,"teacher_disagreement_score":0.0149681205,"about_ca_system_score_codex":0.00063334935,"about_ca_system_score_gemma":0.0007609193,"threshold_uncertainty_score":0.02976203},"labels":[],"label_agreement":null},{"id":"W3210027215","doi":"10.1016/j.media.2021.102295","title":"Diagnosing glaucoma on imbalanced data with self-ensemble dual-curriculum learning","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Retinal Imaging and Analysis","field":"Medicine","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":"Western University","funders":"Science and Technology Project of Nantong City; Fundamental Research Funds for Central Universities of the Central South University; National Key Research and Development Program of China; Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Glaucoma; Artificial intelligence; Computer science; Feature (linguistics); Discriminative model; Weighting; Machine learning; Pattern recognition (psychology); Feature vector; Feature learning; Medicine; Ophthalmology; Radiology","score_opus":0.010087269101009787,"score_gpt":0.2993627430291062,"score_spread":0.28927547392809644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210027215","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39985022,0.0033543697,0.58673567,0.000988577,0.0005872251,0.00021563335,0.0013377352,0.003502603,0.0034279595],"genre_scores_gemma":[0.8581151,0.0004742657,0.13534765,0.00041860645,0.00026269563,0.00010661278,0.0030424164,0.00010384498,0.00212881],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992914,0.00013099707,0.000052618234,0.0002673143,0.00013274753,0.00012488388],"domain_scores_gemma":[0.9983279,0.0008066171,0.00010158921,0.00023788911,0.00040330028,0.00012259958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016857089,0.0012415744,0.0012157735,0.0014533548,0.0005319686,0.0007803548,0.0010853414,0.0014184868,0.0010599798],"category_scores_gemma":[0.0035037831,0.00034835842,0.0013558149,0.0008321012,0.00035305807,0.0013625368,0.0018535577,0.0014694828,0.0005737618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001053031,0.0012804446,0.035437133,0.00021118463,0.0004956726,0.0003255639,0.00022443768,0.10384448,0.022942275,0.0009473352,0.0087112,0.8245272],"study_design_scores_gemma":[0.00003098375,0.00024321016,0.0059324536,0.000022072627,0.00012996577,0.00015116938,0.00007684966,0.9845604,0.0056021777,0.0022248123,0.0010055864,0.000020314757],"about_ca_topic_score_codex":0.0029475442,"about_ca_topic_score_gemma":0.0047474816,"teacher_disagreement_score":0.0029475442,"about_ca_system_score_codex":0.0003415916,"about_ca_system_score_gemma":0.0007912612,"threshold_uncertainty_score":0.008915007},"labels":[],"label_agreement":null},{"id":"W3213990473","doi":"10.1016/j.media.2023.103058","title":"Acquisition-invariant brain MRI segmentation with informative uncertainties","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","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 Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; National Institute on Aging; National Institute for Health and Care Research; Northern California Institute for Research and Education; BioClinica; Biogen; Pfizer; Novartis Pharmaceuticals Corporation; Wellcome Trust; University of Southern California; Engineering and Physical Sciences Research Council; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; University College London Hospitals NHS Foundation Trust; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Segmentation; Computer science; Task (project management); Artificial intelligence; Context (archaeology); Machine learning; Invariant (physics); Quality (philosophy); Data mining; Mathematics","score_opus":0.00644885826553453,"score_gpt":0.29685513854852047,"score_spread":0.29040628028298593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213990473","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075113294,0.0002611747,0.9912101,0.00011050387,0.000015758449,0.000018871408,0.00006629224,0.00031183453,0.00049422204],"genre_scores_gemma":[0.41521624,0.00067842146,0.579859,0.00017889141,0.00012772703,0.00009869924,0.00059572695,0.00051351247,0.0027317605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991167,0.00019675358,0.00005087487,0.00023661212,0.00031910744,0.00007995425],"domain_scores_gemma":[0.9986249,0.0006319742,0.00021410333,0.00029471822,0.00019365437,0.00004050778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017240815,0.00089072774,0.0010001146,0.0009311864,0.00042478438,0.0014448755,0.0010439215,0.0015806186,0.0011689233],"category_scores_gemma":[0.0058995164,0.0009362654,0.001075573,0.0008871475,0.00095586356,0.0014333875,0.0016291062,0.0014556202,0.0005685171],"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":[0.00094509963,0.00009537997,0.0016580136,0.0004706409,0.00021953958,0.00036912572,0.0002677057,0.44679835,0.07601258,0.047097765,0.002509667,0.42355618],"study_design_scores_gemma":[0.000016796865,0.000084860294,0.0011086133,0.00002551944,0.000064708955,0.00032860882,0.00002042192,0.94900197,0.025767585,0.021455316,0.002092584,0.00003292411],"about_ca_topic_score_codex":0.0017138167,"about_ca_topic_score_gemma":0.0022663905,"teacher_disagreement_score":0.0017240815,"about_ca_system_score_codex":0.0006840585,"about_ca_system_score_gemma":0.001239169,"threshold_uncertainty_score":0.009117901},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011825847,0.000077706565,0.99793786,0.00002691893,0.000010207085,0.00002330239,0.000035305522,0.00052136823,0.00018464577],"genre_scores_gemma":[0.06354647,0.00021953548,0.9338565,0.00007638795,0.000033027645,0.00015241303,0.00029140277,0.00030582468,0.0015184034],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953437,0.00008176123,0.000032723914,0.00012021183,0.00016780861,0.00006309191],"domain_scores_gemma":[0.99969554,0.00008747883,0.000030871044,0.00004204075,0.000119846925,0.0000242684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013107406,0.0009004248,0.0012918575,0.0017561034,0.000852172,0.0014329663,0.002465369,0.0016983318,0.0023305812],"category_scores_gemma":[0.0014922105,0.00091122004,0.0020455536,0.0016108542,0.0005849233,0.0014489664,0.0017267624,0.0013684885,0.0010902331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024640688,0.0001518147,0.001014796,0.00034093868,0.0002464842,0.00024607178,0.00038954508,0.40266305,0.037718587,0.031424355,0.0056435815,0.5199144],"study_design_scores_gemma":[0.000004163957,0.000018921988,0.00015395216,0.000010168229,0.00002188048,0.00005751943,0.000016253618,0.986935,0.00463053,0.0066956095,0.001440118,0.000015955975],"about_ca_topic_score_codex":0.008433808,"about_ca_topic_score_gemma":0.011861708,"teacher_disagreement_score":0.008433808,"about_ca_system_score_codex":0.0008990896,"about_ca_system_score_gemma":0.0021070784,"threshold_uncertainty_score":0.016769469},"labels":[],"label_agreement":null},{"id":"W4200066891","doi":"10.1016/j.media.2021.102336","title":"Head and neck tumor segmentation in PET/CT: The HECKTOR challenge","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":203,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Cancer Agency; Université de Sherbrooke","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Segmentation; Thresholding; Artificial intelligence; Computer science; Sørensen–Dice coefficient; Modality (human–computer interaction); Leverage (statistics); Positron emission tomography; Medicine; Nuclear medicine; Medical physics; Pattern recognition (psychology); Image segmentation; Computer vision; Image (mathematics)","score_opus":0.009524723942521691,"score_gpt":0.3205935885383795,"score_spread":0.3110688645958578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200066891","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74765027,0.02286353,0.16425289,0.011192283,0.008652983,0.0037302785,0.017976305,0.008577608,0.015103844],"genre_scores_gemma":[0.62005603,0.003989119,0.2845088,0.0033537846,0.0032160638,0.0015166582,0.057071455,0.0028879973,0.02340016],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9918351,0.003118793,0.0005313309,0.0018333993,0.002101705,0.00057964755],"domain_scores_gemma":[0.9850524,0.0070898132,0.0006117752,0.0021040686,0.0037477275,0.0013941823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010937735,0.0018784245,0.0017896097,0.0021411353,0.0014164688,0.0024932625,0.0027171967,0.004195836,0.0027157033],"category_scores_gemma":[0.0236371,0.00059142447,0.0017982672,0.00096672575,0.0011309582,0.0013198716,0.0038916662,0.002429648,0.0020282373],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035875612,0.0017343431,0.0211241,0.0041734837,0.0018014574,0.0033472893,0.0021857347,0.041435633,0.04568768,0.0026234433,0.21763638,0.65466297],"study_design_scores_gemma":[0.0010267484,0.005132372,0.13170625,0.0014635617,0.0014290538,0.020986568,0.0069267466,0.39610386,0.13303277,0.012625321,0.28864634,0.0009205522],"about_ca_topic_score_codex":0.008916886,"about_ca_topic_score_gemma":0.01857924,"teacher_disagreement_score":0.010937735,"about_ca_system_score_codex":0.001323635,"about_ca_system_score_gemma":0.002349907,"threshold_uncertainty_score":0.057844937},"labels":[],"label_agreement":null},{"id":"W4200557095","doi":"10.1016/j.media.2021.102329","title":"Skin3D: Detection and longitudinal tracking of pigmented skin lesions in 3D total-body textured meshes","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","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":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada; Nvidia","keywords":"Polygon mesh; Artificial intelligence; Computer science; Computer vision; Geodesic; Matching (statistics); Lesion; Pattern recognition (psychology); Skin lesion; Mathematics; Medicine; Pathology; Computer graphics (images); Geometry","score_opus":0.010825215192580057,"score_gpt":0.2798404174109954,"score_spread":0.26901520221841535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200557095","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18932962,0.0014678654,0.7915487,0.00039124175,0.00024215056,0.00028131457,0.0040427437,0.0103759635,0.0023203453],"genre_scores_gemma":[0.5945384,0.0009317108,0.39585006,0.00020371075,0.000074359465,0.0001975829,0.0034469932,0.0013352367,0.003421971],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99979514,0.000030772673,0.000009646809,0.000055131364,0.000084288535,0.000025037936],"domain_scores_gemma":[0.999741,0.00010824073,0.000027609296,0.00003887465,0.00005705284,0.0000271483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038517526,0.00061825005,0.000493556,0.0012981879,0.00017968466,0.0009573591,0.0005409275,0.0008169627,0.0027471466],"category_scores_gemma":[0.0012804924,0.0004829496,0.000808565,0.0006483905,0.0001629838,0.00034707022,0.0007518321,0.00039492015,0.0008578537],"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":[0.0011610222,0.0002473039,0.017654872,0.0006760638,0.00038925957,0.00076607044,0.00049893337,0.10541196,0.15432598,0.0016700458,0.018697152,0.6985014],"study_design_scores_gemma":[0.000035069774,0.00012091915,0.0121489,0.000055498414,0.000059582857,0.0009648935,0.00011548644,0.9585904,0.020856982,0.0011363701,0.005867923,0.000048012793],"about_ca_topic_score_codex":0.0033748508,"about_ca_topic_score_gemma":0.00517872,"teacher_disagreement_score":0.0033748508,"about_ca_system_score_codex":0.0002156708,"about_ca_system_score_gemma":0.00033131178,"threshold_uncertainty_score":0.009190142},"labels":[],"label_agreement":null},{"id":"W4205470984","doi":"10.1016/j.media.2021.102347","title":"ProstAttention-Net: A deep attention model for prostate cancer segmentation by aggressiveness in MRI scans","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","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":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Lyon; Université Claude Bernard Lyon 1; Agence Nationale de la Recherche","keywords":"Prostate cancer; Prostate; Prostatectomy; Magnetic resonance imaging; Grading (engineering); Segmentation; Medicine; Receiver operating characteristic; Prostate biopsy; Computer science; Biopsy; Artificial intelligence; Radiology; Cancer; Internal medicine","score_opus":0.010217428877475912,"score_gpt":0.31484505776938976,"score_spread":0.30462762889191386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205470984","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05173702,0.007232777,0.90405464,0.0015022937,0.00058567285,0.00032359414,0.0063017844,0.023895787,0.0043664677],"genre_scores_gemma":[0.50741637,0.003397727,0.44189674,0.0032552266,0.00057658873,0.00054430997,0.013959639,0.001959956,0.026993483],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997478,0.000041913874,0.000011840133,0.00010226169,0.000048259048,0.000047895825],"domain_scores_gemma":[0.9996939,0.00013431441,0.000022934852,0.000034722296,0.00008019071,0.00003397944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007670098,0.0019852142,0.0012483264,0.0011690502,0.0004625443,0.00086753204,0.0022969807,0.0022092874,0.0032693646],"category_scores_gemma":[0.0014121446,0.000893155,0.0014410232,0.0009211834,0.0003220924,0.0008245081,0.0013702131,0.0020940127,0.0017396848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066771515,0.00031178998,0.0034549762,0.00036236146,0.0004599205,0.0002962287,0.00007883661,0.2048745,0.0119288955,0.0023199439,0.042756893,0.732488],"study_design_scores_gemma":[0.000022589897,0.000074917945,0.0007373099,0.00003087973,0.000077206656,0.00009760637,0.000008973411,0.989419,0.00411797,0.0024651694,0.0029303255,0.00001804295],"about_ca_topic_score_codex":0.024090625,"about_ca_topic_score_gemma":0.045463875,"teacher_disagreement_score":0.024090625,"about_ca_system_score_codex":0.0011340054,"about_ca_system_score_gemma":0.0015154147,"threshold_uncertainty_score":0.047900796},"labels":[],"label_agreement":null},{"id":"W4221132458","doi":"10.1016/j.media.2022.102432","title":"Mapping heterogenous anisotropic tissue mechanical properties with transverse isotropic nonlinear inversion MR elastography","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Elasticity and Material Modeling","field":"Engineering","cited_by":41,"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":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health; National Science Foundation","keywords":"Magnetic resonance elastography; Anisotropy; Isotropy; Elastography; Materials science; White matter; Diffusion MRI; Nuclear magnetic resonance; Transverse plane; Nonlinear system; Biomedical engineering; Magnetic resonance imaging; Physics; Optics; Acoustics; Ultrasound; Anatomy; Radiology; Medicine","score_opus":0.008492318509760332,"score_gpt":0.1900310918492953,"score_spread":0.18153877333953497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221132458","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0674955,0.00044628212,0.92998654,0.00013338105,0.000013701505,0.000029722563,0.00007324092,0.00028206652,0.0015394605],"genre_scores_gemma":[0.6937211,0.0013833186,0.30052486,0.00013604449,0.000046855377,0.000087592736,0.00018299662,0.00018995821,0.0037273203],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999168,0.000025011388,0.0000046250625,0.00001924725,0.000027096268,0.0000072341204],"domain_scores_gemma":[0.9998325,0.00007787727,0.000034242166,0.000031766314,0.000018114493,0.000005473477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003360613,0.0004117148,0.00021726334,0.00045463693,0.000121802244,0.0006944177,0.00025910218,0.00044552409,0.0008311266],"category_scores_gemma":[0.0011494254,0.00026893555,0.00021539997,0.0003753749,0.00028402332,0.00068163575,0.00028355877,0.00029057276,0.00035024856],"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":[0.00017959284,0.00013715039,0.005209406,0.00028299034,0.0001049817,0.0005949627,0.00023089928,0.15069246,0.56398934,0.0067000454,0.0010653038,0.2708129],"study_design_scores_gemma":[0.000012178244,0.0000766764,0.006858852,0.000026458403,0.00005754244,0.0013412117,0.00008913197,0.8764893,0.10435425,0.0076166745,0.003042322,0.000035362365],"about_ca_topic_score_codex":0.0005297597,"about_ca_topic_score_gemma":0.001056337,"teacher_disagreement_score":0.0008311266,"about_ca_system_score_codex":0.00009878137,"about_ca_system_score_gemma":0.00022383277,"threshold_uncertainty_score":0.0027803779},"labels":[],"label_agreement":null},{"id":"W4223609437","doi":"10.1016/j.media.2022.102456","title":"Reasoning discriminative dictionary-embedded network for fully automatic vertebrae tumor diagnosis","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","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":"Western University","funders":"","keywords":"Discriminative model; Artificial intelligence; Computer science; Feature (linguistics); Pattern recognition (psychology); Smoothing; Computer vision","score_opus":0.006446025547585492,"score_gpt":0.2461280501562718,"score_spread":0.2396820246086863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223609437","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040467896,0.00097065896,0.9522209,0.00032404135,0.00015523961,0.000080831414,0.0008357655,0.0026668417,0.0022778742],"genre_scores_gemma":[0.6794838,0.00064195547,0.30465874,0.00043855977,0.00018385256,0.00012996432,0.0033134283,0.00023491007,0.010914887],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999546,0.000051040217,0.000024290675,0.00018728315,0.00011730749,0.000074126445],"domain_scores_gemma":[0.99961853,0.00012389504,0.00004286233,0.000069741895,0.00012100597,0.000023993423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040548661,0.0008189033,0.0009790797,0.000879429,0.00038086635,0.00065599603,0.0016961867,0.0013960127,0.0036375762],"category_scores_gemma":[0.0012247658,0.00048751384,0.0008791839,0.0007867737,0.0003479752,0.0008866979,0.0011409895,0.0010844278,0.0013914076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004750576,0.00025696203,0.0037797801,0.00024963872,0.00019160392,0.00046635943,0.00008741683,0.19136778,0.031512726,0.0059163044,0.014484549,0.7512118],"study_design_scores_gemma":[0.000012574905,0.00003590575,0.0004725016,0.000010087755,0.000033857275,0.00011131546,0.000015079671,0.99024266,0.00430129,0.0038096178,0.00094729644,0.0000078575285],"about_ca_topic_score_codex":0.008089807,"about_ca_topic_score_gemma":0.014157478,"teacher_disagreement_score":0.008089807,"about_ca_system_score_codex":0.00056398305,"about_ca_system_score_gemma":0.0009132943,"threshold_uncertainty_score":0.016085446},"labels":[],"label_agreement":null},{"id":"W4226378137","doi":"10.1016/j.media.2022.102526","title":"Constrained unsupervised anomaly segmentation","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Anomaly Detection Techniques and Applications","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":"European Regional Development Fund; Generalitat Valenciana; European Commission","keywords":"Computer science; Segmentation; Anomaly detection; Constraint (computer-aided design); Artificial intelligence; Hyperparameter; Regularization (linguistics); Pattern recognition (psychology); Machine learning; Mathematics","score_opus":0.006985585779932297,"score_gpt":0.2591613300058138,"score_spread":0.2521757442258815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226378137","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011435122,0.00023797282,0.98535895,0.00014070421,0.000021807156,0.00004554304,0.0001806051,0.0015655229,0.0010137475],"genre_scores_gemma":[0.43734565,0.0005442022,0.5509437,0.00045693232,0.00021408104,0.00029704414,0.0024499646,0.0014575842,0.006290881],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988605,0.00020083653,0.000053399814,0.00046917007,0.00027040517,0.00014579753],"domain_scores_gemma":[0.9986675,0.0005495641,0.00019393615,0.00028488558,0.00024124424,0.00006299176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009712359,0.0013932899,0.0016769415,0.002212179,0.0005891038,0.0016018341,0.0026451112,0.0021343718,0.0023936841],"category_scores_gemma":[0.0034604182,0.000691165,0.0016054866,0.0017807896,0.0016491408,0.0016993127,0.0022545636,0.0013967223,0.0009806256],"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":[0.00025650312,0.00012645028,0.002926444,0.0003361144,0.00024751673,0.00037739993,0.00037409746,0.5407684,0.03311475,0.03649316,0.008577762,0.37640136],"study_design_scores_gemma":[0.000008361483,0.000017778695,0.00047688987,0.0000128969305,0.00001574494,0.00014412291,0.000020839052,0.9724524,0.004438243,0.020605402,0.0017927383,0.000014586429],"about_ca_topic_score_codex":0.004405566,"about_ca_topic_score_gemma":0.0069451663,"teacher_disagreement_score":0.004405566,"about_ca_system_score_codex":0.0010652874,"about_ca_system_score_gemma":0.0019636883,"threshold_uncertainty_score":0.008759856},"labels":[],"label_agreement":null},{"id":"W4229333559","doi":"10.1016/j.media.2022.102476","title":"Bridging the gap between constrained spherical deconvolution and diffusional variance decomposition via tensor‐valued diffusion MRI","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","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":"Philips (Canada); CARE Canada; Université de Sherbrooke","funders":"","keywords":"Diffusion MRI; Deconvolution; Fractional anisotropy; Computer science; Tensor (intrinsic definition); Anisotropy; Orientation (vector space); Algorithm; Diffusion; White matter; Mathematics; Artificial intelligence; Physics; Magnetic resonance imaging; Geometry; Optics","score_opus":0.02731257130503184,"score_gpt":0.34251903067927053,"score_spread":0.3152064593742387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229333559","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00643749,0.006210263,0.9817546,0.0027106889,0.0001434574,0.0000172069,0.000053060463,0.00014312724,0.0025301217],"genre_scores_gemma":[0.28581673,0.025305618,0.68128675,0.0014314835,0.0014139768,0.00011499284,0.00030782528,0.00050742953,0.0038151352],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974694,0.0011100931,0.0001515486,0.00034746784,0.0008148514,0.00010652773],"domain_scores_gemma":[0.99051356,0.0063472497,0.000603244,0.0014460767,0.00085884426,0.00023096021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006122601,0.0009644704,0.0015359803,0.0011498147,0.00047506142,0.0041846796,0.001803672,0.0025445507,0.0017801821],"category_scores_gemma":[0.020355504,0.00067882484,0.00061139127,0.0012716001,0.0028512704,0.0066672005,0.005703963,0.003548484,0.0005501754],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021260096,0.00007609799,0.00075018755,0.0008860208,0.0002476874,0.000207684,0.00059194764,0.03853648,0.015295451,0.6654638,0.004307482,0.27342454],"study_design_scores_gemma":[0.000028049342,0.000062023406,0.0008108192,0.00023642599,0.000060728307,0.00035782953,0.00016652979,0.37594017,0.005215257,0.6007367,0.016310643,0.00007480167],"about_ca_topic_score_codex":0.0017929085,"about_ca_topic_score_gemma":0.0017255972,"teacher_disagreement_score":0.006122601,"about_ca_system_score_codex":0.0006271465,"about_ca_system_score_gemma":0.0022434848,"threshold_uncertainty_score":0.032379806},"labels":[],"label_agreement":null},{"id":"W4281765497","doi":"10.1016/j.media.2022.102507","title":"Deep learning models of cognitive processes constrained by human brain connectomes","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","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":"Université de Montréal; Institut Universitaire de Gériatrie de Montréal","funders":"","keywords":"Human Connectome Project; Connectome; Computer science; Decoding methods; Cognition; Graph; Artificial intelligence; Neural decoding; Machine learning; Inference; Subnetwork; Neuroscience; Theoretical computer science; Psychology; Functional connectivity; Algorithm","score_opus":0.029169179019087586,"score_gpt":0.29471047534253786,"score_spread":0.26554129632345025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281765497","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44271502,0.0006485182,0.54966885,0.0015629982,0.00006083385,0.000087086795,0.0011224475,0.0007333084,0.0034009246],"genre_scores_gemma":[0.9724917,0.00028085953,0.022043128,0.00018985392,0.000045051656,0.00018343715,0.0006970777,0.00007381312,0.0039950158],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974066,0.00008087987,0.0000074761183,0.0001011609,0.000023161292,0.000046669495],"domain_scores_gemma":[0.99884534,0.0007926176,0.00012916145,0.00008060863,0.00009257907,0.000059556518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000988863,0.0009009251,0.0006614642,0.0005998782,0.00025225838,0.00092979905,0.0014086162,0.0012739154,0.0022011288],"category_scores_gemma":[0.0051074573,0.00044242505,0.0008471249,0.000598483,0.0009674979,0.0013250326,0.0008851835,0.0021706014,0.0004702774],"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":[0.0000967323,0.000049388877,0.0017808116,0.0000326172,0.000045472792,0.000070021175,0.000083924104,0.97406435,0.001318626,0.007286448,0.0007118062,0.014459758],"study_design_scores_gemma":[0.000005043232,0.000009072746,0.00038449356,0.0000035963656,0.0000044863477,0.000010135772,0.0000045342213,0.99205583,0.00016363892,0.007284823,0.00007143927,0.0000029396354],"about_ca_topic_score_codex":0.0066569354,"about_ca_topic_score_gemma":0.0074579148,"teacher_disagreement_score":0.0066569354,"about_ca_system_score_codex":0.0010492346,"about_ca_system_score_gemma":0.0006548385,"threshold_uncertainty_score":0.013236403},"labels":[],"label_agreement":null},{"id":"W4283316793","doi":"10.1016/j.media.2022.102520","title":"MVFStain: Multiple virtual functional stain histopathology images generation based on specific domain mapping","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","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":"Western University","funders":"","keywords":"Stain; Staining; Artificial intelligence; Computer science; H&E stain; Pathology; Pattern recognition (psychology); Medicine","score_opus":0.01887046352473579,"score_gpt":0.2305144341979102,"score_spread":0.21164397067317442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283316793","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075589027,0.00008389615,0.9821578,0.00008264036,0.000045119377,0.000072150775,0.00029121776,0.008256459,0.0014518332],"genre_scores_gemma":[0.13253734,0.00024115318,0.8585915,0.00014112321,0.00004121523,0.00017033766,0.0013008525,0.001915422,0.0050610676],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99984527,0.00001891435,0.0000068047057,0.000034971832,0.000073916,0.000020113435],"domain_scores_gemma":[0.9997769,0.000056526613,0.000015218968,0.00005762908,0.00007222442,0.000021460628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042574148,0.00083897257,0.00037284291,0.0015279825,0.00031340364,0.0008112492,0.0008746277,0.0006859605,0.010817923],"category_scores_gemma":[0.0008860463,0.0004159398,0.0007737992,0.0006062462,0.00022292335,0.00064838346,0.0011551518,0.0005525247,0.0022641248],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041999854,0.0001032878,0.0014871139,0.00024255685,0.0000787904,0.00036091855,0.00016590547,0.025588334,0.14563888,0.0049861367,0.020800887,0.8001272],"study_design_scores_gemma":[0.000049659895,0.000111019384,0.0016116304,0.00002231754,0.000039764072,0.0008999787,0.000097593605,0.8459422,0.124549605,0.0067502614,0.019883143,0.000042805503],"about_ca_topic_score_codex":0.000990214,"about_ca_topic_score_gemma":0.0015697686,"teacher_disagreement_score":0.010817923,"about_ca_system_score_codex":0.0002461014,"about_ca_system_score_gemma":0.00048915535,"threshold_uncertainty_score":0.036189556},"labels":[],"label_agreement":null},{"id":"W4285602699","doi":"10.1016/j.media.2022.102532","title":"Cardiac MRI segmentation with sparse annotations: Ensembling deep learning uncertainty and shape priors","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","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":"University of Toronto; Sunnybrook Health Science Centre","funders":"Canadian Institutes of Health Research; GE Healthcare; National Natural Science Foundation of China; Ontario Research Foundation","keywords":"Artificial intelligence; Prior probability; Segmentation; Computer science; Pattern recognition (psychology); Computer vision; Machine learning; Bayesian probability","score_opus":0.006567144916448316,"score_gpt":0.2791082115076999,"score_spread":0.27254106659125155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285602699","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021497346,0.0005190779,0.975608,0.00031461605,0.000046807494,0.00003503667,0.0001448509,0.0012212188,0.0006130978],"genre_scores_gemma":[0.5805001,0.00051993295,0.4133559,0.0005800797,0.0001585095,0.00012650383,0.0011185136,0.00039705023,0.003243349],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926835,0.00019808165,0.000044149754,0.00024818836,0.00015661884,0.00008459466],"domain_scores_gemma":[0.996856,0.0019095601,0.00024558656,0.00038747033,0.000460053,0.00014132321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024760375,0.0012209825,0.0017770116,0.0010768253,0.00055077905,0.0014477272,0.002281383,0.0030618836,0.0016724429],"category_scores_gemma":[0.008188037,0.0013320036,0.0010910102,0.000879797,0.001131993,0.0021063897,0.0021371709,0.0024189546,0.000626147],"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":[0.00036211943,0.00012661751,0.0016861451,0.00013417729,0.000109621076,0.00010103141,0.00015414385,0.73977524,0.0064486032,0.011409306,0.0036522525,0.23604076],"study_design_scores_gemma":[0.0000046052733,0.000016852475,0.00007144991,0.00000929896,0.000006557503,0.000015205558,0.000003289405,0.99497414,0.0007401984,0.0039804177,0.00017300702,0.0000050501963],"about_ca_topic_score_codex":0.00914027,"about_ca_topic_score_gemma":0.016258597,"teacher_disagreement_score":0.00914027,"about_ca_system_score_codex":0.0012483685,"about_ca_system_score_gemma":0.0015927182,"threshold_uncertainty_score":0.018174171},"labels":[],"label_agreement":null},{"id":"W4286256866","doi":"10.1016/j.media.2022.102554","title":"Task relevance driven adversarial learning for simultaneous detection, size grading, and quantification of hepatocellular carcinoma via integrating multi-modality MRI","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","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":"China Scholarship Council","keywords":"Computer science; Modality (human–computer interaction); Artificial intelligence; Pattern recognition (psychology); Machine learning","score_opus":0.008804619070751366,"score_gpt":0.2814590973556853,"score_spread":0.27265447828493394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286256866","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058122113,0.0007429111,0.9385329,0.0005399943,0.00007202928,0.000064985405,0.00013779876,0.00089549227,0.0008917761],"genre_scores_gemma":[0.8861748,0.00033668114,0.109087996,0.00044112865,0.00013608065,0.00009552362,0.00033524606,0.00013053743,0.0032619268],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949634,0.0001924003,0.000020000296,0.00013275082,0.0000927745,0.000065657165],"domain_scores_gemma":[0.9988984,0.00070893107,0.00011026183,0.00008669685,0.00013727492,0.000058401958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019809871,0.0009287785,0.0010224603,0.00057835487,0.0002767549,0.0007220305,0.0013219469,0.0010350504,0.0007435959],"category_scores_gemma":[0.0032892288,0.0005087977,0.0007195282,0.0004168915,0.0006170198,0.0006739589,0.0016680389,0.0012687674,0.00034358355],"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":[0.000512433,0.00020700484,0.0034280755,0.0001448818,0.000212202,0.00018902583,0.00011400102,0.7306985,0.020985829,0.0044202516,0.003883809,0.23520403],"study_design_scores_gemma":[0.0000036495007,0.000021205136,0.00021499883,0.000002956719,0.000010410182,0.000025985537,0.0000028470522,0.9972512,0.0013875881,0.00093435816,0.00014048145,0.0000042832553],"about_ca_topic_score_codex":0.002834265,"about_ca_topic_score_gemma":0.0039163786,"teacher_disagreement_score":0.002834265,"about_ca_system_score_codex":0.0005582274,"about_ca_system_score_gemma":0.0006820201,"threshold_uncertainty_score":0.010476589},"labels":[],"label_agreement":null},{"id":"W4286592278","doi":"10.1016/j.media.2022.102531","title":"Estimating medical image registration error and confidence: A taxonomy and scoping review","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","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":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Image registration; Computer science; Taxonomy (biology); Field (mathematics); Artificial intelligence; Estimation; Systematic error; Data mining; Machine learning; Data science; Image (mathematics); Statistics; Mathematics","score_opus":0.02076406305091679,"score_gpt":0.35695223752247734,"score_spread":0.3361881744715606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286592278","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00066638115,0.9936999,0.0044568228,0.0004641565,0.00010673456,0.000070226706,0.000093639224,0.000020858019,0.00042130903],"genre_scores_gemma":[0.013448503,0.9724832,0.012609734,0.00047042378,0.0003422781,0.000181889,0.00024259444,0.00004075123,0.00018065676],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.98086756,0.0052310904,0.006242585,0.0027246955,0.004640661,0.00029344682],"domain_scores_gemma":[0.6812732,0.28548527,0.0116061885,0.0040837247,0.01710133,0.0004502017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04572213,0.002260998,0.0074626985,0.020067878,0.00091882725,0.008797755,0.0059206984,0.006134137,0.0019286154],"category_scores_gemma":[0.23131625,0.002121195,0.0050418777,0.01679778,0.00439571,0.00898745,0.0031810347,0.0035309687,0.0005461625],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021779713,0.00009320156,0.0062351483,0.13699865,0.0026660888,0.00013059612,0.0008085913,0.0026373283,0.000399003,0.008040168,0.003694476,0.838079],"study_design_scores_gemma":[0.00015048194,0.00091803767,0.019536987,0.6937698,0.02216973,0.00390233,0.0032182864,0.014776345,0.004335178,0.037315827,0.19926901,0.0006379775],"about_ca_topic_score_codex":0.00987728,"about_ca_topic_score_gemma":0.007992183,"teacher_disagreement_score":0.04572213,"about_ca_system_score_codex":0.003556821,"about_ca_system_score_gemma":0.009207678,"threshold_uncertainty_score":0.2418046},"labels":[],"label_agreement":null},{"id":"W4290859860","doi":"10.1016/j.media.2022.102567","title":"Grayscale self-adjusting network with weak feature enhancement for 3D lumbar anatomy segmentation","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","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":"École de Technologie Supérieure","funders":"","keywords":"Segmentation; Artificial intelligence; Computer science; Feature (linguistics); Lumbar; Deep learning; Process (computing); Magnetic resonance imaging; Image segmentation; Pattern recognition (psychology); Computer vision; Anatomy; Medicine; Radiology","score_opus":0.0034965541401365315,"score_gpt":0.23195369293814336,"score_spread":0.22845713879800683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290859860","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06871856,0.00046784172,0.9267474,0.00015951984,0.00006778986,0.000064883985,0.00013190338,0.0014352537,0.0022068801],"genre_scores_gemma":[0.64640415,0.00054497167,0.34461504,0.00017796084,0.00006675084,0.00011541346,0.00055024197,0.0002589063,0.0072666495],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979013,0.00002903113,0.000011296743,0.000074517346,0.00006367567,0.000031409963],"domain_scores_gemma":[0.99978095,0.000062204024,0.000020954503,0.00003367742,0.000088895635,0.000013320154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004638658,0.00051404344,0.0004698009,0.0007613229,0.00033557048,0.00049021794,0.00078367954,0.00064522325,0.0015974962],"category_scores_gemma":[0.0008787614,0.00029430236,0.00056481815,0.0007150706,0.00028038735,0.00068914634,0.00065082364,0.00043757004,0.00041147938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032954535,0.00013944395,0.0029449344,0.000106760206,0.000095069845,0.00015891425,0.00017152472,0.1653042,0.06256884,0.0029378051,0.0039583724,0.7612846],"study_design_scores_gemma":[0.000005754227,0.00003834686,0.0012501857,0.0000066082353,0.00003135211,0.00006774077,0.000016602744,0.98498744,0.011664519,0.0009032409,0.0010181064,0.0000100603875],"about_ca_topic_score_codex":0.0057795392,"about_ca_topic_score_gemma":0.007218022,"teacher_disagreement_score":0.0057795392,"about_ca_system_score_codex":0.0004606048,"about_ca_system_score_gemma":0.0005458447,"threshold_uncertainty_score":0.0114917755},"labels":[],"label_agreement":null},{"id":"W4293581532","doi":"10.1016/j.media.2022.102610","title":"Predicting treatment-specific lesion outcomes in acute ischemic stroke from 4D CT perfusion imaging using spatio-temporal convolutional neural networks","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Acute Ischemic Stroke Management","field":"Medicine","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":"Alberta Children's Hospital; Hotchkiss Brain Institute; University of Calgary","funders":"","keywords":"Convolutional neural network; Deconvolution; Artificial intelligence; Computer science; Deep learning; Pattern recognition (psychology); Dice; Perfusion scanning; Sørensen–Dice coefficient; Perfusion; Machine learning; Medicine; Radiology; Segmentation; Mathematics; Statistics; Algorithm; Image segmentation","score_opus":0.018628474170982328,"score_gpt":0.2860544841063169,"score_spread":0.26742600993533455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293581532","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9418344,0.0016078792,0.051866833,0.0007269814,0.00008086879,0.000053009004,0.0020705468,0.00029561706,0.0014639851],"genre_scores_gemma":[0.9936185,0.00038780193,0.0044175745,0.000047285866,0.000037877497,0.000019628935,0.001007048,0.000011402427,0.00045299073],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999869,0.000026956131,0.000011806758,0.00003593844,0.000023815282,0.00003244551],"domain_scores_gemma":[0.9996062,0.00016968054,0.00009001745,0.000022956867,0.000071108276,0.000040082832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004724127,0.0005854325,0.00045420375,0.0011796165,0.00013985715,0.000659565,0.00041760827,0.0004691104,0.0008037389],"category_scores_gemma":[0.0018990085,0.00021329575,0.0005998739,0.0004802434,0.00018495944,0.00050014467,0.00040191854,0.0005312045,0.00022829587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021742138,0.00086337276,0.47099915,0.0001939046,0.0008599342,0.0010220514,0.00011959875,0.20733152,0.011439147,0.0014531686,0.0048062573,0.29873765],"study_design_scores_gemma":[0.000014838503,0.000102728976,0.071252115,0.00002812013,0.0001224653,0.00021600224,0.00004624162,0.92402375,0.002011894,0.0017683153,0.00039141052,0.000022092465],"about_ca_topic_score_codex":0.014582607,"about_ca_topic_score_gemma":0.021092879,"teacher_disagreement_score":0.014582607,"about_ca_system_score_codex":0.00055531954,"about_ca_system_score_gemma":0.000586198,"threshold_uncertainty_score":0.028995454},"labels":[],"label_agreement":null},{"id":"W4295025017","doi":"10.1016/j.media.2022.102608","title":"Focused Attention in Transformers for interpretable classification of retinal images","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":90,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hôpital Maisonneuve-Rosemont; Polytechnique Montréal","funders":"","keywords":"Interpretability; Computer science; Artificial intelligence; Transformer; Convolutional neural network; Pattern recognition (psychology); Machine learning; Computer vision","score_opus":0.013977527888023797,"score_gpt":0.31444630171166765,"score_spread":0.30046877382364384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295025017","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06343188,0.0004597811,0.931652,0.00023025005,0.00007662646,0.00006816396,0.00015818093,0.0014778145,0.0024453118],"genre_scores_gemma":[0.8572808,0.0004637147,0.13794084,0.00016210746,0.00009672902,0.00005727923,0.00037445762,0.00028129693,0.0033427174],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995109,0.00012079268,0.00003411285,0.00014383842,0.00009975576,0.00009071179],"domain_scores_gemma":[0.99866486,0.00061651255,0.00010215705,0.00024368771,0.00029713937,0.000075632604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087604177,0.00065596885,0.0006029489,0.0010089538,0.0004447421,0.0016211014,0.0010138719,0.00065211946,0.004790252],"category_scores_gemma":[0.004738912,0.0002513013,0.0004902404,0.0009344322,0.0006392407,0.0014766327,0.0012787142,0.0007934936,0.0010782871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016902458,0.00015363033,0.004082659,0.000314139,0.00007639826,0.00055265217,0.00042212222,0.02866128,0.1599031,0.07471984,0.0051553147,0.7242687],"study_design_scores_gemma":[0.00005595591,0.00032663412,0.004954931,0.00006265038,0.000120951256,0.0006805831,0.00026164192,0.73806125,0.13753487,0.110464156,0.0074443347,0.000032047792],"about_ca_topic_score_codex":0.0027594822,"about_ca_topic_score_gemma":0.0021414012,"teacher_disagreement_score":0.004790252,"about_ca_system_score_codex":0.0008008441,"about_ca_system_score_gemma":0.0007204582,"threshold_uncertainty_score":0.016024947},"labels":[],"label_agreement":null},{"id":"W4296199856","doi":"10.1016/j.media.2022.102617","title":"Source-free domain adaptation for image segmentation","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":92,"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; Hôpital Notre-Dame","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Nvidia","keywords":"Artificial intelligence; Computer vision; Computer science; Domain adaptation; Adaptation (eye); Segmentation; Image (mathematics); Domain (mathematical analysis); Image segmentation; Pattern recognition (psychology); Mathematics; Psychology","score_opus":0.013353712860726735,"score_gpt":0.2706699761746786,"score_spread":0.25731626331395185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296199856","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005484347,0.00043172302,0.9922753,0.00009712452,0.000037403224,0.000026653672,0.00008750419,0.0010846328,0.00047526293],"genre_scores_gemma":[0.31937772,0.0013176467,0.66976124,0.00047116683,0.00018710237,0.00019845463,0.0016518885,0.0010497058,0.0059851008],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995347,0.00014185441,0.000022446919,0.00015110608,0.000100768906,0.000049166847],"domain_scores_gemma":[0.99896324,0.00047670043,0.00005354131,0.00023574225,0.00022026381,0.00005040213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013207739,0.0008842238,0.0013980443,0.0010537374,0.000407956,0.000831745,0.0015719755,0.0016476365,0.0021417998],"category_scores_gemma":[0.0034105093,0.0005738788,0.0011941026,0.001055383,0.0007864839,0.0015398785,0.0018621472,0.0019432164,0.0013920141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046862423,0.00024307476,0.0008154003,0.00042806647,0.00026147647,0.0001814919,0.00015811143,0.29731286,0.06120104,0.013565496,0.009980793,0.6153835],"study_design_scores_gemma":[0.000010997093,0.000031023064,0.00034345425,0.000012232721,0.000022569777,0.00010532164,0.000015556232,0.975749,0.009856481,0.012317118,0.0015218287,0.000014388453],"about_ca_topic_score_codex":0.0026446346,"about_ca_topic_score_gemma":0.0029076764,"teacher_disagreement_score":0.0026446346,"about_ca_system_score_codex":0.00053070043,"about_ca_system_score_gemma":0.0008695169,"threshold_uncertainty_score":0.0071650743},"labels":[],"label_agreement":null},{"id":"W4296288620","doi":"10.1016/j.media.2022.102611","title":"Anticipation for surgical workflow through instrument interaction and recognized Signals","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Surgical Simulation and Training","field":"Medicine","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":"Carleton University; University of Ottawa","funders":"","keywords":"Computer science; Anticipation (artificial intelligence); Feature (linguistics); Workflow; Artificial intelligence; Task (project management); Segmentation; Noise (video); Inference; Laptop; Surgical instrument; Machine learning; Human–computer interaction; Computer vision; Pattern recognition (psychology)","score_opus":0.055835172749610903,"score_gpt":0.3775551914670036,"score_spread":0.3217200187173927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296288620","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13550854,0.00041088654,0.8548627,0.0006001445,0.00034179993,0.00011220111,0.00015899447,0.0016215865,0.006383173],"genre_scores_gemma":[0.8830636,0.00032385386,0.11340968,0.0001593126,0.000107847096,0.000065217544,0.00014623864,0.00018680678,0.0025373697],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995117,0.00010653614,0.00002248949,0.00009895216,0.00019049515,0.00006974501],"domain_scores_gemma":[0.998582,0.000803218,0.00020323682,0.00007545861,0.00021334733,0.00012282157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073760987,0.0007682805,0.0003458874,0.0008331632,0.0003836654,0.0016330666,0.00047398283,0.00076087756,0.0030060376],"category_scores_gemma":[0.005567467,0.00046119993,0.00037853667,0.00041083386,0.00036293294,0.0014513604,0.001209475,0.0012975847,0.000929838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003051229,0.0006384011,0.02519031,0.00042312758,0.00011218813,0.0010292577,0.001011038,0.09825839,0.34013054,0.011802209,0.0058762734,0.51247716],"study_design_scores_gemma":[0.00006188007,0.0010599211,0.033562686,0.00012057377,0.00010820239,0.0010012294,0.00039930284,0.869897,0.078478746,0.0097057065,0.0054600197,0.00014477673],"about_ca_topic_score_codex":0.0008357848,"about_ca_topic_score_gemma":0.001169836,"teacher_disagreement_score":0.0030060376,"about_ca_system_score_codex":0.00032534896,"about_ca_system_score_gemma":0.0011428583,"threshold_uncertainty_score":0.010056198},"labels":[],"label_agreement":null},{"id":"W4302774145","doi":"10.1016/j.media.2022.102649","title":"Predicting the evolution trajectory of population-driven connectional brain templates using recurrent multigraph neural networks","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","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 on Aging; Canadian Institutes of Health Research","keywords":"Multigraph; Computer science; Population; Artificial intelligence; Neuroimaging; Normalization (sociology); Graph; Machine learning; Neuroscience; Biology; Theoretical computer science; Medicine","score_opus":0.028425723417708527,"score_gpt":0.2876609425353886,"score_spread":0.25923521911768005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302774145","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52354646,0.00042673838,0.47288644,0.00045080401,0.000057441015,0.000047768233,0.00034699886,0.0008592582,0.0013781389],"genre_scores_gemma":[0.9780318,0.0000853764,0.020684315,0.000027807255,0.00001329828,0.000020450238,0.00021131709,0.00005497756,0.0008706877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999193,0.000014806207,0.0000027777091,0.0000321427,0.000015648453,0.000015427015],"domain_scores_gemma":[0.9991979,0.00049780204,0.00010831748,0.000047226582,0.00010796044,0.00004084841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035496725,0.0003857779,0.00028795286,0.0006302002,0.00018984318,0.00042649335,0.0005437922,0.00069240015,0.00085204124],"category_scores_gemma":[0.002797256,0.00036608684,0.00044576943,0.0004351157,0.00028533235,0.00067338656,0.00028934097,0.00072907587,0.00025762542],"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":[0.00013036636,0.00006861646,0.010293976,0.000051865005,0.000086376866,0.00025010738,0.000096145464,0.90646595,0.011765393,0.004204795,0.0019163875,0.06466994],"study_design_scores_gemma":[0.0000010453867,0.0000048846555,0.00071576674,0.0000012066582,0.0000029873652,0.00001574125,0.0000028609268,0.99812335,0.0003796148,0.00071382156,0.000036602894,0.0000020543318],"about_ca_topic_score_codex":0.008087095,"about_ca_topic_score_gemma":0.010971415,"teacher_disagreement_score":0.008087095,"about_ca_system_score_codex":0.000597566,"about_ca_system_score_gemma":0.00045661745,"threshold_uncertainty_score":0.016080081},"labels":[],"label_agreement":null},{"id":"W4308448228","doi":"10.1016/j.media.2022.102670","title":"Segmentation with mixed supervision: Confidence maximization helps knowledge distillation","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","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":"Centre Hospitalier de l’Université de Montréal; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Distillation; Maximization; Artificial intelligence; Segmentation; Machine learning; Mathematics; Mathematical optimization; Chemistry; Chromatography","score_opus":0.008958241496845029,"score_gpt":0.2593132697323961,"score_spread":0.25035502823555106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308448228","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026992435,0.0007200452,0.96321064,0.0011693798,0.00013944769,0.000103664504,0.00054345466,0.004687151,0.002433845],"genre_scores_gemma":[0.5552192,0.00043269526,0.4358451,0.00081939506,0.00032249707,0.00014947302,0.002407422,0.0009855417,0.0038187227],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99806935,0.0005041323,0.00013904594,0.0006796145,0.00041150162,0.00019627552],"domain_scores_gemma":[0.99266833,0.0042542485,0.00036212752,0.0013599029,0.0010386156,0.00031684115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003162906,0.002072971,0.0021600514,0.0019760919,0.0011322264,0.003170245,0.00374816,0.0040746494,0.006472342],"category_scores_gemma":[0.014704214,0.0014315323,0.001981304,0.0015940336,0.0013617818,0.0045927214,0.004074899,0.0047691483,0.002038241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015538844,0.00052066776,0.003303176,0.00070566306,0.00043754233,0.00035480785,0.0004661331,0.18262486,0.03844233,0.02139624,0.020450745,0.72974384],"study_design_scores_gemma":[0.000035528275,0.000072375595,0.00037376632,0.00004261336,0.000059224207,0.00008702424,0.000036330035,0.96078897,0.014064013,0.022264248,0.0021543277,0.000021605823],"about_ca_topic_score_codex":0.0056479094,"about_ca_topic_score_gemma":0.009131071,"teacher_disagreement_score":0.006472342,"about_ca_system_score_codex":0.0009542497,"about_ca_system_score_gemma":0.0024063075,"threshold_uncertainty_score":0.021652162},"labels":[],"label_agreement":null},{"id":"W4308459649","doi":"10.1016/j.media.2022.102681","title":"From sMRI to task-fMRI: A unified geometric deep learning framework for cross-modal brain anatomo-functional mapping","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","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":"Western University","funders":"Natural Science Foundation of Beijing Municipality; China Scholarship Council; National Natural Science Foundation of China","keywords":"Computer science; Context (archaeology); Artificial intelligence; Deep learning; Graph; Pattern recognition (psychology); Machine learning; Theoretical computer science; Biology","score_opus":0.031196875085315573,"score_gpt":0.3144274947720919,"score_spread":0.2832306196867763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308459649","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031652579,0.00027150923,0.9940441,0.00021831969,0.000038696056,0.00003675708,0.00028318324,0.0012382161,0.00070392713],"genre_scores_gemma":[0.18584514,0.0012587245,0.8016335,0.0005996889,0.0001872437,0.00038138527,0.0022477088,0.0010782789,0.006768299],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999673,0.000093092705,0.000015084744,0.00009624033,0.000077102624,0.000045380275],"domain_scores_gemma":[0.99956244,0.00012679759,0.000049832834,0.00011544722,0.00010603645,0.00003942244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001148791,0.0015037783,0.00095965323,0.00087011245,0.00039519186,0.0011619704,0.0022562225,0.001397456,0.0038121026],"category_scores_gemma":[0.0025884174,0.0007110104,0.0014338045,0.0011900278,0.0005840804,0.0013506938,0.0027729915,0.002187449,0.0020395187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001739734,0.00018380895,0.0010129288,0.00025667308,0.00031336545,0.00017172286,0.00012217385,0.28009528,0.02723235,0.033533216,0.017237356,0.63966715],"study_design_scores_gemma":[0.000008723739,0.0000617373,0.000483832,0.000016855778,0.000030526677,0.00014840478,0.000019916326,0.9502293,0.004509832,0.040250134,0.0042151567,0.000025557369],"about_ca_topic_score_codex":0.005929214,"about_ca_topic_score_gemma":0.014750704,"teacher_disagreement_score":0.005929214,"about_ca_system_score_codex":0.00066495,"about_ca_system_score_gemma":0.0017880006,"threshold_uncertainty_score":0.012752771},"labels":[],"label_agreement":null},{"id":"W4308889883","doi":"10.1016/j.media.2022.102693","title":"SSD-KD: A self-supervised diverse knowledge distillation method for lightweight skin lesion classification using dermoscopic images","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":125,"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 Coastal Health; BC Research (Canada); Vancouver Coastal Health Research Institute; Spinal Cord Injury BC; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Benchmark (surveying); Artificial intelligence; Machine learning; Convolutional neural network; Container (type theory); Feature (linguistics); Pattern recognition (psychology); Engineering","score_opus":0.037007236712825714,"score_gpt":0.35365210019532106,"score_spread":0.3166448634824953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308889883","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046030797,0.0013986676,0.92585397,0.0002789583,0.0003125582,0.00033738185,0.003108346,0.020569071,0.002110248],"genre_scores_gemma":[0.3009867,0.0005886954,0.676122,0.0005441151,0.0001914215,0.00037125935,0.01187198,0.00057658105,0.008747189],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991534,0.00009197488,0.000070439324,0.00030234345,0.00027668627,0.00010523051],"domain_scores_gemma":[0.9992362,0.00021489635,0.00004796321,0.0001619682,0.00028874722,0.000050165647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009269101,0.0014397983,0.0016796228,0.0023227357,0.00076715124,0.0009876502,0.0026988047,0.0015134189,0.0040986035],"category_scores_gemma":[0.0017135296,0.00049334415,0.0014267769,0.0017787853,0.00039221917,0.0014040573,0.0017062902,0.0014909194,0.003157764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031117402,0.0003741037,0.0019391216,0.00020919427,0.000200005,0.000117528325,0.000053091004,0.018138966,0.017542837,0.0006968967,0.018324224,0.9420929],"study_design_scores_gemma":[0.00005573412,0.00015709373,0.0017422263,0.000026809905,0.00008418057,0.00026061106,0.00006504958,0.9714626,0.016556967,0.0028699208,0.0066793943,0.000039344995],"about_ca_topic_score_codex":0.00806576,"about_ca_topic_score_gemma":0.015442746,"teacher_disagreement_score":0.00806576,"about_ca_system_score_codex":0.00056749064,"about_ca_system_score_gemma":0.0014968308,"threshold_uncertainty_score":0.016037643},"labels":[],"label_agreement":null},{"id":"W4309630525","doi":"10.1016/j.media.2022.102684","title":"Guidelines and evaluation of clinical explainable AI in medical image analysis","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":168,"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":"BC Cancer Foundation","keywords":"Guideline; Computer science; Data mining; Artificial intelligence; Medical physics; Medicine; Pathology","score_opus":0.10646275466905696,"score_gpt":0.4673112791791384,"score_spread":0.3608485245100814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309630525","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15247409,0.1254035,0.40070903,0.156161,0.0023705598,0.013609982,0.005310349,0.0047341934,0.13922727],"genre_scores_gemma":[0.41745666,0.014599725,0.5488986,0.006193096,0.00033200617,0.003988579,0.0025175982,0.0005299671,0.0054838127],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9528369,0.031091163,0.006197035,0.0011458421,0.008068644,0.0006604524],"domain_scores_gemma":[0.78920364,0.11786024,0.009148144,0.008429112,0.07077319,0.004585631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.045888044,0.0008471851,0.0008541698,0.006012175,0.001355623,0.005992034,0.0036228315,0.0032067893,0.0043109246],"category_scores_gemma":[0.20926642,0.0005094201,0.0009743534,0.0029341383,0.002244338,0.0025226658,0.0023451517,0.0024030805,0.001612262],"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":[0.0015786716,0.0011806971,0.037061423,0.004847665,0.00041072816,0.0008410404,0.005399383,0.0076833386,0.0036100121,0.06548743,0.066970125,0.8049295],"study_design_scores_gemma":[0.0022771237,0.0044744224,0.08403593,0.03388808,0.0021349501,0.0070199566,0.012657898,0.10018719,0.0477797,0.22218467,0.48268312,0.00067689805],"about_ca_topic_score_codex":0.006923895,"about_ca_topic_score_gemma":0.013058175,"teacher_disagreement_score":0.045888044,"about_ca_system_score_codex":0.004042863,"about_ca_system_score_gemma":0.010382502,"threshold_uncertainty_score":0.2426821},"labels":[],"label_agreement":null},{"id":"W4309763239","doi":"10.1016/j.media.2022.102690","title":"Symmetry-based regularization in deep breast cancer screening","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":13,"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":"European Regional Development Fund; Fundação para a Ciência e a Tecnologia; Canadian Mennonite University; Nvidia","keywords":"Regularization (linguistics); Computer science; Artificial intelligence; Machine learning; Breast cancer; Generalization; Artificial neural network; Theoretical computer science; Mathematics; Medicine; Cancer","score_opus":0.006943077669812815,"score_gpt":0.2585088927304301,"score_spread":0.25156581506061726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309763239","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029568946,0.0006380475,0.96667707,0.00041815834,0.000040175983,0.000037724716,0.00012245255,0.0005418397,0.0019555653],"genre_scores_gemma":[0.6584563,0.00085997465,0.32860884,0.0004053883,0.00012407845,0.00012193853,0.0005130329,0.0003151578,0.010595225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996717,0.00013249028,0.000014842107,0.00004552873,0.000085527885,0.000049782655],"domain_scores_gemma":[0.9993487,0.0003041176,0.00006851327,0.00010006168,0.00013304262,0.00004548937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011913276,0.00048278066,0.00071261305,0.0005780545,0.00025592468,0.0005343581,0.001025506,0.00085307984,0.0018642641],"category_scores_gemma":[0.002238377,0.0004025631,0.0007358379,0.0004888356,0.00056480366,0.00072035805,0.0010358222,0.000951313,0.0005231587],"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":[0.00045922183,0.00023687202,0.0020287419,0.0002809927,0.00019236606,0.0001358295,0.000111172085,0.44319326,0.055468354,0.049375877,0.008301659,0.4402156],"study_design_scores_gemma":[0.0000062333065,0.000023038165,0.00024832835,0.0000053415692,0.000010355771,0.000039970677,0.0000067334768,0.9896039,0.0037409223,0.0057121487,0.0005983116,0.000004820182],"about_ca_topic_score_codex":0.0032194834,"about_ca_topic_score_gemma":0.0040090024,"teacher_disagreement_score":0.0032194834,"about_ca_system_score_codex":0.0004729622,"about_ca_system_score_gemma":0.0009769794,"threshold_uncertainty_score":0.0064014792},"labels":[],"label_agreement":null},{"id":"W4309764145","doi":"10.1016/j.media.2022.102698","title":"Orthogonal latent space learning with feature weighting and graph learning for multimodal Alzheimer’s disease diagnosis","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":62,"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; Department of Science and Technology of Sichuan Province; National Institute on Aging; National Center for Advancing Translational Sciences; Ministry of Science and Technology of the People's Republic of China","keywords":"Discriminative model; Artificial intelligence; Computer science; Feature vector; Feature learning; Weighting; Graph; Pattern recognition (psychology); Machine learning; Probabilistic latent semantic analysis; Theoretical computer science","score_opus":0.011779166621783393,"score_gpt":0.3006232963432875,"score_spread":0.2888441297215041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309764145","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028806519,0.00089799747,0.96809745,0.00038380385,0.00005660923,0.000052843363,0.0003452672,0.00085893535,0.0005006281],"genre_scores_gemma":[0.5678499,0.0008046951,0.42566577,0.00033102534,0.00014755024,0.00020420019,0.0021338256,0.00022225792,0.002640837],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872464,0.0006327031,0.00007106332,0.00025894764,0.00019648738,0.000116227224],"domain_scores_gemma":[0.99771464,0.0013820099,0.00015986945,0.0003193959,0.0003269206,0.00009713186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002265019,0.0007816245,0.0014185963,0.0021349925,0.00054961874,0.0010052957,0.0015067116,0.0012653845,0.0017347381],"category_scores_gemma":[0.006036877,0.00046234787,0.0016962554,0.0020720107,0.00071325543,0.0017116838,0.0019715691,0.0017480986,0.0005668203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060256326,0.0006325423,0.0044010663,0.0002314413,0.00039834026,0.00012025008,0.0001606119,0.22550453,0.0068389503,0.020597942,0.010560974,0.72995085],"study_design_scores_gemma":[0.000017130928,0.000038792066,0.00035153955,0.000008471258,0.00002814225,0.000027034961,0.000016903628,0.9846031,0.0005874644,0.013837314,0.00047368422,0.000010271379],"about_ca_topic_score_codex":0.010026842,"about_ca_topic_score_gemma":0.010609635,"teacher_disagreement_score":0.010026842,"about_ca_system_score_codex":0.00075507455,"about_ca_system_score_gemma":0.0013205003,"threshold_uncertainty_score":0.019936979},"labels":[],"label_agreement":null},{"id":"W4309915768","doi":"10.1016/j.media.2022.102699","title":"Mitosis domain generalization in histopathology images — The MIDOG challenge","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":146,"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":"Amazon Web Services; Siemens Healthineers; Nvidia","keywords":"Artificial intelligence; Computer science; Grading (engineering); Limiting; Generalization; Pattern recognition (psychology); Set (abstract data type); Domain (mathematical analysis); Machine learning; Mathematics; Biology","score_opus":0.007711893702026811,"score_gpt":0.2507850560881752,"score_spread":0.24307316238614837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309915768","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3882798,0.024216814,0.5145325,0.01893357,0.001175739,0.00071718247,0.01554477,0.018619647,0.017980058],"genre_scores_gemma":[0.6952309,0.0054057743,0.2621979,0.0031769348,0.0007906987,0.00012965564,0.01812269,0.0010106546,0.013934776],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999062,0.00022923821,0.00004738138,0.00029371117,0.00025654933,0.00011118428],"domain_scores_gemma":[0.99761117,0.0010950043,0.00013248794,0.0006172457,0.00035998505,0.00018414523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022931537,0.0011338195,0.0016914996,0.0017240307,0.000752815,0.0017345507,0.0016174867,0.0023146756,0.0026507769],"category_scores_gemma":[0.0046492657,0.0004379103,0.0013635372,0.001268109,0.0007465243,0.0012693547,0.0016198447,0.0022969828,0.0020147592],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009964956,0.0004526518,0.008380977,0.00081782445,0.00025766145,0.00072386337,0.0002252715,0.048557956,0.028929109,0.005560893,0.082823634,0.82227373],"study_design_scores_gemma":[0.00017323271,0.0003444385,0.012465644,0.00024053227,0.0001592618,0.003208371,0.0007940511,0.84868795,0.037627768,0.051243637,0.04498648,0.000068627836],"about_ca_topic_score_codex":0.006260317,"about_ca_topic_score_gemma":0.010393703,"teacher_disagreement_score":0.006260317,"about_ca_system_score_codex":0.0009815608,"about_ca_system_score_gemma":0.0012400758,"threshold_uncertainty_score":0.012447715},"labels":[],"label_agreement":null},{"id":"W4318773574","doi":"10.1016/j.media.2023.102761","title":"Generative Sampling in Bundle Tractography using Autoencoders (GESTA)","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","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":"Université de Sherbrooke","funders":"Compute Canada; McDonnell Center for Systems Neuroscience; Réseau en Bio-Imagerie du Quebec; National Institutes of Health; Université de Sherbrooke","keywords":"Tractography; Streamlines, streaklines, and pathlines; Artificial intelligence; Human Connectome Project; White matter; Pattern recognition (psychology); Diffusion MRI; Computer science; Mathematics; Computer vision; Physics; Neuroscience; Psychology; Magnetic resonance imaging","score_opus":0.1638148958616718,"score_gpt":0.45533188993030915,"score_spread":0.29151699406863735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318773574","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003373184,0.00013672271,0.9958324,0.000073735275,0.000021221344,0.0000107618,0.000024772908,0.00026378635,0.00026333216],"genre_scores_gemma":[0.32533735,0.0006340277,0.667134,0.00021788449,0.00016200975,0.00020928343,0.00040002025,0.0005860628,0.0053193853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994522,0.00025474286,0.00002877011,0.000116058596,0.00009815922,0.00005016352],"domain_scores_gemma":[0.99687225,0.0023404143,0.0001789262,0.00027030942,0.0002243542,0.00011373892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017356226,0.0007836889,0.001281839,0.00070469064,0.0005445311,0.001068968,0.0012270561,0.0019397727,0.0021262234],"category_scores_gemma":[0.0049697612,0.0014029966,0.0015307064,0.0008926649,0.0012670506,0.0013503117,0.0017511668,0.0021809682,0.0008220612],"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":[0.00007617212,0.00004254968,0.0006948689,0.000076711876,0.00010017574,0.00010922813,0.00013225115,0.8591357,0.0025569578,0.032847974,0.0015513354,0.10267604],"study_design_scores_gemma":[0.0000026842624,0.0000055070927,0.000049495862,0.000004151651,0.000003923184,0.000013908244,0.0000029340167,0.99267936,0.00026261533,0.0067300955,0.00024155472,0.0000037752177],"about_ca_topic_score_codex":0.009712264,"about_ca_topic_score_gemma":0.012651237,"teacher_disagreement_score":0.009712264,"about_ca_system_score_codex":0.0007389914,"about_ca_system_score_gemma":0.001142696,"threshold_uncertainty_score":0.019311488},"labels":[],"label_agreement":null},{"id":"W4328098743","doi":"10.1016/j.media.2023.102795","title":"An ultrasound-exclusive non-invasive computational diagnostic framework for personalized cardiology of aortic valve stenosis","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":false,"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":"Stenosis; Medicine; Cardiology; Internal medicine; Aortic valve; Aortic valve stenosis; Radiology; Heart valve; Ultrasound","score_opus":0.013441990357171592,"score_gpt":0.3731156302263428,"score_spread":0.3596736398691712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4328098743","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010064181,0.00036874713,0.9858743,0.00029394502,0.000057579335,0.00007187485,0.00018265426,0.0018983686,0.0011884514],"genre_scores_gemma":[0.4007843,0.0006107306,0.59369653,0.00053192116,0.00018451757,0.00024097298,0.0007365574,0.00025146038,0.0029630037],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957675,0.00009870779,0.000025973803,0.00009616831,0.00015475973,0.000047642196],"domain_scores_gemma":[0.9995435,0.00018394446,0.00003282526,0.000055054497,0.00012385633,0.000060941748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071990606,0.0006701697,0.0008542756,0.00069701305,0.0003747783,0.0013732257,0.0015174826,0.00087948097,0.0018119747],"category_scores_gemma":[0.0018920925,0.00031969574,0.0009427799,0.00035322557,0.00033572648,0.0006459016,0.0016990039,0.00086935784,0.00064722117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007991061,0.00072653,0.009310535,0.00033923224,0.00042521796,0.0013094649,0.00033255894,0.3129144,0.040519476,0.03676323,0.0139890425,0.5825712],"study_design_scores_gemma":[0.000013112954,0.000035222274,0.0005094017,0.000010975901,0.000024822106,0.00013639822,0.00001318121,0.9901356,0.0020823956,0.005441273,0.0015859187,0.000011828436],"about_ca_topic_score_codex":0.0033647364,"about_ca_topic_score_gemma":0.0052145994,"teacher_disagreement_score":0.0033647364,"about_ca_system_score_codex":0.00036067545,"about_ca_system_score_gemma":0.001184698,"threshold_uncertainty_score":0.0066903234},"labels":[],"label_agreement":null},{"id":"W4362560246","doi":"10.1016/j.media.2023.102808","title":"MyoPS: A benchmark of myocardial pathology segmentation combining three-sequence cardiac magnetic resonance images","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced MRI Techniques and Applications","field":"Medicine","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":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Segmentation; Benchmark (surveying); Computer science; Preprocessor; Artificial intelligence; Cardiac magnetic resonance; Myocardial infarction; Magnetic resonance imaging; Image segmentation; Medical physics; Medicine; Pattern recognition (psychology); Radiology; Cardiology","score_opus":0.01768570160716264,"score_gpt":0.3377404210989619,"score_spread":0.32005471949179926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362560246","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63755065,0.014649475,0.24551222,0.0015502425,0.0012121762,0.0013046913,0.029788127,0.056543473,0.011888978],"genre_scores_gemma":[0.64075387,0.0025182606,0.26974866,0.00068434526,0.00042702517,0.0003789616,0.07516109,0.0039927615,0.00633502],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983884,0.00035111338,0.00013751778,0.00053416024,0.000439567,0.00014936415],"domain_scores_gemma":[0.9982432,0.00061238767,0.0001301016,0.00027103402,0.00050350773,0.00023977067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003276195,0.00243974,0.0014214863,0.0053300387,0.0008996534,0.002650483,0.0024019927,0.0029293532,0.002941649],"category_scores_gemma":[0.0054273577,0.0006323023,0.0013252912,0.002005324,0.0006268056,0.00097397703,0.0018251505,0.000810804,0.002176667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0064339973,0.0016670837,0.024424851,0.0040824376,0.003562253,0.0023066334,0.0005210337,0.12633851,0.08716467,0.0026282358,0.07380591,0.6670645],"study_design_scores_gemma":[0.0007387767,0.001864487,0.042385414,0.0002615272,0.000891136,0.005044419,0.0004898947,0.8500699,0.064046286,0.0050721127,0.028923437,0.00021262822],"about_ca_topic_score_codex":0.00804751,"about_ca_topic_score_gemma":0.012052694,"teacher_disagreement_score":0.00804751,"about_ca_system_score_codex":0.00074027386,"about_ca_system_score_gemma":0.001420017,"threshold_uncertainty_score":0.017326415},"labels":[],"label_agreement":null},{"id":"W4366831358","doi":"10.1016/j.media.2023.102826","title":"Calibrating segmentation networks with margin-based label smoothing","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","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":"Centre Hospitalier de l’Université de Montréal; École de Technologie Supérieure","funders":"H2020 Marie Skłodowska-Curie Actions; Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Softmax function; Discriminative model; Computer science; Margin (machine learning); Segmentation; Artificial intelligence; Machine learning; Logit; Artificial neural network; Mathematical optimization; Pattern recognition (psychology); Mathematics","score_opus":0.013038805535436885,"score_gpt":0.28536634430265007,"score_spread":0.27232753876721316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366831358","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022714963,0.00017439341,0.97340167,0.0001514717,0.00006335083,0.00005601055,0.00004102989,0.0025821973,0.0008148656],"genre_scores_gemma":[0.34384757,0.00016303081,0.650845,0.00029115146,0.00007089002,0.00017497157,0.00034076622,0.0011014165,0.0031651491],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855894,0.0003528795,0.000067448564,0.0005537923,0.0003423957,0.00012458449],"domain_scores_gemma":[0.9970151,0.0012451014,0.0003247964,0.00048785203,0.0008128654,0.00011429786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033420653,0.0015523097,0.0013271195,0.0015870044,0.00097049365,0.0018361404,0.0022572135,0.003510534,0.0023000052],"category_scores_gemma":[0.010591241,0.0014813,0.0012355185,0.0011656892,0.0012892884,0.002071765,0.002534623,0.0030046843,0.001770347],"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":[0.00048876164,0.00018807712,0.0029753668,0.00017101163,0.00015383044,0.00012218434,0.00033751014,0.5343371,0.047039162,0.008126629,0.0040442226,0.40201622],"study_design_scores_gemma":[0.0000080155205,0.00002256794,0.0002551284,0.0000102948225,0.000011629461,0.000023702321,0.000010856651,0.9889249,0.006936846,0.0031643885,0.0006222532,0.00000943451],"about_ca_topic_score_codex":0.0068953885,"about_ca_topic_score_gemma":0.0105856005,"teacher_disagreement_score":0.0068953885,"about_ca_system_score_codex":0.0017332324,"about_ca_system_score_gemma":0.0016661817,"threshold_uncertainty_score":0.017674744},"labels":[],"label_agreement":null},{"id":"W4367853078","doi":"10.1016/j.media.2023.103073","title":"Automatic motion artefact detection in brain T1-weighted magnetic resonance images from a clinical data warehouse using synthetic data","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":16,"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 Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; Agence Nationale de la Recherche; 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; Alzheimer's Association","keywords":"Computer science; Artificial intelligence; Computer vision; Motion (physics); Ghosting; Magnetic resonance imaging; Medical imaging; Exploit; Pattern recognition (psychology)","score_opus":0.09274629936307639,"score_gpt":0.43112744423604804,"score_spread":0.33838114487297166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367853078","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70048285,0.0010141251,0.29030895,0.0004340365,0.00014222249,0.00015838248,0.0031001137,0.0038253611,0.00053396414],"genre_scores_gemma":[0.84411365,0.0003125565,0.14878258,0.00006208132,0.00004401125,0.00005950056,0.0061616884,0.00015759893,0.00030626144],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937207,0.0001478584,0.00009822093,0.00014922513,0.0001739951,0.00005875822],"domain_scores_gemma":[0.9965814,0.0017239943,0.0003853452,0.0003984105,0.0008108575,0.00010004807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017114427,0.0006163489,0.00043737717,0.0019465523,0.00021885033,0.0011519948,0.000568107,0.00065731845,0.0003682909],"category_scores_gemma":[0.0067299954,0.00025246653,0.00070319866,0.0013720439,0.00024955068,0.0004856092,0.00063530204,0.0004343787,0.00028507703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0040546414,0.00065152516,0.05049596,0.0010868374,0.00058045745,0.0030256999,0.0009617942,0.115600206,0.21887201,0.0017712588,0.008719639,0.59418],"study_design_scores_gemma":[0.00007332693,0.00032247484,0.022704292,0.000048793096,0.00016518947,0.0020881977,0.00030095052,0.90404665,0.06559142,0.0014521814,0.0031496717,0.00005691438],"about_ca_topic_score_codex":0.0023289255,"about_ca_topic_score_gemma":0.0025132643,"teacher_disagreement_score":0.0023289255,"about_ca_system_score_codex":0.0003378229,"about_ca_system_score_gemma":0.000732281,"threshold_uncertainty_score":0.0090510845},"labels":[],"label_agreement":null},{"id":"W4376866854","doi":"10.1016/j.media.2023.102841","title":"BolT: Fused window transformers for fMRI time series analysis","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":106,"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":"Computer science; Landmark; Artificial intelligence; Transformer; Cascade; LEAPS; Pattern recognition (psychology); Time series; Regularization (linguistics); Encoder; Window (computing); Machine learning","score_opus":0.020018020582874607,"score_gpt":0.2881298682667515,"score_spread":0.2681118476838769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376866854","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022796006,0.00008439379,0.9845674,0.000038643528,0.0000498687,0.00004492108,0.0005990743,0.011750729,0.000585427],"genre_scores_gemma":[0.088509515,0.00025156187,0.89676213,0.00013617481,0.00010275918,0.00042556398,0.0019723137,0.0064185024,0.0054214615],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964666,0.00006859954,0.000032575383,0.00007221184,0.00013096805,0.000049014525],"domain_scores_gemma":[0.9990318,0.0004498623,0.00007869395,0.00020814459,0.00015587911,0.00007559908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012121358,0.0011794054,0.0006711115,0.0011582457,0.0004005137,0.0015989967,0.0011529042,0.0010478521,0.031580403],"category_scores_gemma":[0.0055724634,0.00063685916,0.0007325976,0.0011397619,0.0004413356,0.0019777815,0.00175238,0.0014516292,0.008462494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019882368,0.00015463884,0.0008485061,0.0004927097,0.00020391353,0.00070551294,0.00025515992,0.019141816,0.09527544,0.042352855,0.051641222,0.78694],"study_design_scores_gemma":[0.0002520256,0.00041876597,0.0019522756,0.00010987244,0.00012403721,0.0011683758,0.00009888826,0.6850196,0.18101452,0.070141375,0.059569716,0.00013045735],"about_ca_topic_score_codex":0.001017172,"about_ca_topic_score_gemma":0.0017464884,"teacher_disagreement_score":0.031580403,"about_ca_system_score_codex":0.00031871008,"about_ca_system_score_gemma":0.00068411394,"threshold_uncertainty_score":0.10564703},"labels":[],"label_agreement":null},{"id":"W4377695098","doi":"10.1016/j.media.2023.102846","title":"Diffusion models in medical imaging: A comprehensive survey","year":2023,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":652,"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; Artificial intelligence; Probabilistic logic; Diffusion map; Noise (video); Noise reduction; Machine learning; Diffusion; Medical imaging; Data science; Image (mathematics); Nonlinear dimensionality reduction","score_opus":0.08373872611063253,"score_gpt":0.387150647904169,"score_spread":0.30341192179353643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377695098","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015871332,0.995152,0.0032817626,0.00038216924,0.00014072555,0.0000064267974,0.000029574492,0.000021855873,0.0008267769],"genre_scores_gemma":[0.0016644233,0.9947432,0.0025262297,0.00020129615,0.00039800935,0.000009687892,0.00004794156,0.000008966229,0.00040016678],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996152,0.00009183355,0.000052313553,0.00009084982,0.00012593057,0.000023795323],"domain_scores_gemma":[0.99703014,0.0022509606,0.00014277437,0.000095222225,0.00041054678,0.000070384485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018878954,0.0014648499,0.002363331,0.0027628648,0.0003303596,0.0020280597,0.001488296,0.0020693678,0.0032078382],"category_scores_gemma":[0.0038298718,0.00072656095,0.0010825435,0.0043904497,0.0009317269,0.0029006263,0.0008891759,0.001993424,0.0018390084],"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":[0.000076040706,0.000120125915,0.00055685785,0.01709041,0.00018665018,0.000091771166,0.00007551641,0.0036367942,0.0010107135,0.015322219,0.022602612,0.9392302],"study_design_scores_gemma":[0.000049396745,0.0002371589,0.002133328,0.011359572,0.00072392204,0.00114113,0.00017002599,0.010959441,0.002239474,0.0370556,0.9337775,0.00015356264],"about_ca_topic_score_codex":0.0035333685,"about_ca_topic_score_gemma":0.003801804,"teacher_disagreement_score":0.0035333685,"about_ca_system_score_codex":0.00086509774,"about_ca_system_score_gemma":0.0017954406,"threshold_uncertainty_score":0.01073128},"labels":[],"label_agreement":null},{"id":"W4380148842","doi":"10.1016/j.media.2023.102863","title":"A survey on deep learning for skin lesion segmentation","year":2023,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":178,"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; Google; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Canadian Institutes of Health Research; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação para a Ciência e a Tecnologia; Fundação de Amparo à Pesquisa do Estado de São Paulo; BC Cancer Foundation; National Science Foundation","keywords":"Artificial intelligence; Segmentation; Deep learning; Skin lesion; Computer vision; Computer science; Pattern recognition (psychology); Lesion; Medicine; Dermatology; Pathology","score_opus":0.07588093704665991,"score_gpt":0.4023016891922042,"score_spread":0.32642075214554434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380148842","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041868066,0.9941041,0.0035792144,0.00033812362,0.00021886591,0.00001373233,0.00013058154,0.00005778253,0.0011389417],"genre_scores_gemma":[0.0030100197,0.9909243,0.0037119384,0.00045641404,0.00041038287,0.00001801838,0.00032009362,0.000027222646,0.0011216039],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999708,0.000052749674,0.000048842823,0.00007643623,0.000091054295,0.000022882896],"domain_scores_gemma":[0.9989856,0.0006777324,0.00007417042,0.000033691344,0.00019296721,0.000035841378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008953179,0.0011613402,0.0015132878,0.0026994555,0.00020573614,0.0012152791,0.0011263802,0.0011711317,0.0047104578],"category_scores_gemma":[0.0024146412,0.00046458578,0.0013109554,0.003335122,0.00037231794,0.0013559522,0.0008329092,0.0012824783,0.0019847448],"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":[0.00004350066,0.000057721798,0.00033343743,0.010651094,0.00017236671,0.000047416263,0.000018461156,0.0011541619,0.0006783195,0.0010319554,0.01686328,0.9689483],"study_design_scores_gemma":[0.00009134242,0.00056739966,0.0046658623,0.019828014,0.0019101066,0.0018767766,0.00011923995,0.011527434,0.005728536,0.010214071,0.9433198,0.00015151023],"about_ca_topic_score_codex":0.0036313466,"about_ca_topic_score_gemma":0.005005791,"teacher_disagreement_score":0.0047104578,"about_ca_system_score_codex":0.0004882444,"about_ca_system_score_gemma":0.0015526481,"threshold_uncertainty_score":0.015758097},"labels":[],"label_agreement":null},{"id":"W4380183557","doi":"10.1016/j.media.2023.102865","title":"Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the AutoImplant 2021 cranial implant design challenge","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Traumatic Brain Injury and Neurovascular Disturbances","field":"Medicine","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":"University of Alberta; Sunnybrook Health Science Centre; University of Toronto; Sunnybrook Hospital","funders":"Bundesministerium für Digitalisierung und Wirtschaftsstandort; Steirische Wirtschaftsförderungsgesellschaft; Bundesministerium für Verkehr, Innovation und Technologie; Austrian Science Fund","keywords":"Computer science; Implant; Skull; Medical physics; Artificial intelligence; Medicine; Surgery","score_opus":0.10278458159155032,"score_gpt":0.3880010164852415,"score_spread":0.2852164348936912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380183557","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02102716,0.05787021,0.9046504,0.005709063,0.0002894814,0.000105817795,0.00024753792,0.0006505368,0.009449818],"genre_scores_gemma":[0.19460417,0.04958234,0.74766344,0.00088864256,0.0008893793,0.00016016165,0.00038031823,0.0007752039,0.0050564012],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977223,0.0007214742,0.00016889929,0.00028244604,0.001031425,0.00007344719],"domain_scores_gemma":[0.98765135,0.009498643,0.00033092315,0.00097641,0.0013911566,0.00015139543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044330857,0.0011888025,0.0015862113,0.0019509855,0.00029616937,0.004351072,0.002820206,0.0019661644,0.0076361336],"category_scores_gemma":[0.012364169,0.0011350174,0.0011334438,0.0012001279,0.0012725295,0.002460398,0.0017127686,0.0020764295,0.0025195673],"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":[0.00027499924,0.000166698,0.0022511645,0.0019327496,0.00020282685,0.00018594359,0.00020170207,0.09674978,0.021026753,0.041109286,0.005899987,0.829998],"study_design_scores_gemma":[0.00007023306,0.0003967988,0.0029554449,0.00053732307,0.0001298062,0.0015732343,0.0002191655,0.8363099,0.014785732,0.09940962,0.04351396,0.00009878638],"about_ca_topic_score_codex":0.0012818016,"about_ca_topic_score_gemma":0.0016326949,"teacher_disagreement_score":0.0076361336,"about_ca_system_score_codex":0.00082900724,"about_ca_system_score_gemma":0.0010882253,"threshold_uncertainty_score":0.025545418},"labels":[],"label_agreement":null},{"id":"W4381951399","doi":"10.1016/j.media.2023.102871","title":"Automatic labeling of Parkinson’s Disease gait videos with weak supervision","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Human Pose and Action Recognition","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":"British Columbia Centre of Excellence for Women's Health; Vancouver Coastal Health; University of British Columbia Hospital; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gait; Artificial intelligence; Computer science; Physical medicine and rehabilitation; Machine learning; Rating scale; A priori and a posteriori; Parkinson's disease; Estimator; Pattern recognition (psychology); Disease; Medicine; Mathematics; Statistics; Pathology","score_opus":0.011610055587825843,"score_gpt":0.26245370000849594,"score_spread":0.2508436444206701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381951399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34161448,0.0022536241,0.6413186,0.00057188066,0.00029006982,0.00033846687,0.0062306747,0.003299958,0.004082162],"genre_scores_gemma":[0.7757987,0.00090426166,0.20636578,0.00023094828,0.00015845544,0.0001880668,0.011055915,0.00019116054,0.005106755],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977475,0.000035541943,0.000014070168,0.00007934894,0.00004371392,0.000052508352],"domain_scores_gemma":[0.99971384,0.000050587412,0.000045102326,0.00004843649,0.000108439264,0.000033586733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026325163,0.0006116623,0.0005492286,0.0013120965,0.00028659028,0.00049520045,0.00045351827,0.0006370373,0.0010640281],"category_scores_gemma":[0.0008571849,0.0002383562,0.00045682318,0.00068586477,0.00021063448,0.00030215934,0.00045360107,0.0004926412,0.00066846056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00095451524,0.00034695066,0.014047763,0.00035366561,0.00011635309,0.0005550159,0.00009918589,0.015008609,0.14290202,0.0013463403,0.019260094,0.8050096],"study_design_scores_gemma":[0.0000605392,0.0003977508,0.03792661,0.00012450147,0.00011608577,0.00174484,0.00013886548,0.87915105,0.065002024,0.0041881828,0.01110864,0.000040941628],"about_ca_topic_score_codex":0.005918639,"about_ca_topic_score_gemma":0.014942474,"teacher_disagreement_score":0.005918639,"about_ca_system_score_codex":0.00026078298,"about_ca_system_score_gemma":0.0007816044,"threshold_uncertainty_score":0.011768401},"labels":[],"label_agreement":null},{"id":"W4384701644","doi":"10.1016/j.media.2023.102878","title":"Robotic ultrasound imaging: State-of-the-art and future perspectives","year":2023,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":153,"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; Process (computing); Artificial intelligence; Teleoperation; Modalities; Human–computer interaction; Data science; Robot","score_opus":0.01278421280006974,"score_gpt":0.2920633726128263,"score_spread":0.27927915981275653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384701644","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000085517255,0.9986947,0.00030475736,0.00026358056,0.000126229,0.0000029201844,0.000011492332,0.000008855586,0.00050189486],"genre_scores_gemma":[0.00087424665,0.9979381,0.0004472123,0.00021876227,0.00024772494,0.000004763029,0.000019851845,0.0000024340193,0.00024696253],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99955136,0.000085566855,0.00005542507,0.00008658268,0.0001728635,0.000048209273],"domain_scores_gemma":[0.99757403,0.0015272854,0.00021557647,0.00004935128,0.0004939962,0.00013976816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018798941,0.001065695,0.0019550538,0.0032832515,0.0002958282,0.002184492,0.0013594384,0.0018618656,0.004564673],"category_scores_gemma":[0.0022110802,0.0004251998,0.0007896646,0.002853983,0.0010960136,0.0025150115,0.00089323334,0.0020524831,0.0022462157],"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":[0.000093087016,0.00007690684,0.0002572118,0.017336566,0.00007531719,0.00011461447,0.00005458699,0.00049470755,0.0016450513,0.004312919,0.014959333,0.9605797],"study_design_scores_gemma":[0.000029067696,0.00025690038,0.0015695508,0.010894173,0.0003009201,0.0027077193,0.00021272802,0.0007821823,0.0016262631,0.006853343,0.9746908,0.00007637866],"about_ca_topic_score_codex":0.001104788,"about_ca_topic_score_gemma":0.0022215967,"teacher_disagreement_score":0.004564673,"about_ca_system_score_codex":0.00062950957,"about_ca_system_score_gemma":0.0015054847,"threshold_uncertainty_score":0.015270352},"labels":[],"label_agreement":null},{"id":"W4386565864","doi":"10.1016/j.media.2023.102942","title":"Deep learning, data ramping, and uncertainty estimation for detecting artifacts in large, imbalanced databases of MRI images","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","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 Health Centre; McGill University; NeuroRx Research (Canada); Montreal Neurological Institute and Hospital","funders":"","keywords":"Computer science; Artificial intelligence; Artifact (error); Transfer of learning; Neuroimaging; Deep learning; Inference; Machine learning; Data quality; Pattern recognition (psychology); Data mining","score_opus":0.022595957263734208,"score_gpt":0.3643586328800441,"score_spread":0.3417626756163099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386565864","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2635826,0.003564403,0.72964066,0.0010005388,0.000101668906,0.00007625129,0.00047169306,0.0010530197,0.0005091538],"genre_scores_gemma":[0.8947779,0.00058058236,0.10264256,0.00019570749,0.00014638995,0.000047351095,0.00089421764,0.00007525276,0.0006401519],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906963,0.00024463655,0.00009739716,0.00023484885,0.00024095915,0.00011257698],"domain_scores_gemma":[0.99534744,0.0031808445,0.0005125746,0.00035094997,0.00046042062,0.00014784734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043880683,0.0007351633,0.0014231278,0.0012782144,0.00034010332,0.0010711199,0.00121954,0.0011684686,0.0003991958],"category_scores_gemma":[0.010768021,0.00051432283,0.00076312135,0.0008992476,0.00074389315,0.0013596293,0.0015031392,0.0015295722,0.00011554556],"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":[0.0015673001,0.00040301392,0.017912697,0.00034065518,0.00031119311,0.000358634,0.00020342167,0.522486,0.014887649,0.004553254,0.0044140304,0.43256214],"study_design_scores_gemma":[0.000007380519,0.00003614086,0.0012199573,0.000008956357,0.000015970088,0.000039645565,0.000011127239,0.99524313,0.0016237494,0.0016438612,0.00014450365,0.0000056442077],"about_ca_topic_score_codex":0.004464998,"about_ca_topic_score_gemma":0.0044774185,"teacher_disagreement_score":0.004464998,"about_ca_system_score_codex":0.000752518,"about_ca_system_score_gemma":0.0010029505,"threshold_uncertainty_score":0.023206592},"labels":[],"label_agreement":null},{"id":"W4386725051","doi":"10.1016/j.media.2023.102958","title":"Active learning for medical image segmentation with stochastic batches","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":46,"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; Hôpital Notre-Dame","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Fonds de recherche du Québec – Nature et technologies; Réseau en Bio-Imagerie du Quebec","keywords":"Computer science; Segmentation; Artificial intelligence; Set (abstract data type); Machine learning; Sampling (signal processing); Baseline (sea); Metric (unit); Active learning (machine learning); Image segmentation; Code (set theory); Data mining; Pattern recognition (psychology); Computer vision","score_opus":0.007167990610211484,"score_gpt":0.29577817084283925,"score_spread":0.28861018023262774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386725051","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026826768,0.00016180032,0.99622524,0.000130306,0.000030968342,0.00003781514,0.000056752582,0.00044817838,0.00022621632],"genre_scores_gemma":[0.31629542,0.00054264505,0.6714145,0.00041972383,0.00039581239,0.00087948533,0.0010699308,0.00063533353,0.008347067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99852896,0.0005868954,0.00010611417,0.0002914317,0.00035750098,0.00012922291],"domain_scores_gemma":[0.99224466,0.0061049634,0.0003708215,0.0005096914,0.00056827563,0.00020160677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005379123,0.0010761551,0.0027461487,0.0011901141,0.0006705074,0.0016502937,0.004112401,0.0027851954,0.0037366394],"category_scores_gemma":[0.010870468,0.0021496385,0.0019497585,0.0013074029,0.0020821523,0.0020460412,0.0027772526,0.0036705346,0.0009906382],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068291277,0.0001322843,0.0004974858,0.00021390192,0.00014984002,0.00009125376,0.00010976276,0.83538485,0.0050058183,0.03831741,0.003219958,0.11619449],"study_design_scores_gemma":[0.000012042342,0.000014724939,0.000032767584,0.000004353643,0.0000054341863,0.0000068352388,0.0000015550893,0.99349445,0.0005131771,0.0056761284,0.0002336762,0.0000049331165],"about_ca_topic_score_codex":0.008836403,"about_ca_topic_score_gemma":0.009081801,"teacher_disagreement_score":0.008836403,"about_ca_system_score_codex":0.002033367,"about_ca_system_score_gemma":0.0020647184,"threshold_uncertainty_score":0.028447866},"labels":[],"label_agreement":null},{"id":"W4386826462","doi":"10.1016/j.media.2023.102972","title":"Automatic Head and Neck Tumor segmentation and outcome prediction relying on FDG-PET/CT images: Findings from the second edition of the HECKTOR challenge","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","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":"Université de Sherbrooke","funders":"Hasler Stiftung; Siemens Healthineers; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Artificial intelligence; Positron emission tomography; Segmentation; Computer science; Population; Percentile; Medicine; Leverage (statistics); Medical physics; Machine learning; Radiology; Mathematics; Statistics","score_opus":0.014450067475993525,"score_gpt":0.3054342986791619,"score_spread":0.29098423120316835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386826462","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81440485,0.017357795,0.11432044,0.0075300876,0.0047282614,0.0023308864,0.018161127,0.0072816,0.013884943],"genre_scores_gemma":[0.6841236,0.0026539569,0.21660165,0.0021724992,0.0024000155,0.0011996343,0.07118871,0.0014333198,0.018226523],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99187976,0.0028983182,0.0006020419,0.0020298376,0.0021085993,0.0004814245],"domain_scores_gemma":[0.986245,0.006280308,0.00047835216,0.0021760508,0.0035682989,0.0012519339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009349687,0.002662875,0.0024159108,0.0027361484,0.001180828,0.0029178592,0.0025366312,0.0042039123,0.0020878112],"category_scores_gemma":[0.020030815,0.00052414474,0.00224025,0.0010440244,0.0011641601,0.0015948935,0.0039534974,0.0027203837,0.0018682407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0038717412,0.002651011,0.028836459,0.0026946901,0.0016321848,0.0016380606,0.0013436503,0.046938226,0.02556851,0.0019394481,0.17646928,0.7064168],"study_design_scores_gemma":[0.00096653873,0.004331978,0.101863034,0.00056412397,0.0013506437,0.005184338,0.0027726085,0.6939245,0.079853,0.0109260855,0.09757863,0.0006845803],"about_ca_topic_score_codex":0.0072781444,"about_ca_topic_score_gemma":0.009488128,"teacher_disagreement_score":0.009349687,"about_ca_system_score_codex":0.0012489025,"about_ca_system_score_gemma":0.0019297593,"threshold_uncertainty_score":0.049446464},"labels":[],"label_agreement":null},{"id":"W4386826466","doi":"10.1016/j.media.2023.102938","title":"GAMMA challenge: Glaucoma grAding from Multi-Modality imAges","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Retinal Imaging and Analysis","field":"Medicine","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":"DiagnoCure (Canada); École de Technologie Supérieure","funders":"","keywords":"Glaucoma; Optical coherence tomography; Fundus photography; Grading (engineering); Medicine; Optometry; Fundus (uterus); Ophthalmology; Optic disc; Modality (human–computer interaction); Modalities; Artificial intelligence; Blindness; Computer science; Retinal; Fluorescein angiography","score_opus":0.02778191829118971,"score_gpt":0.3494678826587598,"score_spread":0.3216859643675701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386826466","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5443184,0.017349623,0.32413042,0.008217691,0.002192489,0.0046258294,0.047078427,0.029211463,0.022875616],"genre_scores_gemma":[0.572558,0.00490578,0.36960495,0.0018946937,0.0010723699,0.0015557779,0.030880947,0.0028616965,0.014665878],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99893445,0.0003024309,0.000112464964,0.00018039213,0.00037353655,0.00009678568],"domain_scores_gemma":[0.99772805,0.00084802095,0.00013628755,0.00025302684,0.0006033543,0.00043128178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031746905,0.0012640186,0.0008078176,0.0032462582,0.00050246576,0.0017688468,0.0012871148,0.0024024954,0.0054147476],"category_scores_gemma":[0.009039041,0.0004079459,0.0007873163,0.0008209297,0.0002705906,0.0011420061,0.002313062,0.0009399376,0.0030107053],"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":[0.0024437243,0.00065554556,0.024504835,0.0013142189,0.00060328713,0.0017420471,0.00023759117,0.008147406,0.036823418,0.0013249711,0.13141526,0.7907877],"study_design_scores_gemma":[0.0017358175,0.0029883757,0.22880931,0.0017905554,0.0012658009,0.025033709,0.001594927,0.4453054,0.10393424,0.019322291,0.16746254,0.0007570338],"about_ca_topic_score_codex":0.0053987466,"about_ca_topic_score_gemma":0.011012324,"teacher_disagreement_score":0.0054147476,"about_ca_system_score_codex":0.0004528735,"about_ca_system_score_gemma":0.0009537662,"threshold_uncertainty_score":0.01811415},"labels":[],"label_agreement":null},{"id":"W4386954264","doi":"10.1016/j.media.2023.102965","title":"Backdoor attack and defense in federated generative adversarial network-based medical image synthesis","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Adversarial Robustness in Machine Learning","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":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Public Safety Canada; Nvidia","keywords":"Backdoor; Computer science; Generator (circuit theory); Adversarial system; Computer security; Artificial intelligence; Deep learning; Machine learning; Data mining","score_opus":0.014849875500631417,"score_gpt":0.29797350023547114,"score_spread":0.2831236247348397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386954264","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043854985,0.0004193161,0.95164716,0.00042214282,0.000066489156,0.00005132722,0.00006274546,0.0011932019,0.0022825787],"genre_scores_gemma":[0.9322359,0.00017819072,0.064966194,0.0003455681,0.00002670528,0.00006212481,0.00008009679,0.00007979357,0.0020254645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879587,0.00045848434,0.000052446438,0.00022162442,0.00033171457,0.00013990639],"domain_scores_gemma":[0.9981406,0.0010794838,0.00016405187,0.00038033284,0.00016467526,0.00007082899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017514402,0.00076966215,0.0007432926,0.00035467668,0.00033412248,0.0006992552,0.0009281284,0.0010743942,0.0011246953],"category_scores_gemma":[0.0045053144,0.0003581673,0.0007612043,0.0002232422,0.0015177454,0.0012644335,0.0023463916,0.0018516936,0.0002599219],"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":[0.00034291175,0.000072808,0.0015722367,0.00009287991,0.00011153511,0.0004197694,0.00021672926,0.86793023,0.020351602,0.030142104,0.0018908259,0.07685652],"study_design_scores_gemma":[0.0000074827644,0.000032092266,0.00012684001,0.000009753279,0.000007637447,0.000100371435,0.000010197875,0.98663735,0.005223635,0.0073818304,0.0004531466,0.000009630416],"about_ca_topic_score_codex":0.00093249604,"about_ca_topic_score_gemma":0.00087452313,"teacher_disagreement_score":0.0017514402,"about_ca_system_score_codex":0.0006977993,"about_ca_system_score_gemma":0.0005614619,"threshold_uncertainty_score":0.009262621},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017803343,0.00025843983,0.97938675,0.0002354913,0.000028864983,0.00003376107,0.00010868958,0.001899293,0.000245339],"genre_scores_gemma":[0.38499916,0.0008021777,0.6069219,0.0002551968,0.00016793612,0.00023892608,0.0017282751,0.0010787993,0.0038076523],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910516,0.00020089645,0.000049896375,0.00030793506,0.00020740688,0.00012871285],"domain_scores_gemma":[0.997764,0.0011329422,0.000251479,0.00038492522,0.00036215634,0.00010456834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018663673,0.001543492,0.0021383627,0.0017741473,0.00049362006,0.0021248255,0.0020290425,0.0029264677,0.0017075122],"category_scores_gemma":[0.006908397,0.0013028199,0.0021610907,0.0023291637,0.0014529957,0.0018387894,0.001963069,0.0024240655,0.0013139629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004442593,0.00015790018,0.0023813164,0.00022987075,0.00021109561,0.00016886553,0.0002712629,0.32977405,0.03586056,0.0074143526,0.0063674846,0.61671895],"study_design_scores_gemma":[0.000011929706,0.000042991956,0.00047457483,0.0000075539087,0.000020392814,0.000057546218,0.000034492798,0.98583555,0.004434876,0.008498011,0.00056996633,0.000012046422],"about_ca_topic_score_codex":0.0057455236,"about_ca_topic_score_gemma":0.0067059402,"teacher_disagreement_score":0.0057455236,"about_ca_system_score_codex":0.0007871388,"about_ca_system_score_gemma":0.0017821667,"threshold_uncertainty_score":0.011424184},"labels":[],"label_agreement":null},{"id":"W4387004670","doi":"10.1016/j.media.2023.102980","title":"Spatiotemporal knowledge teacher–student reinforcement learning to detect liver tumors without contrast agents","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","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 British Columbia","funders":"","keywords":"Reinforcement learning; Computer science; Contrast (vision); Set (abstract data type); Workflow; Pixel; Artificial intelligence","score_opus":0.02162950575016027,"score_gpt":0.33032660725676816,"score_spread":0.3086971015066079,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387004670","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34929997,0.00023832294,0.6451244,0.00037713113,0.000078047604,0.00011660009,0.00006069451,0.0010497142,0.0036552523],"genre_scores_gemma":[0.9676932,0.000029060511,0.030375976,0.00004530713,0.000010420988,0.000032148713,0.000036448033,0.000016592989,0.0017609484],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998789,0.000031288073,0.0000065420572,0.000037959766,0.000023632347,0.000021644773],"domain_scores_gemma":[0.9991358,0.00047933243,0.000073695526,0.00005504123,0.0001955806,0.00006053952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005343441,0.00040013492,0.00037851825,0.00022164303,0.00019089798,0.00028123337,0.00065284764,0.00073596375,0.00145411],"category_scores_gemma":[0.002132574,0.00016545277,0.00023344524,0.00012494337,0.00023832823,0.00040226197,0.00059197773,0.00063557125,0.00018661468],"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":[0.00069299387,0.0005726891,0.006923472,0.000106976135,0.00009924463,0.00021146469,0.00014732314,0.6116677,0.01583913,0.0035488745,0.002357297,0.35783282],"study_design_scores_gemma":[0.000012461699,0.000062414176,0.0002867033,0.0000015995622,0.0000074099553,0.000013083459,0.000004602197,0.9969081,0.0021781782,0.0003941056,0.00012924222,0.0000020140626],"about_ca_topic_score_codex":0.0053612236,"about_ca_topic_score_gemma":0.004987111,"teacher_disagreement_score":0.0053612236,"about_ca_system_score_codex":0.0004578302,"about_ca_system_score_gemma":0.0007209325,"threshold_uncertainty_score":0.010660052},"labels":[],"label_agreement":null},{"id":"W4387521217","doi":"10.1016/j.media.2023.102985","title":"SurgT challenge: Benchmark of soft-tissue trackers for robotic surgery","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Surgical Simulation and Training","field":"Medicine","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 Toronto","funders":"","keywords":"Artificial intelligence; Computer science; Deep learning; Benchmark (surveying); BitTorrent tracker; Segmentation; Ground truth; Benchmarking; Bounding overwatch; Computer vision; Metric (unit); Machine learning; Eye tracking","score_opus":0.04643213624207263,"score_gpt":0.35713379866465866,"score_spread":0.310701662422586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387521217","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29621798,0.015289313,0.48498443,0.009303262,0.008540561,0.0025674847,0.04195439,0.07188046,0.06926207],"genre_scores_gemma":[0.5706248,0.003086238,0.28135002,0.0019393859,0.0010001448,0.0008677342,0.105291836,0.0066116373,0.029228115],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.994534,0.001550612,0.00025795703,0.0010244277,0.0022880845,0.00034486974],"domain_scores_gemma":[0.99274206,0.0024594099,0.00033015147,0.0011451485,0.002277808,0.0010454395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007045038,0.0017551841,0.0013823604,0.001693692,0.0007508562,0.0024269742,0.0027876692,0.0033970934,0.008886718],"category_scores_gemma":[0.017536419,0.00052498275,0.0013350168,0.0011388367,0.0008581656,0.0018111599,0.0037587672,0.0018070155,0.008934514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0043734396,0.0021240884,0.01173897,0.0028517141,0.00089614466,0.0006258505,0.00041445412,0.14152549,0.028237551,0.006681501,0.24833746,0.55219334],"study_design_scores_gemma":[0.0011647962,0.004719954,0.024584064,0.000428563,0.00027884656,0.0024521619,0.00053380616,0.7757223,0.02913038,0.010579473,0.1501731,0.00023248607],"about_ca_topic_score_codex":0.00420541,"about_ca_topic_score_gemma":0.004596963,"teacher_disagreement_score":0.008886718,"about_ca_system_score_codex":0.0007858921,"about_ca_system_score_gemma":0.0024339699,"threshold_uncertainty_score":0.037258208},"labels":[],"label_agreement":null},{"id":"W4387978272","doi":"10.1016/j.media.2023.103015","title":"Do we really need dice? The hidden region-size biases of segmentation losses","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":45,"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; École de Technologie Supérieure","funders":"H2020 Marie Skłodowska-Curie Actions; Fonds de recherche du Québec – Nature et technologies; HORIZON EUROPE Marie Sklodowska-Curie Actions; Natural Sciences and Engineering Research Council of Canada","keywords":"Dice; Ground truth; Segmentation; Computer science; Artificial intelligence; Perspective (graphical); Matching (statistics); Upper and lower bounds; Pattern recognition (psychology); Mathematics; Statistics","score_opus":0.030278542921383895,"score_gpt":0.3202295275761869,"score_spread":0.289950984654803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387978272","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1480573,0.005939152,0.80461484,0.028278248,0.0008276457,0.000083251536,0.00041559048,0.0008763261,0.010907595],"genre_scores_gemma":[0.85344505,0.0029449635,0.12741739,0.0037637607,0.001011952,0.000088623114,0.00037328826,0.0007482515,0.010206632],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988607,0.00037933284,0.000055554676,0.000273026,0.00031341598,0.00011787043],"domain_scores_gemma":[0.98696554,0.0086170975,0.00079216185,0.0018443733,0.0014488017,0.0003320399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073815035,0.00074879255,0.0011659913,0.0006066736,0.0004963845,0.002141116,0.0015647656,0.002758909,0.0044815824],"category_scores_gemma":[0.051243156,0.0007228667,0.0004838147,0.00052063586,0.0026040298,0.0105602695,0.0017138493,0.0033650352,0.00090882083],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008292783,0.00016763213,0.010961886,0.0006016698,0.00028356406,0.00033194548,0.0005384925,0.15216874,0.022175485,0.3156475,0.025551464,0.47074234],"study_design_scores_gemma":[0.00007891837,0.00013116402,0.006534777,0.0001708326,0.000067652225,0.00061545684,0.00010285535,0.5580589,0.015834078,0.41182777,0.006486932,0.00009063061],"about_ca_topic_score_codex":0.0020675992,"about_ca_topic_score_gemma":0.0023437599,"teacher_disagreement_score":0.0073815035,"about_ca_system_score_codex":0.0014082112,"about_ca_system_score_gemma":0.0007315107,"threshold_uncertainty_score":0.039037585},"labels":[],"label_agreement":null},{"id":"W4388024378","doi":"10.1016/j.media.2023.103011","title":"Anatomically-aware uncertainty for semi-supervised image segmentation","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":57,"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":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec; Compute Canada","keywords":"Segmentation; Artificial intelligence; Computer science; Inference; Pixel; Representation (politics); Scale-space segmentation; Image segmentation; Pattern recognition (psychology); Segmentation-based object categorization; Machine learning","score_opus":0.011542703190844253,"score_gpt":0.33554928277027957,"score_spread":0.3240065795794353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388024378","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008492236,0.00029442844,0.98978955,0.00009375446,0.000015564809,0.000041044666,0.00009284316,0.0009257253,0.00025490718],"genre_scores_gemma":[0.5188533,0.00040706113,0.4762243,0.0002868911,0.00018398592,0.00029216058,0.0012804655,0.0006430074,0.0018288235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977456,0.00050369365,0.00018012796,0.00062314735,0.00072861236,0.00021884652],"domain_scores_gemma":[0.9936655,0.004087842,0.00064843084,0.0006288554,0.00080569723,0.00016358135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028181833,0.0011700485,0.0029648028,0.0019041544,0.00080995966,0.0017474208,0.0028510536,0.0028076957,0.0014186058],"category_scores_gemma":[0.009381275,0.0014496627,0.0021331639,0.001468786,0.0016006199,0.0018729869,0.0030966573,0.0021725707,0.00062835356],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005507946,0.00013381438,0.001282007,0.00040941613,0.00023552621,0.00018122772,0.00030135724,0.6141586,0.018953033,0.009342364,0.0026301886,0.35182157],"study_design_scores_gemma":[0.000006438619,0.00003129246,0.00021890781,0.000014164184,0.000014396887,0.00005534703,0.000009719011,0.99093854,0.0026370957,0.005723624,0.00034066205,0.000009863913],"about_ca_topic_score_codex":0.005445348,"about_ca_topic_score_gemma":0.0072530643,"teacher_disagreement_score":0.005445348,"about_ca_system_score_codex":0.0013169566,"about_ca_system_score_gemma":0.0020752293,"threshold_uncertainty_score":0.014904141},"labels":[],"label_agreement":null},{"id":"W4388806341","doi":"10.1016/j.media.2023.103041","title":"WarpDrive: Improving spatial normalization using manual refinements","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","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":"McGill University Health Centre; Toronto Rehabilitation Institute; McGill University; Douglas Mental Health University Institute; Krembil Foundation; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; National Institute of Neurological Disorders and Stroke; IXICO; H. Lundbeck A/S; Eisai; Deutsche Forschungsgemeinschaft; Servier; EU Joint Programme – Neurodegenerative Disease Research; Northern California Institute for Research and Education; National Institute of Mental Health; BioClinica; Biogen; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association","keywords":"Computer science; Artificial intelligence; Spatial normalization; Normalization (sociology); Inference; Modalities; Pattern recognition (psychology); Neuroimaging; Process (computing); Computer vision; Machine learning; Neuroscience; Psychology","score_opus":0.05912185879799546,"score_gpt":0.42084703398665946,"score_spread":0.361725175188664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388806341","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059344675,0.00027447252,0.9689075,0.00012838679,0.000110103625,0.00010467518,0.0005243781,0.023186063,0.0008300665],"genre_scores_gemma":[0.052021302,0.00030174578,0.9348158,0.00019885738,0.00005295119,0.0003127911,0.001954277,0.008566546,0.0017757533],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9957487,0.001009851,0.00045817494,0.0009707272,0.0015951375,0.00021745476],"domain_scores_gemma":[0.9914402,0.0044870176,0.00083401875,0.0019643295,0.0011383822,0.00013605702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066589317,0.0028542858,0.001678817,0.0036212103,0.0008106499,0.002824925,0.003301589,0.0015704894,0.009529329],"category_scores_gemma":[0.034459546,0.0016450753,0.0020413625,0.0024882616,0.0011973429,0.002541522,0.0043665264,0.0025731837,0.005153246],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006586176,0.00020954483,0.005161286,0.00073457713,0.00056820374,0.000511133,0.0011434258,0.033626623,0.042077236,0.009211241,0.040521078,0.865577],"study_design_scores_gemma":[0.00027189322,0.00038584825,0.007889916,0.00027500087,0.00026471584,0.0021702112,0.0006706959,0.68317825,0.14794023,0.036213253,0.12034172,0.00039827146],"about_ca_topic_score_codex":0.0049588275,"about_ca_topic_score_gemma":0.009891706,"teacher_disagreement_score":0.009529329,"about_ca_system_score_codex":0.0006001685,"about_ca_system_score_gemma":0.0018277585,"threshold_uncertainty_score":0.035216212},"labels":[],"label_agreement":null},{"id":"W4388818696","doi":"10.1016/j.media.2023.103033","title":"An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","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":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Department of Health and Social Care; National Institute for Health and Care Research; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Medical Research Council; Biogen; Engineering and Physical Sciences Research Council; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; University College London Hospitals NHS Foundation Trust; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Artificial intelligence; Computer science; Support vector machine; Process (computing); Set (abstract data type); Machine learning; Quality (philosophy); Feature (linguistics); Class (philosophy); Pattern recognition (psychology)","score_opus":0.018009744842974067,"score_gpt":0.3180659777950156,"score_spread":0.30005623295204154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388818696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008855153,0.00013788437,0.9870351,0.00013175102,0.00004909925,0.00008302659,0.000039949933,0.0031546778,0.0005133345],"genre_scores_gemma":[0.6127872,0.00021420357,0.38188025,0.00039119547,0.00012745298,0.00043409228,0.00044890924,0.0003657221,0.0033510064],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986696,0.00022046005,0.00007744191,0.0005237107,0.000368369,0.00014048886],"domain_scores_gemma":[0.9969585,0.00094862405,0.00049319817,0.0005219504,0.00093367515,0.00014405475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029180741,0.0012100731,0.0014145054,0.0007095984,0.00056752306,0.0013234636,0.0024323354,0.0013197196,0.0021650945],"category_scores_gemma":[0.0075429957,0.0006165953,0.0010554962,0.00044583468,0.001405276,0.0011754328,0.0018717279,0.0023444234,0.0011946873],"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":[0.0005978434,0.00019691502,0.001922364,0.00018057517,0.00014102359,0.00023410478,0.00020630947,0.53444046,0.033906218,0.0051395083,0.0050429166,0.4179918],"study_design_scores_gemma":[0.000014117374,0.000051858427,0.0002378119,0.000008784867,0.000009735754,0.00004319463,0.0000051405736,0.99237496,0.0055016857,0.0012516059,0.00049148966,0.000009721985],"about_ca_topic_score_codex":0.0037055097,"about_ca_topic_score_gemma":0.0025404198,"teacher_disagreement_score":0.0037055097,"about_ca_system_score_codex":0.0011582716,"about_ca_system_score_gemma":0.0016049455,"threshold_uncertainty_score":0.015432477},"labels":[],"label_agreement":null},{"id":"W4388840678","doi":"10.1016/j.media.2023.103030","title":"Combiner and HyperCombiner networks: Rules to combine multimodality MR images for prostate cancer localisation","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","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":"Wilfrid Laurier University","funders":"Knight Cancer Institute, Oregon Health and Science University; Wellcome Trust; University of Manchester; University College London; University of Cambridge; Cancer Research UK","keywords":"Computer science; Modality (human–computer interaction); Artificial intelligence; Segmentation; Pattern recognition (psychology); Parametric statistics; Machine learning; Mathematics; Statistics","score_opus":0.01712218110866384,"score_gpt":0.3275623913581904,"score_spread":0.31044021024952656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388840678","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0089637255,0.00030734547,0.9843864,0.00024379134,0.000060758117,0.00016170644,0.00051658583,0.00383916,0.0015205132],"genre_scores_gemma":[0.16451068,0.0003349676,0.8279633,0.0003682216,0.00011963992,0.00047160714,0.0015774866,0.0008153331,0.0038387987],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975527,0.00056195096,0.00023797654,0.0006253116,0.0008705045,0.0001516532],"domain_scores_gemma":[0.9934136,0.004254514,0.0004978729,0.0006633173,0.00091797556,0.0002526263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050740708,0.0015437542,0.0015238611,0.003149364,0.00071811944,0.0021921208,0.0028220061,0.0019794435,0.0043143723],"category_scores_gemma":[0.015311069,0.0009494835,0.0022565178,0.0012574279,0.0010819832,0.0028165178,0.0032383604,0.0019793569,0.0022343232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008174092,0.00036450967,0.009515235,0.0003980451,0.0007119032,0.00089974626,0.00028551792,0.16554764,0.00878112,0.014057128,0.014094058,0.7845277],"study_design_scores_gemma":[0.00004472392,0.00011457679,0.0013843447,0.00012267537,0.00021087567,0.00041209484,0.000067577814,0.94580024,0.011375363,0.03234996,0.008071893,0.000045625424],"about_ca_topic_score_codex":0.004481542,"about_ca_topic_score_gemma":0.008803205,"teacher_disagreement_score":0.0050740708,"about_ca_system_score_codex":0.000769938,"about_ca_system_score_gemma":0.0008804393,"threshold_uncertainty_score":0.026834548},"labels":[],"label_agreement":null},{"id":"W4389990429","doi":"10.1016/j.media.2023.103066","title":"Placental vessel segmentation and registration in fetoscopy: Literature review and MICCAI FetReg2021 challenge findings","year":2023,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Prenatal Screening and Diagnostics","field":"Medicine","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 Toronto","funders":"UCLH Biomedical Research Centre; HORIZON EUROPE Framework Programme; Wellcome / EPSRC Centre for Interventional and Surgical Sciences; Nvidia; Wellcome Trust; Royal Academy of Engineering; University College London Hospitals NHS Foundation Trust; Horizon 2020; Engineering and Physical Sciences Research Council; National Institute for Health and Care Research","keywords":"Fetoscopy; Computer science; Artificial intelligence; Computer vision; Segmentation; Context (archaeology); Prenatal diagnosis; Pregnancy","score_opus":0.010902822670717885,"score_gpt":0.3113714892789012,"score_spread":0.3004686666081833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389990429","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007340125,0.9645511,0.015687773,0.0032718133,0.0014515661,0.00015934635,0.0029965332,0.00072583463,0.0038159552],"genre_scores_gemma":[0.03436708,0.91451657,0.026879722,0.002637789,0.0022473386,0.00027102968,0.016713334,0.0003945528,0.001972591],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9971625,0.00045835227,0.00068130676,0.00077256566,0.00079144374,0.00013380486],"domain_scores_gemma":[0.9897947,0.0058090626,0.0007608515,0.0005792787,0.0026920072,0.00036415405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004182113,0.0013627588,0.0014321881,0.007408012,0.0007627811,0.002487062,0.0027897884,0.0023979568,0.002486254],"category_scores_gemma":[0.01831748,0.000783094,0.0015030317,0.007495777,0.00097115507,0.0026449312,0.0017052907,0.0014518207,0.0014632285],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025793957,0.00013137865,0.004428213,0.037666906,0.0003752179,0.00061654794,0.00041358112,0.0030084623,0.0025575685,0.0023380097,0.084372364,0.8638338],"study_design_scores_gemma":[0.00007589435,0.0005395568,0.029879037,0.035846476,0.0024680798,0.0068084416,0.0015576698,0.016422966,0.008523438,0.007769469,0.88970315,0.00040570763],"about_ca_topic_score_codex":0.009941462,"about_ca_topic_score_gemma":0.010660519,"teacher_disagreement_score":0.009941462,"about_ca_system_score_codex":0.0010763782,"about_ca_system_score_gemma":0.005049212,"threshold_uncertainty_score":0.022117376},"labels":[],"label_agreement":null},{"id":"W4390747482","doi":"10.1016/j.media.2024.103085","title":"What matters in reinforcement learning for tractography","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","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; Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Compute Canada","keywords":"Reinforcement learning; Tractography; Computer science; Codebase; Artificial intelligence; Function (biology); Machine learning; White matter; Software","score_opus":0.033939239268384484,"score_gpt":0.3895429358701114,"score_spread":0.3556036966017269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390747482","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08382233,0.008327368,0.85157377,0.04273351,0.0013875782,0.0000959847,0.00027921385,0.0010089413,0.010771354],"genre_scores_gemma":[0.880029,0.002491043,0.106644385,0.002636544,0.001988229,0.00009378916,0.00021765183,0.00055145985,0.0053478433],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99594915,0.0025145034,0.00016882196,0.00076157035,0.00042978962,0.0001760764],"domain_scores_gemma":[0.92697996,0.06392754,0.0015650664,0.0026648901,0.003241248,0.0016212987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008931934,0.00080056937,0.0019047027,0.00042950705,0.0010424372,0.0027730048,0.0015993059,0.003631248,0.0072167264],"category_scores_gemma":[0.095930725,0.00051667815,0.00048930995,0.000464098,0.003219274,0.010926872,0.0016686837,0.004057722,0.0009599979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015548881,0.0006154166,0.01208245,0.0014153367,0.00041670015,0.00029434965,0.000428701,0.191731,0.0039476464,0.30170256,0.029098365,0.45671257],"study_design_scores_gemma":[0.000090882044,0.0001803378,0.0012382368,0.00018368354,0.00004596757,0.000102352,0.0001070601,0.50633633,0.0013516865,0.48756292,0.0027570836,0.000043466633],"about_ca_topic_score_codex":0.0024049,"about_ca_topic_score_gemma":0.0026184218,"teacher_disagreement_score":0.008931934,"about_ca_system_score_codex":0.0011247746,"about_ca_system_score_gemma":0.0018536142,"threshold_uncertainty_score":0.047237158},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28537259,0.0018803698,0.6989645,0.00089873635,0.00012165626,0.00014301839,0.00088155665,0.008729946,0.0030077281],"genre_scores_gemma":[0.7566947,0.0004887271,0.2340711,0.0006121892,0.000049682014,0.00013368335,0.002910151,0.0005206546,0.004519188],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996087,0.00007086467,0.000018116207,0.000170291,0.000070045884,0.000061948],"domain_scores_gemma":[0.9992386,0.00035084414,0.000076910015,0.00016173998,0.00011821644,0.000053682525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011460945,0.0011471147,0.0005985136,0.0007265561,0.0003034258,0.0007231816,0.0015947304,0.0013967467,0.0011422858],"category_scores_gemma":[0.0033516528,0.0006637008,0.0010016981,0.00060873205,0.0006542332,0.001511258,0.0016445778,0.0017540985,0.0006320351],"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":[0.0006517565,0.0002330125,0.005755958,0.00018888168,0.00033993967,0.00026483735,0.00020490712,0.67604864,0.026395308,0.0028932013,0.0058208015,0.28120282],"study_design_scores_gemma":[0.000016312317,0.00006413403,0.00069437316,0.000011084501,0.000024159483,0.000045298642,0.000011631435,0.992784,0.0035762193,0.0021391604,0.00062169466,0.000011889848],"about_ca_topic_score_codex":0.011101547,"about_ca_topic_score_gemma":0.02325819,"teacher_disagreement_score":0.011101547,"about_ca_system_score_codex":0.0012093012,"about_ca_system_score_gemma":0.0009129121,"threshold_uncertainty_score":0.022073865},"labels":[],"label_agreement":null},{"id":"W4391264577","doi":"10.1016/j.media.2024.103093","title":"Neural deformation fields for template-based reconstruction of cortical surfaces from MRI","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Segmentation; Polygon mesh; Computer vision; Surface (topology); Flow (mathematics); Surface reconstruction; Pattern recognition (psychology); Mathematics; Geometry; Computer graphics (images)","score_opus":0.03829917376689692,"score_gpt":0.3651151343577,"score_spread":0.3268159605908031,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391264577","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010380538,0.00020837384,0.9875822,0.00016716073,0.000031654577,0.00004385387,0.00015325115,0.0006640276,0.00076892064],"genre_scores_gemma":[0.35376891,0.0010387971,0.63774264,0.00015983656,0.00006822522,0.00024195843,0.0008852679,0.0006799905,0.0054144743],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997991,0.000042337495,0.000013225485,0.00003786497,0.00009007449,0.000017386397],"domain_scores_gemma":[0.99956757,0.00019898819,0.000048986956,0.00006958395,0.00009337059,0.000021422236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006958658,0.00045932032,0.00047135114,0.00088462245,0.00028554298,0.0008786789,0.0008575693,0.0011823468,0.0025656484],"category_scores_gemma":[0.00320976,0.0005339938,0.00084882736,0.0010843829,0.0004935507,0.0008711802,0.00083512446,0.0012840023,0.0010243796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002250699,0.00010659275,0.0011443256,0.00021733351,0.000081574944,0.0001300198,0.0001367848,0.4575572,0.04188629,0.030163743,0.0052277916,0.4631232],"study_design_scores_gemma":[0.0000074058466,0.000018255472,0.0003765123,0.000012452461,0.0000070252217,0.00008765927,0.000010726904,0.98433995,0.0057376157,0.008104313,0.0012854553,0.0000126486675],"about_ca_topic_score_codex":0.00431801,"about_ca_topic_score_gemma":0.0051930253,"teacher_disagreement_score":0.00431801,"about_ca_system_score_codex":0.0005991699,"about_ca_system_score_gemma":0.0011332715,"threshold_uncertainty_score":0.008585751},"labels":[],"label_agreement":null},{"id":"W4391671707","doi":"10.1016/j.media.2024.103101","title":"Blurred streamlines: A novel representation to reduce redundancy in tractography","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","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":"Streamlines, streaklines, and pathlines; Redundancy (engineering); Artificial intelligence; False positive paradox; Computer science; Tractography; Representation (politics); Pattern recognition (psychology); Algorithm; Mathematics; Computer vision; Diffusion MRI","score_opus":0.070228611807333,"score_gpt":0.4499603158126226,"score_spread":0.37973170400528955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391671707","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013679525,0.00036643757,0.9844146,0.00016561603,0.00005664878,0.000041595526,0.00022111968,0.0006441851,0.0004103585],"genre_scores_gemma":[0.16873945,0.0011889013,0.8262077,0.00015538013,0.0002711488,0.0002127632,0.00092967245,0.00046996961,0.0018250267],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949706,0.0001501695,0.00004061057,0.00008428616,0.00017968567,0.00004815501],"domain_scores_gemma":[0.99813074,0.00065481884,0.00039188747,0.000305666,0.00040401088,0.00011284163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009151294,0.0010795225,0.00072876364,0.0020514852,0.00041003077,0.0012434028,0.0009951569,0.0010727983,0.002075491],"category_scores_gemma":[0.0046166247,0.00044453586,0.00080246053,0.0020173427,0.00074548495,0.0022552782,0.0010069218,0.0010153054,0.0006711448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083626946,0.00015676249,0.0034737426,0.0006584971,0.00014635608,0.00069342065,0.0007519484,0.31140304,0.07650701,0.098138645,0.01397414,0.4932601],"study_design_scores_gemma":[0.000038396294,0.00015941342,0.00093047344,0.000042336258,0.000035674442,0.0003816533,0.000038473045,0.9551203,0.013152093,0.017394032,0.012659105,0.000047934936],"about_ca_topic_score_codex":0.002964662,"about_ca_topic_score_gemma":0.0026962261,"teacher_disagreement_score":0.002964662,"about_ca_system_score_codex":0.0006533179,"about_ca_system_score_gemma":0.0010636542,"threshold_uncertainty_score":0.006943226},"labels":[],"label_agreement":null},{"id":"W4391951835","doi":"10.1016/j.media.2024.103109","title":"LESS: Label-efficient multi-scale learning for cytological whole slide image screening","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":20,"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; Vector Institute; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Compute Canada","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Machine learning; Task (project management); Supervised learning; Artificial neural network","score_opus":0.03090864510067017,"score_gpt":0.3249026520584386,"score_spread":0.29399400695776845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391951835","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019092439,0.0005978911,0.93272597,0.00045776606,0.00022366352,0.00021135362,0.0015481575,0.04356017,0.0015825375],"genre_scores_gemma":[0.14948231,0.00027917078,0.8304309,0.00080730655,0.00021878851,0.00034612138,0.0041939947,0.0017353834,0.012505978],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986771,0.00025236746,0.00006407669,0.0003389852,0.00050040963,0.00016709878],"domain_scores_gemma":[0.9981669,0.0006309775,0.00011632982,0.00056208787,0.00037258147,0.00015110712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015945709,0.001671401,0.0018478682,0.0017744495,0.0007809452,0.0016179873,0.0039379126,0.0024872702,0.012003244],"category_scores_gemma":[0.0035576713,0.000891695,0.0015181097,0.0016699885,0.00056542736,0.0020433404,0.0037773869,0.0018810683,0.0064143697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00080868386,0.00037649498,0.0015248555,0.00019066593,0.0001608023,0.00012459955,0.000055845077,0.022585945,0.03850963,0.0014876599,0.029855652,0.9043191],"study_design_scores_gemma":[0.00008709699,0.0001419495,0.0012134674,0.000014481582,0.000035806628,0.00018175696,0.000036385383,0.9672135,0.020276979,0.004807745,0.005951298,0.000039448227],"about_ca_topic_score_codex":0.0047570462,"about_ca_topic_score_gemma":0.010851197,"teacher_disagreement_score":0.012003244,"about_ca_system_score_codex":0.00090987654,"about_ca_system_score_gemma":0.0012573142,"threshold_uncertainty_score":0.040154874},"labels":[],"label_agreement":null},{"id":"W4392349077","doi":"10.1016/j.media.2024.103131","title":"Tracking and mapping in medical computer vision: A review","year":2024,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"Robotics and Sensor-Based Localization","field":"Engineering","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 British Columbia","funders":"","keywords":"Computer science; Field (mathematics); Process (computing); Artificial intelligence; Computer vision; Tracking (education); Data science","score_opus":0.021467946330328388,"score_gpt":0.33339687273021956,"score_spread":0.31192892639989117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392349077","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021447118,0.99087816,0.005298278,0.000570673,0.0007673125,0.000021486949,0.000055283628,0.00006305941,0.002131327],"genre_scores_gemma":[0.0018410631,0.99053967,0.0047559706,0.00047614117,0.0012278601,0.000027181422,0.00014851759,0.000026301457,0.00095736707],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989612,0.00018430794,0.00016025067,0.00019866976,0.00042894707,0.00006653956],"domain_scores_gemma":[0.99669904,0.0020777318,0.00019178464,0.00012213673,0.0008240869,0.00008524485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015104085,0.0013457995,0.0014568056,0.0041816323,0.0005079911,0.0024777974,0.0016174744,0.0021592951,0.0049765096],"category_scores_gemma":[0.0039471705,0.00073405745,0.0010590617,0.006939607,0.0010248994,0.0040353895,0.0010084377,0.0022551925,0.00433011],"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":[0.000049231192,0.000058379133,0.0004279375,0.01632423,0.00013115915,0.00013061319,0.0001503889,0.0013007848,0.0010804556,0.008999998,0.055452242,0.91589457],"study_design_scores_gemma":[0.000010725831,0.0001319338,0.0013541277,0.0066828425,0.00016960262,0.0014993616,0.000117394506,0.00180691,0.0008934554,0.010026119,0.9772341,0.00007338322],"about_ca_topic_score_codex":0.0025941704,"about_ca_topic_score_gemma":0.0016260528,"teacher_disagreement_score":0.0049765096,"about_ca_system_score_codex":0.00095646025,"about_ca_system_score_gemma":0.0020781276,"threshold_uncertainty_score":0.016648114},"labels":[],"label_agreement":null},{"id":"W4392455053","doi":"10.1016/j.media.2024.103134","title":"Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","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":"Google (Canada); Université de Sherbrooke","funders":"Engineering and Physical Sciences Research Council; European Commission; Agencia Estatal de Investigación; Ministerio de Ciencia, Innovación y Universidades; Ministerio de Ciencia e Innovación; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Wellcome Trust","keywords":"Diffusion MRI; Protocol (science); Computer science; Artificial intelligence; Relaxation (psychology); Machine learning; Diffusion; Algorithm; Magnetic resonance imaging; Physics; Medicine","score_opus":0.04037254488641366,"score_gpt":0.40363153447511524,"score_spread":0.36325898958870156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392455053","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017873676,0.00017357341,0.98077697,0.00010675726,0.000012814127,0.00012902767,0.000043357984,0.0005517372,0.00033214802],"genre_scores_gemma":[0.1814097,0.0001957335,0.8166838,0.00011393703,0.000019104049,0.00058666343,0.0002244573,0.00028322102,0.00048334582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991441,0.00036789186,0.00006069125,0.00019388592,0.00018853116,0.000044891207],"domain_scores_gemma":[0.9972709,0.0016502837,0.00035203865,0.00026510673,0.00039586582,0.00006586567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043995488,0.001729677,0.0009833351,0.00094660436,0.00042007858,0.0009198266,0.0013751843,0.0012477665,0.0008377395],"category_scores_gemma":[0.011417483,0.00085906923,0.00096518284,0.000643627,0.0008125853,0.0015831225,0.0013555875,0.0018130911,0.00043870058],"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":[0.00038076116,0.00030461547,0.001598077,0.00045783224,0.00018568742,0.0001229391,0.000284988,0.70149624,0.07425004,0.0046077026,0.00097637164,0.21533474],"study_design_scores_gemma":[0.000046534984,0.00024146072,0.0009186268,0.000027706123,0.00004569815,0.00009163355,0.000021772184,0.971235,0.019285046,0.0068892376,0.0011494794,0.000047791375],"about_ca_topic_score_codex":0.0018337596,"about_ca_topic_score_gemma":0.0025044528,"teacher_disagreement_score":0.0043995488,"about_ca_system_score_codex":0.0008614685,"about_ca_system_score_gemma":0.0021207137,"threshold_uncertainty_score":0.023267329},"labels":[],"label_agreement":null},{"id":"W4392883923","doi":"10.1016/j.media.2024.103143","title":"CellViT: Vision Transformers for precise cell segmentation and classification","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":282,"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","funders":"Universität Duisburg-Essen","keywords":"Computer science; Segmentation; Artificial intelligence; Convolutional neural network; Deep learning; Encoder; Pattern recognition (psychology); Cluster analysis; Computer vision; Scale-space segmentation; Image segmentation","score_opus":0.010348644571440708,"score_gpt":0.308377914807403,"score_spread":0.2980292702359623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392883923","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054129392,0.0012161796,0.8505564,0.00035429234,0.00034695692,0.00041360062,0.0044835275,0.08268483,0.005814745],"genre_scores_gemma":[0.3761413,0.0008934215,0.58405715,0.0007259289,0.000099275865,0.0004830795,0.020490687,0.0030251034,0.014084061],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99970394,0.000026554162,0.0000138762225,0.00012146076,0.00008577562,0.000048423535],"domain_scores_gemma":[0.9996947,0.000076789125,0.00003216887,0.00009421923,0.00006840176,0.00003378994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007492919,0.0011721518,0.0005587019,0.0009606398,0.0002888396,0.0012035862,0.0023888247,0.0012890752,0.0059634084],"category_scores_gemma":[0.0020065906,0.0005325163,0.00085333025,0.0006285599,0.0004864255,0.0013050758,0.0013525023,0.001389923,0.0036518965],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006965998,0.00028510106,0.0059838737,0.00055833865,0.00021633858,0.00022571188,0.0001677382,0.07202178,0.11757031,0.010215867,0.071323246,0.72073513],"study_design_scores_gemma":[0.00007558062,0.0002464138,0.0021367467,0.00004582177,0.00004695235,0.00046055607,0.000055212073,0.8763645,0.09179123,0.008113255,0.02062536,0.000038266688],"about_ca_topic_score_codex":0.0047365553,"about_ca_topic_score_gemma":0.008591624,"teacher_disagreement_score":0.0059634084,"about_ca_system_score_codex":0.0011152697,"about_ca_system_score_gemma":0.00105678,"threshold_uncertainty_score":0.019949615},"labels":[],"label_agreement":null},{"id":"W4393165986","doi":"10.1016/j.media.2024.103160","title":"Plug-and-Play latent feature editing for orientation-adaptive quantitative susceptibility mapping neural networks","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced MRI Techniques and Applications","field":"Medicine","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 Calgary; University of Alberta","funders":"Australian Research Council; Canadian Institutes of Health Research; Canadian HIV Trials Network, Canadian Institutes of Health Research; National Natural Science Foundation of China","keywords":"Quantitative susceptibility mapping; Computer science; Orientation (vector space); Artificial intelligence; Pattern recognition (psychology); Adaptability; Artificial neural network; Magnetic resonance imaging; Mathematics","score_opus":0.02620456957795512,"score_gpt":0.3659482357832057,"score_spread":0.33974366620525054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393165986","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01582894,0.0001842705,0.9801507,0.00011702729,0.000034503322,0.000040359093,0.00010384777,0.0025108666,0.0010295347],"genre_scores_gemma":[0.5266457,0.0003020317,0.46679986,0.00031795434,0.00004276061,0.00021019246,0.00062358327,0.00041040417,0.0046474715],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986935,0.000028497518,0.0000058853116,0.000031025233,0.000047517635,0.000017740877],"domain_scores_gemma":[0.99970704,0.00013063278,0.000032450065,0.000058938902,0.00005166851,0.00001919609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056888815,0.00075197825,0.00030519292,0.00026711507,0.00014745566,0.00040445293,0.0011846038,0.0005473963,0.0024442552],"category_scores_gemma":[0.002028836,0.0002849319,0.00035455986,0.00024695863,0.00038154097,0.0008715309,0.0009621781,0.001033783,0.00050333823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025469068,0.00015899626,0.0012284704,0.0001910507,0.00009012568,0.0002127022,0.00011762061,0.41054612,0.045859516,0.010235071,0.005859542,0.525246],"study_design_scores_gemma":[0.000009068191,0.000036810783,0.00013622089,0.000005145626,0.000006937284,0.000041738345,0.0000063772513,0.9869472,0.008988998,0.002487916,0.001326886,0.0000066976004],"about_ca_topic_score_codex":0.0017689528,"about_ca_topic_score_gemma":0.0044471025,"teacher_disagreement_score":0.0024442552,"about_ca_system_score_codex":0.00036877152,"about_ca_system_score_gemma":0.00049068127,"threshold_uncertainty_score":0.008176863},"labels":[],"label_agreement":null},{"id":"W4393187453","doi":"10.1016/j.media.2024.103145","title":"DermSynth3D: Synthesis of in-the-wild annotated dermatology images","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","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":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; BC Cancer Foundation; Fonds National de la Recherche Luxembourg; Nvidia","keywords":"Artificial intelligence; Computer science; Dermatology; Computer vision; Pattern recognition (psychology); Medicine","score_opus":0.006955562316105821,"score_gpt":0.2746487435462664,"score_spread":0.26769318123016056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393187453","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047134567,0.001848491,0.8725965,0.00075997796,0.0008897996,0.000652585,0.021512263,0.042779494,0.011826241],"genre_scores_gemma":[0.21311551,0.0018601344,0.73238564,0.0005439296,0.00019795654,0.0005781602,0.026837949,0.010557985,0.013922699],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997615,0.000019004958,0.00001411129,0.00006339695,0.000104440085,0.000037589645],"domain_scores_gemma":[0.99963486,0.000108777254,0.000026602613,0.000066794186,0.00013396786,0.00002901201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048015144,0.0013285752,0.00060035125,0.0021226015,0.00036335585,0.0016600981,0.0011156299,0.0012653827,0.022370938],"category_scores_gemma":[0.001386244,0.000849113,0.0012388563,0.0008533652,0.0003042267,0.0005874254,0.0009938172,0.0009853373,0.0067112213],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013946051,0.00029785914,0.002857012,0.0024004458,0.000327786,0.0014061279,0.00050577073,0.05660222,0.1860348,0.0041055684,0.11869851,0.62536937],"study_design_scores_gemma":[0.00022361834,0.00035709882,0.008231878,0.00040516365,0.00026949466,0.0032109704,0.0006132278,0.6033572,0.22557966,0.009879569,0.14764932,0.00022285542],"about_ca_topic_score_codex":0.0033774553,"about_ca_topic_score_gemma":0.008934015,"teacher_disagreement_score":0.022370938,"about_ca_system_score_codex":0.00043414938,"about_ca_system_score_gemma":0.00103504,"threshold_uncertainty_score":0.07483822},"labels":[],"label_agreement":null},{"id":"W4393281987","doi":"10.1016/j.media.2024.103150","title":"Boundary-aware information maximization for self-supervised medical image segmentation","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Domain Adaptation and Few-Shot Learning","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":"École de Technologie Supérieure; Hôpital Notre-Dame","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Artificial intelligence; Computer science; Cluster analysis; Feature learning; Segmentation; Pattern recognition (psychology); Mutual information; Maximization; Image segmentation; Feature (linguistics); Machine learning; Mathematics","score_opus":0.007274381179233694,"score_gpt":0.2781048143756569,"score_spread":0.2708304331964232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393281987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008207381,0.00033571487,0.9902113,0.00012408133,0.000015316395,0.000036270587,0.000055029777,0.00068087864,0.00033393787],"genre_scores_gemma":[0.3915145,0.0005836129,0.6019086,0.00048740063,0.00015258137,0.0002904438,0.0008450007,0.0007318516,0.0034859357],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999159,0.00027338453,0.000054654436,0.0002573,0.00016590106,0.000089717636],"domain_scores_gemma":[0.99766326,0.0014932464,0.0001956648,0.00022865325,0.000329003,0.00009025107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025588,0.00088608643,0.0025774904,0.0015225693,0.0005475936,0.0010240722,0.0025512886,0.0024637955,0.0012168898],"category_scores_gemma":[0.005387033,0.0010864917,0.001495785,0.001162478,0.001381458,0.0015796783,0.0020658004,0.0015069944,0.00066333247],"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":[0.00043269582,0.00028848945,0.0008855628,0.0004990594,0.00026697025,0.00013704835,0.00021714673,0.6049861,0.036389034,0.009200343,0.0055623264,0.34113526],"study_design_scores_gemma":[0.0000058790465,0.00001827684,0.00014795526,0.0000072867465,0.000009745454,0.00003088519,0.000005231496,0.99310136,0.0024329033,0.004030903,0.00020300671,0.000006545741],"about_ca_topic_score_codex":0.003457415,"about_ca_topic_score_gemma":0.0038016762,"teacher_disagreement_score":0.003457415,"about_ca_system_score_codex":0.0009827493,"about_ca_system_score_gemma":0.0014496462,"threshold_uncertainty_score":0.0135324},"labels":[],"label_agreement":null},{"id":"W4396622776","doi":"10.1016/j.media.2024.103184","title":"Self-supervised anatomical continuity enhancement network for 7T SWI synthesis from 3T SWI","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced MRI Techniques and Applications","field":"Medicine","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":"McGill University; University of British Columbia","funders":"University of British Columbia; Compute Canada","keywords":"Susceptibility weighted imaging; Computer science; Task (project management); Pattern recognition (psychology); Artificial intelligence; Magnetic resonance imaging; Medicine","score_opus":0.010741466911618097,"score_gpt":0.32669903471585465,"score_spread":0.31595756780423656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396622776","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021514643,0.00037843004,0.9736191,0.00013453288,0.000076936645,0.00008967635,0.0003167515,0.0022027208,0.0016671055],"genre_scores_gemma":[0.30322132,0.00055287965,0.6807753,0.00026883293,0.00012650159,0.0002892424,0.002796476,0.0006822769,0.0112871425],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998282,0.000025077716,0.000008415425,0.00005779479,0.000052604544,0.000027816484],"domain_scores_gemma":[0.9996543,0.0001093821,0.000035312594,0.00004314961,0.00013667365,0.00002122927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007445875,0.0008411506,0.00054594205,0.00077506003,0.00034689822,0.000522891,0.00078684674,0.0008977204,0.0032066354],"category_scores_gemma":[0.0013175011,0.00047990822,0.0007755377,0.0007018817,0.00026547848,0.0006233025,0.0007946721,0.001131483,0.0015104214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041496064,0.00020380257,0.0016078773,0.00018836887,0.00013874107,0.00021809305,0.00015180807,0.13800456,0.06469573,0.002796007,0.0084856665,0.7830944],"study_design_scores_gemma":[0.000009286998,0.00005454192,0.00085131516,0.000014317266,0.000034216948,0.00010355371,0.000019442034,0.9819018,0.013696364,0.0010038977,0.0022980655,0.0000132143605],"about_ca_topic_score_codex":0.0048072403,"about_ca_topic_score_gemma":0.010594603,"teacher_disagreement_score":0.0048072403,"about_ca_system_score_codex":0.0003009315,"about_ca_system_score_gemma":0.00085045665,"threshold_uncertainty_score":0.010727286},"labels":[],"label_agreement":null},{"id":"W4396669394","doi":"10.1016/j.media.2024.103197","title":"Multi-scale relational graph convolutional network for multiple instance learning in histopathology images","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","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 British Columbia; University of British Columbia Hospital","funders":"Canadian Institutes of Health Research; Michael Smith Health Research BC","keywords":"Convolutional neural network; Magnification; Computer science; Graph; Pattern recognition (psychology); Artificial intelligence; Embedding; Theoretical computer science","score_opus":0.014425377078651583,"score_gpt":0.2764111777591579,"score_spread":0.2619858006805063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396669394","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05150106,0.0012154068,0.9383186,0.0005931455,0.00007750978,0.00010455287,0.000704874,0.005774184,0.001710507],"genre_scores_gemma":[0.6350504,0.00084176275,0.35604578,0.00036577904,0.00007736478,0.00014396854,0.002533281,0.00035297213,0.004588715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951816,0.00009543472,0.000022021755,0.00019993038,0.000103946506,0.000060564882],"domain_scores_gemma":[0.9993678,0.00021515065,0.00009499987,0.00016066377,0.000117129086,0.000044360077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000910321,0.0011366535,0.0007692002,0.0013222131,0.00029829438,0.00093948684,0.0022143312,0.001358165,0.0024079918],"category_scores_gemma":[0.002529619,0.00044091046,0.0010887914,0.0013605977,0.0004698202,0.0020250732,0.0010239274,0.0017993066,0.000718039],"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":[0.00025860046,0.00020162069,0.0025107842,0.00020338155,0.00020717169,0.0001875108,0.00009111899,0.5511547,0.013448662,0.011535371,0.007483973,0.41271713],"study_design_scores_gemma":[0.000004140286,0.00001371419,0.00022135966,0.0000036372167,0.000012216955,0.000016948272,0.0000058781725,0.99420446,0.0018732925,0.0032465174,0.00039309415,0.0000047431868],"about_ca_topic_score_codex":0.014349246,"about_ca_topic_score_gemma":0.018829094,"teacher_disagreement_score":0.014349246,"about_ca_system_score_codex":0.0017152154,"about_ca_system_score_gemma":0.0008369261,"threshold_uncertainty_score":0.028531432},"labels":[],"label_agreement":null},{"id":"W4398201688","doi":"10.1016/j.media.2024.103211","title":"BrainDAS: Structure-aware domain adaptation network for multi-site brain network analysis","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","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; Adaptation (eye); Artificial intelligence; Domain (mathematical analysis); Domain adaptation; Network analysis; Neuroscience; Psychology; Mathematics; Engineering","score_opus":0.03182547527032829,"score_gpt":0.3158843965732074,"score_spread":0.2840589213028791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398201688","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012732337,0.0002056276,0.97094023,0.00012885813,0.00008041403,0.00012319403,0.0012021188,0.013621142,0.0009660766],"genre_scores_gemma":[0.20204909,0.00045488682,0.7825348,0.00021798852,0.00008279153,0.00084630004,0.005057008,0.0025800688,0.0061770193],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998486,0.00003709055,0.000004973387,0.00005293979,0.00003536509,0.000020923311],"domain_scores_gemma":[0.99967027,0.00015174327,0.00002938397,0.00006139718,0.00006101095,0.0000261976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006174466,0.0011705941,0.0005829764,0.0010940743,0.0004810106,0.00068398163,0.0011447797,0.00086551247,0.005397999],"category_scores_gemma":[0.002206095,0.00048433634,0.0010542962,0.001112636,0.0002984119,0.000752404,0.0012551616,0.0013866099,0.0016553649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059557264,0.00032766841,0.002883262,0.00037921852,0.00060553855,0.00042882966,0.00030176036,0.22625831,0.04361883,0.013203541,0.051507104,0.6598903],"study_design_scores_gemma":[0.00003719131,0.000056773282,0.0013582159,0.000014141681,0.00005270341,0.00014017579,0.000042471256,0.9709372,0.00931251,0.011852351,0.006165145,0.000031262687],"about_ca_topic_score_codex":0.0054474636,"about_ca_topic_score_gemma":0.013890317,"teacher_disagreement_score":0.0054474636,"about_ca_system_score_codex":0.00047455973,"about_ca_system_score_gemma":0.0007573044,"threshold_uncertainty_score":0.018058121},"labels":[],"label_agreement":null},{"id":"W4399107553","doi":"10.1016/j.media.2024.103225","title":"MMGPL: Multimodal Medical Data Analysis with Graph Prompt Learning","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Graph Neural Networks","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; Artificial intelligence; Graph; Machine learning; Theoretical computer science","score_opus":0.01196817928201128,"score_gpt":0.30379009180225885,"score_spread":0.2918219125202476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399107553","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006468911,0.000286284,0.82762873,0.00055634935,0.00017821336,0.0003349702,0.0088537065,0.15379205,0.0019007693],"genre_scores_gemma":[0.09306851,0.0003270673,0.878721,0.00054236467,0.00009423757,0.000833235,0.014860913,0.006196587,0.0053560855],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99961406,0.0000933541,0.000022771554,0.000118825104,0.00011780756,0.000033158365],"domain_scores_gemma":[0.99904245,0.00046096797,0.000054136653,0.00023957618,0.00013152315,0.00007135803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007941656,0.0014091361,0.00084911735,0.0016556571,0.00045131083,0.0012097192,0.0021459719,0.0015906533,0.02665816],"category_scores_gemma":[0.0048613604,0.0007490284,0.0011802467,0.0011787118,0.00040488326,0.0017262684,0.002693836,0.0019224334,0.0078726085],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063579273,0.00030156306,0.0015478102,0.0006786845,0.00020554109,0.00043432452,0.00013082392,0.05943068,0.010663916,0.009650791,0.15571481,0.7606052],"study_design_scores_gemma":[0.00011378342,0.00011256252,0.00063484453,0.00004037542,0.000033441673,0.00017656243,0.000037240872,0.9358125,0.010025205,0.031211931,0.021762537,0.00003906336],"about_ca_topic_score_codex":0.0046421457,"about_ca_topic_score_gemma":0.007691097,"teacher_disagreement_score":0.02665816,"about_ca_system_score_codex":0.0006935548,"about_ca_system_score_gemma":0.0009895539,"threshold_uncertainty_score":0.08918041},"labels":[],"label_agreement":null},{"id":"W4399708787","doi":"10.1016/j.media.2024.103239","title":"CCSI: Continual Class-Specific Impression for data-free class incremental learning","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Domain Adaptation and Few-Shot Learning","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":"Vancouver General Hospital; Vector Institute; University of British Columbia","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Artificial intelligence; Forgetting; Machine learning; Class (philosophy); Margin (machine learning); Cross entropy; Normalization (sociology); Deep learning; Pattern recognition (psychology); Data mining","score_opus":0.03183739835390364,"score_gpt":0.3246820824993474,"score_spread":0.29284468414544373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399708787","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008340976,0.00032991136,0.97841483,0.00020398993,0.00020810711,0.00020121307,0.0004662003,0.009730372,0.0021043967],"genre_scores_gemma":[0.26920432,0.0002979983,0.7156983,0.0003968112,0.00028818552,0.00047255476,0.0028849214,0.0016851902,0.009071762],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988211,0.0001596239,0.000049870294,0.0003937991,0.00044287616,0.00013274708],"domain_scores_gemma":[0.9961169,0.00096565584,0.00013398488,0.0015922249,0.0008767248,0.00031457262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026091794,0.0010328267,0.0016818567,0.0014442268,0.00084428594,0.0014777762,0.0044730487,0.0019644604,0.011288287],"category_scores_gemma":[0.010917013,0.0006758352,0.0010587177,0.001240916,0.0013806071,0.0025988682,0.0042500217,0.0032710866,0.0030440933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006540215,0.00030915806,0.0011922914,0.00027404228,0.000121421275,0.00011505258,0.00014481615,0.04302473,0.016191335,0.023157172,0.03413292,0.880683],"study_design_scores_gemma":[0.000035932582,0.000118510325,0.00069061323,0.000026666232,0.000027724609,0.000096177915,0.00002818961,0.95940757,0.011339896,0.022158423,0.006038559,0.00003176436],"about_ca_topic_score_codex":0.006179045,"about_ca_topic_score_gemma":0.010347672,"teacher_disagreement_score":0.011288287,"about_ca_system_score_codex":0.0013291909,"about_ca_system_score_gemma":0.0020648844,"threshold_uncertainty_score":0.03776306},"labels":[],"label_agreement":null},{"id":"W4399709995","doi":"10.1016/j.media.2024.103231","title":"Interpretable deep clustering survival machines for Alzheimer’s disease subtype discovery","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":7,"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 Institutes of Health; Genentech; IXICO; U.S. National Library of Medicine; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; University of Southern California; Biogen; BioClinica; Meso Scale Diagnostics; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Canadian Institutes of Health Research; National Science Foundation","keywords":"Cluster analysis; Discriminative model; Artificial intelligence; Machine learning; Computer science; Categorization; Pattern recognition (psychology); Data mining","score_opus":0.019210361013252256,"score_gpt":0.3603439240123461,"score_spread":0.34113356299909386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399709995","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10487991,0.0030887506,0.8656522,0.0023357242,0.00038669648,0.00027257594,0.0067882724,0.013183534,0.0034123084],"genre_scores_gemma":[0.63875353,0.0009458606,0.3287893,0.0010228835,0.00036230768,0.00040539593,0.018403586,0.0006358637,0.010681357],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990972,0.00025866408,0.000081576974,0.0002779261,0.00015600176,0.00012856362],"domain_scores_gemma":[0.99712,0.0014398374,0.0001772196,0.00050844165,0.0006272808,0.00012728837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025092652,0.0015364892,0.0012807784,0.0020404118,0.00089717953,0.0014804426,0.0021155041,0.0023577653,0.003543975],"category_scores_gemma":[0.0067581288,0.00057318574,0.0018635136,0.001353566,0.0005196713,0.0011193964,0.0015177954,0.0025891785,0.0028158927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006948056,0.0004106629,0.014896166,0.00019656835,0.00034697217,0.0003353792,0.00026228302,0.13257803,0.005726284,0.0073247827,0.044320054,0.7929079],"study_design_scores_gemma":[0.00002733039,0.0000663144,0.0014655157,0.000053373413,0.00005259597,0.00011370441,0.00006398265,0.97302294,0.002239735,0.020380132,0.002497321,0.000017029664],"about_ca_topic_score_codex":0.011103987,"about_ca_topic_score_gemma":0.021427296,"teacher_disagreement_score":0.011103987,"about_ca_system_score_codex":0.001275481,"about_ca_system_score_gemma":0.0020719513,"threshold_uncertainty_score":0.022078693},"labels":[],"label_agreement":null},{"id":"W4400016384","doi":"10.1016/j.media.2024.103243","title":"CP-Net: Instance-aware part segmentation network for biological cell parsing","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","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":"CReATe Fertility Centre; University of Toronto","funders":"Temerty Faculty of Medicine, University of Toronto; Natural Sciences and Engineering Research Council of Canada; Vector Institute; Government of Ontario; Ontario Research Foundation","keywords":"Segmentation; Parsing; Computer science; Artificial intelligence; Market segmentation; Image segmentation; Pattern recognition (psychology); Feature (linguistics); Computer vision","score_opus":0.02439601499852344,"score_gpt":0.3100596373806054,"score_spread":0.2856636223820819,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400016384","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03781213,0.0015773503,0.92756003,0.0004319521,0.00018504681,0.00025426183,0.005147148,0.022909855,0.004122227],"genre_scores_gemma":[0.34152865,0.0011372528,0.6213577,0.00089408486,0.00011625282,0.0005597058,0.023674766,0.0012183808,0.009513251],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999723,0.0000273037,0.000017298495,0.00013920249,0.00006024085,0.000033028693],"domain_scores_gemma":[0.999723,0.00009175608,0.000026893655,0.00006904297,0.00006200036,0.000027287277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049555366,0.0013183032,0.0009722039,0.0012051924,0.00046282914,0.001121498,0.002319633,0.001454326,0.0049156626],"category_scores_gemma":[0.0013679594,0.00055081287,0.0012216194,0.0012735407,0.0004567757,0.0018534184,0.0011612816,0.0012402802,0.0014172608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083716976,0.0002061768,0.003745685,0.0005196732,0.0002644917,0.0005235331,0.00016740234,0.2479349,0.03450108,0.00984514,0.040442817,0.66101193],"study_design_scores_gemma":[0.000022648785,0.000061709914,0.00083565,0.000023945757,0.00005557381,0.00016359103,0.000030553576,0.97284275,0.011719137,0.0062080994,0.008014597,0.00002176526],"about_ca_topic_score_codex":0.0077864025,"about_ca_topic_score_gemma":0.011172604,"teacher_disagreement_score":0.0077864025,"about_ca_system_score_codex":0.0011349947,"about_ca_system_score_gemma":0.0011743293,"threshold_uncertainty_score":0.016444504},"labels":[],"label_agreement":null},{"id":"W4400188830","doi":"10.1016/j.media.2024.103257","title":"The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","field":"Computer Science","cited_by":27,"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":"Orionin Tutkimussäätiö; Turun Yliopisto; Vetenskapsrådet; Cancerfonden; VINNOVA; Karolinska Institutet; European Commission; Swedish e-Science Research Centre; Turun Yliopistosäätiö; Syöpäsäätiö; European Federation of Pharmaceutical Industries and Associations","keywords":"Computer science; Artificial intelligence; Image registration; Breast cancer; Computer vision; Pattern recognition (psychology); Cancer; Medicine; Image (mathematics)","score_opus":0.0076311122557927704,"score_gpt":0.3052339521346472,"score_spread":0.29760283987885444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400188830","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29071268,0.017681247,0.50488293,0.0068702516,0.0066229976,0.0026609641,0.07107416,0.07668898,0.022805758],"genre_scores_gemma":[0.32879233,0.0028714114,0.45748925,0.002203457,0.00083286053,0.001491506,0.18220979,0.009695827,0.014413658],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9940713,0.001395048,0.00052210374,0.0019396363,0.0016695296,0.00040231593],"domain_scores_gemma":[0.9941163,0.0016613348,0.00048625644,0.0024273912,0.00098869,0.00031994964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008162214,0.002093426,0.001777729,0.002248797,0.00134676,0.0034314152,0.0026091472,0.0029170075,0.0042112223],"category_scores_gemma":[0.017188933,0.0008295417,0.0024128086,0.0021490352,0.0012922062,0.0018983056,0.0043974356,0.0027275677,0.0069339513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029605525,0.0008672832,0.01769871,0.0030437028,0.0014929329,0.00095433823,0.00066803664,0.032728564,0.112320065,0.007057912,0.32124358,0.4989643],"study_design_scores_gemma":[0.0009116941,0.0018392848,0.052359555,0.00055495155,0.00057755824,0.009204036,0.0010995369,0.39453217,0.17573564,0.02100463,0.34163684,0.00054408755],"about_ca_topic_score_codex":0.004708676,"about_ca_topic_score_gemma":0.0075744265,"teacher_disagreement_score":0.008162214,"about_ca_system_score_codex":0.00085970626,"about_ca_system_score_gemma":0.0024706656,"threshold_uncertainty_score":0.04316646},"labels":[],"label_agreement":null},{"id":"W4400768355","doi":"10.1016/j.media.2024.103278","title":"Generating multi-pathological and multi-modal images and labels for brain MRI","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Generative Adversarial Networks and Image Synthesis","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":"EPSRC Centre for Doctoral Training in Medical Imaging; Engineering and Physical Sciences Research Council; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Avid Radiopharmaceuticals; Genentech; Royal Academy of Engineering; IXICO; H. Lundbeck A/S; Centre For Medical Engineering, King’s College London; Servier; Eisai; King's College London; National Institutes of Health; National Institute on Aging; National Institute for Health and Care Research; Northern California Institute for Research and Education; Nvidia; Wellcome Trust; University of Southern California; Pfizer; BioClinica; Biogen; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; European Commission; National Center for Advancing Translational Sciences; Medical Research Council; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Alzheimer's Association","keywords":"Computer science; Segmentation; Artificial intelligence; Categorical variable; Generative model; Synthetic data; Modal; Pattern recognition (psychology); Machine learning; Generative grammar","score_opus":0.018465209063195195,"score_gpt":0.3049406073021934,"score_spread":0.2864753982389982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400768355","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041865993,0.0003767846,0.95281076,0.0008875409,0.0001019125,0.00009504419,0.00062290113,0.0013632979,0.0018757337],"genre_scores_gemma":[0.56347877,0.00049257727,0.4284463,0.0007090786,0.000110583635,0.00019260727,0.0020525982,0.0006127827,0.0039047222],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99953735,0.00017917954,0.0000137481775,0.00014082754,0.00009361475,0.000035360794],"domain_scores_gemma":[0.9989611,0.0005676916,0.000096249336,0.00023964855,0.0000816725,0.000053677035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014443583,0.00071537116,0.00044961792,0.0007367244,0.0002585134,0.0009158873,0.0009856937,0.0011344537,0.0017758168],"category_scores_gemma":[0.0037489238,0.00049812905,0.0008832273,0.00054383394,0.0010219729,0.0008976464,0.001199937,0.0018061469,0.00073635275],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005472918,0.00020609607,0.0051085064,0.0002868341,0.00014775715,0.0006229839,0.00032032546,0.6767379,0.049299113,0.030479975,0.009797572,0.22644576],"study_design_scores_gemma":[0.000016597585,0.00004995145,0.00065720495,0.000021483298,0.000012492817,0.00034695838,0.000038442144,0.960806,0.012944244,0.022786774,0.002296482,0.00002343789],"about_ca_topic_score_codex":0.0010907431,"about_ca_topic_score_gemma":0.0026330987,"teacher_disagreement_score":0.0017758168,"about_ca_system_score_codex":0.0007383459,"about_ca_system_score_gemma":0.0005002465,"threshold_uncertainty_score":0.0076385736},"labels":[],"label_agreement":null},{"id":"W4401170997","doi":"10.1016/j.media.2024.103287","title":"Image-level supervision and self-training for transformer-based cross-modality tumor segmentation","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","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":"Centre Hospitalier de l’Université de Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Fonds de Recherche du Québec - Santé; Compute Canada","keywords":"Computer science; Artificial intelligence; Segmentation; Modality (human–computer interaction); Image segmentation; Modalities; Pattern recognition (psychology); Deep learning; Machine learning; Computer vision","score_opus":0.0338594690691731,"score_gpt":0.35617742369687416,"score_spread":0.3223179546277011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401170997","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048920784,0.0005847805,0.9384469,0.00033113375,0.00009520231,0.00014781576,0.0003330746,0.008292315,0.002848001],"genre_scores_gemma":[0.6499152,0.00037467465,0.33883777,0.00085255754,0.000096559306,0.00033892933,0.002918285,0.0012370558,0.005428942],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992906,0.00015596516,0.000037767728,0.00028531684,0.00014113558,0.00008914298],"domain_scores_gemma":[0.9988783,0.00037520658,0.00013427588,0.00027523996,0.00025250742,0.000084380554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016772258,0.0012475206,0.00087315514,0.0008516435,0.00040892125,0.0010406228,0.002376104,0.0016233943,0.0027734465],"category_scores_gemma":[0.004226196,0.0006425342,0.0011979715,0.00065441185,0.0011168219,0.0012744871,0.0021103204,0.0021534632,0.0013861603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061339914,0.00037011085,0.0034339111,0.00033926507,0.00023975615,0.0003152211,0.00034279382,0.39055067,0.064961605,0.0067691146,0.012384715,0.5196794],"study_design_scores_gemma":[0.000017826404,0.000076030134,0.00043031303,0.000015631413,0.000021368685,0.00011355594,0.000025846939,0.9807293,0.013694563,0.0035635252,0.001298934,0.000013187308],"about_ca_topic_score_codex":0.003551188,"about_ca_topic_score_gemma":0.0060349754,"teacher_disagreement_score":0.003551188,"about_ca_system_score_codex":0.00077253825,"about_ca_system_score_gemma":0.0014516183,"threshold_uncertainty_score":0.009278059},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01868782,0.00012461808,0.97942126,0.00009457065,0.00002475349,0.000026739543,0.000051089923,0.00068332336,0.00088579574],"genre_scores_gemma":[0.45552835,0.00037748695,0.5371421,0.00019102552,0.000060093458,0.00014474647,0.0004335424,0.0005368276,0.0055858092],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99971956,0.000047781603,0.000014554038,0.000043154898,0.00014683044,0.000028096856],"domain_scores_gemma":[0.9993129,0.00036757262,0.0000691776,0.00009223596,0.000124315,0.00003373525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005334299,0.0006515969,0.0009442101,0.00064429356,0.00023576304,0.00095849176,0.0013724229,0.001968449,0.0024381191],"category_scores_gemma":[0.0021962516,0.0009832957,0.00091684295,0.0007099643,0.00070382183,0.0010293081,0.0012839944,0.0012593769,0.00065810635],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012316668,0.000051963503,0.00055211486,0.00013936145,0.000056175064,0.00013146008,0.00012234067,0.79863197,0.017495599,0.0073226285,0.0015576471,0.17381562],"study_design_scores_gemma":[0.0000021869214,0.000009158993,0.00003662539,0.0000033952942,0.0000021171722,0.00002047634,0.0000030280562,0.9981026,0.0010436613,0.00058221654,0.00019177524,0.0000027890972],"about_ca_topic_score_codex":0.0061965035,"about_ca_topic_score_gemma":0.008671037,"teacher_disagreement_score":0.0061965035,"about_ca_system_score_codex":0.000419282,"about_ca_system_score_gemma":0.0012003175,"threshold_uncertainty_score":0.012320876},"labels":[],"label_agreement":null},{"id":"W4401852142","doi":"10.1016/j.media.2024.103309","title":"Establishing group-level brain structural connectivity incorporating anatomical knowledge under latent space modeling","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","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 Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; 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":"Inference; Computer science; Generative model; Artificial intelligence; Machine learning; Statistical inference; Bayesian inference; Bayesian probability; Pattern recognition (psychology); Generative grammar; Mathematics; Statistics","score_opus":0.07956383460373577,"score_gpt":0.39229125966041684,"score_spread":0.31272742505668105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401852142","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029502325,0.00006161764,0.96939474,0.00017921114,0.00001280075,0.000033970326,0.00008499837,0.00036412897,0.00036608858],"genre_scores_gemma":[0.779261,0.00017119567,0.21806142,0.00012914224,0.00008846011,0.00017125542,0.00080168684,0.0001534468,0.001162447],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823904,0.0007241722,0.000069762646,0.000530891,0.00029177224,0.00014437287],"domain_scores_gemma":[0.9928639,0.004729116,0.00073793955,0.0008834895,0.0005428669,0.00024264726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002754307,0.00086640945,0.0012579329,0.0015807127,0.00072525523,0.0014321937,0.0018509825,0.0020587612,0.001716962],"category_scores_gemma":[0.014152261,0.0007093279,0.0014017179,0.0014536891,0.0016190645,0.002770408,0.0022800236,0.002435406,0.00075195637],"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":[0.00046636365,0.0006778172,0.015211252,0.00033367134,0.0006528888,0.0005543648,0.0008863694,0.58062136,0.018329674,0.07523413,0.0057036444,0.3013284],"study_design_scores_gemma":[0.000009287016,0.000036974205,0.0007625684,0.00000742016,0.000023997725,0.000046018085,0.000032632674,0.9680378,0.0008470715,0.029974692,0.00021264752,0.000009010834],"about_ca_topic_score_codex":0.0040436545,"about_ca_topic_score_gemma":0.0067277583,"teacher_disagreement_score":0.0040436545,"about_ca_system_score_codex":0.0006825949,"about_ca_system_score_gemma":0.0017780021,"threshold_uncertainty_score":0.014566302},"labels":[],"label_agreement":null},{"id":"W4402282873","doi":"10.1016/j.media.2024.103342","title":"ATEC23 Challenge: Automated prediction of treatment effectiveness in ovarian cancer using histopathological images","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","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 British Columbia","funders":"National Taiwan University of Science and Technology; Tri-Service General Hospital; National Science and Technology Council","keywords":"Bevacizumab; Medicine; Ovarian cancer; Perforation; Oncology; Cancer; Internal medicine; Adverse effect; Medical physics; Chemotherapy","score_opus":0.0252842723934936,"score_gpt":0.33213852774531333,"score_spread":0.3068542553518197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402282873","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6387985,0.018018136,0.061702747,0.0046656267,0.0016634311,0.0022010207,0.2293653,0.03187749,0.01170768],"genre_scores_gemma":[0.655446,0.0021677914,0.095801674,0.0013922303,0.00062462076,0.0009045796,0.23051089,0.0008835196,0.012268769],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987286,0.00031569623,0.00008578203,0.00033996615,0.00038650745,0.0001434796],"domain_scores_gemma":[0.99788827,0.0010945665,0.00013367106,0.00026623977,0.00039926526,0.00021795525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020450319,0.0019124965,0.0017116406,0.0026468693,0.0004995872,0.0013515404,0.0020198538,0.0024921396,0.0039074933],"category_scores_gemma":[0.0062914635,0.0003299397,0.0018711915,0.0010912337,0.0003128979,0.0006302864,0.00097429304,0.0010313247,0.0026518523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003135814,0.0017774255,0.05863455,0.0019277385,0.0016308101,0.00094672345,0.0000924786,0.056561776,0.01724528,0.00072266546,0.282918,0.5744067],"study_design_scores_gemma":[0.0011241231,0.0023264268,0.069641866,0.0003211942,0.0010196838,0.0028237284,0.0002814802,0.81430566,0.035594404,0.0031151806,0.06924412,0.00020211576],"about_ca_topic_score_codex":0.020725,"about_ca_topic_score_gemma":0.024049846,"teacher_disagreement_score":0.020725,"about_ca_system_score_codex":0.0008789192,"about_ca_system_score_gemma":0.0017732735,"threshold_uncertainty_score":0.041208744},"labels":[],"label_agreement":null},{"id":"W4402565067","doi":"10.1016/j.media.2024.103346","title":"Semi-supervised ViT knowledge distillation network with style transfer normalization for colorectal liver metastases survival prediction","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","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":"Université de Montréal; Centre Hospitalier de l’Université de Montréal; Polytechnique Montréal","funders":"Fonds de Recherche du Québec - Santé; Institut TransMedTech; Institut de Valorisation des Données; Natural Sciences and Engineering Research Council of Canada; Institut Du Cancer de Montréal; Canada First Research Excellence Fund; Université de Montréal","keywords":"Normalization (sociology); Distillation; Computer science; Artificial intelligence; Transfer of learning; Machine learning; Chemistry; Chromatography","score_opus":0.011029659896524527,"score_gpt":0.25581270989714217,"score_spread":0.24478305000061765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402565067","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14115278,0.0041617043,0.8293083,0.0009284185,0.0007759397,0.0002341509,0.002773567,0.01388218,0.0067830416],"genre_scores_gemma":[0.7684981,0.00082449283,0.20020169,0.0007753534,0.00033836265,0.00028007253,0.0067528505,0.00044660337,0.021882588],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993581,0.00014877882,0.000033525863,0.00023463902,0.000113546615,0.00011145645],"domain_scores_gemma":[0.99922895,0.00034571125,0.000043660606,0.000114163944,0.00022788292,0.000039595052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012835665,0.0012383868,0.0009244089,0.001052743,0.0006002949,0.00071012875,0.0019658662,0.0014925466,0.0031587337],"category_scores_gemma":[0.0024966137,0.00043832755,0.0012461726,0.0009271372,0.0004177319,0.0010245948,0.0011692179,0.0021137232,0.0019915455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005081602,0.00036561585,0.002400529,0.00014294998,0.00020558313,0.00013242569,0.00008881233,0.09928255,0.014923715,0.0015026913,0.013657604,0.86678934],"study_design_scores_gemma":[0.000017566674,0.00007579555,0.00086208316,0.000015545675,0.00005442361,0.000044433233,0.0000157353,0.9893293,0.006824177,0.0015083561,0.0012337988,0.000018776665],"about_ca_topic_score_codex":0.009662292,"about_ca_topic_score_gemma":0.015425139,"teacher_disagreement_score":0.009662292,"about_ca_system_score_codex":0.0006101278,"about_ca_system_score_gemma":0.0011666912,"threshold_uncertainty_score":0.019212127},"labels":[],"label_agreement":null},{"id":"W4403015917","doi":"10.1016/j.media.2024.103357","title":"A Foundation Language-Image Model of the Retina (FLAIR): encoding expert knowledge in text supervision","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Retinal Imaging and Analysis","field":"Medicine","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":"DiagnoCure (Canada); Centre Hospitalier de l’Université de Montréal; Université du Québec","funders":"Fonds de recherche du Québec","keywords":"Encoding (memory); Computer science; Foundation (evidence); Image (mathematics); Fluid-attenuated inversion recovery; Artificial intelligence; Natural language processing; Retina; Computer vision; Psychology; Medicine; Neuroscience; Radiology; Magnetic resonance imaging","score_opus":0.01632020237077082,"score_gpt":0.34574680577638284,"score_spread":0.32942660340561203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403015917","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014026039,0.00014290801,0.98037714,0.0007097906,0.000037446396,0.00010254586,0.0008763165,0.0023795334,0.0013483263],"genre_scores_gemma":[0.46836498,0.0002701737,0.5254568,0.00034510263,0.00009865419,0.0002858998,0.0017246709,0.0003498289,0.0031038362],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929583,0.00025550614,0.00005339354,0.00019599247,0.00013868792,0.00006067633],"domain_scores_gemma":[0.9960198,0.0023763138,0.0002720133,0.0005190037,0.0006375549,0.00017532712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00199311,0.0005149011,0.0006201025,0.0007140583,0.00041993085,0.0014119927,0.0020544922,0.0013188613,0.0031906595],"category_scores_gemma":[0.009050811,0.0003500124,0.0009722655,0.0005584879,0.0007404492,0.002393789,0.0010800837,0.0010965781,0.00089199917],"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":[0.0010403099,0.00037507486,0.0043839514,0.00035859214,0.00017105964,0.0005833423,0.0007842822,0.51339835,0.007552593,0.11155444,0.018339247,0.3414588],"study_design_scores_gemma":[0.000016517935,0.000026741212,0.00015115178,0.000016086777,0.000014796221,0.000029003098,0.0000147823985,0.97505444,0.0011844911,0.022454333,0.0010296636,0.000007953013],"about_ca_topic_score_codex":0.015450751,"about_ca_topic_score_gemma":0.01687554,"teacher_disagreement_score":0.015450751,"about_ca_system_score_codex":0.0011359677,"about_ca_system_score_gemma":0.0027851453,"threshold_uncertainty_score":0.030721605},"labels":[],"label_agreement":null},{"id":"W4403243140","doi":"10.1016/j.media.2024.103365","title":"RFMiD: Retinal Image Analysis for multi-Disease Detection challenge","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Retinal Imaging and Analysis","field":"Medicine","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":"Western University","funders":"Agence Nationale de la Recherche","keywords":"Computer science; Artificial intelligence; Diabetic retinopathy; Disease; Macular degeneration; Glaucoma; Central retinal artery occlusion; Preprocessor; Medicine; Fundus (uterus); Contextual image classification; Optometry; Retinal; Ophthalmology; Pattern recognition (psychology); Pathology; Image (mathematics); Diabetes mellitus","score_opus":0.02624280849333781,"score_gpt":0.3576893885822046,"score_spread":0.3314465800888668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403243140","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15108632,0.022823993,0.6779936,0.015809212,0.0022125337,0.0018824174,0.044115774,0.06338491,0.02069122],"genre_scores_gemma":[0.2712555,0.0056050043,0.65959823,0.0033391854,0.0009466625,0.0005927284,0.041454893,0.0026673707,0.014540473],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980034,0.00033726037,0.00011841007,0.00041704957,0.0009136863,0.00021015195],"domain_scores_gemma":[0.9975224,0.0007177196,0.00014297653,0.00054814876,0.00079637364,0.00027243225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031692085,0.0015215499,0.0015263255,0.0033071514,0.0008315559,0.002131737,0.0017099995,0.0027472642,0.004144708],"category_scores_gemma":[0.006545972,0.00044661414,0.0012796286,0.0011212886,0.0003642338,0.0011383129,0.0022612668,0.0015792144,0.004406403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009011715,0.0004161365,0.010271786,0.0011292647,0.00037805922,0.0010934833,0.00009689624,0.0058022095,0.06751238,0.0031344842,0.19977163,0.7094925],"study_design_scores_gemma":[0.0003385027,0.0010945961,0.046501197,0.0004778759,0.0004993817,0.015584508,0.000467955,0.5199565,0.1808963,0.022110127,0.21187069,0.00020232378],"about_ca_topic_score_codex":0.004444371,"about_ca_topic_score_gemma":0.006561125,"teacher_disagreement_score":0.004444371,"about_ca_system_score_codex":0.0007327788,"about_ca_system_score_gemma":0.0014191328,"threshold_uncertainty_score":0.016760588},"labels":[],"label_agreement":null},{"id":"W4403909094","doi":"10.1016/j.media.2024.103381","title":"A cross-attention-based deep learning approach for predicting functional stroke outcomes using 4D CTP imaging and clinical metadata","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Acute Ischemic Stroke Management","field":"Medicine","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":"Alberta Children's Hospital; University of Calgary","funders":"","keywords":"Metadata; Computer science; Artificial intelligence; Deep learning; Stroke (engine); Machine learning; World Wide Web","score_opus":0.033867064350132356,"score_gpt":0.3710186549519243,"score_spread":0.33715159060179195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403909094","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36399305,0.0060022185,0.6160565,0.0028488236,0.00039892536,0.00022908465,0.0018899433,0.004244371,0.004337035],"genre_scores_gemma":[0.9536306,0.00076540647,0.03891327,0.0008005824,0.00021548738,0.00013706293,0.0021962635,0.00006789405,0.0032733318],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994742,0.000114662624,0.00004152908,0.000167554,0.00009655432,0.0001054533],"domain_scores_gemma":[0.999474,0.00021033098,0.000067830595,0.000041221494,0.0001551305,0.000051533356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013037819,0.0015063128,0.0010957014,0.0015309345,0.00037905504,0.0008589481,0.0015174268,0.0015762076,0.0013423586],"category_scores_gemma":[0.0021615534,0.00043988653,0.0013700749,0.0011170135,0.000517652,0.0010950911,0.001629021,0.0016360445,0.00057859835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00085919636,0.0008748283,0.027122812,0.00020429598,0.0005757237,0.00068745995,0.00018595942,0.4398737,0.010587743,0.0018997695,0.010047097,0.5070814],"study_design_scores_gemma":[0.000011392185,0.000070403126,0.0010499548,0.000011880709,0.00004392232,0.000040527902,0.000011405317,0.9968225,0.00073294644,0.0009303442,0.0002648678,0.000009830273],"about_ca_topic_score_codex":0.012841236,"about_ca_topic_score_gemma":0.011539065,"teacher_disagreement_score":0.012841236,"about_ca_system_score_codex":0.00081497256,"about_ca_system_score_gemma":0.0011671203,"threshold_uncertainty_score":0.02553296},"labels":[],"label_agreement":null},{"id":"W4404055034","doi":"10.1016/j.media.2024.103389","title":"Editorial for Special Issue on Foundation Models for Medical Image Analysis","year":2024,"lang":"en","type":"editorial","venue":"Medical Image Analysis","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","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 British Columbia","funders":"","keywords":"Foundation (evidence); Computer science; Image (mathematics); Artificial intelligence; Data science; Geography","score_opus":0.0070945737540653265,"score_gpt":0.34453436390558956,"score_spread":0.33743979015152425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404055034","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000020892196,0.0033862242,0.00035138233,0.030035382,0.96500957,0.00001695434,0.00007863635,0.00008639933,0.0010145337],"genre_scores_gemma":[0.00025720385,0.0017909341,0.0001918356,0.011277252,0.98107225,0.000022329288,0.000042342275,0.000059638875,0.005286227],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99446774,0.0012306613,0.00061677705,0.00065337523,0.0027621216,0.00026928735],"domain_scores_gemma":[0.96697706,0.014869119,0.0018355185,0.0012199527,0.01208232,0.0030161396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010060871,0.005053643,0.0051206965,0.005941162,0.003323197,0.00957938,0.0036448212,0.01804618,0.025626736],"category_scores_gemma":[0.03555325,0.0018892032,0.0047070384,0.0017167454,0.0027346914,0.0044262824,0.0021268134,0.02042158,0.015177562],"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":[0.000023484976,0.000006776767,0.000014455089,0.00008280994,0.000023453462,0.0000459017,0.0000027611209,0.00003620411,0.000020379246,0.00022203989,0.9970989,0.0024228184],"study_design_scores_gemma":[0.0002047225,0.000039098177,0.00029713212,0.00056001305,0.00017370266,0.00028376564,0.000025870479,0.0012461767,0.00012784441,0.005239649,0.9917624,0.00003971381],"about_ca_topic_score_codex":0.0025044195,"about_ca_topic_score_gemma":0.006950993,"teacher_disagreement_score":0.025626736,"about_ca_system_score_codex":0.0034403806,"about_ca_system_score_gemma":0.0032208597,"threshold_uncertainty_score":0.085730016},"labels":[],"label_agreement":null},{"id":"W4404768704","doi":"10.1016/j.media.2024.103400","title":"Prediction of the upright articulated spine shape in the operating room using conditioned neural kernel fields","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","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":"Centre Hospitalier Universitaire Sainte-Justine; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Centre hospitalier universitaire Sainte-Justine; Centre Hospitalier Universitaire de Québec; Chung Hua University; Compute Canada","keywords":"Artificial intelligence; Computer science; SPINE (molecular biology); Computer vision; Artificial neural network; Kernel (algebra); Pattern recognition (psychology); Mathematics; Machine learning; Biology; Combinatorics","score_opus":0.013660729993830322,"score_gpt":0.2557247947042363,"score_spread":0.24206406471040598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404768704","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5923808,0.00038961196,0.4046554,0.00030716468,0.00005573623,0.000055701155,0.00041070304,0.0009369107,0.00080790627],"genre_scores_gemma":[0.98328537,0.00007513305,0.015400682,0.00004933812,0.000012385226,0.00003310358,0.00048532564,0.00002868285,0.0006298795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986017,0.000028522378,0.0000068792574,0.00004632907,0.00002703436,0.000030936782],"domain_scores_gemma":[0.99942493,0.00029951005,0.00007302205,0.000044894663,0.00011130308,0.000046400946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049627834,0.00058993086,0.0005456312,0.0003560326,0.00017449142,0.00036230794,0.000680484,0.0006504079,0.0008801009],"category_scores_gemma":[0.0018239365,0.00032110175,0.0006760619,0.00020749021,0.0004125009,0.0003876884,0.0005253572,0.0008248928,0.00025908463],"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":[0.00021758613,0.00009839612,0.008846117,0.000023910028,0.00003366922,0.00011255058,0.00004121126,0.9283977,0.0036602342,0.00049445615,0.0005566705,0.05751739],"study_design_scores_gemma":[0.0000041133194,0.000023691815,0.0007698828,0.000002217144,0.0000035004366,0.000015303298,0.0000034760258,0.99844414,0.000458121,0.00023421102,0.000038345126,0.000003039185],"about_ca_topic_score_codex":0.014959096,"about_ca_topic_score_gemma":0.012218291,"teacher_disagreement_score":0.014959096,"about_ca_system_score_codex":0.0006271178,"about_ca_system_score_gemma":0.0010073219,"threshold_uncertainty_score":0.029744029},"labels":[],"label_agreement":null},{"id":"W4405494868","doi":"10.1016/j.media.2024.103439","title":"Personalized dental crown design: A point-to-mesh completion network","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"3D Shape Modeling and Analysis","field":"Engineering","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":"Polytechnique Montréal","funders":"MEDTEQ+; Natural Sciences and Engineering Research Council of Canada; King Fahd University of Petroleum and Minerals","keywords":"Crown (dentistry); Point (geometry); Computer science; Artificial intelligence; Orthodontics; Mathematics; Medicine; Geometry","score_opus":0.013217806012718668,"score_gpt":0.25885082124515635,"score_spread":0.2456330152324377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405494868","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03596632,0.00018053476,0.9579174,0.00017858435,0.000048825503,0.00009054436,0.00021548966,0.0034866289,0.00191565],"genre_scores_gemma":[0.40660793,0.00023173164,0.5821502,0.00027716323,0.000055062224,0.0002477486,0.0012837409,0.00042981954,0.00871651],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997706,0.000027637614,0.000009067052,0.000068949324,0.00009391561,0.000029875297],"domain_scores_gemma":[0.99967444,0.00012060558,0.000028657101,0.000071380775,0.00007778168,0.000027131842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049675326,0.00069508434,0.0005936544,0.00050754927,0.00027419132,0.00047825716,0.0014454954,0.0009498322,0.0042117587],"category_scores_gemma":[0.0013749943,0.0005297122,0.00078751286,0.00038584817,0.0003337799,0.0007055643,0.0009782442,0.0010703814,0.0010907089],"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":[0.00020634495,0.00012770225,0.001271323,0.00007778392,0.000054635675,0.00010912093,0.000078192694,0.54947275,0.010873314,0.0029594826,0.0067784493,0.42799088],"study_design_scores_gemma":[0.0000054857273,0.000021120411,0.00010688763,0.0000024884773,0.0000032838755,0.000020563466,0.000005397044,0.99683845,0.001443634,0.0009283361,0.00062124815,0.0000031019376],"about_ca_topic_score_codex":0.007318256,"about_ca_topic_score_gemma":0.01076918,"teacher_disagreement_score":0.007318256,"about_ca_system_score_codex":0.0006909121,"about_ca_system_score_gemma":0.0007876202,"threshold_uncertainty_score":0.0145513415},"labels":[],"label_agreement":null},{"id":"W4406642521","doi":"10.1016/j.media.2025.103473","title":"Towards contrast-agnostic soft segmentation of the spinal cord","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","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 de Montréal; Mila - Quebec Artificial Intelligence Institute; Polytechnique Montréal","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Neurological Disorders and Stroke; Fonds de recherche du Québec – Nature et technologies; Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Réseau en Bio-Imagerie du Quebec; Canadian Institutes of Health Research; National Institutes of Health; Canada Research Chairs; Canada First Research Excellence Fund; Boettcher Foundation; Canada Foundation for Innovation","keywords":"Segmentation; Artificial intelligence; Contrast (vision); Computer science; Computer vision; Pattern recognition (psychology); Anatomy; Medicine","score_opus":0.006067156222850755,"score_gpt":0.27762898365622074,"score_spread":0.27156182743337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406642521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09944674,0.0018102621,0.88828665,0.0005947784,0.00013562925,0.00016621573,0.0008433214,0.006946894,0.0017695067],"genre_scores_gemma":[0.53116727,0.00091943605,0.45595297,0.0010057788,0.00018063928,0.00026681536,0.0035465388,0.0014749924,0.0054855873],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993724,0.0001137238,0.000039804556,0.00027963877,0.00012279037,0.00007166535],"domain_scores_gemma":[0.99877983,0.0005576874,0.00020271043,0.00018887826,0.00019227742,0.00007856141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015843449,0.0015426243,0.0010287508,0.0015317423,0.00042735474,0.0017397702,0.0013633001,0.0021022754,0.00135588],"category_scores_gemma":[0.004652314,0.0007662863,0.001678034,0.00080238434,0.0010053038,0.0011690184,0.0017221966,0.0021029667,0.0013386761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001147356,0.0002870136,0.005451142,0.0005725996,0.00033063206,0.00043483358,0.00043563845,0.3349866,0.103524,0.0042064628,0.0079671275,0.54065657],"study_design_scores_gemma":[0.000028915758,0.0001188818,0.0016149839,0.000054227905,0.00004774409,0.00024319191,0.000043664902,0.9727413,0.017912354,0.005763523,0.0014073149,0.000023980785],"about_ca_topic_score_codex":0.0053045787,"about_ca_topic_score_gemma":0.009056356,"teacher_disagreement_score":0.0053045787,"about_ca_system_score_codex":0.000847918,"about_ca_system_score_gemma":0.0014476542,"threshold_uncertainty_score":0.0105473995},"labels":[],"label_agreement":null},{"id":"W4407080228","doi":"10.1016/j.media.2025.103483","title":"Harmonizing flows: Leveraging normalizing flows for unsupervised and source-free MRI harmonization","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced MRI Techniques and Applications","field":"Medicine","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":"Centre Hospitalier Universitaire Sainte-Justine; École de Technologie Supérieure; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Compute Canada","keywords":"Harmonization; Computer science; Artificial intelligence; Econometrics; Mathematics; Physics","score_opus":0.022535602287254545,"score_gpt":0.3132272608554435,"score_spread":0.29069165856818896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407080228","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007947733,0.00011475238,0.9884298,0.000103475155,0.00003676419,0.00006986072,0.00011358553,0.0021941436,0.0009898823],"genre_scores_gemma":[0.29916278,0.000449455,0.68919134,0.0005948709,0.0001544592,0.00040701215,0.0016796804,0.0019157488,0.0064446535],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921834,0.00020207469,0.00004243434,0.00029709912,0.00015957626,0.00008049129],"domain_scores_gemma":[0.99883276,0.00037703133,0.00014733813,0.00033398188,0.00025350347,0.000055475088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022729393,0.0013412156,0.00077819015,0.0013914454,0.0006613841,0.0013037602,0.0018208537,0.001129273,0.0025858728],"category_scores_gemma":[0.006100805,0.00057482754,0.0009856387,0.0010320944,0.0012807181,0.00249111,0.0024613475,0.0017601319,0.0013150834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029412267,0.00021166101,0.003350569,0.00014669819,0.00015693276,0.00022643857,0.0004938775,0.2841136,0.024779877,0.03136634,0.010528218,0.64433163],"study_design_scores_gemma":[0.00002916591,0.00007032978,0.00068701577,0.000026736494,0.000033798577,0.00016844051,0.00006945131,0.949911,0.013346235,0.028797897,0.006823078,0.000036939804],"about_ca_topic_score_codex":0.0047140764,"about_ca_topic_score_gemma":0.0057104537,"teacher_disagreement_score":0.0047140764,"about_ca_system_score_codex":0.000762165,"about_ca_system_score_gemma":0.0018020716,"threshold_uncertainty_score":0.012020588},"labels":[],"label_agreement":null},{"id":"W4407269389","doi":"10.1016/j.media.2025.103491","title":"HistoKernel: Whole slide image level Maximum Mean Discrepancy kernels for pan-cancer predictive modelling","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","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":"EPSRC Centre for Doctoral Training in Medical Imaging; Engineering and Physical Sciences Research Council; Sierra Oncology","keywords":"Artificial intelligence; Mathematics; Computer science; Pattern recognition (psychology); Computer vision","score_opus":0.02527294268600958,"score_gpt":0.311818302738887,"score_spread":0.2865453600528774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407269389","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01138662,0.00039308102,0.9852749,0.00018750441,0.000028305041,0.000034433488,0.00024051165,0.0019623092,0.00049228984],"genre_scores_gemma":[0.45563403,0.00069434784,0.5354947,0.00033163192,0.00009121535,0.0002652748,0.0018703507,0.001105218,0.0045133224],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946564,0.00016878829,0.000027938131,0.00014351732,0.00015134558,0.00004282648],"domain_scores_gemma":[0.99840987,0.00084981805,0.00016669933,0.00024618907,0.0002586474,0.00006874578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017759815,0.0007932633,0.00087980786,0.0007681388,0.000278341,0.001262015,0.0018570148,0.0011269534,0.0019580966],"category_scores_gemma":[0.0060233017,0.0004691878,0.0010408236,0.0006925364,0.00076289417,0.0016587687,0.0015157646,0.0017042803,0.00092959794],"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":[0.00033726147,0.000116921,0.0045925835,0.00028434722,0.00018154157,0.00018149069,0.00019373235,0.7112147,0.016145747,0.02389946,0.0094041405,0.23344809],"study_design_scores_gemma":[0.0000043626796,0.000012539303,0.00024270604,0.000005845594,0.00000398752,0.000028249859,0.000007915864,0.9931206,0.0013364477,0.004182994,0.0010454548,0.000008803162],"about_ca_topic_score_codex":0.004207463,"about_ca_topic_score_gemma":0.00375383,"teacher_disagreement_score":0.004207463,"about_ca_system_score_codex":0.0010171303,"about_ca_system_score_gemma":0.0010582438,"threshold_uncertainty_score":0.009392381},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008418692,0.00008557573,0.9901074,0.00007346283,0.000019731415,0.000026000349,0.000031858177,0.00034193427,0.00089523237],"genre_scores_gemma":[0.37108755,0.00024608694,0.62433565,0.00012644006,0.000054764816,0.00017694541,0.000407803,0.0006654024,0.002899408],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833775,0.00055788626,0.0000721605,0.0004514158,0.00048095325,0.000099924626],"domain_scores_gemma":[0.997191,0.0013122741,0.000329825,0.0004450657,0.0006020174,0.000119812255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019456011,0.0011322076,0.0011589775,0.0011819522,0.00072155346,0.0016872463,0.0022974168,0.0020484447,0.0023658269],"category_scores_gemma":[0.009912276,0.0012220162,0.0008496447,0.0014295902,0.0010072917,0.0020709508,0.0023792812,0.0018663087,0.00086163974],"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":[0.00019713466,0.000072615214,0.0009969241,0.00012748998,0.000060876824,0.000085856875,0.00016860003,0.83516276,0.012078836,0.012646053,0.0017770272,0.13662574],"study_design_scores_gemma":[0.000004356187,0.000012283776,0.00011264999,0.000007806316,0.0000044135345,0.000028836788,0.0000075697058,0.99452835,0.0025151873,0.0022374569,0.00053411035,0.000007017518],"about_ca_topic_score_codex":0.005256632,"about_ca_topic_score_gemma":0.00685441,"teacher_disagreement_score":0.005256632,"about_ca_system_score_codex":0.0012015845,"about_ca_system_score_gemma":0.0015853567,"threshold_uncertainty_score":0.010452032},"labels":[],"label_agreement":null},{"id":"W4407763442","doi":"10.1016/j.media.2025.103503","title":"Hyperfusion: A hypernetwork approach to multimodal integration of tabular and medical imaging data for predictive modeling","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Machine Learning in Healthcare","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":"McGill University","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Mental Health; Biotechnology and Biological Sciences Research Council; Child Mind Institute; Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; University of California; National Institutes of Health; Ministry of Health, State of Israel; Stavros Niarchos Foundation; U.S. Department of Defense; James S. McDonnell Foundation; Alzheimer's Disease Neuroimaging Initiative; Israel Science Foundation; National Institute on Aging; Commonwealth Scientific and Industrial Research Organisation; University of Cambridge; Canadian Institute for Advanced Research; Leon Levy Foundation; Harvard University; Massachusetts General Hospital; Alzheimer's Association; Medical Research Council; Howard Hughes Medical Institute","keywords":"Computer science; Artificial intelligence; Computer vision; Machine learning","score_opus":0.02037254151537225,"score_gpt":0.3356412056160552,"score_spread":0.31526866410068294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407763442","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009300349,0.0006318868,0.9852743,0.00072989595,0.00005780396,0.00005403235,0.00081733405,0.0016637002,0.0014706283],"genre_scores_gemma":[0.54930454,0.0019344102,0.4343195,0.0012465193,0.00036407652,0.00057154347,0.003913147,0.00062420213,0.0077220164],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915075,0.0003528715,0.000051035317,0.00021051115,0.00016572699,0.000069222864],"domain_scores_gemma":[0.99776995,0.0014176854,0.00019806418,0.00026904276,0.00023814986,0.00010719872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00253848,0.0011911591,0.00082871335,0.0017220792,0.0005703763,0.0016554483,0.002289258,0.0015235244,0.004674194],"category_scores_gemma":[0.0066225193,0.0007456292,0.0016123548,0.0017563743,0.0011281715,0.0036536148,0.0027572669,0.0026859115,0.0008492489],"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":[0.00022458281,0.000111111796,0.0022916186,0.00012147161,0.00027793716,0.00025609104,0.00015513196,0.8217199,0.0021838413,0.03308947,0.006333864,0.13323498],"study_design_scores_gemma":[0.0000057886527,0.000011364891,0.00014425143,0.000014213878,0.00001460701,0.00001918086,0.0000091678,0.9742407,0.00047011155,0.024104536,0.0009570143,0.000008977688],"about_ca_topic_score_codex":0.014361045,"about_ca_topic_score_gemma":0.013789637,"teacher_disagreement_score":0.014361045,"about_ca_system_score_codex":0.0016984281,"about_ca_system_score_gemma":0.0014688743,"threshold_uncertainty_score":0.028554916},"labels":[],"label_agreement":null},{"id":"W4407942234","doi":"10.1016/j.media.2025.103494","title":"Exploring the values underlying machine learning research in medical image analysis","year":2025,"lang":"en","type":"review","venue":"Medical Image Analysis","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","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":"Artificial intelligence; Computer science; Image (mathematics); Machine learning; Computer vision","score_opus":0.6769723147849942,"score_gpt":0.6144296277153716,"score_spread":0.06254268706962263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407942234","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014345102,0.9910142,0.0014990161,0.0040415246,0.00024549573,0.000008274655,0.000010076954,0.0000071751983,0.0030307637],"genre_scores_gemma":[0.003924805,0.9907204,0.0020183937,0.0021859834,0.00052955013,0.000035558267,0.000021260103,0.000008567581,0.0005554901],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99675083,0.0015279443,0.00033584688,0.0002967051,0.0009713928,0.00011741613],"domain_scores_gemma":[0.9899421,0.008381977,0.00036990276,0.00025658583,0.0008972841,0.00015227194],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007259279,0.0006336633,0.0013312546,0.0042097573,0.0007441623,0.0041394485,0.0012269153,0.0030026908,0.0018312033],"category_scores_gemma":[0.010722622,0.00045830043,0.00062924175,0.0046635577,0.005173689,0.00648606,0.002192829,0.0060473066,0.0011305129],"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":[0.000050172464,0.00006168188,0.000261874,0.018670578,0.0001351526,0.00018494306,0.0008528208,0.0009535336,0.0007060902,0.29155037,0.017488163,0.66908467],"study_design_scores_gemma":[0.000020636331,0.00007886565,0.0007219051,0.025616534,0.000098963275,0.00087655993,0.0006194316,0.00037711303,0.0005173613,0.1717147,0.79929656,0.00006137027],"about_ca_topic_score_codex":0.001261064,"about_ca_topic_score_gemma":0.001719852,"teacher_disagreement_score":0.99274075,"about_ca_system_score_codex":0.0024301405,"about_ca_system_score_gemma":0.003738987,"threshold_uncertainty_score":0.038391173},"labels":[],"label_agreement":null},{"id":"W4408052092","doi":"10.1016/j.media.2025.103538","title":"Graph-based prototype inverse-projection for identifying cortical sulcal pattern abnormalities in congenital heart disease","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","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":"Holland Bloorview Kids Rehabilitation Hospital; University of Toronto","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke; National Institutes of Health; National Heart, Lung, and Blood Institute; Ministry of Science and ICT, South Korea","keywords":"Artificial intelligence; Projection (relational algebra); Computer science; Graph; Computer vision; Pattern recognition (psychology); Anatomy; Medicine; Algorithm; Theoretical computer science","score_opus":0.019769023957759167,"score_gpt":0.3153291550869829,"score_spread":0.29556013112922375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408052092","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2914772,0.00091077434,0.70026004,0.0002819403,0.000047014077,0.00027026777,0.0011452052,0.0033441556,0.0022633693],"genre_scores_gemma":[0.66171885,0.0005422234,0.33505052,0.000058528105,0.00003571421,0.00010804903,0.0009803525,0.00033804716,0.0011675986],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998461,0.00004213185,0.000008663278,0.000033741402,0.000047999856,0.000021264676],"domain_scores_gemma":[0.99955803,0.00019182387,0.000032493223,0.00004453287,0.00014225805,0.000030801042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030788922,0.00054015574,0.00045985568,0.0019983384,0.00025585294,0.0008846043,0.0004909325,0.00062963465,0.0022176206],"category_scores_gemma":[0.0015163119,0.00027062593,0.00058917847,0.0009579933,0.00027366244,0.0004261646,0.0006737762,0.00040040186,0.0006438399],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014801968,0.00021087966,0.017225282,0.0005700026,0.00024314405,0.0010521594,0.0002706153,0.053725604,0.12575278,0.003335742,0.0057049533,0.7904287],"study_design_scores_gemma":[0.000039640247,0.00012746007,0.013977573,0.000030477666,0.00008889589,0.0017653155,0.00014010223,0.9579727,0.021361895,0.002861073,0.0015943842,0.00004041861],"about_ca_topic_score_codex":0.0047606775,"about_ca_topic_score_gemma":0.00605917,"teacher_disagreement_score":0.0047606775,"about_ca_system_score_codex":0.00018806137,"about_ca_system_score_gemma":0.000666284,"threshold_uncertainty_score":0.009465933},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015631588,0.00031524696,0.91843575,0.00026890798,0.00015543932,0.00017677294,0.0006118984,0.062499825,0.0019046578],"genre_scores_gemma":[0.25062665,0.00061551627,0.730455,0.0010661144,0.0001221794,0.00038928603,0.0036549973,0.00726778,0.005802566],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963915,0.00006512571,0.000028972156,0.00013738078,0.00009311672,0.000036360023],"domain_scores_gemma":[0.99947006,0.00019577592,0.000035599525,0.00017036012,0.00007949257,0.00004873908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000866733,0.0010865047,0.00058086327,0.0007060361,0.0002631126,0.0010730977,0.001820152,0.0015641527,0.007368224],"category_scores_gemma":[0.003096613,0.0005739013,0.0011880983,0.00052361924,0.00042140312,0.0013910249,0.0018134,0.0011706951,0.0025313583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013014136,0.00025095008,0.003822048,0.00064489245,0.0002505142,0.0007432549,0.0006090323,0.08694043,0.11367554,0.008260483,0.039688744,0.7438127],"study_design_scores_gemma":[0.00009064732,0.00027022083,0.0018150813,0.000047634538,0.000064579275,0.0007569401,0.0001539236,0.893561,0.06213989,0.010074918,0.030935783,0.00008940296],"about_ca_topic_score_codex":0.001706702,"about_ca_topic_score_gemma":0.0024753262,"teacher_disagreement_score":0.007368224,"about_ca_system_score_codex":0.00040442633,"about_ca_system_score_gemma":0.00056655,"threshold_uncertainty_score":0.024649203},"labels":[],"label_agreement":null},{"id":"W4409161079","doi":"10.1016/j.media.2025.103559","title":"Improved unsupervised 3D lung lesion detection and localization by fusing global and local features: Validation in 3D low-dose computed tomography","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","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":"Artificial Intelligence in Medicine (Canada)","funders":"Samsung Medical Center, Sungkyunkwan University; Institute for Information and Communications Technology Promotion; Seoul National University; Ministry of Health and Welfare; Ministry of Trade, Industry and Energy; Ministry of Food and Drug Safety; Korea Medical Device Development Fund","keywords":"Artificial intelligence; Computed tomography; Computer science; Computer vision; Pattern recognition (psychology); Radiology; Medicine","score_opus":0.004103300644462484,"score_gpt":0.2765436820288232,"score_spread":0.2724403813843607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409161079","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40093884,0.001151027,0.585039,0.0006182826,0.0001588006,0.00032656954,0.0011968816,0.008762442,0.0018080968],"genre_scores_gemma":[0.857651,0.00029690767,0.13774604,0.00029537993,0.000039160357,0.00013872964,0.001991612,0.00032725648,0.0015139693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99930716,0.00016983307,0.00004182522,0.000226127,0.00017348574,0.000081613805],"domain_scores_gemma":[0.99895006,0.0004670859,0.00009649725,0.00017751285,0.00021375758,0.000095150805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021146033,0.0013199319,0.00093714014,0.0010464595,0.0003487974,0.0012141151,0.0015633084,0.0014782012,0.0010381453],"category_scores_gemma":[0.0035561267,0.0005710209,0.0015989282,0.0006916806,0.00076180405,0.00073265034,0.0013325923,0.00127507,0.0005317017],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003677102,0.00027310295,0.014572502,0.00016700676,0.0003605671,0.00019240791,0.000115069226,0.8271371,0.016987013,0.00085235195,0.0022864845,0.1366887],"study_design_scores_gemma":[0.000010730365,0.0000462937,0.0011614297,0.000007913335,0.000014121812,0.00004819879,0.000009207159,0.9947509,0.0034318573,0.00028185366,0.00022595457,0.00001149013],"about_ca_topic_score_codex":0.013212641,"about_ca_topic_score_gemma":0.014357456,"teacher_disagreement_score":0.013212641,"about_ca_system_score_codex":0.00096312567,"about_ca_system_score_gemma":0.0011862145,"threshold_uncertainty_score":0.026271462},"labels":[],"label_agreement":null},{"id":"W4409328585","doi":"10.1016/j.media.2025.103571","title":"From tissue to sound: A new paradigm for medical sonic interaction design","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Music Technology and Sound Studies","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":"Sound (geography); Human–computer interaction; Computer science; Acoustics; Artificial intelligence; Physics","score_opus":0.018957600114458614,"score_gpt":0.3358606046118199,"score_spread":0.31690300449736125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409328585","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029513468,0.00072992424,0.99073225,0.00078891986,0.00013817851,0.000067898975,0.000034932134,0.00035372077,0.004202872],"genre_scores_gemma":[0.14063613,0.0027199613,0.8439145,0.0013606611,0.0004534484,0.00065404666,0.00014768675,0.00035768855,0.009755863],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990742,0.00025239153,0.000049399012,0.00015039441,0.00042643238,0.000047172758],"domain_scores_gemma":[0.99934024,0.0003212634,0.000053159245,0.000102160644,0.00010993317,0.00007327662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012248829,0.0007393982,0.0004550922,0.00082856056,0.00045445908,0.0021860995,0.0014454576,0.0014209192,0.004043199],"category_scores_gemma":[0.0025166867,0.0004990696,0.0007262712,0.000370929,0.0020378374,0.0026994953,0.0029685167,0.0014450103,0.0014297476],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023280291,0.000106195475,0.0008515247,0.0008792399,0.0000865528,0.0006016929,0.0020829933,0.034190226,0.28601733,0.3029545,0.010279578,0.36171743],"study_design_scores_gemma":[0.00012556512,0.0010228397,0.0011222353,0.0003893335,0.00012168001,0.0029475433,0.00076189794,0.27684686,0.07736161,0.35166264,0.28743097,0.00020689092],"about_ca_topic_score_codex":0.00029843155,"about_ca_topic_score_gemma":0.00031776467,"teacher_disagreement_score":0.004043199,"about_ca_system_score_codex":0.000450918,"about_ca_system_score_gemma":0.0006635203,"threshold_uncertainty_score":0.013525844},"labels":[],"label_agreement":null},{"id":"W4409762848","doi":"10.1016/j.media.2025.103600","title":"ProtoASNet: Comprehensive evaluation and enhanced performance with uncertainty estimation for aortic stenosis classification in echocardiography","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","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":"Vancouver General Hospital; University of British Columbia","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Stenosis; Cardiology; Internal medicine; Medicine; Radiology; Artificial intelligence; Computer science","score_opus":0.015351166396583077,"score_gpt":0.37529175444290136,"score_spread":0.3599405880463183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409762848","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73932904,0.031034881,0.12912028,0.0041234093,0.004294856,0.0009293558,0.027153574,0.045538526,0.018476147],"genre_scores_gemma":[0.84986264,0.0032734636,0.08525515,0.0014390504,0.0006317717,0.00035423145,0.051268768,0.0006771757,0.0072376877],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997352,0.0005794294,0.00023906294,0.0008376455,0.000728111,0.00026377122],"domain_scores_gemma":[0.99794966,0.0008000379,0.00014394586,0.00024937236,0.0006288166,0.00022814356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038245074,0.0035186002,0.002195887,0.0024135043,0.0008423132,0.0017593774,0.002910188,0.0028300674,0.0023013875],"category_scores_gemma":[0.008288509,0.00052153086,0.0011478611,0.0013472069,0.000766,0.002381477,0.0019273398,0.0017639685,0.0016760763],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004131261,0.0015836989,0.03623431,0.0016749502,0.001181063,0.0011713954,0.00027071865,0.20222461,0.012751364,0.0017629644,0.12750672,0.60950696],"study_design_scores_gemma":[0.00018838658,0.0004644379,0.0045464966,0.00014584218,0.0001358665,0.00042985697,0.0001566072,0.9787257,0.006943351,0.0022651255,0.0059236162,0.00007476974],"about_ca_topic_score_codex":0.019081173,"about_ca_topic_score_gemma":0.02153586,"teacher_disagreement_score":0.019081173,"about_ca_system_score_codex":0.0015795437,"about_ca_system_score_gemma":0.0022093798,"threshold_uncertainty_score":0.037940204},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029191037,0.00008674242,0.99551564,0.000050286464,0.000009092187,0.000015502777,0.000041169635,0.0009985524,0.00036401785],"genre_scores_gemma":[0.20277742,0.00028669697,0.7918696,0.00024597207,0.000053946853,0.00015984109,0.00062334526,0.0014853607,0.002497859],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999358,0.00014796168,0.000032180676,0.0001596415,0.00022079807,0.00008150588],"domain_scores_gemma":[0.99872094,0.0005757796,0.00011418885,0.00029661244,0.00021121636,0.00008126452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010172186,0.0012168741,0.0016987935,0.0011332721,0.00058094267,0.0020815984,0.0029627357,0.002552211,0.0029105893],"category_scores_gemma":[0.004625602,0.0012893314,0.0013513081,0.0010727108,0.0010035875,0.002000919,0.002730506,0.0025340528,0.0014614488],"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":[0.00019190637,0.000117389136,0.000584934,0.00019491209,0.00010877842,0.0000783944,0.00016693496,0.68059474,0.033203818,0.017517602,0.003795033,0.2634455],"study_design_scores_gemma":[0.000002952578,0.000007398355,0.000051297644,0.0000059494023,0.000004058688,0.000014792795,0.0000070364918,0.99282384,0.001680203,0.0048804684,0.00051641505,0.0000054809134],"about_ca_topic_score_codex":0.0068772575,"about_ca_topic_score_gemma":0.011024707,"teacher_disagreement_score":0.0068772575,"about_ca_system_score_codex":0.0011296828,"about_ca_system_score_gemma":0.0016288551,"threshold_uncertainty_score":0.013674438},"labels":[],"label_agreement":null},{"id":"W4410781874","doi":"10.1016/j.media.2025.103648","title":"Corrigendum to “LESS: Label-efficient multi-scale learning for cytological whole slide image screening” [Medical Image Analysis 94 (2024): 103109]","year":2025,"lang":"en","type":"erratum","venue":"Medical Image Analysis","topic":"AI in cancer detection","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; Vector Institute; University of British Columbia","funders":"","keywords":"Artificial intelligence; Image (mathematics); Computer science; Scale (ratio); Computer vision; Pattern recognition (psychology); Machine learning; Cartography; Geography","score_opus":0.025653145658127943,"score_gpt":0.31852275399436714,"score_spread":0.2928696083362392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410781874","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011496776,0.0011897387,0.0025479838,0.053300668,0.9318263,0.000055875105,0.001419018,0.0018700026,0.0076754824],"genre_scores_gemma":[0.005293238,0.004008457,0.011393271,0.11287039,0.24590157,0.0002579526,0.0059963176,0.0039758463,0.6103029],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99477565,0.0007710608,0.0004833516,0.00070913584,0.0028603899,0.00040036687],"domain_scores_gemma":[0.9728105,0.0043900847,0.00067607994,0.001732915,0.01912855,0.0012618586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003205322,0.0023860445,0.0028403818,0.0046261554,0.0042898473,0.0040773363,0.004296992,0.0077048573,0.17501055],"category_scores_gemma":[0.03977332,0.001387374,0.0026400618,0.0026545185,0.001964941,0.0022947306,0.0024973976,0.007438977,0.124219805],"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":[0.000010666425,0.0000050949866,0.000011765955,0.000027592163,0.0000040683426,0.00003269718,0.0000030892372,0.000023458339,0.00005543184,0.0001555311,0.99668247,0.0029881282],"study_design_scores_gemma":[0.00003498752,0.000026020634,0.000724696,0.00011909316,0.000036341004,0.00020394976,0.000026455644,0.0010647943,0.0008088038,0.0018479024,0.9950399,0.00006714206],"about_ca_topic_score_codex":0.043591075,"about_ca_topic_score_gemma":0.073017456,"teacher_disagreement_score":0.17501055,"about_ca_system_score_codex":0.004636438,"about_ca_system_score_gemma":0.003956185,"threshold_uncertainty_score":0.58546865},"labels":[],"label_agreement":null},{"id":"W4410836011","doi":"10.1016/j.media.2025.103619","title":"Deep learning detection of acute and sub-acute lesion activity from single-timepoint conventional brain MRI in multiple sclerosis","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Multiple Sclerosis Research Studies","field":"Medicine","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":"NeuroRx Research (Canada); Montreal Neurological Institute and Hospital","funders":"Biogen","keywords":"Multiple sclerosis; Artificial intelligence; Medicine; Lesion; Deep learning; Neuroscience; Psychology; Computer science; Pathology; Psychiatry","score_opus":0.037144037094959184,"score_gpt":0.31749190746002515,"score_spread":0.280347870365066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410836011","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95177025,0.002093956,0.043797377,0.00038846608,0.00007090277,0.000035964964,0.00074117776,0.00027269253,0.0008291292],"genre_scores_gemma":[0.9883089,0.0005994582,0.009369192,0.00005034086,0.00005507732,0.000016326925,0.0006014446,0.000014152015,0.0009852112],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987924,0.00002360186,0.000009270597,0.00003005364,0.000018955116,0.000038822593],"domain_scores_gemma":[0.99969685,0.00012893286,0.000050268194,0.000018703413,0.00006248542,0.000042779884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005403447,0.00045833713,0.000413445,0.0009059293,0.0001330852,0.000537111,0.0003411288,0.00056901696,0.0005442156],"category_scores_gemma":[0.0012813635,0.0001579945,0.00038846827,0.00039378405,0.00016618261,0.00034914407,0.00045430573,0.0004661757,0.0002205155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0034928003,0.000851671,0.13483004,0.00041741575,0.00047295028,0.0011440056,0.0002834187,0.049984943,0.0833533,0.001204607,0.0076980274,0.7162669],"study_design_scores_gemma":[0.000052092448,0.00045756705,0.14154638,0.00008273302,0.00023287356,0.0010859976,0.00025345097,0.8283267,0.023163801,0.002940746,0.0018094445,0.00004822885],"about_ca_topic_score_codex":0.004610233,"about_ca_topic_score_gemma":0.0064083156,"teacher_disagreement_score":0.004610233,"about_ca_system_score_codex":0.00019541105,"about_ca_system_score_gemma":0.00031527274,"threshold_uncertainty_score":0.009166837},"labels":[],"label_agreement":null},{"id":"W4411122136","doi":"10.1016/j.media.2025.103650","title":"Evaluation of techniques for automated classification and artery quantification of the circle of Willis on TOF-MRA images: The CROWN challenge","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cerebrovascular and Carotid Artery Diseases","field":"Medicine","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":"Artificial intelligence; Crown (dentistry); Circle of Willis; Computer vision; Computer science; Pattern recognition (psychology); Mathematics; Medicine; Radiology; Orthodontics","score_opus":0.02559972452756481,"score_gpt":0.3423675114762819,"score_spread":0.3167677869487171,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411122136","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35501873,0.0063550924,0.59991735,0.0026846493,0.0021686263,0.0034440295,0.0027674823,0.020626204,0.007017741],"genre_scores_gemma":[0.44338018,0.00097287656,0.5432997,0.00051325397,0.0005212506,0.0014873999,0.0039623147,0.00335161,0.0025113919],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.92543364,0.04297521,0.0059270035,0.010042602,0.013934544,0.0016870489],"domain_scores_gemma":[0.8027317,0.08685384,0.009215804,0.028000796,0.06875628,0.004441657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09387091,0.0030715156,0.0019779012,0.005367388,0.0020728004,0.0052518495,0.0046330458,0.0034759128,0.0027866315],"category_scores_gemma":[0.16880071,0.0008212039,0.001511597,0.0022097982,0.0018058555,0.0030309155,0.004970867,0.0016728481,0.003337427],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032201898,0.0008959058,0.042375486,0.003243133,0.0012206736,0.0008802119,0.0065664337,0.015503281,0.051827382,0.0023007614,0.027608734,0.8443579],"study_design_scores_gemma":[0.0011528744,0.006472688,0.16267571,0.002166594,0.0012443251,0.009209122,0.009914985,0.5332676,0.15446348,0.021827841,0.09644055,0.0011642114],"about_ca_topic_score_codex":0.0023831395,"about_ca_topic_score_gemma":0.0030697086,"teacher_disagreement_score":0.09387091,"about_ca_system_score_codex":0.0012539887,"about_ca_system_score_gemma":0.0024997161,"threshold_uncertainty_score":0.49644274},"labels":[],"label_agreement":null},{"id":"W4411438887","doi":"10.1016/j.media.2025.103677","title":"When evidence modeling meets knowledge distillation: Towards reliable contrast-enhanced knowledge distillation for non-contrast medical image segmentation","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","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":"Contrast (vision); Computer science; Artificial intelligence; Segmentation; Domain knowledge; Distillation; Pattern recognition (psychology); Machine learning; Chemistry","score_opus":0.01639260718222609,"score_gpt":0.3587668278257788,"score_spread":0.3423742206435527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411438887","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022121323,0.00056640845,0.9736824,0.00047666798,0.000052239764,0.0001270775,0.00015499136,0.0010947691,0.0017241187],"genre_scores_gemma":[0.48318022,0.0006292952,0.5117001,0.0005827294,0.000061476654,0.000239861,0.00071046205,0.00037425294,0.0025216546],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997829,0.0004869139,0.00019065512,0.00061853847,0.00069548126,0.00017935637],"domain_scores_gemma":[0.9956701,0.0021959979,0.00038938728,0.00078827806,0.0007325258,0.00022366496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003141402,0.0011938558,0.0010611694,0.0013145567,0.0006339995,0.002474201,0.0018716156,0.0016672886,0.0026939705],"category_scores_gemma":[0.014797304,0.00059491466,0.00116114,0.00093201513,0.0014074215,0.0061184447,0.0048640855,0.0033328086,0.0006982766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011992209,0.0003408858,0.00239743,0.0008299372,0.00020237402,0.000472351,0.0008066571,0.26805815,0.045953866,0.06337082,0.0036522378,0.612716],"study_design_scores_gemma":[0.00006213245,0.00013659528,0.00047250165,0.000059668888,0.00006472667,0.00015907499,0.00010535685,0.9198605,0.033893086,0.04096717,0.0041765845,0.00004250712],"about_ca_topic_score_codex":0.0033091623,"about_ca_topic_score_gemma":0.0029819203,"teacher_disagreement_score":0.0033091623,"about_ca_system_score_codex":0.0010239332,"about_ca_system_score_gemma":0.0026149796,"threshold_uncertainty_score":0.016613543},"labels":[],"label_agreement":null},{"id":"W4412433844","doi":"10.1016/j.media.2025.103711","title":"CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image classification","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","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 Cancer Institute; ARUP Laboratories; National Institutes of Health; Huntsman Cancer Institute; University of Utah","keywords":"Artificial intelligence; Class (philosophy); Pattern recognition (psychology); Stain; Computer science; Computer vision; Image (mathematics); Mathematics; Pathology; Medicine; Staining","score_opus":0.014088078634863782,"score_gpt":0.3105851636818431,"score_spread":0.2964970850469793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412433844","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016044969,0.00032770916,0.9771855,0.00017530538,0.000053325293,0.00015169477,0.00021632548,0.0047764513,0.0010686404],"genre_scores_gemma":[0.3042736,0.0002952005,0.6858374,0.0005258509,0.00012272164,0.00055682054,0.001374351,0.0006498599,0.006364184],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999471,0.00011456246,0.000020323034,0.00019308804,0.00014773321,0.000053226773],"domain_scores_gemma":[0.9992072,0.00031252822,0.000080824306,0.00019067046,0.00016196087,0.00004670838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013784879,0.0008847214,0.0007635415,0.00096756883,0.00034889017,0.0007719689,0.0029382762,0.0013458142,0.0024784754],"category_scores_gemma":[0.0022388264,0.00046189153,0.001247429,0.0007052164,0.00073195423,0.0012488537,0.0012445688,0.0015500336,0.0012829596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046323723,0.00037339338,0.0041859956,0.0001900817,0.00018744449,0.00012616524,0.000118282565,0.16078742,0.02554356,0.008590625,0.01301822,0.7864155],"study_design_scores_gemma":[0.000011647871,0.000049051345,0.00039948424,0.0000068409095,0.000010638713,0.000048837283,0.0000049446408,0.9896086,0.00535077,0.0029117556,0.0015878122,0.000009680647],"about_ca_topic_score_codex":0.0023805278,"about_ca_topic_score_gemma":0.0039348276,"teacher_disagreement_score":0.0029382762,"about_ca_system_score_codex":0.00084382424,"about_ca_system_score_gemma":0.0008643183,"threshold_uncertainty_score":0.008291364},"labels":[],"label_agreement":null},{"id":"W4412584687","doi":"10.1016/j.media.2025.103716","title":"PitVis-2023 challenge: Workflow recognition in videos of endoscopic pituitary surgery","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":7,"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":"Department for Science, Innovation and Technology; Horizon 2020 Framework Programme; Cancer Research UK; Wellcome / EPSRC Centre for Interventional and Surgical Sciences; Wellcome Trust; Royal Academy of Engineering; Engineering and Physical Sciences Research Council; National Institute for Health and Care Research","keywords":"Workflow; Computer science; Artificial intelligence; Database","score_opus":0.03061313576358627,"score_gpt":0.3268501300808107,"score_spread":0.29623699431722444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412584687","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18235615,0.026451003,0.11428552,0.014198231,0.03023793,0.006131311,0.4754994,0.08825156,0.062588885],"genre_scores_gemma":[0.12844916,0.0030590887,0.094093695,0.0020262813,0.0017567072,0.0012940114,0.7218756,0.003037113,0.04440834],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9955226,0.0011941468,0.00028766284,0.0011323176,0.0012716053,0.0005916861],"domain_scores_gemma":[0.9937728,0.0019217534,0.00024746972,0.001099652,0.0019232624,0.0010350056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040429267,0.0049225856,0.0019406066,0.0016487231,0.0015037518,0.0029895396,0.0030588561,0.004315448,0.018425707],"category_scores_gemma":[0.014901693,0.0006542372,0.0025602595,0.0017423815,0.0008185375,0.0028790121,0.0032036372,0.0035593756,0.014423957],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017816712,0.00076215406,0.0019083914,0.002501464,0.0002997986,0.00070199295,0.00022917429,0.010780735,0.008521093,0.0012064428,0.80193603,0.16937101],"study_design_scores_gemma":[0.0012812653,0.0029303993,0.028581193,0.0017263439,0.00030144988,0.0038063563,0.0018205798,0.29980364,0.035432506,0.013535059,0.61023945,0.00054181763],"about_ca_topic_score_codex":0.03068032,"about_ca_topic_score_gemma":0.07006395,"teacher_disagreement_score":0.03068032,"about_ca_system_score_codex":0.0028173644,"about_ca_system_score_gemma":0.0040171617,"threshold_uncertainty_score":0.061640143},"labels":[],"label_agreement":null},{"id":"W4412860472","doi":"10.1016/j.media.2025.103715","title":"Attend-and-Refine: Interactive keypoint estimation and quantitative cervical vertebrae analysis for bone age assessment","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Dental Radiography and Imaging","field":"Dentistry","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":"Institute for Information and Communications Technology Promotion; Korea Advanced Institute of Science and Technology; Ministry of Health and Welfare; Korea Health Industry Development Institute; National Research Foundation of Korea; Ministry of Science and ICT, South Korea; Ministry of Health and Family Welfare; Ministry of Food and Drug Safety; Ministry of Trade, Industry and Energy","keywords":"Computer science; Consistency (knowledge bases); Annotation; Process (computing); Artificial intelligence; Vertebra; Cervical vertebrae; Machine learning; Data mining; Pattern recognition (psychology); Medicine","score_opus":0.011626643756951978,"score_gpt":0.35715661818151534,"score_spread":0.34552997442456335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412860472","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014815703,0.000733357,0.89860684,0.0001467585,0.00009859441,0.00031398764,0.0023562093,0.08121376,0.0017147624],"genre_scores_gemma":[0.09786162,0.0005162982,0.8864299,0.00016701115,0.00007402034,0.0003682284,0.002607077,0.0059522097,0.006023556],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99946624,0.000055434724,0.000031431602,0.000120518154,0.00023979992,0.000086492284],"domain_scores_gemma":[0.9993006,0.00034115015,0.00004269459,0.00009940573,0.00015106004,0.000065183856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000824844,0.001810953,0.0012147889,0.002694883,0.0004745651,0.0011399643,0.0021763532,0.0018099414,0.037135724],"category_scores_gemma":[0.0028220543,0.001031507,0.0011225294,0.00121098,0.00025993775,0.00092338916,0.0026435845,0.00078075164,0.007968423],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010505023,0.00015242439,0.0030691626,0.0005169779,0.00022434589,0.0002614054,0.00034150007,0.006596335,0.063256584,0.0012345545,0.026105503,0.8971907],"study_design_scores_gemma":[0.0003557485,0.00036028365,0.020102082,0.00013828659,0.00031112053,0.0020633913,0.00040198298,0.7667073,0.13998792,0.005765421,0.063522495,0.00028397387],"about_ca_topic_score_codex":0.0086052455,"about_ca_topic_score_gemma":0.02355903,"teacher_disagreement_score":0.037135724,"about_ca_system_score_codex":0.00040409146,"about_ca_system_score_gemma":0.00089054194,"threshold_uncertainty_score":0.12423134},"labels":[],"label_agreement":null},{"id":"W4412979322","doi":"10.1016/j.media.2025.103749","title":"MedCLIP-SAMv2: Towards universal text-driven medical image segmentation","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"COVID-19 diagnosis using AI","field":"Medicine","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":"Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Computer vision; Segmentation; Image segmentation; Image (mathematics); Pattern recognition (psychology)","score_opus":0.012013374653484442,"score_gpt":0.3527614810408809,"score_spread":0.34074810638739644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412979322","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049646287,0.0013216882,0.58954215,0.0008624965,0.0005092924,0.0005984911,0.02363351,0.37423033,0.0043373993],"genre_scores_gemma":[0.05615639,0.00095172087,0.8355512,0.002213074,0.0003239706,0.0011091259,0.06643624,0.029123409,0.008134855],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854326,0.00024360577,0.00014393288,0.0004376348,0.0004917749,0.0001399077],"domain_scores_gemma":[0.9981013,0.0008128016,0.0001040382,0.00033185017,0.00048850273,0.00016153202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019889902,0.0033629155,0.001963152,0.0045122616,0.00084784924,0.0032394824,0.0049331775,0.0042853076,0.031746916],"category_scores_gemma":[0.00817128,0.0015747614,0.0027425194,0.0029013862,0.00071952783,0.0016813132,0.0043682866,0.0021603229,0.02232664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015349037,0.0002897677,0.0014354998,0.0020673492,0.00066316786,0.00049271685,0.00028135258,0.013701037,0.043619234,0.006701505,0.36530998,0.5639035],"study_design_scores_gemma":[0.00044388586,0.00031484987,0.0021914488,0.00042224646,0.00020829705,0.0013868664,0.00018867002,0.66989917,0.11367617,0.03314532,0.17790924,0.00021390493],"about_ca_topic_score_codex":0.0063112746,"about_ca_topic_score_gemma":0.009761026,"teacher_disagreement_score":0.031746916,"about_ca_system_score_codex":0.001158797,"about_ca_system_score_gemma":0.0017411702,"threshold_uncertainty_score":0.10620403},"labels":[],"label_agreement":null},{"id":"W4413159892","doi":"10.1016/j.media.2025.103743","title":"Exploring the robustness of TractOracle methods in RL-based tractography","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","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":"Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Artificial intelligence; Computer science; Tractography; Computer vision; Diffusion MRI; Biology; Magnetic resonance imaging; Radiology; Medicine","score_opus":0.16667939381837615,"score_gpt":0.47539322498763364,"score_spread":0.3087138311692575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413159892","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26176316,0.0034391698,0.72544277,0.0012215296,0.00018127306,0.0001558118,0.00072035723,0.003603099,0.0034727708],"genre_scores_gemma":[0.81144273,0.0008490309,0.18257034,0.00023013192,0.00016788694,0.00007183644,0.0014081689,0.0014397532,0.0018201226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99541134,0.0024511833,0.00031465365,0.00087582564,0.00071123394,0.00023570852],"domain_scores_gemma":[0.93985254,0.04678631,0.0034431804,0.005472395,0.003624349,0.0008211798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01319676,0.0018265967,0.0011963898,0.00295781,0.0010328005,0.003962961,0.0018921376,0.0031896436,0.0030760043],"category_scores_gemma":[0.08949001,0.00076188834,0.0012476452,0.0015431606,0.0018649359,0.003195662,0.0022240386,0.002087863,0.0012357632],"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":[0.0026922082,0.00023548474,0.022508051,0.0010298061,0.0014501119,0.00043554025,0.00054087833,0.62034196,0.029966742,0.0119726015,0.0035481374,0.30527848],"study_design_scores_gemma":[0.000040759885,0.00015265329,0.0037969956,0.00008092732,0.000085293635,0.00022328517,0.00008590483,0.98164535,0.007422393,0.005574884,0.0008569728,0.000034674908],"about_ca_topic_score_codex":0.011893181,"about_ca_topic_score_gemma":0.010934171,"teacher_disagreement_score":0.01319676,"about_ca_system_score_codex":0.0008949102,"about_ca_system_score_gemma":0.0017107674,"threshold_uncertainty_score":0.06979203},"labels":[],"label_agreement":null},{"id":"W4413275632","doi":"10.1016/j.media.2025.103764","title":"BiasPruner: Mitigating bias transfer in continual learning for fair medical image analysis","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Domain Adaptation and Few-Shot Learning","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":"Simon Fraser University; University of Victoria; University of British Columbia; University of British Columbia Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Transfer of learning; Computer science; Image (mathematics); Computer vision; Machine learning","score_opus":0.01483735090395529,"score_gpt":0.30610355023936614,"score_spread":0.29126619933541087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413275632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051672444,0.000945165,0.9413605,0.00070824125,0.00008825964,0.00015845457,0.000119629,0.0037572484,0.0011901594],"genre_scores_gemma":[0.68810004,0.00029672144,0.305812,0.0010271718,0.00013503677,0.0003231574,0.00039303937,0.0006032144,0.003309681],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856144,0.00051849824,0.00006800432,0.00044991923,0.00025713292,0.0001451055],"domain_scores_gemma":[0.9945111,0.0031031556,0.0004567592,0.0011750248,0.0004604066,0.00029357694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067257765,0.0013812851,0.0013589077,0.0010735231,0.00081517297,0.0011595306,0.0038775338,0.0022512742,0.0027268662],"category_scores_gemma":[0.01932089,0.00080351107,0.0010991755,0.00058965996,0.0018714142,0.0025004821,0.0033712413,0.0030109745,0.0006476389],"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":[0.00059583655,0.0003057526,0.0069820834,0.00021979574,0.00023957236,0.00028077658,0.00037710372,0.63441634,0.008366476,0.012528054,0.005162989,0.33052522],"study_design_scores_gemma":[0.000032295073,0.00006498246,0.00028344325,0.00001645044,0.000016465587,0.000047132628,0.000012311202,0.98503125,0.0019531518,0.012035465,0.0004966573,0.000010334103],"about_ca_topic_score_codex":0.004480644,"about_ca_topic_score_gemma":0.0066408454,"teacher_disagreement_score":0.0067257765,"about_ca_system_score_codex":0.001393576,"about_ca_system_score_gemma":0.0020083857,"threshold_uncertainty_score":0.035569727},"labels":[],"label_agreement":null},{"id":"W4413384488","doi":"10.1016/j.media.2025.103772","title":"A doppler-exclusive computational diagnostic framework to enhance conventional 2-D clinical ultrasound with 3-D mitral valve dynamics and cardiac hemodynamics","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","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":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hemodynamics; Mitral valve; Cardiology; Doppler effect; Dynamics (music); Internal medicine; Medicine; Ultrasound; Doppler echocardiography; Computer science; Radiology; Artificial intelligence; Physics; Diastole; Acoustics","score_opus":0.004639162723404445,"score_gpt":0.32223845265999806,"score_spread":0.31759928993659364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413384488","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005915835,0.00009891289,0.9926426,0.00012078158,0.000038111197,0.000029775605,0.000054019183,0.00047272572,0.0006272371],"genre_scores_gemma":[0.24141018,0.00039214367,0.75528425,0.00028591818,0.00013419054,0.00015415833,0.00026016924,0.00021059395,0.001868471],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99977607,0.00005584942,0.000013509233,0.00003951817,0.00009194412,0.000023224466],"domain_scores_gemma":[0.99950683,0.00018891896,0.00003754636,0.000053071853,0.00015897304,0.000054688433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006440188,0.000635144,0.0005880721,0.0008898129,0.00031735172,0.0011930418,0.0011746114,0.0007438577,0.002069554],"category_scores_gemma":[0.0019750062,0.00038094164,0.00084867096,0.0004633987,0.0003891994,0.00055326225,0.0015740733,0.0007717972,0.00068890967],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033740688,0.00040956348,0.0038885064,0.00023867423,0.00018306411,0.0006111549,0.00018017845,0.45584354,0.07352036,0.029369893,0.0052922256,0.43012547],"study_design_scores_gemma":[0.000004611934,0.000020803393,0.00021079545,0.000005952802,0.000009775251,0.00006575959,0.000005569791,0.9949509,0.0018912753,0.001958596,0.0008685802,0.0000073967194],"about_ca_topic_score_codex":0.0031345363,"about_ca_topic_score_gemma":0.003967072,"teacher_disagreement_score":0.0031345363,"about_ca_system_score_codex":0.00029043318,"about_ca_system_score_gemma":0.001007403,"threshold_uncertainty_score":0.0069233775},"labels":[],"label_agreement":null},{"id":"W4414919761","doi":"10.1016/j.media.2025.103832","title":"Fusion and pure feature extraction framework for intraoperative hyperspectral of thyroid lesion","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Infrared Thermography in Medicine","field":"Medicine","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":"Xi'an Institute of Optics and Precision Mechanics; Canadian Anesthesiologists' Society","keywords":"Hyperspectral imaging; Pattern recognition (psychology); Thyroid cancer; Thyroid nodules; Robustness (evolution); Fusion; Cascade; Thyroid","score_opus":0.008032420561623182,"score_gpt":0.3375897405711067,"score_spread":0.3295573200094835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414919761","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054479055,0.00087741326,0.94026226,0.0001487822,0.00010211813,0.00006682033,0.00043521478,0.0018115412,0.0018167626],"genre_scores_gemma":[0.6521023,0.00087920105,0.33937055,0.00020456089,0.00018098322,0.00016414326,0.001894393,0.00017756225,0.005026286],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996051,0.000035845344,0.000024359599,0.0001084283,0.00015485786,0.00007141177],"domain_scores_gemma":[0.99984014,0.000022983402,0.000016092401,0.000025227459,0.00008076621,0.000014692946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038859088,0.0007779989,0.0008162172,0.0010638386,0.0003169115,0.0007043278,0.0006669444,0.00055821624,0.0016544043],"category_scores_gemma":[0.00053557527,0.0002591012,0.0010076329,0.00078562286,0.00015801136,0.0006922751,0.0009760388,0.0006081081,0.0007990813],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041017352,0.00029053044,0.0041120653,0.00015177985,0.00019828246,0.00021605928,0.000108723834,0.026368408,0.13983212,0.0015777762,0.005008112,0.8217261],"study_design_scores_gemma":[0.00001934079,0.00022460257,0.009922739,0.000019451314,0.00014112647,0.00048144878,0.00008648843,0.93201745,0.050438803,0.0025431893,0.00405908,0.00004621491],"about_ca_topic_score_codex":0.0026504793,"about_ca_topic_score_gemma":0.0042173197,"teacher_disagreement_score":0.0026504793,"about_ca_system_score_codex":0.00022372801,"about_ca_system_score_gemma":0.00060028257,"threshold_uncertainty_score":0.0055345893},"labels":[],"label_agreement":null},{"id":"W4414982617","doi":"10.1016/j.media.2025.103807","title":"A novel gradient inversion attack framework to investigate privacy vulnerabilities during retinal image-based federated learning","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Privacy-Preserving Technologies in Data","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":"Alberta Children's Hospital; University of Calgary","funders":"Canada Research Chairs","keywords":"Generalizability theory; Convolutional neural network; Deep learning; Data set; Differential privacy; Vulnerability (computing); Artificial neural network; Inversion (geology); Information privacy; Training set","score_opus":0.020796750789016834,"score_gpt":0.29876690050068483,"score_spread":0.277970149711668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414982617","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09719955,0.00038139222,0.8976235,0.0008392245,0.00005333235,0.00016047896,0.00024368931,0.0017878169,0.0017110779],"genre_scores_gemma":[0.9133631,0.00015009214,0.08490083,0.00023213784,0.00003573119,0.00012039936,0.0002421465,0.00006353398,0.000892031],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972458,0.0010997639,0.00015998891,0.00046688304,0.0007099651,0.00031759084],"domain_scores_gemma":[0.9942129,0.0026631365,0.000790996,0.001532025,0.00058338576,0.00021749469],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036500683,0.00081656186,0.0008456358,0.0010309033,0.0005248054,0.0015064153,0.0016870896,0.001591766,0.0009800582],"category_scores_gemma":[0.01356474,0.00031178928,0.00082374393,0.00055584114,0.0016629186,0.002629855,0.0029925215,0.0018687645,0.00021824912],"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":[0.0009190998,0.0005032912,0.019777302,0.00017830072,0.00028654237,0.0014592033,0.0005501765,0.6949474,0.017852217,0.08670204,0.0056375456,0.17118679],"study_design_scores_gemma":[0.000009805588,0.00005236327,0.0005564301,0.000008825732,0.000012070332,0.00015628611,0.00002695682,0.9787975,0.0039313603,0.016017567,0.00042025605,0.000010591981],"about_ca_topic_score_codex":0.0024482135,"about_ca_topic_score_gemma":0.001346656,"teacher_disagreement_score":0.0036500683,"about_ca_system_score_codex":0.0014680456,"about_ca_system_score_gemma":0.0014330824,"threshold_uncertainty_score":0.01930362},"labels":[],"label_agreement":null},{"id":"W4416040400","doi":"10.1016/j.media.2025.103857","title":"Adaptive mix for semi-supervised medical image segmentation","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","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":"Fundamental Research Funds for the Central Universities; National University's Basic Research Foundation of China; China Scholarship Council; Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China","keywords":"Overfitting; Segmentation; Perturbation (astronomy); Image segmentation; Pattern recognition (psychology); Image (mathematics)","score_opus":0.012633380039165325,"score_gpt":0.31778882447609685,"score_spread":0.30515544443693154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416040400","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017552543,0.00032687324,0.97897345,0.0001197149,0.0000372431,0.00010168999,0.00016690276,0.001971374,0.0007502645],"genre_scores_gemma":[0.2372527,0.00027438518,0.753057,0.0002736415,0.00013923543,0.00036320576,0.00090284256,0.00078160485,0.0069553647],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988073,0.00029414555,0.00007003787,0.00031564946,0.00039636344,0.000116457864],"domain_scores_gemma":[0.9987399,0.0005478512,0.00012849645,0.00021743985,0.00027956438,0.00008677283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018050289,0.0009156639,0.0015129923,0.0016013377,0.0006534592,0.0014007578,0.0022694925,0.0018711818,0.0041008717],"category_scores_gemma":[0.0029308244,0.0009853392,0.0012423499,0.0012286772,0.00089187257,0.0016519852,0.0028590125,0.0013988308,0.0017990677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013469567,0.00025834623,0.0017508217,0.00036847545,0.00032186805,0.00015223325,0.00025042068,0.10546341,0.08558996,0.00959795,0.004860079,0.79003954],"study_design_scores_gemma":[0.000018427678,0.00010742196,0.00060127553,0.000014966803,0.000034121458,0.00013697102,0.00003031944,0.9700095,0.020859756,0.005847091,0.0023229138,0.000017346649],"about_ca_topic_score_codex":0.001876198,"about_ca_topic_score_gemma":0.004684005,"teacher_disagreement_score":0.0041008717,"about_ca_system_score_codex":0.00082909415,"about_ca_system_score_gemma":0.001247931,"threshold_uncertainty_score":0.013718724},"labels":[],"label_agreement":null},{"id":"W4416535178","doi":"10.1016/j.media.2025.103883","title":"Extreme cardiac MRI analysis under respiratory motion: Results of the CMRxMotion challenge","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","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":"Science and Technology Innovation Plan Of Shanghai Science and Technology Commission; International Science and Technology Cooperation Programme; National Key Research and Development Program of China; Shanghai Rising-Star Program; Fudan University; Science and Technology Commission of Shanghai Municipality; Key Technologies Research and Development Program; Shanghai Municipal Education Commission; National Natural Science Foundation of China","keywords":"Robustness (evolution); Segmentation; Magnetic resonance imaging; Cardiac magnetic resonance; Deep learning; Image quality; Motion (physics)","score_opus":0.021587272762190364,"score_gpt":0.30409470299316577,"score_spread":0.2825074302309754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416535178","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7702094,0.0074789734,0.20889594,0.004096488,0.00031773633,0.00020504974,0.0015717086,0.0019073344,0.0053173816],"genre_scores_gemma":[0.79980403,0.003888031,0.18883322,0.0010175796,0.00043858017,0.00008192393,0.0021548027,0.00094802555,0.0028337953],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9992367,0.0003266767,0.000039336148,0.00014272046,0.00020268909,0.000051910796],"domain_scores_gemma":[0.9951918,0.0034067489,0.00014954791,0.00043966103,0.0005245755,0.0002876013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022680352,0.000601134,0.0005817054,0.00074996223,0.00038436335,0.0009285225,0.0007388611,0.0013134961,0.0017486664],"category_scores_gemma":[0.007395943,0.00027772895,0.00049038575,0.0004597744,0.00066911575,0.00072300865,0.0014269977,0.0010276282,0.0007223836],"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":[0.004622329,0.0012953761,0.017606549,0.0014647737,0.00066026207,0.003346219,0.0015419811,0.046449624,0.3819262,0.003154613,0.013936346,0.5239957],"study_design_scores_gemma":[0.0007240269,0.00413037,0.19795506,0.0004844647,0.00060647266,0.030398294,0.0023033908,0.47427297,0.21409698,0.022837052,0.051763687,0.00042724612],"about_ca_topic_score_codex":0.00090952474,"about_ca_topic_score_gemma":0.0016341077,"teacher_disagreement_score":0.0022680352,"about_ca_system_score_codex":0.00019133987,"about_ca_system_score_gemma":0.0003095495,"threshold_uncertainty_score":0.01199466},"labels":[],"label_agreement":null},{"id":"W4417483711","doi":"10.1016/j.media.2025.103915","title":"DTG: Dual transformers-based generative adversarial networks for retinal 2D/3D OCT image classification","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Retinal Imaging and Analysis","field":"Medicine","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":"Polytechnique Montréal; Université de Montréal; Hôpital Maisonneuve-Rosemont","funders":"Université de Montréal","keywords":"Optical coherence tomography; Pattern recognition (psychology); Convolutional neural network; Classifier (UML); Transformer; Deep learning; Contextual image classification; Generative grammar","score_opus":0.014424068914491498,"score_gpt":0.3305399040351102,"score_spread":0.3161158351206187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417483711","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009091107,0.00043542404,0.9847556,0.00031294595,0.000115212155,0.00006612652,0.0004030791,0.003290989,0.0015295334],"genre_scores_gemma":[0.60237813,0.0007492795,0.3751203,0.0010890045,0.00020588805,0.00031991504,0.0027161443,0.0010009768,0.016420279],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996153,0.000104454295,0.000016167502,0.00009853759,0.00010517821,0.000060219605],"domain_scores_gemma":[0.9993405,0.00032777418,0.000051875257,0.000105871346,0.00012261957,0.000051463907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089454226,0.0011302711,0.0007753537,0.0006721507,0.0003387265,0.00075663504,0.0017093045,0.0015842883,0.0039409418],"category_scores_gemma":[0.0021734487,0.000579406,0.0010780719,0.00055980723,0.0006434767,0.0008879729,0.0020885833,0.0023083098,0.0019454883],"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":[0.00031055944,0.00010376776,0.001141569,0.00010064434,0.00013615373,0.00016409013,0.000055438526,0.6514575,0.007522154,0.009263369,0.013135203,0.31660944],"study_design_scores_gemma":[0.000004138438,0.000012546141,0.00006095564,0.000004631879,0.0000056241706,0.000023175347,0.0000027974847,0.99639696,0.0008930663,0.0021588115,0.00043368395,0.0000035387404],"about_ca_topic_score_codex":0.006385697,"about_ca_topic_score_gemma":0.008886138,"teacher_disagreement_score":0.006385697,"about_ca_system_score_codex":0.0008343394,"about_ca_system_score_gemma":0.0007701695,"threshold_uncertainty_score":0.013183773},"labels":[],"label_agreement":null},{"id":"W4417530586","doi":"10.1016/j.media.2025.103920","title":"GloW-VSNet: A scribble-based weakly supervised framework for global-view vitiligo lesion segmentation","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"melanin and skin pigmentation","field":"Biochemistry, Genetics and Molecular Biology","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; Vancouver Coastal Health","funders":"Shenzhen Science and Technology Innovation Program; Natural Sciences and Engineering Research Council of Canada; Shenzhen University; University of British Columbia; National Natural Science Foundation of China","keywords":"Segmentation; Vitiligo; Pattern recognition (psychology); Image segmentation; Cluster analysis; Noise (video); Consistency (knowledge bases); Market segmentation","score_opus":0.012372055032451954,"score_gpt":0.3405596409592523,"score_spread":0.3281875859268003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417530586","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031660005,0.0012441674,0.95125794,0.00042759537,0.00011127855,0.00027138638,0.0011084503,0.011108403,0.0028108135],"genre_scores_gemma":[0.36255288,0.001097798,0.6106905,0.0012471795,0.00021262035,0.00066699524,0.008195791,0.002754887,0.012581315],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920005,0.00015466349,0.00004112802,0.00035181915,0.00015576657,0.0000965457],"domain_scores_gemma":[0.99937433,0.0001930561,0.000086324966,0.0001279995,0.00015540495,0.00006295915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010590259,0.0018157449,0.0016220186,0.001672032,0.0007721437,0.0014509141,0.0030530104,0.0027085661,0.0026583122],"category_scores_gemma":[0.0024495872,0.00091295113,0.001787975,0.00093706144,0.0010756565,0.0012663738,0.0018749125,0.0020036087,0.0019343097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008102753,0.00031244688,0.0040540877,0.00062772166,0.00027407703,0.00046862388,0.0005430479,0.3550181,0.05878752,0.00879447,0.029348593,0.540961],"study_design_scores_gemma":[0.000019920839,0.00006837076,0.00053343095,0.000038473932,0.00002362817,0.00015065695,0.000038583094,0.9802932,0.007853025,0.007221689,0.003738755,0.000020279558],"about_ca_topic_score_codex":0.008774911,"about_ca_topic_score_gemma":0.019453878,"teacher_disagreement_score":0.008774911,"about_ca_system_score_codex":0.0015235702,"about_ca_system_score_gemma":0.0016880208,"threshold_uncertainty_score":0.01744765},"labels":[],"label_agreement":null},{"id":"W7117294798","doi":"10.1016/j.media.2025.103925","title":"Spatial-frequency dual-constrained Mamba diffusion model for cross-modal generation from CFP to FFA","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Retinal Imaging and Analysis","field":"Medicine","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":"Science and Technology Innovation Plan Of Shanghai Science and Technology Commission; Science and Technology Commission of Shanghai Municipality; Shanghai Pudong New Area Health Commission; National Natural Science Foundation of China","keywords":"Noise reduction; Feature (linguistics); Pattern recognition (psychology); Constraint (computer-aided design); Retinal; Wavelet; Gamut; Convolution (computer science)","score_opus":0.01580299769263544,"score_gpt":0.3460638778647273,"score_spread":0.3302608801720919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117294798","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01808536,0.0007947509,0.9767237,0.000404149,0.00009712395,0.00004112902,0.00015243763,0.0002211158,0.003480266],"genre_scores_gemma":[0.76312786,0.0021200145,0.20975225,0.00040060395,0.00012573451,0.00021790815,0.00060842425,0.00020190049,0.023445334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999194,0.000022173928,0.0000040247082,0.000020679574,0.000020722335,0.000012903113],"domain_scores_gemma":[0.99980885,0.00007336806,0.000018577772,0.000018395127,0.00006409866,0.000016641196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036759287,0.00044221952,0.00039610214,0.00028636333,0.0002937557,0.000528755,0.00077289564,0.0010671417,0.0021941368],"category_scores_gemma":[0.0009858763,0.00026361705,0.0005455979,0.00042859293,0.0003429356,0.0008140516,0.0005664215,0.0009055402,0.00044742884],"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":[0.00019338884,0.00007757698,0.0009680007,0.00025590946,0.000094850904,0.00029293724,0.00015848101,0.8295799,0.03794972,0.06039041,0.0034734719,0.06656532],"study_design_scores_gemma":[0.000002436166,0.000005502748,0.000070277165,0.0000036832466,0.000004394191,0.000021526837,0.000004187839,0.9971022,0.000650636,0.0017539926,0.000375963,0.0000050997724],"about_ca_topic_score_codex":0.008529659,"about_ca_topic_score_gemma":0.0069857845,"teacher_disagreement_score":0.008529659,"about_ca_system_score_codex":0.00045621043,"about_ca_system_score_gemma":0.00071007595,"threshold_uncertainty_score":0.016960025},"labels":[],"label_agreement":null},{"id":"W7117463490","doi":"10.1016/j.media.2025.103927","title":"Multi-cancer framework with cancer-aware attention and adversarial mutual-information minimization for whole slide image classification","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"AI in cancer detection","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":"Israel Innovation Authority; Technion-Israel Institute of Technology; Ministry of Science and Technology, Israel; Israel Science Foundation; University of Toronto; Israel Cancer Association; Israel Cancer Association USA","keywords":"Leverage (statistics); Generalizability theory; Adversarial system; Regularization (linguistics); Scalability; Focus (optics); Construct (python library); Minification","score_opus":0.01122081343523291,"score_gpt":0.30923430122183787,"score_spread":0.29801348778660497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117463490","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013563107,0.0009948161,0.98020464,0.0007368091,0.000068956004,0.00010907089,0.000350111,0.0026864314,0.0012861465],"genre_scores_gemma":[0.53437376,0.0011096016,0.44018576,0.00227243,0.00040265883,0.00059272675,0.0032028789,0.0009466545,0.01691355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998911,0.00019024509,0.0000374151,0.00043396835,0.00025755996,0.0001698508],"domain_scores_gemma":[0.99914277,0.0002992508,0.0001253753,0.00015271471,0.00018645536,0.00009348306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019982157,0.0016522643,0.0017955652,0.0015669917,0.0006540699,0.0014748016,0.0049783657,0.0027409743,0.0025806306],"category_scores_gemma":[0.0025667949,0.0008581149,0.0024142724,0.00113767,0.0011685003,0.0013588055,0.0026887308,0.0028183267,0.0011531052],"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":[0.00026631466,0.00019012297,0.004268689,0.0002190651,0.0003397719,0.0003030566,0.0002187052,0.69001555,0.013624277,0.014468684,0.012062412,0.2640234],"study_design_scores_gemma":[0.0000068740646,0.00002468743,0.00027033425,0.000006317731,0.000021074231,0.00006212003,0.000007366795,0.9932371,0.0014141618,0.0039646947,0.0009750524,0.000010208009],"about_ca_topic_score_codex":0.013504082,"about_ca_topic_score_gemma":0.015962027,"teacher_disagreement_score":0.013504082,"about_ca_system_score_codex":0.002325699,"about_ca_system_score_gemma":0.0020004858,"threshold_uncertainty_score":0.026850998},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014317206,0.00018946055,0.98476726,0.00008811633,0.000010809048,0.000026427764,0.000041428833,0.0004329592,0.00012626608],"genre_scores_gemma":[0.3515709,0.00042487076,0.6459884,0.0001521401,0.000064137756,0.00011372186,0.00029782357,0.00022965328,0.0011583611],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999471,0.00020456963,0.000024975,0.00009218944,0.00016276554,0.00004448091],"domain_scores_gemma":[0.9977156,0.001632939,0.00021214478,0.00013357963,0.00023507625,0.00007072729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019564554,0.0004823975,0.0006589009,0.0012938221,0.0003152878,0.0005393912,0.0010743841,0.0009826588,0.0008560331],"category_scores_gemma":[0.0038738707,0.0005445191,0.00094567897,0.0006355879,0.00070477964,0.0006967215,0.0008101414,0.0010637145,0.00022882587],"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":[0.00076371466,0.00022867139,0.00241191,0.0002980973,0.00014235408,0.00016674047,0.0001704641,0.54268456,0.10796647,0.014722863,0.0023658937,0.32807827],"study_design_scores_gemma":[0.00000877508,0.000022210768,0.0007527563,0.0000062635204,0.000016062864,0.000052377138,0.0000037709704,0.9904199,0.0066512604,0.0018089114,0.00024572105,0.000011945118],"about_ca_topic_score_codex":0.00566531,"about_ca_topic_score_gemma":0.0070035174,"teacher_disagreement_score":0.00566531,"about_ca_system_score_codex":0.0008865724,"about_ca_system_score_gemma":0.0010833658,"threshold_uncertainty_score":0.011264682},"labels":[],"label_agreement":null}]}